Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. *Remote Sensing of Environment, 114*(3), 504–513. https://doi.org/10.1016/j.rse.2009.10.008

INE, I. N. de E. (n.d.). *Censos 2021*. Retrieved September 10, 2024, from https://censos.ine.pt/xportal/xmain?xpgid=censos21_main&xpid=CENSOS21&xlang=pt

Mariën, B., Balzarolo, M., Dox, I., Leys, S., Lorène, M. J., Geron, C., Portillo-Estrada, M., AbdElgawad, H., Asard, H., & Campioli, M. (2019). Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone. *New Phytologist*, 224(1), 166–176. https://doi.org/10.1111/nph.15991

Menezes, G. de O. (2017). *Análise do Clima Urbano da Cidade de Bragança (2012-2016): Estudo da Ilha de Calor* [Instituto Politécnico de Bragança]. https://core.ac.uk/reader/153415740

Miles, V., & Esau, I. (2020). Surface urban heat islands in 57 cities across different climates in northern Fennoscandia. *Urban Climate, 31*(December 2019), 100575. https://doi.org/10.1016/j.uclim.2019.100575

Mullerova, D., & Williams, M. (2019). Satellite Monitoring of Thermal Performance in Smart Urban Designs. *Remote Sensing, 11*(19), 2244. https://doi.org/10.3390/rs11192244

NASA. (n.d.-a). *Earth Engine Data Catalog - Landsat*. Retrieved October 10, 2024, from https://developers.google.com/earth-engine/datasets/catalog/landsat

NASA. (n.d.-b). *NASA Applied Remote Sensing Training (ARSET) program*.

Nations, U. (n.d.). *The 17 Goals*. Retrieved October 21, 2024, from https://sdgs.un.org/goals

Oke, T. R. (1982). The energetic basis of the urban heat island. *Quarterly Journal of the Royal Meteorological Society*, 108(455), 1–24. https://doi.org/10.1002/qj.49710845502

Oke, T. R. (1987). *Boundary Layer Climates* (Routledge (Ed.); 2nd ed.). http://library1.nida.ac.th/termpaper6/ sd/2554/19755.pdf

Oke, T. R. (1988). Street design and urban canopy layer climate. *Energy and Buildings*, 11(1–3), 103–113. https://doi.org/10.1016/0378-7788(88)90026-6

Oke, T. R., Mills, G., Christen, A., & Voogt, J. A. (2017). *Energy Balance*. Cambridge University Press. https://doi.org/https://doi.org/10.1017/9781139016476.007

ONU. (2019). World population prospects 2019. In *Department of Economic and Social Affairs. World Population Prospects 2019*. (Issue 141). http://www.ncbi.nlm.nih.gov/pubmed/12283219

Pachêco, A. P. (2001). *Sensoriamento Remoto na Faixa espectral do Infravermelho termal (8 - 12 µm)*. Revista Da Comissão Brasileira de Geodésia. https://agencia.ufpe.br/documents/39451/1778670/GE ODE'SIA+online++2%252F2001.pdf/1a8b61d5- da8c-487e-9103-485313f03aa7

Rose, G. (2019). Satellite Monitoring of Thermal Performance in Smart Urban Designs. *The Routledge Companion to Urban Imaginaries, Figure 1*, 103–112. https://doi.org/10.4324/9781315163956-8

Saher, R., Stephen, H., & Ahmad, S. (2021). Understanding the summertime warming in canyon and non-canyon surfaces. *Urban Climate, 38*, 100916. https://doi.org/10.1016/j.uclim.2021.100916

Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. *Bulletin of the American Meteorological Society*, 93(12), 1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1

Tang, J., Di, L., Xiao, J., Lu, D., & Zhou, Y. (2017). Impacts of land use and socioeconomic patterns on urban heat island. *International Journal of Remote Sensing*, 38(11), 3445–3465. https://doi.org/10.1080/01431161.2017.1295485

US Geological Survey. (n.d.-a). *Landsat Missions*. Retrieved October 23, 2024, from https://www.usgs.gov/landsat-missions/landsat-9

US Geological Survey. (n.d.-b). *Landsat Missions*. Retrieved September 12, 2024, from https:// www.usgs.gov/core-science-systems/nli/landsat/ landsat-8?qt-science_support_page_related_con=0#qt- science_support_page_related_con

*User manual - HTMercury33*. (n.d.). Retrieved July 1, 2020, from https://www.ht-instruments.com/en/products/mercury/download/manual/

Voogt, J., & Oke, T.. (2003). Thermal remote sensing of urban climates. *Remote Sensing of Environment*, 86(3), 370–384. https://doi.org/10.1016/S0034- 4257(03)00079-8

Weng, Q. (2003). Fractal analysis of satellite-detected urban heat island effect. *Photogrammetric Engineering and Remote Sensing*, 69(5), 555–566. https://doi.org/10.14358/PERS.69.5.555

Weng, Q., & Fu, P. (2014). Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. *ISPRS Journal of Photogrammetry and Remote Sensing*, 97, 78–88. https://doi.org/10.1016/j.isprsjprs.2014.08.009

Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. *Remote Sensing of Environment*, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005

Yu, K., Chen, Y., Wang, D., Chen, Z., Gong, A., & Li, J. (2019). Study of the Seasonal Effect of Building Shadows on Urban Land Surface Temperatures Based on Remote Sensing Data. *Remote Sensing*, 11(5), 497. https://doi.org/10.3390/rs11050497

Zheng, H., Chen, Y., Pan, W., Cai, Y., & Chen, Z. (2019). Impact of Land Use/Land Cover Changes on the Thermal Environment in Urbanization: A Case Study of the Natural Wetlands Distribution Area in Minjiang River Estuary, China. *Polish Journal of Environmental Studies*, 28(4), 3025–3041. https://doi.org/10.15244/pjoes/93743

Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., & Sobrino, J. A. (2019). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. *Remote Sensing*, 11(1), 1–36. https://doi.org/10.3390/rs11010048
# Hazard Modeling Using Sentinel Data for Risk Assessment and Management of Tsho Rolpa Glacier, Nepal

Prasil Poudel, Nikesh Budha Chettri, Nabin Sah and Drabindra Pandit

Department of Physics, St. Xavier's College, Maitighar, Kathmandu, Nepal
{021bscphy017, 021bscphy011, 021bscphy009}@sxc.edu.np, drabindra@sxc.edu.np

**Keywords:** Glacier Lake Outburst Flood (GLOF), Synthetic Aperture Radar (SAR), Geographic Information System (GIS), and Hazard Modeling.

**Abstract:** The accelerated melting of the Himalayan glaciers due to climate change has caused significant issues including Glacier Lake Outburst Floods (GLOFs), to the downstream communities and the infrastructure. This study focuses on the Tsho Rolpa Glacier in Nepal, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data to measure ice velocity and analyze the impacts of glacier melting on lake dynamics. A GLOF occurs when the volume and surface area of glacier lake water exceeds the capacity of the moraine dam. For hazard modeling, SAGA GIS 8.3.0 was used for the terrain modeling and hydrological analysis, which highlighted the flood-prone areas. ArcGIS 10.5 facilitated the integration of Sentinel-2 imagery with local topographic data to predict the flood scenarios. The integration of these tools enhanced the accuracy of flood paths predictions and provided much needed valuable insights into the impacts on the local infrastructure. The results highlights the growing risks associated with the climate-induced GLOFs, demonstrating the importance on real-time glacier monitoring and predictive hazard modeling. This study tries to be a helpful tool for decision-support framework for mitigating the socio-economic impacts of GLOFs in vulnerable regions such as the Himalayas.

## 1 INTRODUCTION

Glaciers are a very important component of the hydrological cycle of the earth system i.e. glaciers play a significant role as a fresh water reservoir in maintaining global sea level and regional water availability. Himalayan Glaciers acts as a freshwater reservoir to rivers downstream, which acts as a lifeline for millions of people living downstream. However, in the past few decade due to the ongoing impacts of the climate change, it has accelerated the rate at which the glaciers are melting, increasing the risk of Glacier Lake Outburst Flood (GLOF), landslides and other environmental disasters that threatens the local infrastructure, ecosystems and the daily livelihoods of the people living in those GLOFs prone areas.

In particular, the threat of GLOFs has raised a significant concern to the local demographic, as these events cause catastrophic flooding downstream when the moraine dam holding back glacier lake water fails due to excessive increase in the glacier lake due to melting of the glacier lake. The accurate and timely monitoring of glaciers and the hazards associated to it

would play a critical factor for the risk assessment and disaster management in the event of GLOF incident. Thanks to the advancement in satellite technology, monitoring of glacier dynamics has become more precise and accessible. The Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data, both provided by the European Space Agency (ESA), have proven to be indispensable tools for glacier monitoring and the development of hazard models. While Sentinel-1's radar capabilities enables the year-round monitoring (even in challenging weather conditions due to cloud penetrating radar capabilities) and are ideal for measuring ice velocity which is a key factor in understanding glacier dynamics (Copernicus Climate Sentinel-1, n.d.) but Sentinel-2's high-resolution optical imagery is particularly useful for assessing surface changes and developing detailed hazard models and predict the flood-path and the subsequent impacts of potential GLOFs.

Our approach to this study, was to generate awareness towards the potential disaster than could be caused by the GLOFs incidents in high impact areas such as Tsho Rolpa Glacial Lake in the Rolwaling Valley (Rounce, Watson, & McKinney, 2017). This

Poudel, P., Chettri, N. B., Sah, N. and Pandit, D.
Hazard Modeling Using Sentinel Data for Risk Assessment and Management of Tsho Rolpa Glacier, Nepal.

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 165-171

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Proceedings Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Ltda.
study utilized the Sentinel-1 SAR data to measure the ice velocity of the Tsho Rolpa Glacier from 2020 to 2023, enabling the analysis of glacier dynamics. In addition to this, Sentinel-2 imagery was used to create an extensive hazard model. This study serves as a small yet significant step toward mitigating the impacts.

# 2 RELATED WORKS

## 2.1 Glacier Hazard Monitoring and Risk Assessment

Glacier hazard modeling is very important for the prediction of associated flood path in case of GLOF incident. Various studies have already been published on this topic, especially the effects of climate change in glaciers (Bolch et al., 2012).

Studied glacier retreat and thinning pattern in Himalayan glaciers. This study suggests the increase in potential risk of GLOFs as ice volume declines.

## 2.2 Sentinel-1 SAR Applications in Glacier Monitoring

Sentinel-1 SAR data provides all-weather year-round glacier monitoring, which acts as a great tool to monitor areas such as Himalayan region where cloud coverage is common. Several glaciers in previous studies such as (Rankl, Kienholz, & Braun, 2014) have utilized Sentinel-1 data to monitor changes in Ice velocity and surface deformations.

## 2.3 Sentinel-2 Imagery Data for Hazard Modeling

Since Sentinel-2 provides a higher spatial resolution of 10 meters for visible and Near Infrared band (NIR), 20

meters for red-edge and SWIR band and 60 meters for atmospheric correction bound as compared to 5-20 meters of Sentinel-1 SAR (ChatGPT, Personal Communication, 2024). Hence, Sentinel-2 can be an important tool for monitoring small changes in glacier flood path making it more effective in Hazard modeling as compared to Sentinel-1 SAR (Paul et al., 2016).

## 2.4 Glacier Lake Area and Volume Change

The previous studies by (Maskey et al., 2013) provides insights on the significant expansion of Tsho Rolpa Glacier Lake, driven by impacts of climate change and accelerated melting of its parent glacier. The lake's surface area, recorded at merely 0.23 km² in 1957, increased substantially to 1.65 km² by 1997, reflecting a dramatic growth of approximately 617% over four decades. This expansion of Tsho Rolpa Glacial Lake has heightened the threat of GLOFs.

# 3 METHODOLOGY

## 3.1 Study Area

Tsho Rolpa glacier, located in Rolwaling Valley of Himalayan region of Nepal is prone to GLOF incidents because of its large glacier lake that is actually growing because of melting glaciers (Damen, 1992). This study analyzes the spatial and temporal aspects of glaciers and glacier lakes.

## 3.2 Data Acquisition

Our study utilizes Sentinel-1 and Sentinel-2 data obtained from the open-access Copernicus Open

<figure><img src="image_112.png" /><figcaption>Figure 1: Google. (2021). [Map showing Tsho Rolpa Glacier] [Map]. Google Earth Pro (Version 7.3). Imagery date: March 25, 2021. Retrieved January 20, 2025, from https://earth.google.com/.</figcaption></figure>
Access hub (Copernicus.eu). Since Sentinel-1 data were used for different purpose like monitoring natural disasters, environmental monitoring and different climatic analysis

(Copernicus Sentinel-1, n.d.). In our study, we have used Sentinel-1 as well as Sentinel-2 data for analyzing Ice velocity and Hazard modeling respectively provided by the European Space Agency (ESA). The datasets were taken in following ways:-

### 3.2.1 Sentinel-1 Data

Sentinel-1 uses a spatial resolution of 5m in range with 20m in azimuth (Du et al., 2021). The Twin polar-orbiting satellites i.e Sentinel-1A and Sentinel-1B are equipped with supply geographical data for environmental and security warranting for the expansion of the remote sensing, world economy and business which means the satellites function both during the day and at night and perform a synthetic aperture with radar imaging i.e. it process radar signal to create high-resolution image of Earth which is obtained by simulating a large synthetic aperture using the motion of the radar platform. We can obtain the imagery using Sentinel-1 bands in any weather. An active phased array antenna called C-SAR was developed to offer faster azimuth and elevation scanning which allows to cover bigger areas of incidence angle to support the SAR operation (Yulianto et al., 2021). In this research, High-resolution SAR images from the Sentinel-1 satellites were downloaded in Ground Range Detected High resolution (GRDH; Level-1).

Sentinel-1 is a sun-synchronous, near-polar orbit satellite with 175 orbits around the earth and 12 days repeat cycle. Both Sentinel-1A and Sentinel-1B share the same orbit plane with a 180- orbital phasing difference. The orbit altitude of Sentinel-1 Satellite is 693 km (Yulianto et al., 2021, September).

We have acquired the imageries in Interferometric Wide Swath (IW) Mode, which helps to monitor large area, and in Vertical-Horizontal (VH) Polarization i.e the signal is transmitted vertically and received horizontally. Also, all the images were downloaded in GRD.

### 3.2.2 Sentinel-2 Optical Imagery Data

Sentinel-2 provides a multispectral imaging that allows for high-resolution imagery data, useful for analyzing surface characteristics, glacier extent etc. For this study, Sentinel-2 imagery data was assessed from Copernicus Open Access hub/Copernicus browser, which is a platform for Sentinel data. A processing level of 2A was used, which provides atmospherically correct surface reflectance (Copernicus Open Access Hub, n.d.)

## 3.3 Methods of Hazard Modeling

Hazard Modeling is crucial for predicting the potential flood paths in case of GLOF incidents. Knowing the potential flood path/flow paths would prepare local governmental bodies and demography to be more prepared in such incidents. Along with that it is also crucial for the planning infrastructural development around such GLOF prone areas.

For hazard modeling Sentinel-2 imagery data was utilized which was taken from the Copernicus hub and then pre-processed using SAGA GIS 8.3.0 software's "Filled sink (Wang & Liu)" pre-processing tool to correct the elevation layer in Digital Elevation Model (DEM) map and from DEM map, channel network vector was created using the Hydrology tool in Terrain analysis of SAGA GIS 8.3.0. Then the original DEM map and channel network was imported into ARCGIS 10.5 and merged into a single layer. The channel of order greater than 0.1 in terms of width and length was specified and connected creating the hazard modeling layer in 2D. Now the map was converted into a KML file and 3D visualization was done in Google earth. Both the DEM model and TIN visualization layers in Google Earth Pro are taken as final results as 2D and 3D hazard models. Both DEM layer and TIN layer were used as results as they both could high light the aspect of elevation to present the actual terrain features.

## 3.4 Ice Velocity

Glacier ice velocity plays a crucial role in understanding

<table><caption>Table 1: Parameter of images.</caption><thead><tr><td>Acquisition date</td><td>Acquisition date</td><td>Acquisition date</td><td>Acquisition date</td></tr></thead><tbody><tr><td>2020-01-04</td><td>2021-01-10</td><td>2022-01-05</td><td>2023-01-12</td></tr><tr><td>2020-02-09</td><td>2021-02-15</td><td>2022-02-10</td><td>2023-02-17</td></tr></tbody></table>
<figure><img src="image_113.png" /><figcaption>Figure 2: 2D DEM layer Hazard Model.</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>Figure 3: 3D Hazard Model.</figcaption></figure>

the overall glacier dynamics influenced due to climate change. The velocity of glaciers caused by different factors including geographical location, ice thickness, and environmental conditions (Hyde, 2024). In regions like Himalayans the glacier dynamics affects the availability of fresh drinking water highlighting socio-economic effect to the country and measurement of glacier's ice velocity are essential for modeling future glacier changes and potential natural calamities like flood and landslide (Millan et al., 2023). Ice Velocity of glaciers can be measured using different techniques but in this study we have used optical and SAR imageries to analyze the glacier ice velocity since high resolution optical imageries provides high accurate measurement (Gu et al., 2024). In this research study, we have use offset tracking analysis calculated by Sentinel

Application Platform (SNAP) to analyze image intensity information for identifying and matching points between image (Gu et al., 2024). In offset tracking random points are generated and compared between two points which allows to analyze glacier movement with high accuracy and calculated by

$$Offset = \sqrt{A^2 + D^2} \qquad (1)$$

Where, Offset is the offset of the drifted glacier area, A is the average value of offset drifted glacier area, D is the standard deviation of the drifted glacier area (Gu et al., 2024).

We have used the following technique for the analysis of the Ice velocity:
<figure><img src="image_115.png" /><figcaption>Figure 5: Flowchart of Ice Velocity Processing.</figcaption></figure>

# 4 RESULTS AND DISCUSSION

## 4.1 Results from Hazard Modeling

The Hazard Modeling of Tsho Rolpa glacier lake reveals detailed potential flood paths and hazard zones in case of a GLOFs event. This model shows flood paths (purple and blue lines as in figure 2 and figure 3) where water could flow as a direct result of an outburst event of the Tsho Rolpa glacier lake. From this model, it was clearly shown that rural communities such as Na Village (as seen in map) are at a high risk which serves as a critical tool for identifying vulnerable areas and guiding mitigation strategies.

## 4.2 Results from Ice Velocity Measurement

From the figure 5(a), (b), (c), (d) it was found that the ice velocity in the area in the year 2020 January-February was a maximum of 0.129m/day and minimum of 0.016m/day indicated by Red color and White color respectively similarly in the year 2021 January-February there was a slightly fluctuation in the velocity with a maximum velocity of 0.056m/day and minimum of 0.002m/day indicated by Red color and White color respectively. In the year 2022, we can observe that the ice velocity became twice as of 2021 with a maximum velocity of 0.106m/day and minimum of 0.006m/day indicated by the red and white color. In the year 2023, we observed a decrease

in the velocity of ice with maximum of 0.058m/day and minimum of 0.002m/day.

# 5 DISCUSSION

## 5.1 Interpretation of Hazard Modeling Results

The Hazard Modeling of Tsho Rolpa glacial lake highlights the GOLF risks of the region. The flood paths visualized in the Figure 2 and Figure 3 shows those rural communities such as Na Village which are in a direct hazard zone. The visualized flood paths demonstrate the utility of integrating remote sensing data with the hazard assessment tools. The accuracy of the flood paths can be further refined which was discussed in the future works.

## 5.2 Interpretation of Ice Velocity of Measurement

From the above result, we can conclude that the Ice velocity in the area has fluctuation over time and can lead to moraine dam collapse due to continuous flow of water in the Tsho Rolpa lake and with fluctuation in ice velocity the possibility of GLOF can be occurred anytime. With Global temperature rising (Copernicus Climate Change Service, n.d.), the possibility of glacier melting with numerous amount of water flowing into the lake can lead to a possible GLOF
<figure><img src="image_116.png" /><figcaption>Figure 5(a): Ice velocity of 2020.</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>Figure 5(b): Ice velocity of 2021.</figcaption></figure>

<figure><img src="image_118.png" /><figcaption>Figure 5(c): Ice velocity of 2022.</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>Figure 5(d): Ice velocity of 2023.</figcaption></figure>

condition in the area which can badly affect the country's economic as well as long-term effect can be seen.

# 6 CONCLUSION AND FUTURE WORKS

GLOFs are natural disasters that will happen eventually. The only viable method of risk reduction are the early warning systems. The study of glacier ice velocity provides us an insight about the dynamics of glaciers, sea-level rise and response to climate change. With the help of ice velocity integrated with hydrology, we can design an early warning system. The hazard modeling provides crucial insights on the nature of flood paths and high-risk zones, where the early warning can effectively reduce the risk on life and properties. The rapid expansion of glacial lake and the resulting potential flood path emphasizes the urgent need for intervention for the mitigation of GLOFs.

One of the major challenges many countries that has direct access to the ocean or sea are facing is continuous sea-level rise and one of the major cause for sea level rise is melting of glaciers, which add tons and tons of water continuously in the ocean resulting in rise of sea level. Future work should focus on refining the accuracy of flood paths generated by hazard models by incorporating real-time data. Further works can be done analyzing different parameters like ice thickness, surface displacement using satellite data as well high precision GPS stations and InSAR to get high accuracy results. Similarly more sophisticated modes can be developed that can simulate ice velocity in different weather and climate variables taking in note about the surface as well as internal glacier melting and deformation. The current hazard model relies on the geospatial data and the assumption of the lake stability which might not full capture the dynamic nature of GLOF events so direct field-based data collections and validation of observations along with advanced hydrodynamic modeling could address these gaps and provide a more refined hazard model.
REFERENCES

Bolch, T. K. (2012). The state and fate of Himalayan glaciers. *Science*, **336**(6079), 310–314.
doi:https://doi.org/10.1126/science.1215828

ChatGPT. (2024). Explanation on spatial resolution of Sentinel-2 and Sentinel-1.

Copernicus Climate Change Service. (n.d.). Home Page (or Copernicus Climate Change Service Website). Retrieved from European Centre for Medium-Range Weather Forecasts: https://climate.copernicus.eu/

Damen, M. C. (1992). *Study on the potential outburst flooding of Tsho Rolpa glacier lake, Rolwaling valley, East Nepal*. International Institute for Geo-Information Science and Earth Observation.

Du, Q. L. (2021). Deformation Monitoring in an Alpine Mining Area in the Tianshan Mountains Based on SBAS-InSAR Technology. *Advances in Materials Science and Engineering, 2021*(1), 9988017. doi:https://doi.org/10.1155/2021/9988017

Gu, F. Z. (2024). The velocity extraction and feature analysis of glacier surface motion in the Gongar region based on multi-source remote sensing data. *Frontiers in Earth Science*, **12**(Article 1413531). doi:https://doi.org/10.3389/feart.2024.1413531

Hyde, A. (2024, January 15). *Measuring glacier velocity*. Retrieved from Antarctic Glaciers: https://www.antarcticglaciers.org/glaciers-and-climate/observing-and-monitoring-glaciers-and-ice-sheets/measuring-glacier-velocity

Maskey, R. K. (2013). *Water and energy security in the mountainous region of Nepal: Lessons from Dig-Tsho, Tsho-Rolpa, and Imja glacial lakes*.

Millan, R. M. (2023). Author correction: Ice velocity and thickness of the world's glaciers. *Nature Geoscience*, **16**(1), 188.

Paul, F., Winsvold, S. H., Kääb, A., Nagler, T., & Schwaizer, G. (2016). Glacier remote sensing using Sentinel-2. Part II: Mapping glacier extents and surface facies, and comparison to Landsat 8. *Remote Sensing*, **8**(7), 575. doi:10.3390/rs8070575

Rankl, M. K. (2014). Glacier changes in the Karakoram region mapped by multimission satellite imagery. *The Cryosphere*, **8**, 977–989. doi:https://doi.org/10.5194/tc-8-977-2014

Rounce, D., Watson, C., & McKinney, D. C. (2017). Identification of hazard and risk for glacial lakes in the Nepal Himalaya using satellite imagery from 2000–2015. *Remote Sensing*, **9**(7), 654. doi:https://doi.org/10.3390/rs9070654

Yulianto, S. A. (2021, September). Spatial Distribution of Paddy Growth Stage Using Sentinel-1 based on CART Model. *2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)*. Institute of Electrical and Electronics Engineers. doi:https://doi.org/10.1109/AGERS53903.2021.9617317

Google, E. (2021). Google Earth Pro [Map].
# Planning Delivery Services: Depot Clustering Based on Socio-Economic Indicators and Geospatial Metrics

Iñaki Cejudo<sup>a</sup>, Laura Rabadán<sup>b</sup>, Eider Irigoyen<sup>c</sup> and Harbil Arregui<sup>d</sup>

Intelligent Systems for Mobility and Logistics, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA),
Mikeletegi 57, Donostia, Spain
{icejudo, lrabadan, eirigoyen, harregui}@vicomtech.org

**Keywords:** Depot Clustering, Delivery Logistics, Socio-Economic Indicators, Urban Network, Decision Support Systems.

**Abstract:** People's lifestyles have evolved in recent years, making home deliveries a necessity for various types of services. Moreover, with the growth of big data and Artificial Intelligence, predicting the performance and customer demand of new businesses is a key aspect of logistics and last-mile delivery planning. By using examples and predictions as a foundation for goods delivery services, initial over-sizing costs can be significantly reduced. In this paper, we analyze and compare operational zone similarities for food and parcel delivery services in Spain, considering socio-economic indicators and urban network features. The study leverages motorbike delivery metrics to complement the analysis. The results demonstrate how similar depots can be clustered, providing a foundational performance scenario for decision-making when planning the launch of a new service.

## 1 INTRODUCTION

The rapid evolution of urban lifestyles has significantly transformed our habits in many aspects. One of them is how e-commerce has altered our way of buying things. We now buy from home and expect a fast delivery. As customer habits were evolving, last-mile delivery logistics in cities have adapted too. Furthermore, these habits have been extended to ordering food, where in the last few years there has been a game changer in cities with many riders delivering food.

Opening new commerce now entails planning an efficient last-mile delivery system, and it requires a delicate balance between cost management, operational scalability, and customer satisfaction. Moreover, the diversity of urban environments, shaped by socio-economic and geographical factors, adds layers of complexity to this process. Understanding these dynamics is essential for predicting the performance of a new commerce in terms of demand, and therefore correctly sizing the service needs, optimizing delivery operations, and ensuring their sustainability in an increasingly competitive market.

One of the biggest challenges is to predict the service demand and profile and location of the potential customers. This is essential for successful fleet sizing and demand categorization. Recent studies have highlighted various approaches to tackle these challenges. For instance, Hu et al. (2024) explored how information and communication technology (ICT) impacts the micro-location choices of stores in urban areas, emphasizing the role of digital platforms in optimizing food delivery operations in densely populated districts. Using real data and machine learning methods, the work found the importance of considering location and traffic patterns when designing efficient delivery zones.

Similarly, Ko et al. (2020) proposed a collaboration model for service clustering in last-mile delivery, demonstrating that cooperative approaches can enhance service efficiency and reduce costs. This highlights the relevance of clustering methodologies for planning delivery zones, especially in scenarios with heterogeneous demand patterns.

Another perspective is provided by Ramírez-Villamil et al. (2022), who used clustering techniques to link clients to the satellites and improve last-mile parcel delivery. Their findings reveal that integrating data-driven clustering with logistics algorithms can significantly reduce operational costs and improve delivery times.

<footnote>a https://orcid.org/0000-0002-7325-9350</footnote>

<footnote>b https://orcid.org/0000-0001-5912-046X</footnote>

<footnote>c https://orcid.org/0000-0001-9486-0906</footnote>

<footnote>d https://orcid.org/0000-0002-7934-9250</footnote>
In another context, Regal et al. (2023) highlighted
in their analysis how clustering can capture the di-
verse socio-economic and functional characteristics
of urban regions, easing more tailored and efficient
delivery strategies. This work aligns with the need for
approaches that integrate socio-economic and geospa-
tial data to address the complexities of urban logistics.

The analysis of Kang (2020) complements the dis-
cussion by examining the spatial evolution of ware-
house and logistics center locations, emphasizing
their tendency to move from urban centers to the pe-
riphery despite the significant growth of online shop-
ping and the demand for instant delivery services.

Regarding machine learning algorithms, Sarkar (2024), and Wangwattanakool and Laesanklang (2024) explored customer segmentation and delivery zone partitioning, both using advanced clustering algorithms. Their research demonstrates how clustering can be leveraged not only to understand customer behavior but also to establish delivery zones. K-means algorithm is shown to be effective for these tasks. Besides, Dupas et al. (2024) propose a K-means clustering approach to allocate customers to depots and optimize vehicle routing, evaluating both the operational efficiency and the impact of last-mile depot locations.

Lastly, Zheng et al. (2023) applied fuzzy clustering analysis to optimize logistics distribution based on customer demand attributes.

Overall, these studies have demonstrated how
clustering urban areas and clients is a meaningful
method for a first-base idea for business sizing and
logistics planning. Most of them focus on clustering
customer demand based on geographical coordinates
or on client profiles.

This paper proposes a clustering approach for
food and parcel delivery services in Spain, that in-
corporates socio-economic indicators and urban net-
work features, based on the amount of demand from
specific small areas within a city. By analyzing the
clustering differences and similarities in operation
zones concerning the group of indicators used, our
study provides insights into depot performance and
decision-making in last-mile delivery planning.

2 DATA DRIVEN APPROACH

Building depot clustering algorithms requires know-
ing specific metrics of stores performance. Using de-
livery electric and combustion motorbike data from
food and parcel delivery services, in a prior work the
process to link that data to specific depots and extract
performance metrics and statistics of each service was
made (Arregui et al., 2024). The result was the de-

tection of many food and parcel delivery services in
Spain, the motorbikes that operate within each ser-
vice, geolocated trips, and delivery data of each mo-
torbike. The data used in this work is from 2024 and
overall, we have detected 854 parcel and 579 food de-
livery services.

Additionally, this data is enhanced with open data
population indicators and geospatial information of
the urban areas.

## 2.1 Depot's Performance Metrics

After the corresponding data cleaning and processing,
the obtained daily performance metrics of each depot
are the following:

• Number of bikes

• Time of use of each bike

• Distance covered by each bike

• Number of deliveries per km

• Consumption per km

• Average delivery radius

• Maximum delivery radius

• Average time per trip

• Number of deliveries per trip

• Total number of deliveries

With this data, we are capable of knowing the de-
livery demand in every area of a city and the fleet met-
rics, therefore we are able to use this information as
a benchmark for future service planning. The depot
clustering analysis increases the usability of this in-
formation, and it relies on socio-economic and net-
work features.

## 2.2 Socio-Economic Data

In Spain, the National Statistics Institute (Instituto Nacional de Estadística, 2024) offers insights into many social, demographic, and economic indicators with a high granularity. These indicators show, for instance, inhabitants, genre, origin, educational level, working status, marital status, and housing. The data is updated to the year 2022. The full list of indicators is depicted in Table 1.

## 2.3 Network Features

The heterogeneity of urban areas can be captured us-
ing Open Street Map (OSM) data enhanced with ele-
vation data. We can obtain interesting geospatial indi-
cators to cluster the services based on metrics such as
<table><caption>Table 1: Socio-economic indicators published by the Statistics National Institute (INE) of Spain.</caption><thead><tr><th>Code</th><th>Indicator</th><th>Code</th><th>Indicator</th></tr></thead><tbody><tr><td>t1.1</td><td>Total people</td><td>t17.3</td><td>Percentage of widowed people</td></tr><tr><td>t2.1</td><td>Percentage of women</td><td>t17.4</td><td>Percentage of people with unknown marital status</td></tr><tr><td>t2.2</td><td>Percentage of men</td><td>t17.5</td><td>Percentage of people legally separated or divorced</td></tr><tr><td>t3.1</td><td>Average age</td><td>t18.1</td><td>Total dwellings</td></tr><tr><td>t4.1</td><td>Percentage of people under 16</td><td>t19.1</td><td>Primary dwellings</td></tr><tr><td>t4.2</td><td>Percentage of people aged 16 (inclusive) to 64 (inclusive)</td><td>t19.2</td><td>Non-Primary dwellings</td></tr><tr><td>t4.3</td><td>Percentage of people over 64</td><td>t20.1</td><td>Owner-occupied dwellings</td></tr><tr><td>t5.1</td><td>Percentage of foreigners</td><td>t20.2</td><td>Rented dwellings</td></tr><tr><td>t6.1</td><td>Percentage of people born abroad</td><td>t20.3</td><td>Dwellings under other tenure types</td></tr><tr><td>t7.1</td><td>Percentage of people pursuing higher education over population 16+</td><td>t21.1</td><td>Total households</td></tr><tr><td>t8.1</td><td>Percentage of people pursuing university education over population 16+</td><td>t22.1</td><td>Single-person households</td></tr><tr><td>t9.1</td><td>Percentage of people with higher education over population 16+</td><td>t22.2</td><td>Two-person households</td></tr><tr><td>t10.1</td><td>Percentage of unemployed people over active population</td><td>t22.3</td><td>Three-person households</td></tr><tr><td>t11.1</td><td>Percentage of employed people over population 16+</td><td>t22.4</td><td>Four-person households</td></tr><tr><td>t12.1</td><td>Percentage of active population over population 16+</td><td>t22.5</td><td>Five-or-more-person households</td></tr><tr><td>t13.1</td><td>Percentage of disability pensioners over population 16+</td><td>r1</td><td>Average net income per person</td></tr><tr><td>t14.1</td><td>Percentage of retirement pensioners over population 16+</td><td>r2</td><td>Average net income per household</td></tr><tr><td>t15.1</td><td>Percentage of people in other inactive situations over population 16+</td><td>r3</td><td>Average income per unit of consumption</td></tr><tr><td>t16.1</td><td>Percentage of students over population 16+</td><td>r4</td><td>Median income per unit of consumption</td></tr><tr><td>t17.1</td><td>Percentage of single people</td><td>r5</td><td>Average gross income per person</td></tr><tr><td>t17.2</td><td>Percentage of married people</td><td>r6</td><td>Average gross income per household</td></tr></tbody></table>

area, elevation, road speed, slope, etc. After a process to extract the metrics, these are depicted in Table 2.

# 3 METHODOLOGY

## 3.1 Weighting the Variables

Socio-economic and network features are obtained at the census section level. A census section is the smallest administrative unit used for statistical purposes in Spain. It is defined by the INE and typically corresponds to a neighborhood or a similar small geographic area within a municipality.

For each depot, this data is aggregated from all the individual census sections where deliveries are made. However, the number of deliveries in each census section can vary a lot. For instance, we can have a big census section with just a few deliveries and a small one with many deliveries. This particular case makes the smallest census section's indicators and metrics more meaningful for that service than the ones of the bigger area. Therefore, it is necessary to weigh every

socio-economic and geospatial indicator according to the number of deliveries made in each area.

Clustering analysis is carried out separately for parcel and food delivery. This separation attends to the performance, demand, and delivery differences between these two service types. For each of them, socio-economic and network indicators from all depots are analyzed. Although some clustering methods, such as hierarchical clustering or density-based methods could be used, because of the regular distribution of data, the ease of finding an optimal number of clusters, and results interpretability, K-means algorithm, a widely used unsupervised machine learning algorithm was chosen. It divides the data by a pre-defined number of clusters, where each data point belongs to the cluster with the nearest mean, minimizing the variance within clusters. It has proven to be a good method for customer segmentation among other applications.

Once the clustering is made, a classification dataset is created with the 854 parcel delivery services and their corresponding socio-economic and network features. The clustering group is added to the dataset as the target variable. The same is done with the 579
<table><caption>Table 2: Network features.</caption><thead><tr><td>Code</td><td>Description</td></tr></thead><tbody><tr><td>surface_m2</td><td>Average area of the census sections</td></tr><tr><td>way_distance_meters</td><td>Average road distance within the census sections</td></tr><tr><td>num_of_nodes</td><td>Average number of nodes in the census sections</td></tr><tr><td>avg_speed</td><td>Average maximum speed of roads in the census sections</td></tr><tr><td>max_max_speed</td><td>Average of the maximum speed of roads in the census sections</td></tr><tr><td>min_max_speed</td><td>Average of the minimum speed of roads in the census sections</td></tr><tr><td>speed_percentil_10</td><td>10th percentile of the average maximum speeds in the census sections</td></tr><tr><td>speed_percentil_90</td><td>90th percentile of the average maximum speeds in the census sections</td></tr><tr><td>avg_elev</td><td>Average of the average elevation in the census sections</td></tr><tr><td>max_elev</td><td>Average of the maximum elevations in the census sections</td></tr><tr><td>min_elev</td><td>Average of the minimum elevations in the census sections</td></tr><tr><td>elev_percentil_10</td><td>10th percentile of the average elevations in the census sections</td></tr><tr><td>elev_percentil_90</td><td>90th percentile of the average elevations in the census sections</td></tr><tr><td>avg_slope</td><td>Average of the average slope in the census sections</td></tr></tbody></table>

food delivery services. These datasets are used to create classification models with random forest machine learning algorithms, and these models can serve as a tool for decision-making.

## 3.2 Parcel Delivery Clustering

K-means algorithm needs the optimal number of clusters to be predefined. For choosing the best number in each iteration, we use 3 different methods: K-means Inertia, GMM (Gaussian Mixture Model) BIC, and GMM AIC methods. The optimal number depends on the data used, therefore we have different numbers of optimal clusters when using socio-economic indicators or network features. These numbers are:

* For socio-economic indicators: 6 clusters

* For network features: 4 clusters

The generated clusters distribution is depicted in Figure 1.

<figure><img src="image_120.png" /><figcaption>Figure 1: Parcel socio-economic vs network features cluster distribution.</figcaption></figure>

An overview of the indicator's impact on forming each cluster is shown in Figure 2. We can observe for example the variables that do not have almost an impact or the ones that have an impact in more than one cluster. Focusing on geospatial clustering, elevation metrics are meaningful to form two of the clusters.

With all the depots linked to a cluster, we have created random forest classification models to predict new depots. The models show an accuracy of 90% for the socio-economic analysis and 93% for the geospatial analysis.

To better understand the model performance and have more information for future decision-making, we have looked into the model explainability through a feature importance method. SHAP (SHapley Additive exPlanations) values allow understanding a machine learning model prediction by assigning each feature a contribution to the output. It shows which indicators have a bigger impact on each class prediction, Figure 3. In the case of socioeconomic indicators, apart from the salary variables, t6_1, t20_2, and t4_3 are the ones with a higher importance. These are related to age, origin, and housing. Although there are other indicators like t12_1 and t11_1, related to employment, that have a considerable impact in some specific clusters. For the network features, we appreciate that speed_percentil_10 is taken into account for two clusters followed by avg_elev in four clusters.

## 3.3 Food Delivery Clustering

Food delivery services work on a different basis than parcel ones. For instance, in every food delivery trip, the rider usually serves a few customers and then returns to the depot. Therefore, we have different performance metrics and a separate clustering study. The optimal number of cluster groups, using the same methods as in parcel analysis, are:

* For socio-economic indicators: 5 clusters

* For network features: 6 clusters

The generated clusters and their distribution are depicted in Figure 4. Unlike for socio-economic clus-
<figure><img src="image_121.png" /><figcaption>Figure 2: Parcel socio-economic vs network variables in clustering.</figcaption></figure>

<figure><img src="image_122.png" /><figcaption>Figure 3: Parcel socio-economic vs network features importance.</figcaption></figure>

ters, we see a balanced distribution for those with the network features.

Figure 5 shows the impact of the indicators to cre-

ate each cluster. It is worth mentioning that the most impacting network features are the same as in parcel delivery analysis.

Regarding the classification models, they give an accuracy of 94% in the socio-economic case and 91% in the geospatial case.

Finally, features importance for the model prediction, Figure 6, shows how for the socio-economic analysis, salary variables are again important, but now we have t14_1 and t9_1, related to retired people and education level respectively. In the geospatial case, now the lowest maximum speeds impact considerably in all the clusters while surface and average slope do not have any impact again.

# 4 APPLICATION USE CASE

A module has been built to plan services and their locations. The process starts with the user selecting the estimated delivery area of a new store on a map. Possible depot locations can also be selected, for which accessibility and centrality road network metrics are obtained. These metrics help decide the best possible location for the depot.

The socio-economic and network features of the
<figure><img src="image_123.png" /><figcaption>Figure 5: Food socio-economic vs network variables in clustering.</figcaption></figure>

<figure><img src="image_124.png" /><figcaption>Figure 6: Food socio-economic vs network features importance.</figcaption></figure>

census sections within the designed delivery area are aggregated and serve as input for the classification model. The model classifies the new depot within a

group, and every depot's performance metrics of that cluster are shown. Then, filtering can be made to keep only the most similar services according to some specific metrics, such as depots with a similar delivery area extension. Additionally, the average demand and fleet performance indicators of the filtered group are shown.

With this application, when a company designs a new service delivery area, it will be classified into a group of services with similar socio-economic and geographic conditions. These similar services show their fleet characteristics, performance, and delivery metrics. Therefore, this information can be used as a benchmark for decision-making regarding the sizing needs of the new service.

# 5 CONCLUSIONS

In this work, we have followed a methodology to cluster food and parcel delivery services from delivery motorbikes data, based on socio-economic indicators and geospatial metrics of the census sections where deliveries are made. The clustering results show patterns to classify these services based on how the inhabitants are or where they live. Additionally, the classification models show high accuracy and serve as a tool to obtain insights into the most meaningful variables and similarities of services already tested and working at the moment. Although the study has been done in the context of Spain, the same methodology can be followed in other places where service performance metrics and socio-economic or urban characteristics data could be obtained.

Opening a commerce poses several challenges and uncertainty regarding the social scenario and delivery fleet needs. Depot's performance metrics such as number of motorbikes, average delivery radius, con-
sumption per km, or total number of deliveries are
crucial indicators to ease this uncertainty.

This study can be used as a benchmark for store
owners to plan and size new stores, their location, and
delivery logistics.

mization: Fuzzy clustering analysis of e-commerce
customers' demands. *Computers in Industry*,
151:103960.

REFERENCES

Arregui, H., Cejudo, I., Arandia, I., Mujika, A., Eider, I., Laura, R., and Estibaliz, L. (2024). Last-mile delivery through electric motorbikes: Modelling considerations for parcel vs. food delivery. In *10th Conference of Transport Research Arena, TRA 2024*. In press.

Dupas, R., Hsu, T., and Taniguchi, E. (2024). A clustering-routing approach for assigning customers to depots in last mile delivery. *Transportation Research Procedia*, 79:13–20.

Hu, X., Zhang, G., Shi, Y., and Yu, P. (2024). How information and communications technology affects the micro-location choices of stores on on-demand food delivery platforms: Evidence from xinjiekou's central business district in nanjing. *ISPRS International Journal of Geo-Information*, 13(2).

Instituto Nacional de Estadística (2024). Instituto Nacional de Estadística. https://www.ine.es/. Retrieved February 5, 2024.

Kang, S. (2020). Relative logistics sprawl: Measuring changes in the relative distribution from warehouses to logistics businesses and the general population. *Journal of Transport Geography*, 83:102636.

Ko, S. Y., Sari, R. P., Makhmudov, M., and Ko, C. S. (2020). Collaboration model for service clustering in last-mile delivery. *Sustainability*, 12(14).

Ramírez-Villamil, A., Montoya-Torres, J. R., Jaegler, A., Cuevas-Torres, J. M., Cortés-Murcia, D. L., and Guerrero, W. J. (2022). Integrating clustering methodologies and routing optimization algorithms for last-mile parcel delivery. In de Armas, J., Ramalhinho, H., and Voß, S., editors, *Computational Logistics*, pages 275–287, Cham. Springer International Publishing.

Regal, A., Gonzalez-Feliu, J., and Rodriguez, M. (2023). A spatio-functional logistics profile clustering analysis method for metropolitan areas. *Transportation Research Part E: Logistics and Transportation Review*, 179:103312.

Sarkar, M., P. A. R. . C. F. R. (2024). Optimizing marketing strategies with rfm method and k-means clustering-based ai customer segmentation analysis. *Journal of Business and Management Studies*, 6(2):54–60.

Wangwattanakool, J. and Laesanklang, W. (2024). Delivery zones partitioning considering workload balance using clustering algorithm. In *14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2024*, pages 378–385. Science and Technology Publications, Lda.

Zheng, K., Huo, X., Jasimuddin, S., Zhang, J. Z., and Battaïa, O. (2023). Logistics distribution opti-
# Advancing Airport Land Subsidence Monitoring Through Time-Series InSAR Technology

Anuphao Aobpaet<sup>a</sup>, Intira Thanomsin, Chanikan Yodya<sup>b</sup> and Suchanpong Obnam<sup>c</sup>

Department of Civil Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
fengaha@ku.ac.th, {intita.tha, chanikan.yod, suchanpong.ob}@ku.th

**Keywords:** InSAR, Time-series InSAR, Land Subsidence Monitoring, Airport.

**Abstract:** The airport is a pivotal infrastructure project, serving as a hub for parking, transporting, and maintaining aircraft carrying passengers, freight, and cargo. However, the substantial usage of the airport leads to the challenge of land subsidence, necessitating ongoing monitoring and assessment. This study focuses on monitoring land subsidence at Suvarnabhumi Airport, Thailand's premier international airport catering to passengers and aircraft. In a lowland area with soft soil, applying advanced technology becomes imperative for continuous monitoring and analysis of subsidence over time. Employing InSAR Time-Series technology, researchers processed data from Sentinel-1 satellites spanning October B.E. 2017 to December 2023 to analyse the evolving conditions at Suvarnabhumi Airport. Results reveal that the most significant subsidence occurs in the Runway and Taxiway areas, with values ranging between -9.1 and 5.1 millimeters. per year. This subsidence is likely attributed to the constant heavy air traffic on these surfaces. Continuous monitoring and evaluation are crucial to planning effective maintenance. InSAR technology is valuable for monitoring land subsidence or displacement, alleviating data constraints and streamlining operational processes.

## 1 INTRODUCTION

An airport is a critical infrastructure connecting air and ground transportation systems and facilitating links between national and global economies (Pornpiboon, 1997; Chimtawan, 2005). Runways and taxiways experience continuous use and are subject to heavy loads and temperature fluctuations, leading to wear, deformation, and subsidence, posing risks to safety and infrastructure integrity.

Suvarnabhumi Airport, located in Samut Prakan Province, is in a soft soil zone on the lower Chao Phraya floodplain (Department of Mineral Resources, 2016). The area's geological structure comprises low-density soft sedimentary soils, making them unsuitable for supporting heavy loads. Groundwater extraction has exacerbated subsidence, with approximately 28–100 mm/year rates before airport operations (Srisompong, 2008). Post-construction monitoring indicates continued subsidence at 20–30 mm/year in the apron and runway areas. Ongoing

monitoring and evaluation are essential to mitigate potential damage.

Traditional subsidence monitoring methods, such as benchmarks and GNSS-based elevation surveys, provide high accuracy but are limited in spatial coverage and costly for large-scale monitoring (Kheerinarat, 2020; Eiaurattanawadi, 2023). InSAR technology, which uses phase differences in SAR satellite images, offers a cost-effective and efficient alternative for detecting land movement over large areas (Piyamarat, 2022). Time-series InSAR further enhances this by providing average subsidence rates in millimeters per year using widely available satellite imagery without requiring field installations.

This research applies time-series InSAR to analyse Sentinel-1 satellite images (October 2017 to December 2023) to monitor subsidence at Suvarnabhumi Airport, Thailand's largest airport. Subsidence is achieved through maps created in QGIS, which provide insights into subsidence patterns for improved infrastructure management and safety.

<footnote><sup>a</sup> https://orcid.org/0000-0001-7638-853X</footnote>

<footnote><sup>b</sup> https://orcid.org/0009-0004-7114-7066</footnote>

<footnote><sup>c</sup> https://orcid.org/0009-0001-0548-5639</footnote>
# 1.1 Scope of the Research

## 1.1.1 Study Area

Samut Prakan Province is located along the Chao Phraya River, at the river's endpoint just above the Gulf of Thailand, between latitudes 13-14 degrees north and longitudes 100-101 degrees east. The province covers an area of approximately 1,004 square kilometers, or about 627,557 rai, in central Thailand, about 29 kilometers southeast of Bangkok. The general landscape is predominantly lowland plains, with the Chao Phraya River flowing through the center, dividing the province into western and eastern parts (Samut Prakan Province, n.d.; Office of Project Administration, Royal Irrigation Department, 2018; Department of Mineral Resources, 2016).

Suvarnabhumi Airport (IATA: BKK, ICAO: VTBS) is located on Debaratana Road and Burapha Withi Expressway, approximately at kilometer 15, in the subdistricts of Nong Prue and Racha Thewa, Bang Phli District, Samut Prakan Province. It lies about 31 kilometers east of Bangkok and covers an area of approximately 22,000 rai. As Thailand's largest airport and the tenth largest globally, Suvarnabhumi Airport opened for commercial service on September 28, 2006. It serves as Thailand's primary airport, featuring modern design and advanced technology, providing excellent service across all areas (Airports of Thailand Public Company Limited, n.d.). Currently, Suvarnabhumi Airport has two runways, capable of handling 68 flights per hour. A third runway, 4,000 meters in length, is under construction on the airport's west side to accommodate increasing air traffic in the future and to ensure capacity during maintenance closures of Runways 1 and 2. The third runway will increase the airport's capacity to 94 flights per hour. In optimal conditions with entire runway and taxiway operations, it will support an average of 800-1,000 flights per day (Suvarnabhumi Airport Construction Project Management Office, n.d.; AEC Consortium Group, 2020). Thus, the airport is a vital infrastructure component that influences the future of the aviation industry, symbolising economic growth and facilitating international business operations.

## 1.1.2 Duration of Implementation

This study uses Sentinel-1 satellite images from October 2017 to December 2023 for analysis based on Synthetic Aperture Radar (SAR) technology.

<figure><img src="image_125.png" /><figcaption>Figure 1: Suvarnabhumi Airport Layout (Suvarnabhumi Airport Construction Project Management Office, n.d.).</figcaption></figure>

# 2 PRINCIPLES AND THEORY

## 2.1 Sentinel-1 Satellite Data

The Sentinel-1 satellite, part of the European Space Agency's (ESA) Copernicus program, began operations in 2014 and includes Sentinel-1A and Sentinel-1B satellites (Bunyapoluk, 2022). However, Sentinel-1B cannot transmit data back to Earth due to an issue with its power supply (Eiaurattanawadi, 2023). Sentinel-1 utilises a radar imaging system that uses microwave signals from a satellite-based energy source and transmits them to Earth at an oblique angle (GISTDA, 2021). It operates in the C-band with a frequency of 5.405 GHz, providing images with varying spatial resolutions and swath widths, as detailed in Table 1. For this research, the Interferometric Wide-Swath Mode (IW) is selected, covering a 250-kilometer swath and offering an image resolution of 5 x 20 meters in range and azimuth directions. Additionally, it operates in dual polarisation mode with an incidence angle ranging from 31 to 46 degrees (European Space Agency, n.d.).

<figure><img src="image_126.png" /><figcaption>Figure 2: SAR Operating Mode (European Space Agency, n.d.).</figcaption></figure>
<table><caption>Table 1: SAR Operating Mode Under Sentinel-1 Satellite (European Space Agency, n.d.).</caption><thead><tr><td>Modes</td><td>Swath (km)</td><td>Spatial resolution (m)</td></tr></thead><tbody><tr><td>Strip Map (SM)</td><td>80</td><td>5 x 5</td></tr><tr><td>Interferometric Wide Swath (IW)</td><td>250</td><td>5 x 20</td></tr><tr><td>Extra-Wide Swath (EW)</td><td>400</td><td>20 x 40</td></tr><tr><td>Wave (WV)</td><td>20 x 20</td><td>5 x 5</td></tr></tbody></table>

The Sentinel-1A satellite operates in a near-polar, sun-synchronous orbit at 693 kilometers, with an inclination angle of 98.18 degrees and a 12-day repeat cycle. Equipped with a C-SAR sensor, it provides data under all weather conditions, day and night. The satellite requires precise orbital control to ensure accurate InSAR measurements for land and maritime monitoring, emergency management, and infrastructure analysis.

## 2.2 Interferometric Synthetic Aperture Radar (InSAR)

InSAR (Interferometric Synthetic Aperture Radar) technology is an advancement of SAR technology, which combines Synthetic Aperture Radar (SAR) images with wave interferometry technology (Aobpaet & Trisirisatayawong, 2012). The working principle of InSAR involves analysing phase differences between two or more SAR images. Synthetic Aperture Radar is a radar system that produces high-resolution images (Piromthong, 2015). When images of the same area taken at different times are compared, the resulting phase differential indicates surface movement (Geoscience Australia, n.d.; Chelbi, Khireddine, & Charles, 2011). The difference due to movement creates discrepancies between the two images. The phase difference allows the study of deformation patterns in various forms of land changes (Chaitawee, 2015). However, while InSAR technology can operate in all weather conditions, both day and night, a significant limitation is the signal distortion caused by the atmosphere, which leads to phase measurement errors (Lu, Kwoun, & Rykhus, 2007).

## 2.3 Time-Series InSAR

The analysis using Time-series InSAR provides a sufficient density of checkpoints to resolve issues related to sparse checkpoints. It also addresses the limitations of using such points to monitor displacement or subsidence. As a result, this technique offers high accuracy and precision.

Persistent Scatterer Interferometry (PS-InSAR) is a technique that uses phase data from Synthetic Aperture Radar (SAR) images to analyse changes in land displacement over time or across specific periods. It relies on the consistent and permanent backscatter (Permanent Scatterer, PS) of radar signals, which are transmitted to objects and reflected to the satellite antenna. The method generates multiple Differential Interferogram pairs, each referenced to a master image for image matching (Chaitawee, 2015). This study processes time-series radar images using the Persistent Scatterers method, another approach in the Time-series InSAR technique.

## 2.4 Persistent Scatterers InSAR

Persistent Scatterers InSAR (PSInSAR) is an advanced remote sensing technique developed from InSAR to address issues related to the lack of data correlation and signal distortions caused by the atmosphere (Piromthong, 2015). It utilises the backscatter of microwave signals from prominent and permanent reflectors (Permanent Scatterers, PS), which reflect off objects and return to the signal receiver, resulting in backscattering values for each pixel. The amplitude and phase of each pixel are vector sums of the backscattering from various scatterers. Over time, any changes in these objects, regardless of the cause, lead to changes in the amplitude and phase of the pixel, indicating movement, as shown in Figure 3 (Sricharoenpramong, 2015; Hooper, Segall, & Zebker, 2007). This technique is, therefore, suitable for monitoring subsidence at airports.

<figure><img src="image_127.png" /><figcaption>Figure 3: Simulation of Scattering Characteristics a) Distributed Scatterer Pixel (b) Persistent Scatterer Pixel (Yhokha, Goswami, & Chang, 2018).</figcaption></figure>

## 2.5 Stanford Method for Persistent Scatterers (StaMPS)

StaMPS is a software used for InSAR processing with the Persistent Scatterers (PS) method, developed to
work even in areas without human-made structures or regions experiencing unstable deformation (Hooper, Bekaert, & Spaans, 2013). The core of the PS technique involves identifying PS pixels and using only the values from the selected pixels for displacement processing. In the early stages of the PS technique, amplitude values were primarily used for filtering, which limited its use to areas with bright scatterers, such as regions with many structures. Another limitation was that a model of surface displacement had to be known beforehand, or the surface needed to exhibit stable movement to provide reliable results. However, StaMPS uses amplitude and the positional correlation of phase values for PS pixel filtering. This allows StaMPS to identify PS pixels even in areas with few structures. It enables it to work in regions with non-stable displacement without prior knowledge of the surface displacement rate (Chaitawee, 2015).

# 3 METHODOLOGY

## 3.1 Preparation of Satellite Image Data

The data used in this study consists of images from the Sentinel-1 satellite in the ascending orbit, which moves from south to north over the Earth's surface, covering the area of Suvarnabhumi Airport, Samut Prakan Province, as shown in Figure 4. The dataset to be used includes 75 Synthetic Aperture Radar (SAR) satellite images recorded from October 25, 2017, to December 29, 2023, covering 7 years. These images are captured in the ascending orbit, with L1 Single Look Complex (SLC) data, operating in VH and VV polarisation modes, and recorded in Interferometric Wide (IW) mode. The coordinate system used is WGS 1984, in Path 172 and Frame 1222, ensuring all images overlap perfectly for subsequent analysis.

<figure><img src="image_128.png" /><figcaption>Figure 4: Scope of Ascending Orbit Satellite Images Covering the Study Area.</figcaption></figure>

## 3.2 Data Processing

Interferometric processing (InSAR) relies on the phase difference between two SAR images, referred to as the master image and the slave image, from the same area. Creating the interferogram uses the SNAP software to select the master image from the 75 images available. The image from October 21, 2020 (Subswath: IW2, Polarization VV, Burst 6-8) was chosen as the master image for reference and image matching, as shown in Figure 5. Then, the Co-registration algorithm aligned the sub-images from nearby areas, ensuring their coordinates were consistent (Laohudomchoke, 2023).

<figure><img src="image_129.png" /><figcaption>Figure 5: Image Matching of Slave Using the Master Image as a Reference.</figcaption></figure>

The algorithm for processing time-series satellite data works as follows: first, Sentinel-1 satellite images are downloaded. Once completed, the SNAP software is used to read and convert the data by performing image matching between the Master and Slave images to create the interferogram. Then, a PS candidate file is created (using the command `mt_prep_snap`), and MATLAB is used to process the data with the Persistent Scatterers method in StaMPS to identify PS pixels. Phase unwrapping is performed to obtain complete phase values, and atmospheric noise is corrected using a linear process in TRAIN. Finally, time series plots are generated (Ladawadee, 2022). The workflow for the algorithm to determine subsidence rates can be illustrated in the flowchart shown in Figure 6.

## 3.3 Displaying Data on the Subsidence Map

Using MATLAB, the application of time-series InSAR for monitoring subsidence at Suvarnabhumi Airport allows for calculating displacement (Deformation) and coordinates (Latitude and longitude). The displacement data can then be
<figure><img src="image_130.png" /><figcaption>Figure 6: Flowchart of the Algorithm Workflow.</figcaption></figure>

displayed on a map in QGIS, as shown in Figure 9. This is done by creating a data layer and defining coordinates along the axis, followed by classification to display the frequency of the data. The resulting data is shown as points, with varying colors representing the different subsidence values across the study area, as depicted in Figure 7.

<figure><img src="image_131.png" /><figcaption>Figure 7: Results of Subsidence Analysis from MATLAB.</figcaption></figure>

## 3.4 CORS Station

This research used data from one CORS station, BPLE, operated by the Department of Lands. This station is located at the Samut Prakan Land Office, Bang Phli branch, as shown in Figure 8. It provides

automatic GNSS satellite signal surveying data under the supervision of the Royal Thai Survey Department. The data was collected from 2021 to 2023 and processed to display horizontal and vertical displacement. The results are presented in positional data (Latitude, Longitude, Height), which can be used as a reference for comparing subsidence trends alongside time-series InSAR analysis using Sentinel-1 satellite images from the same period. The GNSS data were obtained through high-precision single-point positioning, calculated by GPS surveying using the online AUSPOS service, developed by Geoscience Australia, a government agency. This service offers free online GPS data processing. The data processing utilises MicroCosm software, using high-precision satellite orbit and IGS high-precision satellite clock corrections. The processing can only be done with static survey data using dual-frequency receivers in RINEX format (Receiver Independent Exchange Format). The accuracy of the results may depend on the quality of the receiver, the survey duration, and the distance from the reference station (Sukwimonsaree et al., 2011).

<figure><img src="image_132.png" /><figcaption>Figure 8: Position of BPLE CORS Station.</figcaption></figure>

<figure><img src="image_133.png" /><figcaption>Figure 9: A subsidence rate map of Suvarnabhumi Airport, focusing on the runways and taxiways that require special attention, derived from time-series InSAR processing.</figcaption></figure>
# 4 RESULTS

## 4.1 Checking the Subsidence Value

The study on the application of time-series InSAR for monitoring subsidence at Suvarnabhumi Airport, using 75 Sentinel-1 satellite images from October 2017 to December 2023, detected 44,343 PS pixels indicating subsidence and uplift across the airport area. The analysis of subsidence values in the ascending satellite orbit revealed land subsidence ranging from -9.1 mm/year (subsidence) to 5.1 mm/year (uplift), as shown in Figure 9. These subsidence values are within the normal range for airport design in typical lowland areas, allowing subsidence of up to 300 to 450 mm over 10 years without causing operational or safety issues (Srisompong, 2008).

As shown in Figure 13, the runway and taxiway areas have a clear difference in subsidence compared to other areas. For example, areas marked with orange to red indicate subsidence ranging from -9.1 to -4 mm per year, showing more significant subsidence than other locations. These areas should, therefore, be closely monitored and inspected. However, time-series InSAR provides preliminary information about subsidence.

## 4.2 Verification of Time-Series InSAR Technique

The processing using the Persistent Scatterer Interferometry (PSI) time-series technique to monitor subsidence at Suvarnabhumi Airport, specifically in the runway and taxiway areas (Runway 2), referenced at coordinates Lat 13.6637, Lon 100.7536, was conducted within a 100-meter radius. As shown in Figure 10, the airport's cumulative subsidence trend is increasing. The graph in Figure 11 illustrates daily cumulative subsidence values in millimeters. The detailed time-series measurements of PS deformation reveal a clear subsidence pattern.

## 4.3 Comparison of Subsidence Trends

The results obtained from processing RINEX data from 2021 to 2023 for station BPLE, reported as positional data including Latitude (X), Longitude (Y), and Height (Z), reveal movement in three directions: X, Y, and Z.

This research focuses on vertical displacement (Z) or land subsidence. From data processing, the height of station BPLE on January 25, 2021 (initial date), was -18.319 meters, and on December 29, 2023

<figure><img src="image_134.png" /><figcaption>Figure 10: The points within the 100-meter inspection radius on Runway 2, located at coordinates Lat 13.6637, Lon 100.7536, are measured in the Line of Sight (LOS).</figcaption></figure>

<figure><img src="image_135.png" /><figcaption>Figure 11: The cumulative subsidence trend from 2017 to 2023 within a 100-meter radius on Runway 2, located at coordinates Lat 13.6637, Lon 100.7536.</figcaption></figure>

(final date), it was -18.390 meters. A height difference of 0.071 meters, equivalent to 71 millimeters, was observed from 2021 to 2023. Over the three years of processing, this indicates a land subsidence rate of 71 millimeters or 23.7 millimeters per year. As shown in Figure 12, when compared to the subsidence rates obtained using the InSAR technique at Suvarnabhumi Airport, which range from -9.1 to -5.1 millimeters per year, the results demonstrate an increasing trend in land subsidence that is consistent with the overall pattern.

The subsidence rate trends derived from InSAR time-series processing and the permanent GNSS station BPLE, under the Department of Lands, from 2021 to 2023, exhibit a consistent pattern. The subsidence rate increases over time. The subsidence in the Suvarnabhumi Airport area is lower due to soil improvements and specialised construction techniques designed to support massive loads. In contrast, station BPLE, located approximately 10
<figure><img src="image_136.png" /><figcaption>Figure 12: The height statistics and subsidence trends of station BPLE.</figcaption></figure>

kilometers from the airport, shows a higher subsidence rate despite being in a nearby area.

## 5 CONCLUSIONS

Time-series InSAR technology is an effective method for monitoring airport subsidence. This study analysed Sentinel-1 satellite data using the Persistent Scatterer (PS) technique from October 2017 to December 2023, utilising 75 ascending orbit SLC images. The analysis identified 44,343 monitoring points with subsidence rates ranging from -9.1 to 5.1 mm/year, where negative values indicate subsidence and positive values indicate uplift. Runway and taxiway areas exhibited significant cumulative subsidence due to continuous air traffic use.

GNSS data from the BPLE station (10 km from Suvarnabhumi Airport) recorded a subsidence rate of 23.7 mm/year (2021–2023), aligning with InSAR-derived trends. Differences in subsidence values reflect varying construction techniques and land use. Still, both highlight Samut Prakan's susceptibility to subsidence due to its soft soil and impacts from urbanisation and groundwater extraction.

The findings underscore the importance of continuous subsidence monitoring to mitigate risks. InSAR technology proves valuable in engineering surveys, offering preliminary insights into ground stability, reducing fieldwork, and optimising time and costs. Thus, it is a practical tool for infrastructure management and hazard prevention.

## ACKNOWLEDGEMENTS

The authors thank the European Space Agency (ESA) for providing the Sentinel-1 satellite images from the Copernicus program from 2017 to 2023, distributed by ASF DAAC and the SNAP (Sentinel Application

Platform) software. I also thank Prof. Andy Hooper from the School of Earth and Environment, University of Leeds, for the StaMPS (Stanford Method for Persistent Scatterers) software. Thanks to MathWorks for providing MATLAB (Matrix Laboratory) and to the software development team behind QGIS (Quantum GIS), which was used for processing and visualising data with the time-series InSAR technique. I would also like to thank the Royal Thai Survey Department (RTSD) and the Department of Lands (DOL) for providing the RINEX Data. Finally, I would like to thank the AUSLIG Online GPS Processing Service (AUSPOS) for providing free GNSS data processing software. This research has received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [grant number B11F670110]

## REFERENCES

* Pornpiboon, M. (1997). *Airports*. In *Thai Encyclopedia for Youth, Volume 22, Topic 9: Airports*.

* Chimtawan, P. (2005). *Suvarnabhumi Airport and Its Potential as an Aviation Hub of Southeast Asia*. Academic report for the Advanced Certificate in Public Economics for Senior Executives, King Prajadhipok's Institute.

* Airports of Thailand Public Company Limited. (n.d.). *Suvarnabhumi Airport*. Retrieved from https://suvarnabhumi.airportthai.co.th/about-us

* Samut Prakan Province. (n.d.). *Geography and Climate*. Retrieved from https://www.samutprakan.go.th

* Office of Project Administration, Royal Irrigation Department. (2018). *Master Plan Report for Basin Development in Samut Prakan Province*.

* Department of Mineral Resources. (2016). *Zoning for Geological and Mineral Resource Management in Samut Prakan Province*.

* Suvarnabhumi Airport Construction Project Management Office. (n.d.). *Runway 3 Construction Project, Suvarnabhumi Airport*. Retrieved from https://www.bkk-runway3.com

* AEC Consortium Group. (2020). *Runway 3 Construction Project, Suvarnabhumi Airport: Monthly Service Report for September 2020*.

* LeGrand Beltran. (2021). *Understanding Airport Design and Operations*. Retrieved from https://www.scribd.com/document

* GISTDA. (2021). *How Radar Satellites Can Detect Ships*. Retrieved from https://www.gistda.or.th/news_view.php?n_id=1511&lang=EN

* Bunyapoluk, T. (2022). *Development of Dam Safety Monitoring Using Time-Series InSAR Technology*. In *The 27th National Civil Engineering Conference*.

* European Space Agency. (n.d.). *Sentinel-1 Data Access and Products*. Retrieved from https://sentinels.
copernicus.eu/documents/247904/1653440/Sentinel-1_Data_Access_and_Products

Piyamarat, N. (2022). *Application of Time-Series InSAR Technology for Monitoring the Movement of Srinagarind Dam*. In *The 27th National Civil Engineering Conference*.

Geoscience Australia. (n.d.). *Interferometric Synthetic Aperture Radar*. Retrieved from https://www.gov.au/scientific-topics/positioning-navigation/geodesy/geodetic-techniques/interferometric-synthetic-aperture-radar

Aobpaet, A., & Trisirisatayawong, I. (2012). *InSAR Time Series Analysis for Land Subsidence Monitoring in Bangkok and Its Vicinity Area*. Chulalongkorn University.

Piromthong, P. (2015). *Detection of Land Subsidence Rate During 1996-2000 and Subsidence Trends in Bangkok Metropolitan Region Using Time-Series InSAR Technique*. In *The 20th National Civil Engineering Conference*.

Chelbi, S., Khireddine, A., & Charles, J. (2011). *Interferometry process for satellite images SAR*. Paper presented at the 2011 7th International Conference on Electrical and Electronics Engineering (ELECO).

Chaitawee, S. (2015). *Monitoring Land Subsidence in the Eastern Bangkok Metropolitan Area Using Time-Series InSAR Technique*. In *The 19th National Civil Engineering Conference*.

Lu, Z., Kwoun, O., & Rykhus, R. (2007). *Interferometric Synthetic Aperture Radar (InSAR): Past, Present and Future*. Photogrammetric Engineering & Remote Sensing.

Hooper, A., Segall, P., & Zebker, H. (2007). *Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos*. Journal of Geophysical Research, 112, B07407.

Hooper, A., Bekaert, D., & Spaans, K. (2013). *StaMPS/MTI Manual Version 3.3b1*. School of Earth and Environment, University of Leeds.

Ladawadee, A. (2022). *Study of the Relationship between Changes in Groundwater Pressure and Land Subsidence Rate Using Time-Series InSAR Technique*. In *The 27th National Civil Engineering Conference*.

Laohudomchoke, C. (2023). *Monitoring Infrastructure Movements Using InSAR Technology: A Case Study of the Upper Reservoir of Lam Takhong Pumped Storage Power Plant*. In *The 28th National Civil Engineering Conference*.

Srisompong, S. (2008). *Causes and Remedies for Runway and Taxiway Damage at Suvarnabhumi Airport*. Journal of the Faculty of Architecture, King Mongkut's Institute of Technology Ladkrabang.

Eiaurattanawadi, N. (2023). *Monitoring Land Subsidence in Bangkok and Vicinity During 2017-2022 Using Time-Series InSAR Technique with MintPy Software*. In *The 28th National Civil Engineering Conference*.

Yhokha, A., Goswami, P.K., & Chang, C.P. (2018). *Application of Persistent Scatterer Interferometry (PSI) in monitoring slope movements in Nainital,*

*Uttarakhand Lesser Himalaya, India*. Journal of Earth System Science, 127.

Kheerinarat, C. (2020). *Analysis of Land Subsidence Rate in Bangkok Using InSAR Data*. In *The 25th National Civil Engineering Conference*.

NCDC (National Continuous Coordinate Reference Data Center). (n.d.). *Thai CORS Stations*. Retrieved from https://ncdc.in.th

Sukwimonsaree, T., et al. (2011). *Accuracy Assessment of Online GPS Survey Data Processing Services*. Engineering Journal of Kasetsart University, 76(24), April-June 2011.
# An Innovative Model Based on Carvalho Rodrigues's Entropy to Assess Governance in Africa: A Guinea-Bissau Case Study

João Serras<sup>a</sup>, Paulo Morgado<sup>b</sup> and Jorge Malheiros<sup>c</sup>

Centre of Geographic Studies, Institute of Geography and Spatial Planning, Associate Laboratory TERRA,
University of Lisbon, Lisbon, Portugal
{joao26, paulo}@edu.ulisboa.pt, jmalheiros@campus.ul.pt

**Keywords:** Governance Cohesion, Governance Modelling, Cohesion Phase Transition.

**Abstract:** Governance is an abstract, intersubjective, fluid concept, meaning that it is built upon shared beliefs, norms, and practices that are collectively agreed upon by a society, community, or group. It means different things to different cultural environments and scopes. Our current research addresses the challenge of obtaining insight into Governance's underlying mechanisms by using the concept of Cohesion derived by Carvalho Rodrigues from applying Shannon's Entropy. Our study is based on empirical data obtained from field observations across Guinea-Bissau.

This paper presents our cohesion model and a first outlook for using it in the available data sets. It shows the impact of several potential Governance Determinants over a set of specific Governance Dimensions, demonstrating that Ethnicity Variation, local Community Morphology and the distance of Central Government facilities are the most impacting determinants for better cohesions.

## 1 INTRODUCTION

There's no consensus about the meaning and scope of Governance, which may be related to organisation sizes, from simple families to countries. Within the specific context of our work, governance is understood as the *structuring of Governance*, which is distinct from *government*, which refers to the act of governing. As defined by the Institute on Governance in Canada, Governance refers to the way society or groups within it organise themselves to make decisions (Institute on Governance, 2022). Consequently, in this study, the term *Governance* focus on social Governance rather than narrower concepts such as corporate Governance. Specifically, it pertains to how public institutions and systems of authority operate to manage public affairs and serve citizens' needs and interests.

Governance is a complex intersubjective phenomenon influenced by various political, economic, social, and geographic factors. Significant and recurring failures of international policies and institutions in effectively stabilising societies and promoting global

prosperity and well-being are of particular concern. Examples include governance crises in regions such as West Africa, e.g., Mali, Burkina Faso, Guinea-Coakry, and Nigeria.

In recent years, there has been a growing interest in understanding the drivers that impact Governance, particularly in peripheral sphere of the European Union, such as West Africa as it is illustrated by the work of many scholars such as (Abubakar et al., 2020; Achanso, 2022; Krawczyk & Sweet-Cushman, 2016).

Guinea-Bissau, a small country in this region characterised by its ethnic, religious, and social diversity, irregular political trajectory—including a civil war, political instability and economic underdevelopment, in par with formal institutions, such as a constitution derived from external concepts, presents a compelling case for exploring this issue.

Our research seeks to obtain insight into how some candidate Governance determinants influence governance outcomes in Guinea-Bissau, taken as a proxy of West African societies. To achieve this, our group used as a keystone the concept of Cohesion as proposed by Carvalho Rodrigues (Carvalho Rodrigues,

<footnote>a https://orcid.org/0000-0002-7619-509X</footnote>

<footnote>b https://orcid.org/0000-0002-3220-4943</footnote>

<footnote>c https://orcid.org/0000-0002-0976-044X</footnote>
1994; Carvalho Rodrigues & Peixoto, 1991). This was used to assess several Governance dimensions and for several possible drivers or Determinants.

# 2 CONCEPTUAL FRAMEWORK

## 2.1 Baseline Model

### 2.1.1 Complexity of Behaviour and Information

In the book Systems, Entropy and Cohesion (Carvalho Rodrigues & Peixoto, 1991, p. 213), the authors introduce a fundamental distinction: the differentiation between Complexity and Behavioural Complexity, quoting, "[...] there are systems that contain or encompass many elements, or parts, with very predictable behaviour, we would even say simple, and there are very simple systems with few parts or elements with enormous behavioural complexity", a distinction that is considered fundamental for the research we have set to carry out, which focuses precisely on the behavioural elements derived from Governance. The authors add: "Complexity comes primarily from the number of components, while behavioural complexity is due to the type and degree of links [between the components]".

They go on to say that there is a relationship between these two concepts, which, in the authors' interpretation, is achieved through the "amount of information needed to describe or interpret the behaviour of the system", which does not depend on the number of constituent elements, but instead on the type and diversity of their connections.

An example of this situation, in terms of urban organisation figure below shows two communities with roughly the same population: Gendo and Sansanto, in the Oio region, north of Bissau. Gendo is a type of agglomeration with no orientation and Sansanto is a street type Community, with a clear orientation and organisation around the road.

<figure><img src="image_137.png" /><figcaption>Figure 1: Spatial organisation of two typical communities in Guinea-Bissau in the Oio region. Gendo (left) and Sansanto (right).</figcaption></figure>

Thus, Gendo exhibits a higher complexity in household distribution with a similar number of constituents; however, it requires more information to properly describe as it exhibits greater entropy.

### 2.1.2 Information, Entropy and Cohesion

It is possible to determine the degree of Cohesion of a structure maintained by information, as is the case in social structures, based on the quantity of information in that structure, which in human society can translate into a sense of well-being or degradation (Carvalho Rodrigues & Peixoto, 1991, pp. 217–223). To determine this, they use the results of Fisher as mentioned by Frieden (Frieden, 1998), Shannon (Shannon, 1948) and the equation derived by F Carvalho Rodrigues for the quantity of information (Carvalho Rodrigues, 1994, p. 22):

$$H(N, \sigma, p) = - \frac{\sigma}{N} \log_e p = - p \log_e p \quad (1)$$

Where σ is the number of occurrences of an event and N the number of possible events. Estimating H for the communities and for each of the Governance dimensions and determinants is the keystone constituent of our work.

### 2.1.3 The Cohesion Curves

Carvalho Rodrigues (Carvalho Rodrigues, 1994, pp. 42–43), established the behaviour of a system cohesion as it depends on a relevant factor. The figures below, illustrate the situation.

<figure><img src="image_138.png" /><figcaption>Figure 2: Justice Dimension Cohesion function extracted from the actual Guinea-Bissau dataset. The maximum Cohesion was obtained at 0.368.</figcaption></figure>
<figure><img src="image_139.png" /><figcaption>Figure 3: Justice Dimension Cohesion function. This plot illustrates that, for Justice, the larger portion of the communities have a slope greater than -1, or 45°, which can interpret as this being strongly cohesive and *export entropy* to a relatively small number of communities – the ones having slope greater than 1. In social terms it may mean that people are comfortable with Justice, a natural outcome, since most of the Justice is practices by *Big Men* and *Good Men* representatives. A compound slope greater than -1, actually, -0.806 may be interpreted as an overall positive cohesion, in this case, for the *Justice Governance Dimension*.</figcaption></figure>

### 2.1.4 Governance and Cohesion

Vergolini (Vergolini, 2011, pp. 198–199), quoting Canadian Heritage, defines Social Cohesion as "a process that contributes to building a common sense of belonging to the same community" and Lafaye (Lafaye, 2011) refuses a clear definition, preferring to describe and discuss various types of interpretation of social Cohesion, including societal, individualistic and mixed models, and thus finding multiple definitions.

It is important to clarify that in our study, the concept of Cohesion does not directly correspond to social Cohesion, but to the more abstract concept of *Cohesion of a System*, regardless of its specific type.

It is also assumed that governing a society consists of introducing mechanisms to organise it. In other words, governing consists on reducing the Entropy of the society system, which, by direct implication, corresponds to increasing the Cohesion of that system.

To explain the behaviour of the constituents of an anarchic society with maximum Entropy requires a vast amount of information describing all the individual behaviours; to describe the behaviour of a completely organised society with recognised, shared and obeyed laws, you need to know the rules that shape the personal behaviours, which are necessarily far fewer in number. Thus, it is essential to distinguish

between two broad classes of concepts: the dimensions of Governance, i.e. the elements that allow us to gauge how Governance is practised, and the determinants of Governance, i.e. the elements that can potentially impact the sense of Governance, a sure sense and coherence, concepts that we intend to clarify and characterise below.

The essential assumption here is that Governance contributes to structuring a society, which globally corresponds to lowering its Entropy, which will be reflected in the fact that cohesion indicators indicate a change of phase at higher values on the scale of loss of Cohesion.

## 2.2 Governance Dimensions

Various groups of *Governance Indicators* have been proposed by various organisations.

To move forward with the identification of indicators, we have taken the following indicators commonly accepted as universal as a basis for governance indicators: Education, Health, Justice, Food Security and Infrastructure. This selection is based on a combination of perspectives from three public sources: the World Bank indicators (World Bank, 2023), the UNDP indicators to make up the HDI<fnref n="1" />, and the United Nations SDGs<fnref n="2" /> (United Nations Department of Economic and Social Affairs, 2015, 2023). From these, the below *Governance Dimensions* were structured to obtain an understanding of the possible impact.

<table><caption>Table 1: The studied Governance Dimensions.</caption><thead><tr><td>ACRONYM</td><td>DESCRIPTION</td></tr></thead><tbody><tr><td>uer</td><td>Education</td></tr><tr><td>usr</td><td>Health</td></tr><tr><td>ujr</td><td>Justice</td></tr><tr><td>uar</td><td>Food Security</td></tr><tr><td>urr</td><td>Family Wealth</td></tr><tr><td>uir</td><td>Infrastructure equipment</td></tr><tr><td>ucr</td><td>Culture</td></tr><tr><td>upr</td><td>Community Assets</td></tr><tr><td>ufr</td><td>Happiness</td></tr></tbody></table>

## 2.3 Possible Impacting Governance Determinants

The authors found no significant body of research work linked to empirical data regarding drivers of governance at a Community level, leaning to the conclusion that its scope and granularity are not mainstream investigations. In fact, mainstream work is much related to abstract concepts such as some key

<footnote n="1">¹ HDI: Human Development Index</footnote>

<footnote n="2">² SDG: Sustainable Development Goals</footnote>
determinants reported in the African Governance Report (Africa Governance Report, 2023) and by the World Bank (World Bank, 2023), to not only account for actual livelihoods but also carry a pure top-down perspective on Governance. Two simple examples illustrate the situation in rural West-Africa: on *Rule-of-Law* villagers find, regularly, that the Koran along with their social norms, provides any requitable rule to be obeyed, thus the ultimate State Law, the Constitution, written somewhere by non-related (normally dead) people, is not really to be considered, and *Control-of-Corruption*, villagers find that this concept somehow contradicts what is, for them, considered normal business procedures.

Nevertheless, the work of scholars such as Rotberg (Rotberg, 2009), Alence (Alence, 2004), and Chabal (Chabal, 2009), should be noted as they address relevant elements of livelihoods and their linkage to forms of governing.

Nevertheless, the African Union published its own Development Agenda for 2063, linked to the SDGs, containing 7 major aspirations from which potential drivers for Governance could be drawn and would constitute the foundation of our set of Determinants.

In fact, the determinants of Governance will be the elements that contribute to Governance taking place in the society under analysis, in this case Guinea-Bissau, taken as a proxy for West Africa, which implies, that in terms of the basic principles of information theory which are directly related to the concept of Entropy in the 2nd principle of thermodynamics: the lower the Entropy, the more organised the system is and likely to attain the ultimate goal of Governance: organise.

The final list of potential Determinants to evaluate is listed in the below table

<table><caption>Table 2: The suggested Governance Determinants.</caption><thead><tr><td>ACRONYM</td><td>DESCRIPTION</td></tr></thead><tbody><tr><td>mrp</td><td>Community Morphology</td></tr><tr><td>dst</td><td>Distance to Government Service.</td></tr><tr><td>etn</td><td>Ethnical Variation</td></tr><tr><td>pss</td><td>Chabal Rules</td></tr><tr><td>nds</td><td>Needs and Wants Satisfaction</td></tr><tr><td>env</td><td>Environment Stress</td></tr><tr><td>piv</td><td>Public Investment</td></tr><tr><td>sec</td><td>Secularity Bias</td></tr><tr><td>soc</td><td>Social Organisation and relations with neighbour communities</td></tr></tbody></table>

## 2.4 Modelling Determinants' Impact

The next step in the work is to propose a method to evaluate the impact on Governance, of the several Determinants under analysis. To assess this, the following set of axioms was accepted:

* Governance intimately implies the structuring of a society; this means that the higher the cohesion value of an indicator, the best Governance is in that specific indicator<fnref n="3" />.

* The impact of a specific Determinant, can be measured by identifying the Communities for which the cohesion value (H) is higher than the median, doing the same for the Communities with H lower than the median, and computing for the specific Dimension analysis, the values of H on the Dimension for both sets and the picture below illustrates this strategy.

<figure><img src="image_140.png" /><figcaption>Figure 4: The impact of the *Determinant Environment Stress on Community Wealth*. It may be noticed that both curves are pretty much aligned, which suggests that there is no real noticed impact of the *Environment* on local wealth conditions.</figcaption></figure>

# 3 ASSESSING AND COMBINING DATA

## 3.1 Strategy to Address Core Concerns

### 3.1.1 Key Concerns

The strategy to address data quality and completeness of the available data sets is bound to two sets of core

their sustainability and/or development and happiness, so best must be framed under the limited concept of retaining better cohesion.

<footnote n="3">³ Naturally, this situation must be taken with extreme caution as there is no distinction from democracy to dictatorship which may carry similar cohesion values but may also have wildly different outcomes in respect to</footnote>
concerns: first, there exist no surveys that directly question the adopted Governance Dimensions or Determinants, so a means of converting the available information into observed variables had to be established and, secondly, the available data, itself, needed to be assessed for its quality, namely in what regards the explanation of the variations, i.e., the independency of the available 1,400 data points per each of the 208 communities, which conducted to a separated study whose results are summarised below.

### 3.1.2 Raw Data Analysis Outlook

Raw Data Analysis, consisted in obtaining preliminary insight into the data collected, and checking some of their key parameters, such as the likeliness of its consistency, namely for explaining the variation, identification of correlations, etc...

### 3.1.3 Correlation Analysis

Correlation analysis was carried using both Pearson and Spearman methods.

<figure><img src="image_141.png" /><figcaption>Figure 5: The figure suggests an overall comprehensive set of data points, with 90% of them having correlation factors less than 0.3. The negative correlation, the % is much higher, suggesting that the Data Set displays a significant orthogonality.</figcaption></figure>

### 3.1.4 Principal Component Analysis

The goal of carrying out this analysis was to identify the most relevant questions as measured by their contribution to the most relevant Factors. For this, the following SPREE plot was obtained.

### 3.1.5 Combining Raw Data into Dimensions and Determinants

To achieve this all the data elements were analysed and classified into one of the *Dimensions* or one of the *Determinants*, avoiding any overlap to ensure orthogonality.

<figure><img src="image_142.png" /><figcaption>Figure 6: The plot displays the relative relevance of the PC and, Kaiser Criterion, in red in the figure. Principal Component with an eigenvalue less than 1 explains less variance than one of the original variables in standardised data. However, a quick observation hints that, with roughly 20 components, we could explain most of the variation.</figcaption></figure>

## 4 COHESION ANALYSIS

### 4.1 Cohesion Assessment Highlights

A thorough analysis is out of the scope of the current communication, so we present its current outcome as per the estimated impact of Determinants on Governance.

The next table presents a quick outlook of the overall Cohesion analysis.

<table><caption>Table 3: Cohesion Highlights.</caption><thead><tr><th>DESCRIPTION</th><th>p @<br>max H</th><th>max H</th><th>global<br>slope</th></tr></thead><tbody><tr><td><strong>DIMENSIONS</strong></td><td></td><td></td><td></td></tr><tr><td>Education</td><td>0.370</td><td>0.368</td><td>1.394</td></tr><tr><td>Health</td><td>0.414</td><td>0.365</td><td>1.078</td></tr><tr><td>Justice</td><td>0.368</td><td>0.368</td><td>0.806</td></tr><tr><td>Food Security</td><td>0.367</td><td>0.368</td><td>0.705</td></tr><tr><td>Family Wealth</td><td>0.365</td><td>0.368</td><td>1.357</td></tr><tr><td>Infrastructure equipment</td><td>0.368</td><td>0.375</td><td>1.118</td></tr><tr><td>Culture</td><td>0.368</td><td>0.368</td><td>0.499</td></tr><tr><td>Community Assets</td><td>0.368</td><td>0.378</td><td>1.447</td></tr><tr><td>Happiness</td><td>0.368</td><td>0.361</td><td>0.724</td></tr><tr><td><strong>DETERMINANTS</strong></td><td></td><td></td><td></td></tr><tr><td>Community Morphology</td><td>0.368</td><td>0.378</td><td>1.507</td></tr><tr><td>Distance to Government Service.</td><td>0.369</td><td>0.368</td><td>1.083</td></tr><tr><td>Ethnical Variation</td><td>0.374</td><td>0.368</td><td>1.225</td></tr><tr><td>Chabal Rules</td><td>0.353</td><td>0.368</td><td>0.778</td></tr><tr><td>Needs and Wants Satisfaction</td><td>0.358</td><td>0.368</td><td>0.600</td></tr><tr><td>Environment Stress</td><td>0.003</td><td>0.016</td><td>1.541</td></tr><tr><td>Public Investment</td><td>0.080</td><td>0.201</td><td>2.728</td></tr><tr><td>Secularity Bias</td><td>0.368</td><td>0.368</td><td>0.385</td></tr><tr><td>Social Organisation and relations<br>with neighbour communities</td><td>0.367</td><td>0.368</td><td>0.716</td></tr></tbody></table>

The interpretation is that the lower the *max H* and/or the *p@max H*, the less cohesive the structure. Subsequently, the outline values shown in Table 3,
suggest that the *Health Dimension* is more cohesive
than any other *Dimensions* or *Determinants*, as it
peaks at 0.414 for the *p@max H* parameter. However,
its global scope of 1.078 hints that the number of com-
munities with low entropy levels is less than *Justice,
Food Security* or *Culture*. A likely interpretation is
that, for instance, *Culture* and *Justice* are more homo-
geneous than *Health* across the country. A special note
is the value of the slope found for *Happiness*, which is
consistent with *Culture*, *Food Security* and *Justice*,
which is likely to suggest that they are interrelated, po-
tentially due to the type of West-African communities.

Finally, we should mention two outliers, *Environ-
ment Stress* and *Public Investment* that clearly require
a close inspection of the source data, as they present
slope values very high, accompanied by *p*-axis values
very low.

4.2 Impact Evaluation Highlights

4.2.1 Impact on Dimensions

To estimate the overall impact of the studied Determi-
nants, our project obtained the sum of the lags between
the *High Cohesion* and *Low Cohesion* curves as illus-
trated above in Figure 4 for each specific *Dimension*.

The open source *meethere app*, developed within
our project provides a detailed output of this analysis
and it is not in the scope of the current communica-
tions to analyse the full set of outcomes. Neverthe-
less, we show the result for the Education Dimension
in the below figure to illustrate our analysis.

<figure><img src="image_143.png" /><figcaption>Figure 7: The impact of the *Determinants on the Education Dimension*.</figcaption></figure>

The above figure shows that the different Deter-
minants have different impacts and, out of the nine
Determinants, only four, *Ethnicity Variation*, *Com-
munity Morphology*, *Public Investment* and *Govern-
mental Distance* carry better results. These findings
suggest that if the Government wants to focus its mea-
gre resources on improving Education, it will benefit
by addressing its policies, investment, and budget
spending, specifically in those factors.

4.2.2 Overall Impact on Governance

To obtain the Overall impact on Governance for each *Determinant*, the project added all individual contri-
butions to each dimension:

<figure><img src="image_144.png" /><figcaption>Figure 8: The figure show that the most relevant drivers of *Governance*.</figcaption></figure>

The above figure shows that *Public Investment, Chabal Principles*<fnref n="4" /> (Chabal, 2009), *Ethnic Variation, Government Services availability*, and *Community Morphology*, have an edge over *Secularity*<fnref n="5" />, *Societal Organisation, Environment Stress* and *Satisfaction of immediate Needs*.

This can be interpreted as, being drivers of Gov-
ernance, namely the availability of Government Ser-
vices at community level, expenditure in them will
likely potentially bring the best outcomes. A good ex-
ample, when we look at the detailed inputs for this list
*Determinant*, is the benefit of replacing the process of
Citizen card, which currently is at the Region<fnref n="6" /> level,
to at a Sector<fnref n="7" /> level, which would come at almost
zero spending.

<footnote n="4">⁴ The Chabal Principles represent the local societal norms</footnote>

<footnote n="5">⁵ Several scholars, such as (Fox, 2015), incorporate the concept of *Secularity* within *Liberalism* they consider it a</footnote>

<footnote>vital element within liberalism, acting as a safeguard for
individual freedoms, equality, and pluralism</footnote>

<footnote n="6">⁶ Region: Admin 1</footnote>

<footnote n="7">⁷ Sector: Admin 2</footnote>
## 4.3 Applicability Outside Guinea-Bissau

Societies across West Africa, such as in Senegal, Mali, Guinea and Guinea-Bissau do have similitudes, both Cultural, Linguistic and Norms as it is proposed by Ameka *et alia* (Ameka & Breedveld, 2004), when studying local norms and do not consider the specific country level separation, but West-Africa as a whole, which concurs with scholars such as Herskovitz in several publications such as (Melville & Herskovits, 1924), Conton (Conton W.F., 1961), (Achebe, 2022) and (Bohas et al., 2018). All these authors stress commonality either due either to similitude of local norms and mores or the impact of roughly 1,400 years of Islam governing framework. Illustrating this situation, the two below Figures evidence the similitude of the Social Norms, Mores and Religion.

<figure><img src="image_145.png" /><figcaption>Figure 9: Map produced by Herskovits evidencing areas of cultural similitude.</figcaption></figure>

<figure><img src="image_146.png" /><figcaption>Figure 10: Map sourced from Wikipedia (Wegomakity-Tri noyesi, 2024), showing the current impact of Islam in West Africa.</figcaption></figure>

## 5 CONCLUSIONS AND FURTHER WORK

The present communication highlights the use of Cohesión as a means to uncover and substantiate political drivers and subsequent options for Governance. Its first findings are positive and provide clues for detailed modelling, namely enabling machine learning geographic prediction algorithms, which is the next step in our work.

In addition, the authors are keen to, after completion of the current work, obtain insight into how the memes related to the Dimensions and Determinants propagate within the geographical space, testing their propagation through several methods.

## ACKNOWLEDGEMENTS

We would like to express our profound gratitude to Prof. Fernando Carvalho Rodrigues for his insightful introduction to cohesion modelling and for his patience in detailing interpretations, which greatly enhanced the depth of this study. We also extend our heartfelt thanks to Eng. Inussa Baldé, General Secretary of the Guinea-Bissau/Senegal Cooperation Agency, for his invaluable advice and guidance in interpreting the outcomes of this research. Our sincere appreciation goes to the Government of the Republic of Guinea-Bissau for authorising the use of official data, which was essential for this work. Finally, we are deeply thankful to Dr. Muminatu Jaló for her invaluable hints and expertise regarding local cultural elements, which provided important contextual insights for this study.

## REFERENCES

* Abubakar, Y. I., Mustapha, R. A., & Ajiboye, E. S. (2020). Impact of Governance on Financial Development: evidence from West Africa. *Hasanuddin Economics and Business Review*, 3(3), 103. https://doi.org/10.26487/hebr.v3i3.2055

* Achanso, A. S. (2022). *The Nexus between Good Governance and Citizen Participation in Local Governance: Insights from Afrobarometer Round 7*. https://www.researchgate.net/publication/364329702

* Achebe, N. (2022). *Islam and West Africa - Introduction, spread and effects*. WASSCE - West African Senior School Certificate Examination. https://wasscehistory-textbook.com/

* Africa Governance Report. (2023). *Unconstitutional Changes of Government in Africa*. African Peer Review Mechanism.
Alence, R. (2004). Political institutions and developmental Governance in sub-Saharan Africa. *Journal of Modern African Studies*, 42(2), 163–187.
https://doi.org/10.1017/S0022278X04000084

Ameka, F. K., & Breedveld, A. (2004). Areal cultural scripts for social interaction in West African communities. *Intercultural Pragmatics*, 1(2), 167–187.
https://doi.org/10.1515/iprg.2004.1.2.167

Bohas, G., Lélouma, A. M., Saguer, A., Sailvaing, B., & Sinno, A. (2018). *Islam et Bonne Gou-vernance - Au XIXème Siècle dans les sources arabes du Fouta-Djalon - Textes de Tierno Sadou Dalen, Modi Lelouma, Almami Ibrahi-ma Sori Dara et Alfa Gassimou Labe. Geuther*.

Carvalho Rodrigues, F. (1994). *As novas Tecnologias, o Futuro dos Impérios e os Quatro cavaleiros do Apocalipse*. Publicações Europa-América.

Carvalho Rodrigues, F., & Peixoto, J. P. (1991). *Sistemas, Entropia, Coesão* (3/91). Discórdia.

Chabal, P. (2009). *Africa - The Politics of Suffering and Smiling*. University of KwaZulu-Natal Press.

Conton W.F. (1961). *West Africa in History* (Vol. 1). Amazon Fulfilment.

Fox, J. (2015). *A World Survey of Religion and the State*. Cambridge University Press.

Frieden, B. R. (1998). *Physics from Fisher Information a Unification*. Press Syndicate of the University of Cambridge.

Institute on Governance. (2022). *What is Governance*. https://iog.ca/about-us/#:~:text=Governance%20is%20how%20organiza-tions%20and%20societies%20organize%20themselves,put%20in%20place%20to%20achieve%20that%20common%20purpose.

Krawczyk, K., & Sweet-Cushman, J. (2016). Understanding political participation in West Africa: the relationship between good Governance and local citizen engagement. *International Review of Administrative Sciences*.

Lafaye, C. G. (2011). Évolutions de l'interprétation de la cohésion sociale. *International Review of Sociology*, 21(1), 41–65.
https://doi.org/10.1080/03906701.2011.544179

Melville, J., & Herskovits, J. (1924). *A Preliminary Consideration of the Culture Areas of Africa*. https://doi.org/10.1525/aa.1924.26.1.02 a00050

Rotberg, R. I. (2009). A Theory of Governance and Leadership in Africa: Measures, Methods and Results. In *Journal of International Affairs* (Vol. 62, Issue 2).

Shannon, C. E. (1948). *A Mathematical Theory of Communication Part III: Mathematical Preliminaries*. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

United Nations Department of Economic and Social Affairs. (2015). *Sustainable Development Goals*. SDG. https://sdgs.un.org/goals

United Nations Department of Economic and Social Affairs. (2023). *SDG_Progress_Report_Special_Edition_2023_Advance_Unedited_Version*.

Vergolini, L. (2011). Social Cohesion in europe: How do the different dimensions of inequality affect social Cohesion? *International Journal of Comparative Sociology*, 52(3), 197–214. https://doi.org/10.1177/0020715211405421

Wegomakity-Trinoyesi. (2024). *Islam in Africa*. Wikipedia. https://commons.wikimedia.org/w/index.php?curid=88513364

World Bank. (2023). *World Bank Governance Indicators*. https://Info.Worldbank.Org/Governance/Wgi/. https://info.worldbank.org/governance/wgi/
# LiDAR-Based 3D Reconstruction for Robotic Pipelines Inspection

Monika Sara Kawka¹, Lazaros Grammatikopoulos¹,², Ilias Kalisperakis¹ and Christos Stentoumis¹

¹up2metric, Michail Mela 21, Athens, Greece

²Department of Surveying and Geoinformatics Engineering, University of West Attica, Agiou Spiridonos 28, Egaleo, Greece
{monika-sara.kawka, ilias, christos}@up2metric.com, lazaros@uniwa.gr

**Keywords:** Robotics, LiDAR, Pipe Inspection, Computer Vision.

**Abstract:** Robotic platforms have transformed pipe inspection from routine checks into an automatic data-driven process. Such robotic systems often integrate computer vision technology to collect and analyze inspection data in an automated and efficient way and offer additional capabilities such as 3D reconstruction of pipes and precise measurement of deformations (e.g., dents, buckling). This work presents an initial case study of a robotic inspection system equipped with LiDAR and camera sensors capable of performing automatic pipeline inspections. This proof-of-concept study is dedicated to the 3D reconstruction of the pipeline using LiDAR data collected during inspections. Reconstruction accuracy is evaluated by computing the RMSE for pipe surface reconstruction and the deviation from the reference diameter of a single pipe in a controlled laboratory setting. Reconstruction results reach an accuracy higher than 2cm based on computed RMSE and a precision higher than 0.5cm in pipe diameter estimation. The current implementation is limited to the inspection of matte and non-reflective pipes. Still, it offers a straightforward and scalable solution for various industrial sectors. Future work will incorporate camera data to integrate color mapping into the 3D reconstruction model and detect potential defects and deformations in a pipe.

## 1 INTRODUCTION

Robotic platforms are widely adopted, at least in the research community, as an automated alternative to traditional pipe exploration. Robust inspection requires developing a robot that can adapt to various pipe conditions and diameters, which remains an active area of investigation (Kazeminasab et al., 2020; Zhao et al., 2020; Ab Rashid et al., 2020; Elankavi et al., 2022; Baballe et al., 2022). For the scope of this work, such a robotic platform was developed; however, in this contribution, emphasis is given on sensors' data fusion, the methodology employed for 3D reconstruction of pipelines in the form of point clouds and estimating the actual diameter of pipes, rather than on robot integration.

Visual information can enhance the automated pipe inspection process and facilitate quick and direct identification of the pipe's interior features. In (Kakogawa et al., 2019) a camera-equipped pipe inspection robot is used to perform shadow-based autonomous navigation in straight and winding pipe. In (Gunatilake et al., 2021) two IR cameras are used for stereo vision processing and reconstruction and one RGB camera to map the color information to the recon-

structed 3D points. In (Tian et al., 2023), the visual information provided by a monocular camera is essential for the presented RGB-D SLAM algorithm. Others, exploit visual data to generate a digital twin using the Structure from Motion (SfM) algorithm (Summan et al., 2018; Kannala et al., 2008).

The highly symmetrical cylindrical shape of pipes is challenging to capture entirely with a conventional camera. Hence, to overcome the limitation of a narrow view visual sensor, cameras with wide-angle lenses are selected to collect data of the entire interior surface of the pipe. Most approaches use fisheye lens cameras (Tian et al., 2023; Summan et al., 2018; Kannala et al., 2008) or omnidirectional cameras (Matsui et al., 2010; Karkoub et al., 2021) to inspect pipes effectively.

Camera calibration is an essential step before the inspection process to ensure the accuracy of the collected data, especially when using a wide-angle camera lens. Camera calibration is usually performed using a planar calibration object, like a checkerboard or a grid of circles. Various techniques, camera models, and tools are available for the calibration process. In (Summan et al., 2018) the OCamCalib Toolbox is applied (Scaramuzza et al., 2006) for the calibra-
tion of omnidirectional cameras, while in (Kannala et al., 2008) the generic camera model (Kannala and Brandt, 2006) is adopted for their fisheye lens cameras. The Matlab Calibration Toolbox<fnref n="1" /> is used by (Hansen et al., 2011) to undistort the collected image data and the CamOdoCal (Heng et al., 2013) method is used by (Tian et al., 2023) to conduct intrinsics calibration.

Visual sensors are indeed valuable, since they provide a quick view of the pipes surface during inspection. However, the quality of the inspections depends highly, on the camera resolution and on lighting conditions. Therefore, robotic platforms for pipe inspection, often integrate laser technology, which is proven to significantly benefit the inspection process. LiDAR data, allow the detection of existing deformations that might not be visible through the camera data and are suitable for accurate geometric recording of the internal surface of pipelines. In (Gunatilake et al., 2021) 3D laser profiling is implemented, to generate 3D RGB-Depth maps, utilizing an IR laser beam with RGB and IR cameras. In (Matsui et al., 2010) an omnidirectional laser with an omnidirectional camera are combined to ensure full pipe coverage. Data from both omnidirectional sensors are used to reconstruct the pipe, by means of a light section method and SfM analysis. In the work of (Sepulveda-Valdez et al., 2023; Sepulveda-Valdez et al., 2024) a Technical Vision System (TVS) is employed, as a measuring tool to generate a complete point cloud of the pipe's interior. Another commonly used laser technology in pipe inspection is the Light Detection and Ranging (LiDAR) sensor. In (Tian et al., 2023), LiDAR-based constraints, derived from the pipes' underlying geometry, are combined with the proposed SLAM method to reduce long-term odometry drift. The method presented in (Zhao et al., 2023) improves the detection process in the inspection system by using a LiDAR system which is based on non-repetitive technology and an Inertial Measurement Unit (IMU). In (Moein and Himan, 2022) an automated framework is presented, that uses LiDAR data to identify the pipes' diameter and deflection.

In addition to using laser technologies for pipe surface reconstruction, the estimation of the pipe's diameter is widely adopted as a metric for the evaluation of the effectiveness of pipe surface reconstructions, for the estimation of the reconstructed surface's accuracy, but also for detecting possible deformations. In (Matsui et al., 2010) cylinder fitting is employed to measure the pipe's diameter. In contrast, (Sepulveda-Valdez et al., 2024; Moein and Himan, 2022) utilize

circle fitting to find the diameter of the pipe. The k-nearest neighbors algorithm is also proposed for the same purpose (Moein and Himan, 2022).

This work represents a subset of the broader LASER4TWIN project, supported by the EU-funded initiative PIMAP4SUSTAINABILITY<fnref n="2" />. It introduces an initial case study of a robotic inspection system equipped with LiDAR and camera sensors for automated pipeline inspections. Specifically, it focuses on methodologies developed for data pre-processing, calibration, two-step registration method for the 3D reconstruction of a pipe, and its diameter estimation via cylinder fitting. For the evaluation of the 3D reconstruction method, the accuracy provided by RMSE is computed by measuring the average deviation between the observed 3D points and the approximated cylindrical surface. A confidence interval is employed to assess the precision of the estimated diameter.

## 2 METHODOLOGY

This section describes the approach adopted to develop the robotic inspection system. A short outline of the robotic platform is given in subsection 2.1. Subsection 2.2 covers the selected LiDAR and camera sensors, 2.3 discusses the calibration procedures adopted to ensure the optimal sensors fusion, and 2.4 focuses on the data collection process. In subsection 2.5, the pre-processing of point cloud data is addressed. Subsection 2.6 explains the two-step registration method using the Iterative Closest Point (ICP) algorithm. Finally, subsection 2.7 focuses on pipe diameter estimation.

### 2.1 Robotic Platform

In LASER4TWIN project, a custom robotic platform was designed and built by CIS Robotics<fnref n="3" /> to meet the inspection system requirements. The robot has six-wheeled arms, providing enhanced stability and manoeuvrability within pipelines. It features DC motors integrated with wheel encoders and IMU. The IMU measurements are aligned with the robot's axes to ensure precise odometry data for accurate inspection. The robot's arms were designed to accommodate a range of pipe diameters from 30 cm to 45 cm. Notably, the range limitation of 30 cm to 45 cm applies specifically to the robot's arm design and does not reflect the constraints of the sensors. The LiDAR and

<footnote n="1">¹mathworks.com/help/vision/ref/cameracalibrator-app.html</footnote>

<footnote n="2">²https://pimap4sustainability2023.b2match.io/</footnote>

<footnote n="3">³https://www.cis-robotics.com/, Av. Mar Cantábrico 17, Gijón-Asturias</footnote>
camera sensors are placed at the front of the robot
to enable data acquisition, along with two arc-shaped
LED light arrays of 30 NeoPixels.

## 2.2 LiDAR and Camera Sensors

This section describes the selection of the LiDAR sen-
sor and the digital cameras of the proposed inspec-
tion system. The MID-70 LiDAR was selected, due
to its circular field of view, which was considered es-
sential for scanning the pipe's interior. It uses a non-
repetitive laser pattern and its compact size and small
blind zone allow for adaptability in confined spaces.

The primary camera selected for the inspection system is the XIMEA MC050CG-SY, a 5 MP RGB camera with a Sony CMOS Pregius™ sensor and a USB 3.1 interface. It is paired with the Theia MY125M lens, an ultra-wide lens that reduces distortion using linear optical technology. The Theia MY125M lens was specifically chosen for its wide field of view, which maximizes the pipe’s interior coverage. In addition, a 2 MP 1080p USB camera with a fisheye lens and IMX322 sensor was also integrated into the robot, for backup and future use. This camera was not actively used in the inspection process despite its integration.

## 2.3 Calibration

The camera-LiDAR fusion, adopted in the proposed platform, ensures a robust inspection system. LiDAR is used to effectively capture the geometry of the pipelines, while cameras capture RGB data to color the derived point cloud but also to allow visual inspection of the pipelines (identifying obstacles, defects etc.). To get optimal results, but also to allow the localization of objects detected in the camera feed, precise calibration of all sensors is required before executing an inspection mission. Therefore, as a first step, the camera calibration was performed using the Matlab Calibration Toolbox (Bouguet, 2023).

Next, a camera-LiDAR calibration algorithm was
implemented in MATLAB. It is based on the method
developed by the research team in the context of a
mobile mapping platform (Grammatikopoulos et al.,
2022). This algorithm uses a simple calibration planar
board consisting of two crossing retroreflective stripes
and an AprilTag at the intersection of the stripes.
First, the four corners of AprilTag are used to detect
the 2D position of the center of the marker on each
image. Corresponding 3D LiDAR points are detected
by estimating the center of two crossing retroreflec-
tive stripes (Figure1). Finally, based on established
2D to 3D point correspondences, the algorithm es-

estimates a rigid transformation of the camera system
relative to the Lidar frame (Grammatikopoulos et al.,
2022). This transformation is introduced to the soft-
ware tool described in Subsection 2.4, which synchro-
nizes the robot's sensors.

<figure><img src="image_147.png" /><figcaption>Figure 1: Calibration board consisting of two crossing retroreflective stripes and an AprilTag.</figcaption></figure>

## 2.4 Data Collection

A software tool for data collection and synchroniza-
tion was developed using the Robot Operating Sys-
tem (ROS)<fnref n="4" />. A Graphical User Interface (GUI) was
implemented to allow users to initiate the data col-
lection process. Once initialized, the cameras, the
LiDAR sensor, and the other sensors and encoders
of the robot publish data in different channels, while
the implemented algorithm synchronizes the incom-
ing data from all sources. Finally, the collected data
are recorded and saved as a ROS Bag file.

## 2.5 Point Cloud Pre-Processing

Once a data collection mission is performed by the
robot, the synchronized data are extracted from the
ROS Bag and converted into more adequate formats
for further processing, which are fed to the two-step
registration process.

The raw point clouds recorded and collected by
the LiDAR sensor often contain noise that should be
removed to ensure accurate 3D reconstruction. As
a pre-processing step, only low-noise points that be-
long to the pipe surface are retained 2. Firstly, the
points with low confidence based on a relevant Tag
value provided by the LiDAR manufacturer are re-
jected. The LiDAR has a blind zone of up to 5 cm
and a circular field of view (70.4 degrees). Based on
the range of the pipe's diameters (30cm-45cm), Li-
DAR can scan the first points of the pipe's surface at
a distance of 21cm to 32 cm from it (3). However,

<footnote n="4">⁴https://ros.org/</footnote>
the first measurements are prone to errors and present
high noise levels (2). Therefore, it was decided to re-
ject points closer than a certain distance (1 meter in
the test missions). Finally, to further increase relia-
bility, statistical filtering that removes any remaining
points with noise levels exceeding 3σ is added.

<figure><img src="image_148.png" /><figcaption>Figure 2: Pre-processing steps are applied to each scan to remove inaccurate data.</figcaption></figure>

<figure><img src="image_149.png" /><figcaption>Figure 3: For a 45 cm diameter pipe, LiDAR can scan the first points of its surface at a distance of 32 cm from its current position.</figcaption></figure>

## 2.6 3D Reconstruction via Two-Step Registration

Due to a pipe's cylindrical shape, ICP registration of consecutive point clouds in linear parts of a pipe becomes an ill-posed problem, as it is not possible to accurately estimate all six degrees of freedom (DOF) without additional constraints. Specifically, translation along the cylinder's axis and rotation around this axis cannot be determined without additional information about the robot movement. To address this issue, external data, such as odometry, is required to estimate the complete rigid body transformation.

In this work, the individual collected point clouds
are merged using a two-step process based on the It-
erative Closest Point (ICP) registration method, and
data from the robot encoders to perform a 3D re-
construction of the pipeline. Initially, the ICP al-
gorithm aligns two consecutive point clouds in each
iteration by estimating a rigid-body transformation
between them using a point-to-plane ICP approach
(Chen and Medioni, 1992; Rusinkiewicz and Levoy,

2001). These transformations are then expressed in
the coordinate system of the first point cloud to en-
sure the global alignment of all consecutive point
clouds. This step is referred to as *local-to-global reg-
istration*. After the first *local-to-global registration*,

<figure><img src="image_150.png" /><figcaption>Figure 4: Local-to-global registration: the ICP algorithm aligns two consecutive point clouds in each iteration by estimating a rigid-body transformation between them using a point-to-plane ICP approach algorithm. These transformations are expressed in the coordinate system of the first point cloud to ensure the global alignment of all consecutive point clouds.</figcaption></figure>

the difference between the encoder-provided trans-
formation, which provides the necessary informa-
tion about robot's movement, and the current ICP-
estimated transformation, is calculated and used as
the new initial transformation for the next local-to-
global registration. A lower threshold is then applied
for the final local-to-global registration, allowing the
algorithm to integrate the absolute motion data from
the encoders and the geometric constraints of the reg-
istered point clouds.

## 2.7 Pipe Diameter Estimation

The diameter of the 3D reconstruction model is es-
timated by fitting a cylinder surface. A cylinder fit-
ting process is often employed in inspection systems
for multiple tasks such as diameter estimation (Mat-
sui et al., 2010), optimization (Tian et al., 2023), sec-
tioned fitting (Kannala et al., 2008), or establishing a
coordinate frame (Summan et al., 2018). The cylinder
is defined by:

• $\mathbf{P}_0 = [X_0 \enspace Y_0 \enspace Z_0]^T$: A point on the cylinder's central axis,

• $\mathbf{P}_i = [X_i \enspace Y_i \enspace Z_i]^T$: A point on the surface of the cylinder, and

• **d** = [cos(φ) cos(θ) cos(φ) sin(θ) sin(φ)]^T: A cylinder's central axis defined by a direction vector. Here, φ is an elevation angle and θ is an azimuthal angle.
* $S_i$: Cylinder's radius, which is perpendicular from the surface point $\mathbf{P}_i$ to cylinder's axis (direction vector $\mathbf{d}$).

The above parameters form a triangle, where vector $\overrightarrow{P_0P_i}$ corresponds to the triangle's hypotenuse. The adjacent side of the triangle is the projection of $\overrightarrow{P_0P_i}$ onto the direction vector $\mathbf{d}$, which aligns with the axis of the cylinder. The opposite side of the triangle corresponds to the radius $S$ of the cylinder, which can be seen as the shortest distance from the point $\mathbf{P}_i$ to the line through $\mathbf{P}_0$ with direction vector $\mathbf{d}$.

The robot moves along the X-axis. Thus, $\mathbf{P}_0$ can be simplified and assumed to be located at $\mathbf{P}_0 = [X_0 = 0 \ Y_0 \ Z_0]^T$. Similarly, since the robot's moving axis approximately corresponds to the cylinder's axis, the initial values of $Y_0$, $Z_0$, $\phi$, and $\theta$ are set to zero for simplicity. The radius $S_i$ is calculated using the cross-product of $\overrightarrow{P_0P_i}$ and the direction vector $\mathbf{d}$, as shown in the equation below:

$$S_i = \frac{\| (\mathbf{P}_i - \mathbf{P}_0) \times \mathbf{d} \|}{\| \mathbf{d} \|} \quad (1)$$

The error $e_i = S_i - R$ measures how far a point $\mathbf{P}_i$ deviates from the cylinder's surface, where radius $R$ is defined as half of the nominal pipe's diameter.

Levenberg-Marquardt algorithm (Morć, 1978) is used to minimize the error function $\sum_{i=1}^{n} e_i^2$, where $n$ are multiple points sampled from the simplified surface of the reconstructed pipeline. The optimization parameters are:

$$\mathbf{x} = [Y_0, Z_0, R, \phi, \theta]^T \quad (2)$$

Thus, the optimization problem can be defined as:

$$\min_{\mathbf{x}} E(\mathbf{x}) \quad (3)$$

Finally, the optimized radius can be converted to the diameter. Note that a pipe can be modelled as a perfect cylinder with a consistent diameter along its length because of its cylindrical shape and the assumption that it is straight. This assumption is crucial, as it allows the diameter estimation without accounting for any variations in curvature that would arise in a non-straight pipe.

## 3 RESULTS AND EVALUATION

Before executing an inspection mission, a camera-LiDAR calibration was performed to assure optimal results and provide the camera-LiDAR transformation to the ROS integration tool. A checkerboard selected for the calibration had seven rows and ten columns;

its cell size was 25 mm. The robot with an integrated camera and LiDAR sensors was kept fixed while the checkerboard was moved and rotated for every subsequent image. A total of 25 images were taken for camera calibration purpose. The camera calibration process allowed for the estimation of the camera's interior orientation, which is essential for subsequent estimation of the relative position and orientation of the camera with respect to the LiDAR sensor. The camera intrinsics are given in Table 1 together with their estimation errors.

<table><caption>Table 1: Camera Intrinsics.</caption><thead><tr><th>Camera Parameter</th><th>Computed Value</th><th>Estimation Error</th></tr></thead><tbody><tr><td><strong>Focal Length</strong></td><td></td><td></td></tr><tr><td>c<sub>x</sub> (pixel)</td><td>370.46</td><td>±0.36</td></tr><tr><td>c<sub>y</sub> (pixel)</td><td>370.97</td><td>±0.35</td></tr><tr><td><strong>Principal Point</strong></td><td></td><td></td></tr><tr><td>x<sub>o</sub> (pixel)</td><td>1221.11</td><td>±0.11</td></tr><tr><td>y<sub>o</sub> (pixel)</td><td>1057.92</td><td>±0.13</td></tr><tr><td><strong>Radial Distortion</strong></td><td></td><td></td></tr><tr><td>k<sub>1</sub></td><td>0.013138</td><td>±0.000222</td></tr><tr><td>k<sub>2</sub></td><td>-0.008703</td><td>±0.000117</td></tr><tr><td>k<sub>3</sub></td><td>0.000747</td><td>±0.000017</td></tr><tr><td><strong>Tangential Distortion</strong></td><td></td><td></td></tr><tr><td>p<sub>1</sub></td><td>0.000436</td><td>±0.000050</td></tr><tr><td>p<sub>2</sub></td><td>0.000965</td><td>±0.000050</td></tr><tr><td><strong>Mean Reprojection Error</strong></td><td></td><td></td></tr><tr><td>σ<sub>o</sub> (pixel)</td><td>0.29</td><td></td></tr></tbody></table>

<table><caption>Table 2: Camera-LiDAR Calibration Results.</caption><thead><tr><th>Rigid transformation Parameters</th><th>Computed Value</th></tr></thead><tbody><tr><td><strong>Translation</strong></td><td></td></tr><tr><td>ΔX (cm)</td><td>3.53</td></tr><tr><td>ΔY (cm)</td><td>-0.76</td></tr><tr><td>ΔZ (cm)</td><td>5.92</td></tr><tr><td><strong>Orientation</strong></td><td></td></tr><tr><td>ω (deg)</td><td>-34.76</td></tr><tr><td>φ (deg)</td><td>87.58</td></tr><tr><td>κ (deg)</td><td>-53.57</td></tr><tr><td><strong>Mean Reprojection Error</strong></td><td></td></tr><tr><td>σ<sub>o</sub> (pixel)</td><td>1.48</td></tr></tbody></table>

Next, to estimate a rigid transformation of the camera system relative to the LiDAR frame, the camera-LiDAR calibration described in 2.3 was applied using a simple calibration object consisting of two crossing retroreflective stripes and an April-Tag in their intersection. A total of 29 images and point clouds were captured simultaneously. Table 2 presents the camera-LiDAR calibration results.

To verify the effectiveness and reliability of the proposed inspection system, tests were performed in a controlled laboratory environment. For these tests, a polished steel pipe with a diameter of 45 cm and a length of approximately 2.5 meters (Figure 5) was
<figure><img src="image_151.png" /><figcaption>Figure 5: Polished steel pipe.</figcaption></figure>

used.

During the early stages of testing, a challenge with LiDAR producing noisy data was encountered, as a result of the highly reflective nature of the polished surface material. This challenge was addressed by applying a matte white spray to the pipe's interior. This solution effectively minimized reflectivity and allowed the LiDAR to capture accurate data. It is important to note that this adjustment was specific to the reflective properties of polished surfaces, and the system can perform optimally without surface modifications in less reflective environments such as PVC, HDPE, clay or plastic pipes.

<figure><img src="image_152.png" /><figcaption>Figure 6: Robot platform inside the pipe.</figcaption></figure>

To showcase the effectiveness of the proposed two-step registration method outlined in 2.6, firstly, 3D reconstruction was performed using only the provided odometry measurements from the robot encoders. Due to the noise in the data of the robot's IMU sensors, it was determined that an accurate 3D reconstruction could not be performed without further optimization, such as the proposed ICP registration. As detailed in 2.6, the *local-to-global* registration effectively addresses this challenge. The comparison presented in Figure 9 highlights the effectiveness of the proposed approach.

After completing the two-step registration, the pipe's diameter was calculated. Table 3 summarizes the pipe diameter estimation method results. The estimated pipe diameter was 45.431 cm, which closely matches the nominal pipe diameter of 45 cm.

Root Mean Square Error (RMSE) was calculated to evaluate the accuracy of the 3D reconstruction method. It measures the average deviation between

<figure><img src="image_153.png" /><figcaption>Figure 7: 3D reconstruction model using only odometry data.</figcaption></figure>

<figure><img src="image_154.png" /><figcaption>Figure 8: 3D reconstruction model using the proposed two-step registration method.</figcaption></figure>

the observed 3D points of the 3D reconstruction model and the approximated cylindrical surface.

$$ \sigma_o = \sqrt{\frac{1}{n-5} \sum_{i=1}^{n} (S_i - R)^2} \quad (4) $$

The precision of the estimated pipe's diameter is assessed by calculating the confidence interval, expressed as the two standard deviations of twice the optimized radius, indicating a 95% confidence level. Estimation error of all optimized parameters in Table 3 except radius and diameter corresponds to one standard deviation.

<table><caption>Table 3: Results of the pipe diameter estimation.</caption><thead><tr><td>Optimization parameters</td><td>Computed Value</td><td>Estimation Error</td></tr></thead><tbody><tr><td>Optimized Yo (m)</td><td>-0.059</td><td>±0.003</td></tr><tr><td>Optimized Zo (m)</td><td>-0.016</td><td>±0.003</td></tr><tr><td>Optimized R (cm)</td><td>22.715</td><td>±0.032</td></tr><tr><td>Optimized φ (deg)</td><td>0.469</td><td>±0.281</td></tr><tr><td>Optimized θ (deg)</td><td>-7.030</td><td>±0.279</td></tr><tr><td>Diameter (cm)</td><td>45.431</td><td>±0.064 (precision)</td></tr><tr><td>σ<sub>o</sub> (cm)</td><td colspan="2">0.48 (RMSE)</td></tr></tbody></table>
<figure><img src="image_155.png" /><figcaption>(a) Comparison between the 3D reconstruction model created using only odometry data (left image, Figure 7) and the 3D reconstruction model generated using the proposed method (right image, Figure 8)</figcaption></figure>

<figure><img src="image_156.png" /><figcaption>(b) Comparison of the registration process using raw data (top image), using only odometry measurements (middle image, Figure 7), and using the proposed two-step method (bottom image, Figure 8).</figcaption></figure>

<caption>Figure 9: Comparison of the registration processes.</caption>

# 4 CONCLUSIONS

This work developed and evaluated a preliminary implementation of a robotic inspection system for non-reflective pipelines. The robotic system features six-wheeled arms, which enhance stability and adaptability for pipeline diameters ranging from 30 cm to 45 cm. Combining the MID-70 LiDAR and the XIMEA camera enables wide coverage and high-resolution data acquisition. Using accurate calibration of LiDAR and camera sensors, it allows 3D reconstruction via a two-step registration method. Evaluation results validate the proof-of-concept, demonstrating the system's efficiency by achieving a precision higher than 0.5 cm in diameter estimation and accuracy higher than 2cm in 3D reconstruction.

This study presents a pilot implementation and is not without limitations. First, the robot must be redesigned to inspect pipes with diameters larger than 45cm. Although the camera is integrated and ready to be used in the robot platform, its usage is not presented in this initial study. Experiments are conducted only in a controlled laboratory environment with a

single pipe type, and real-world scenarios with varying pipe materials, diameters, and environmental conditions should be explored. Finally, this initial case study lacks a comparison analysis of the proposed method with other state-of-the-art systems.

Future improvements will focus on leveraging camera data to integrate color mapping into a 3D reconstruction method and allow visual inspection of the pipelines (identifying obstacles, defects, etc.). Moreover, a robot platform will be redesigned to refine the system for diverse pipeline materials and diameters. Two-step registration and diameter estimation methods will also be refined to handle more challenging pipes with non-constant diameters. Finally, a refined robotic system will be benchmarked to identify its potential limitations, validate its robustness, and demonstrate its practical usability in real-world pipeline inspection scenarios.

# ACKNOWLEDGEMENTS

This work was part of LASER4TWIN project, supported by the European Union through the EU-funded project PIMAP4SUSTAINABILITY "Photonics for International Markets and Applications for Sustainability", in its Innovation Open Call. The authors would also like to express their gratitude to their partner in the LASER4TWIN project, CIS Robotics, particularly Christian J. Robledo and Adrián Álvarez García, for designing and manufacturing the robot and for integrating the developed technologies.

# REFERENCES

* Ab Rashid, M. Z., Mohd Yakub, M. F., Zaki bin Shaikh Salim, S. A., Mamat, N., Syed Mohd Putra, S. M., and Roslan, S. A. (2020). Modeling of the in-pipe inspection robot: A comprehensive review. *Ocean Engineering*, 203:107206.

* Baballe, M. A., Bello, M. I., Hussaini, A., and Musa, U. S. (2022). Pipeline inspection robot monitoring system. *Journal of Advancement in Robotics*, 9(2):27-35.

* Bouguet, J.-Y. (2023). Camera Calibration Toolbox for Matlab.

* Chen, Y. and Medioni, G. (1992). Object modelling by registration of multiple range images. *Image and Vision Computing*, 10(3):145-155.

* Elankavi, R. S., Dinakaran, D., Chetty, R. K., Ramya, M., and Samuel, D. H. (2022). A review on wheeled type in-pipe inspection robot. *International Journal of Mechanical Engineering and Robotics Research*, 11(10).

* Grammatikopoulos, L., Papanagnou, A., Venianakis, A., Kalisperakis, I., and Stentoumis, C. (2022). An Ef-
fective Camera-to-Lidar Spatiotemporal Calibration
Based on a Simple Calibration Target.

Gunatilake, A., Piyathilaka, L., Tran, A., Vishwanathan, V. K., Thiyagarajan, K., and Kodagoda, S. (2021). Stereo Vision Combined With Laser Profiling for Mapping of Pipeline Internal Defects. *IEEE Sensors Journal*, 21(10):11926–11934.

Hansen, P., Alismail, H., Rander, P., and Browning, B. (2011). Monocular visual odometry for robot localization in LNG pipes. In *2011 IEEE International Conference on Robotics and Automation*, pages 3111–3116. IEEE.

Heng, L., Li, B., and Pollefeys, M. (2013). CamOd-oCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry. In *2013 IEEE/RSJ International Conference on Intelligent Robots and Systems*, pages 1793–1800. IEEE.

Kakogawa, A., Komurasaki, Y., and Ma, S. (2019). Shadow-based operation assistant for a pipeline-inspection robot using a variance value of the image histogram. *Journal of Robotics and Mechatronics*, 31(6):772–780.

Kannala, J. and Brandt, S. S. (2006). A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 28(8):1335–1340.

Kannala, J., Brandt, S. S., and Heikkilä, J. (2008). Measuring and modelling sewer pipes from video. *Machine Vision and Applications*, 19(2):73–83.

Karkoub, M., Bouhali, O., and Sheharyar, A. (2021). Gas pipeline inspection using autonomous robots with omni-directional cameras. *IEEE Sensors Journal*, 21(14):15544–15553.

Kazeminasab, S., Aghashahi, M., and Banks, M. K. (2020). Development of an inline robot for water quality monitoring. In *2020 5th International Conference on Robotics and Automation Engineering (ICRAE)*, pages 106–113.

Matsui, K., Yamashita, A., and Kaneko, T. (2010). 3-D shape measurement of pipe by range finder constructed with omni-directional laser and omni-directional camera. In *2010 IEEE International Conference on Robotics and Automation*, pages 2537–2542. IEEE.

Moein, E. and Himan, H. J. (2022). *Automated Condition Assessment of Sanitary Sewer Pipes Using LiDAR Inspection Data*, pages 136–144. Proceedings. American Society of Civil Engineers (ASCE).

Moré, J. J. (1978). The levenberg-marquardt algorithm: Implementation and theory. In Watson, G. A., editor, *Numerical Analysis*, pages 105–116, Berlin, Heidelberg. Springer Berlin Heidelberg.

Rusinkiewicz, S. and Levoy, M. (2001). Efficient variants of the ICP algorithm. In *Proceedings Third International Conference on 3-D Digital Imaging and Modeling*, pages 145–152. IEEE.

Scaramuzza, D., Martinelli, A., and Siegwart, R. (2006). A Toolbox for Easily Calibrating Omnidirectional Cameras. In *2006 IEEE/RSJ International Conference on Intelligent Robots and Systems*, pages 5695–5701. IEEE.

Sepulveda-Valdez, C., Sergiyenko, O., Alaniz-Plata, R., Núñez-López, J. A., Tyrsa, V., Flores-Fuentes, W., Rodriguez-Quiñonez, J. C., Mercorelli, P., Kolen-dovska, M., Kartashov, V., Miranda-Vega, J. E., and Murrieta-Rico, F. N. (2023). Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application. In *IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society*, pages 1–6. IEEE.

Sepulveda-Valdez, C., Sergiyenko, O., Tyrsa, V., Mercorelli, P., Rodríguez-Quiñonez, J. C., Flores-Fuentes, W., Zhirabok, A., Alaniz-Plata, R., Núñez-López, J. A., Andrade-Collazo, H., Miranda-Vega, J. E., and Murrieta-Rico, F. N. (2024). Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection.

Summan, R., Jackson, W., Dobie, G., MacLeod, C., M neo, C., West, G., Offin, D., Bolton, G., Marshall, S., and Lille, A. (2018). A novel visual pipework inspection system. *AIP Conference Proceedings*, 1949(1):220001.

Tian, T., Wang, L., Yan, X., Ruan, F., Aadityaa, G. J., Choset, H., and Li, L. (2023). Visual-Inertial-Laser-Lidar (VILL) SLAM: Real-Time Dense RGB-D Mapping for Pipe Environments. In *2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, pages 1525–1531. IEEE.

Zhao, M., Fang, Z., Ding, N., Li, N., Su, T., and Qian, H. (2023). Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR.

Zhao, W., Zhang, L., and Kim, J. (2020). Design and analysis of independently adjustable large in-pipe robot for long-distance pipeline. *Applied Sciences*, 10(10).
# Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine

Dan Abudu¹, Lucy Bastin², Katie Chong³ and Mirjam Röder¹

¹Energy and Bioproducts Research Institute, Aston University, B4 7ET, Birmingham, U.K.

²School of Computer Science and Digital Technologies, Aston University, B4 7ET, Birmingham, U.K.

³Energy Systems Catapult, Cannon House, B4 6BS, Birmingham, U.K.

{dabud22, l.bastin, m.roeder}@aston.ac.uk, Katie.Chong@es.catapult.org.uk

**Keywords:** Biomass Density, Carbon Stocks, LULC Classification, GIS, Remote Sensing, Google Earth Engine, Uganda, SDG 13, SDG 15.

**Abstract:** Addressing climate change requires timely and accurate biomass and carbon stocks information. Traditional biomass estimation techniques rely on infrequent ground surveys and manual processing, limiting their scalability. This study proposes a novel framework that advances land cover classification to estimate biomass and carbon stocks using machine learning algorithms in Google Earth Engine. By integrating remote sensing data, machine learning algorithms, and allometric models, the framework automates above-ground biomass (ABG) and below-ground biomass (BGB) calculations, facilitating large-scale carbon stock assessments. The methodology leverages Landsat imagery, alongside derived Normalized Difference Vegetation Indices, to classify seven land cover types and estimate biomass. Equations are applied to derive AGB, with BGB calculated as a fraction of AGB. Carbon stock is estimated using a standard conversion factor of 0.47. Real-time processing capabilities of GEE ensure continuous monitoring and updates, enhancing accuracy and scalability. Findings demonstrate the potential for real-time biomass mapping and the identification of carbon-dense regions. The proposed approach is vital for sustainable land practices, carbon accounting, and forest conservation initiatives, to provide policymakers with accurate, real-time data, that supports climate mitigation efforts and contribute to realizing the Sustainable Development Goals 13 and 15.

## 1 INTRODUCTION

Forests and other vegetated landscapes are natural carbon sinks, playing a key role in mitigating climate change effects (Ma et al., 2022). Biomass, the total mass of living plant material, serves as a critical indicator of ecosystem health, carbon sequestration and energy potential (Makepa & Chihobo, 2024). Real-time biomass and carbon stock assessments are essential for meeting local commitments such as Nationally Determined Contributions (NDC) and international climate agreements, such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation), which aim to incentivize sustainable forest management practices (Nakakaawa et al., 2011). Such real-time data empowers local authorities

and conservation organizations to respond effectively to deforestation, and other environmental threats, contributing to the attainment of United Nations' Sustainable Development Goals 13 (climate action) and 15 (better life on land).

However, traditional methods of biomass and carbon stock estimation such as ground survey and manual image interpretation are time-consuming, expensive, and mostly constrained to small-scale applications (Paneque-Gálvez et al., 2014).

Recent developments in remote sensing technologies have enabled large-areal assessments of biomass and carbon stocks (Flores Lanza et al., 2024). Satellite imagery from programs such as Landsat, Sentinel, and MODIS provides the required localized data for monitoring land cover and vegetation

<footnote>a https://orcid.org/0000-0002-9321-0829</footnote>

<footnote>b https://orcid.org/0000-0003-1321-0800</footnote>

<footnote>c https://orcid.org/0000-0002-3800-8302</footnote>

<footnote>d https://orcid.org/0000-0002-8021-3078</footnote>
dynamics. However, data processing limitations, inconsistent temporal updates, and complex modelling requirements hinder the scalability and real-time applicability of biomass and carbon monitoring systems. This is particularly pronounced in low-income countries, where technical and financial constraints limit the uptake of high-computational geospatial modelling (Kilama Luwa et al., 2020).

Google Earth Engine (GEE) provides a sustainable solution to these challenges. GEE is a cloud-based geospatial analysis platform that facilitates ingesting and processing satellite data in real-time (Gorelick et al., 2017). GEE's potential for continuous land cover monitoring is demonstrated by its ability to ingest large archives of remote sensing data, integrated with advanced classification algorithms, can be scaled to biomass and carbon stocks estimation at flexible scales.

This study proposes a framework for leveraging GEE to perform real-time land cover classification and forest biomass density estimation, with the goal of enhancing carbon stock assessments and informing climate mitigation policies. The proposed framework focuses on automating the classification of seven land cover classes, and the calculation of Above-Ground Biomass (AGB) and Below-Ground Biomass (BGB) for the forest land cover class. By applying allometric models and vegetation indices, the framework enables accurate mapping of forest biomass distribution across the landscape. Carbon stock is estimated by converting biomass values using established carbon fractions, providing insights into the role of forest land cover in carbon sequestration.

The study's contributions include (1) a framework for real-time biomass and carbon stocks estimation, (2) custom JavaScript code for real-time pre-processing of Landsat, Sentinel-2 A/B and Sentinel-1 SAR imagery in GEE, and (3) custom Python code for estimating forest biomass and carbon stocks for climate planning.

# 2 METHODOLOGIES

## 2.1 Study Area and Datasets

The developed land cover classification, biomass density, and carbon stock estimation framework was tested in Uganda, an East African country (Figure 1). Uganda experiences high rates of deforestation and forest degradation. However, the country holds significant potential for sustainable forest landscape restoration due to relatively low restoration costs and

large socio-economic benefits compared to other countries (Brancalion et al., 2019). Several forest restoration hotspots have been identified (Figure 1).

<figure><img src="image_157.png" /><figcaption>Figure 1: Map of the study area.</figcaption></figure>

Uganda's diverse environmental and socio-economic conditions, driven by varying levels of forest degradation, restoration potential, and exposure to climate change impacts, presented an ideal setting to validate and refine our proposed framework.

A summary of the datasets used to achieve the study objectives is provided in Table 1. These datasets have been imported, pre-processed, and analyzed within the Google Earth Engine (GEE) environment to ensure efficient and scalable data handling.

<table><caption>Table 1: Data and data sources used in the study.</caption><thead><tr><td>Data</td><td>Scale</td><td>Date</td><td>Purpose</td><td>Source</td></tr></thead><tbody><tr><td>Landsat 7 ETM+, Landsat 8 OLI/TIRS</td><td>30 m</td><td>2000 - 2020</td><td>Land cover, Biomass, Carbon stocks</td><td>USGS ingested in GEE</td></tr><tr><td>Sentinel 2A/B</td><td>10 m</td><td>2019 - 2020</td><td>Land cover, Biomass, Carbon</td><td>ESA ingested in GEE</td></tr><tr><td>Sentinel 1C</td><td>10 m</td><td>2019 - 2020</td><td>Land cover classification</td><td>ESA ingested in GEE</td></tr><tr><td>Biomass data</td><td>30 m</td><td>2000, 2005</td><td>Validation / ground truthing</td><td>Uganda's Forest Authority</td></tr></tbody></table>

## 2.2 Development of Land Cover Classification Framework

Figure 2 illustrates the real-time land cover classification framework developed in GEE. The framework is applicable to satellite imagery with scalable temporal resolutions, such as the 16- and 10-day repeat cycles of Landsat 8/9 and Sentinel-2 A/B
respectively. This allows for the selection and matching of imagery that aligns with specific temporal requirements, ensuring consistency and accuracy in land cover assessments.

## 2.2.1 Pre-Processing the Satellite Imagery

Pre-processing of satellite imagery followed the developed GEE JavaScript code (Abudu et al., 2024). to automatically select Blue, Green, Red and Near-infrared (RGBN) optical bands from available Landsat 7 ETM+, Landsat 8 OLI/TIRS, Landsat 9 archives and Sentinel 2A/B. The script calculates the Normalized Difference Vegetation Index (NDVI) using Equation 1 and applies GEE's quality mosaic algorithm (Gorelick et al., 2017) for each study period. This process ensures that the final mosaic imagery comprises pixels with the highest NDVI values. The ratioing approach, effectively reducing the impact of cloud cover and mixed-pixel effects, which are common in tropical regions. By prioritizing pixels associated with high biomass (high NDVI values), the approach enhances the accuracy of land cover classification.

$$NDVI = \frac{(NIR - Red)}{(NIR + Red)} \quad (1)$$

Given that forest biophysical properties change gradually, we pre-processed optical imagery into annual composites. To align with Uganda's National Biomass Survey (NBS) periods (NFI, 2016), imagery for 2000, 2005, 2015, and 2020 were selected. The 2015 NBS period, being the most recent, served as the

validation reference for biomass and carbon stock estimation, as detailed in Section 2.3.

To maintain spatial and geometric consistency during analysis, all datasets were reprojected to a spatial resolution of 30 meters (matching Landsat's resolution) and transformed to the WGS84-UTM Zone 36N projection, localized for the study area.

## 2.2.2 Land Cover Classification

We prioritized Landsat imagery for both land cover classification and estimation of biomass and carbon stocks due to rich temporal archives. Additionally, the focus on forest biomass estimation detailed in Section 2.3 meant that the 30-meter spatial resolution of Landsat imagery was sufficient for forest areal extents. Land cover classification was performed using a supervised Random Forest (RF) algorithm. Previous studies have explored different classification techniques, including Maximum Likelihood Classification (Abudu et al., 2019), Support Vector Machines (Opedes et al., 2022), and RF-based methods (Coker et al., 2021). RF demonstrated superior performance over other pixel-based methods in Uganda.

The choice of RF was influenced by its proven advantages, such as the ability to handle high-dimensional datasets and resilience to noise and outliers, owing to its ensemble approach of multiple decision trees (Coker et al., 2021). These attributes are particularly valuable for accurate characterization of Uganda's complex and heterogeneous landscapes.

The RF model was configured with 50 decision trees (n=50) and trained on 70% of the dataset, while

<figure><img src="image_158.png" /><figcaption>Figure 2: Land cover classification framework.</figcaption></figure>
the remaining 30% reserved for validation. Classification targeted seven land cover classes, informed by local expertise, and previous study (Opedes et al., 2022). Table 2 summarizes the land cover classes. To work within GEE's memory limits, we utilized GEE Python API to develop custom code for land cover classification and estimation of biomass density and carbon stocks (Abudu et al., 2024).

<table><caption>Table 2: Land cover classification classes.</caption><thead><tr><td>Class No.</td><td>Class name</td><td>Description</td></tr></thead><tbody><tr><td>1</td><td>Forests</td><td>Natural and artificial tree covers, and woodlands.</td></tr><tr><td>2</td><td>Bushlands</td><td>Closed, open or very open shrubs</td></tr><tr><td>3</td><td>Grasslands</td><td>Graminoids and herbaceous areas for grazing, sports, etc</td></tr><tr><td>4</td><td>Agriculture</td><td>Small- and large-scale farmlands</td></tr><tr><td>5</td><td>Wetlands</td><td>Wet graminoids and herbaceous areas</td></tr><tr><td>6</td><td>Built up</td><td>Buildings, weathered roads, human settlements, and other artificial surfaces.</td></tr><tr><td>7</td><td>Open water</td><td>Standing and flowing water and water dams</td></tr></tbody></table>

### 2.2.3 Accuracy Assessment of the Land Cover Classification

The accuracy of the classification was assessed in two stages. The first stage involved testing the trained RF model on the reserved 30% of unseen data. In the second stage, accuracy was assessed on the final classified image. For this assessment, 300 random pixels were stratified by each land cover class, resulting in a total of 2,100 sampled pixels per year.

A confusion matrix; an accuracy assessment method previously utilized in this study area (Abudu et al., 2019; Kuule et al., 2022), was developed to summarize the counts of correct and incorrect predictions for each of the seven land cover classes (Table A1 in Appendix). Computed accuracy metrics included the Overall Accuracy (OA) which measures the proportion of correctly classified pixels across all classes, User Accuracy (UA) which is a classification precision indicator per class from user's perspective, Producer's Accuracy (PA) which is the model's recall classification and the Kappa Coefficient (K); a statistic measure with values toward one representing stronger agreement between predicted and true labels

while accounting for chance agreement with values between zero and one.

Although, stratified sampling approach is a robust method, in practice biases still arise. In our case, while sampling 300 points per land cover class (N=300), we assumed equal distribution across all classes (n=7). However, within class distributions may vary, resulting into stratum variances ($S_{PA}^V$ and $S_{UA}^V$). We applied Card's correction (Card, 1982), following the steps outlined in Olofsson et al., (2013), to check and correct any stratum variances, and correct the producer's ($PA_n$) and user's ($UA_n$) accuracies per class and overall accuracy ($OA_c$). The classified LULC sample size ($N_n$) varies per class. Equations 2, 3 and 4 were applied on results of the confusion matrix to correct PA, UA and OA.

$$S_{PA}^V = \sum_{n=1}^{n=7} \left( \frac{(1 - PA_n) * PA_n}{N} \right) \quad (2)$$

$$S_{UA}^V = \sum_{n=1}^{n=7} \left( \frac{(1 - UA_n) * UA_n}{N_n} \right) \quad (3)$$

$$OA_c = OA - \left( \frac{S_{PA}^V + S_{UA}^V}{2 \times n} \right) \quad (4)$$

## 2.3 Biomass Density and Carbon Stock Estimation

We formulated a workflow for biomass density calculation in tons per hectare (t/ha). Biomass density is directly correlated with carbon stock, making it a critical parameter for estimating carbon reserves and evaluating the role of vegetation in sequestering atmospheric carbon and informing climate change mitigation strategies (UNFCCC, 2015).

Egeru et al., (2014) affirms that Normalized Difference Vegetation Index (NDVI) is an effective indicator of vegetation and biomass presence in north-eastern Uganda. NDVI values near +1 reflect dense vegetation, while values approaching zero indicating sparse or absent cover. The strong correlation between NDVI and biomass highlights its value for monitoring vegetation health and coverage.

We calculated NDVI from optical Landsat imagery using Equation 1. To establish the relationship between biomass and vegetation indices, we utilized classified forest land cover data, with field-measured biomass serving as the dependent variable and vegetation indices as independent variables. We applied linear regression models to determine the empirical constants (a and b) in Equation 5, using existing biomass data of 2000 and
2005, and calculated the above-ground biomass (AGB) from NDVI. Equation 6 was applied determine the below-ground biomass (BGB) as a fraction of the AGB according to the root-to-shoot ratio (r) for each land cover class. In Uganda, the default Intergovernmental Panel on Climate Change (IPCC) root-to-shoot ratio of 0.24 is commonly applied for such conversions (MWE-IPCC, 2014), which was adopted by study. Biomass density was calculated per hectare by reprojecting the Landsat's 30m pixel size to 100m and then calculating biomass per 100 x 100F m² pixel area.

Carbon stock in Uganda's forests is determined to be 47% of the total biomass stocks (NFA, 2009). Consequently, we focused on forest land cover for estimating biomass and carbon stocks. However, in cases where biomass conversion factors for other land covers exist, the model can be tested for other land cover types. Equations 7 and 8 were used to calculate the total forest biomass and carbon stocks respectively.

$$AGB = a \times e^{b \times NDVI} \qquad (5)$$

$$BGB = r \times AGB \qquad (6)$$

$$Total Biomass = AGB + BGB \qquad (7)$$

$$Carbon Stocks = 0.47 \times Total Biomass \qquad (8)$$

# 3 RESULTS AND DISCUSSIONS

## 3.1 Characterization of Land Cover Changes in Uganda

Classification achieved an overall accuracy of 89% (Table A1 in Appendix). Over the past decade, agriculture had the largest expanded from 51% to 60% while open water remained stable at 11% of the total land area. In contrast, forests have experienced the worst instability, declining by over 3% (3.1 – 2.4 million hectares) respectively, as they are converted to agricultural land and grasslands.

Figure 3 highlights the scale of deforestation in Uganda, with the northern and eastern regions most affected. Deforestation also intensified in the western and central regions from 2015 onwards, where forest losses were previously minimal.

<figure><img src="image_159.png" /><figcaption>Figure 3: Uganda's Land use land cover (LULC) changes.</figcaption></figure>

### 3.1.1 Temporal Transition of Land Cover Classes

Using Markov Chain transition matrix calculations (Abudu et al., 2019; Kuule et al., 2022), we analyzed the shifts between various land cover classes to understand the dynamics and extent of land cover changes over the study period. The results, illustrated in Figures 4 and 5, reveal significant patterns of change, with key transitions highlighting the widespread conversion of forest land into grasslands and agricultural areas. These transitions suggest increasing pressure from human activities such as agricultural expansion, settlement growth, and resource extraction, which are driving the reduction of natural vegetation cover. Notably, bushlands and forests are the most affected by land cover changes, experiencing significant losses with transition rates of approximately 80% and 75%, respectively, as they are increasingly converted to other land cover classes.

<figure><img src="image_160.png" /><figcaption>Figure 4: Markov transition matrix of land cover classes.</figcaption></figure>
<figure><img src="image_161.png" /><figcaption>Figure 5: Land cover transitions from 2000 to 2020.</figcaption></figure>

From Figure 5, forests primarily transition to agriculture, bushlands and grasslands. In Uganda, bushlands often represent secondary recovery stages of previously deforested areas. The transition patterns suggest that as forests are cleared, the land typically shifts to agricultural use or remains within the forest-bushland cycle. Agricultural expansion is the primary driver of deforestation in Uganda as initially cleared bushlands becomes grassland and is later cultivated for farming.

## 3.2 Biomass Density and Carbon Stock in Uganda

Biomass density is a strong indicator for carbon stocks potential and is also a key indicator of energy potential of an area because biomass is a primary resource for renewable energy. Since the energy potential is directly proportional to the biomass quantity and its calorific value (Barasa et al., 2022), areas with higher biomass densities represent more energy potential per unit area.

Figure 6 shows the baseline biomass density in the year 2000, and Figure 7 shows the results modelling biomass density from 2000 – 2040. We present a normalized data to show the trend of biomass density and carbon stocks, to inform future modelling, management, and policy decisions. In the trend analysis, biomass and carbon stocks are directly proportional following similar trends. To add context,

we plotted the energy demand based on data from Ritchie et al., (2022) indicating a strong inverse relationship and suggesting Uganda's biomass loss is greatly influenced by the country's energy demand.

Uganda's biomass density is concentrated around the western and eastern hilly plains with highest biomass densities of 343 t/ha with most areas in the northern parts exhibiting the lowest densities. Other parts of the country exhibit low biomass density and consequently low carbon stocks (Figure 6).

<figure><img src="image_162.png" /><figcaption>Figure 6: Biomass density in tons per hectare (t/ha) for 2000.</figcaption></figure>
Figure 7 presents the projected trends in Uganda’s biomass, carbon stocks, and energy landscape, reflecting the country's Vision 2040. The results show that Uganda’s goal of reducing greenhouse gas emissions by 24.7% below the current 148.80 Mt CO₂e by 2030 (MWE, 2022) can only be feasible under targeted interventions. However, under a business-as-usual scenario, reducing carbon emissions and attaining sustainable biomass for energy consumption remains unattainable.

<figure><img src="image_163.png" /><figcaption>Figure 7: Projecting Uganda’s carbon, energy, and biomass from 2000 - 2040.</figcaption></figure>

# 4 CONCLUSIONS

Uganda’s key forest and climate policy challenges are weak institutional capacity, limited coordination and insufficient financing (Renner, 2020). These challenges are exacerbated by a lack of up-to-date monitoring information and limited data-centric decision-making routines. Our results (data and analyses) are vital for policymakers to prioritize conservation efforts and design strategies that enhance carbon sequestration. Results such as trend analysis in Figure 7 indicate the need for urgent change from business-as-usual scenario to abate the dwindling biomass and carbon stocks in the future and meet the increasing energy demands. The results also underscore the significance of protecting diverse land cover classes as part of Uganda’s strategy to meet climate goals, enhance biodiversity, and promote sustainable development.

This geospatial modelling approach offers a cost-effective and scalable method for carbon stocks assessment, particularly in low-resource settings. Future work will refine the model’s accuracy, addressing uncertainties around biomass density and carbon stock estimation and improving confidence levels. This will be achieved through improved ground-truthing, model fit and confidence interval

analyses and exploring its adaptability to related areas, such as energy demand forecasting.

# REFERENCES

Abudu, D., Bastin, L., Chong, J. K., & Röder, M. (2024). GEE-BioCarbonStocks: Advancing real-time LULC for biomass density and carbon stocks estimation in GEE. Zenodo. https://doi.org/10.5281/zenodo.14837327

Abudu, D., Echima, R. A., & Andogah, G. (2019). Spatial assessment of urban sprawl in Arua Municipality, Uganda. *The Egyptian Journal of Remote Sensing and Space Science*, 22(3), 315–322. https://doi.org/10.1016/j.ejrs.2018.01.008

Barasa, B., Turyabanawe, L., Akello, G., Gudoyi, P. M., Nabatta, C., & Mulabbi, A. (2022). The Energy Potential of Harvested Wood Fuel by Refugees in Northern Uganda. *The Scientific World Journal*, 2022, 1–10. https://doi.org/10.1155/2022/1569960

Brancalion, P. H. S., Niamir, A., Broadbent, E., Crouzeilles, R., Barros, F. S. M., Almeyda Zambrano, A. M., Baccini, A., Aronson, J., Goetz, S., Reid, J. L., Strassburg, B. B. N., Wilson, S., & Chazdon, R. L. (2019). Global restoration opportunities in tropical rainforest landscapes. *Science Advances*, 5(7), 3223–3226. https://doi.org/10.1126/sciadv.aav3223

Card, D. H. (1982). Using Known Map Category Marginal Frequencies To Improve Estimates of Thematic Map Accuracy. *Photogrammetric Engineering and Remote Sensing*, 48(3), 431–439. https://www.asprs.org/wp-content/uploads/pers/1982journal/mar/1982.mar_431-439.pdf

Coker, E. S., Amegah, A. K., Mwebaze, E., Ssematimba, J., & Bainomugisha, E. (2021). A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda. *Environmental Research*, 199(April), 111352. https://doi.org/10.1016/j.envres.2021.111352

Egeru, A., Wasonga, O., Kyagulanyi, J., Majaliwa, G., MacOpiyo, L., & Mburu, J. (2014). Spatio-temporal dynamics of forage and land cover changes in Karamoja sub-region. *Pastoralism: Research, Policy and Practice*, 4(1), 6. https://doi.org/10.1186/2041-7136-4-6

Flores Lanza, M., Leonard, A., & Hirmer, S. (2024). Geospatial and socioeconomic prediction of value-driven clean cooking uptake. *Renewable and Sustainable Energy Reviews*, 192(October 2023), 114199. https://doi.org/10.1016/j.rser.2023.114199

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. *Remote Sensing of Environment*, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Kilama Luwa, J., Bamutaze, Y., Majaliwa Mwanjalolo, J.-G., Waiswa, D., Pilesjö, P., Mukengere, E. B., Luwa, J., Bamutaze, Y., Majaliwa, J.-G., Waiswa, D., Pilesjö,
P., Espoir, & Mukengere, B., Bagula, E., Luwa, J. K., Mwanjalolo, J.-G. M., & Mukengere, E. B. (2020). Impacts of land use and land cover change in response to different driving forces in Uganda: evidence from a review. *African Geographical Review*, 40(4), 378–394. https://doi.org/10.1080/19376812.2020.1832547

Kuule, D. A., Ssentongo, B., Magaya, P. J., Mwesigwa, G. Y., Okurut, I. T., Nyombi, K., Egeru, A., & Tabuti, J. R. S. (2022). Land Use and Land Cover Change Dynamics and Perceived Drivers in Rangeland Areas in Central Uganda. *Land*, 11(9), 1402. https://doi.org/10.3390/land11091402

Ma, Z., Hu, C., Huang, J., Li, T., & Lei, J. (2022). Forests and Forestry in Support of Sustainable Development Goals (SDGs): A Bibliometric Analysis. *Forests*, 13(11), 1960. https://doi.org/10.3390/f1311960

Makepa, D. C., & Chihobo, C. H. (2024). Sustainable pathways for biomass production and utilization in carbon capture and storage—a review. *Biomass Conversion and Biorefinery*, 0123456789. https://doi.org/10.1007/s13399-024-06010-5

MWE-IPCC. (2014). Annex 9: Estimating forest degradation in Uganda. 1999 (table 2). https://www.mwe.go.ug/sites/default/files/library/Annex 09-Biomass Degradation in Uganda.pdf

MWE. (2022). Updated Nationally Determined Contribution (NDC)-Uganda (Issue September). https://mwe.go.ug/library/updated-nationally-determined-contribution-ndc

Nakakaawa, C. A., Vedeld, P. O., & Aune, J. B. (2011). Spatial and temporal land use and carbon stock changes in Uganda: Implications for a future REDD strategy. *Mitigation and Adaptation Strategies for Global Change*, 16(1), 25–62. https://doi.org/10.1007/s11027-010-9251-0

NFA. (2009). National Biomass Study Technical Report 2009. In *Issue December* (Vol. 2009, Issue December 2009). https://www.nfa.go.ug/images/reports/biomasstechnicalreport2009.pdf

NFI. (2016). UGANDA NATIONAL FOREST INVENTORY (NFI). https://microdata.fao.org/index.php/catalog/2047

Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. *Remote Sensing of Environment*, 129, 122–131. https://doi.org/10.1016/j.rse.2012.10.031

Opedes, H., Mücher, S., Baartman, J. E. M. M., Nedala, S., & Mugagga, F. (2022). Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020. *Remote Sensing*, 14(10), 2423. https://doi.org/10.3390/rs14102423

Panéque-Gálvez, J., McCall, M., Napoletano, B., Wich, S., & Koh, L. (2014). Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas. *Forests*, 5(6), 1481–1507. https://doi.org/10.3390/f5061481

Renner, J. (2020). New Power Structures and Shifted Governance Agendas Disrupting Climate Change Adaptation Developments in Kenya and Uganda. *Sustainability 2020*, Vol. 12, Page 2799, 12(7), 2799. https://doi.org/10.3390/SU12072799

Ritchie, H., Roser, M., & Rosado, P. (2022). Energy. Our World in Data; Our World in Data. https://ourworldindata.org/energy

UNFCCC. (2015). *Measurements for Estimation of Carbon Stocks in Afforestation and Reforestation Project Activities under the Clean Development Mechanism: A Field Manual*. http://unfccc.int/resource/docs/publications/cdm_afforestation_field-manual_web.pdf

APPENDIX

<table><caption>Table 3: Confusion matrix for accuracy assessment of land cover classification.</caption><thead><tr><th colspan="12">Reference data</th></tr><tr><th></th><th>Class Number</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>Total</th><th>User accuracy</th><th>Commission Error</th><th>Variance (Card's)</th></tr></thead><tbody><tr><th rowspan="7">Classified land cover Map</th><th>1</th><td>262</td><td>10</td><td>14</td><td>20</td><td>5</td><td>1</td><td>0</td><td>312</td><td>0.8397</td><td>0.1603</td><td>0.0004</td></tr><tr><th>2</th><td>16</td><td>265</td><td>10</td><td>6</td><td>5</td><td>3</td><td>2</td><td>307</td><td>0.8632</td><td>0.1368</td><td>0.0004</td></tr><tr><th>3</th><td>6</td><td>8</td><td>261</td><td>23</td><td>8</td><td>2</td><td>4</td><td>312</td><td>0.8365</td><td>0.1635</td><td>0.0004</td></tr><tr><th>4</th><td>9</td><td>5</td><td>4</td><td>238</td><td>6</td><td>4</td><td>2</td><td>268</td><td>0.8881</td><td>0.1119</td><td>0.0004</td></tr><tr><th>5</th><td>2</td><td>4</td><td>5</td><td>4</td><td>267</td><td>4</td><td>4</td><td>290</td><td>0.9207</td><td>0.0793</td><td>0.0003</td></tr><tr><th>6</th><td>3</td><td>6</td><td>2</td><td>4</td><td>5</td><td>286</td><td>0</td><td>306</td><td>0.9346</td><td>0.0654</td><td>0.0002</td></tr><tr><th>7</th><td>2</td><td>2</td><td>4</td><td>5</td><td>4</td><td>0</td><td>288</td><td>305</td><td>0.9443</td><td>0.0557</td><td>0.0002</td></tr><tr><th>Total</th><td></td><td>300</td><td>300</td><td>300</td><td>300</td><td>300</td><td>300</td><td>300</td><td>1867</td><td></td><td></td><td></td></tr><tr><th>Producer accuracy</th><td></td><td>0.8733</td><td>0.8833</td><td>0.8700</td><td>0.7933</td><td>0.8900</td><td>0.9533</td><td>0.9600</td><td></td><td>Overall accuracy (standard):</td><td></td><td>88.90476</td></tr><tr><th>Omission Error</th><td></td><td>0.1267</td><td>0.1167</td><td>0.1300</td><td>0.2067</td><td>0.1100</td><td>0.0467</td><td>0.0400</td><td></td><td></td><td></td><td></td></tr><tr><th>Variance strata (Card's)</th><td></td><td>0.0004</td><td>0.0003</td><td>0.0004</td><td>0.0005</td><td>0.0003</td><td>0.0001</td><td>0.0001</td><td></td><td>Overall accuracy (Card corrected):</td><td></td><td>88.90444</td></tr><tr><th>Row x Column totals</th><td></td><td>93600</td><td>92100</td><td>93600</td><td>80400</td><td>87000</td><td>91800</td><td>91500</td><td></td><td>Kappa coefficient (K):</td><td></td><td>0.87</td></tr></tbody></table>
# Low-Cost GNSS Receivers Reliability Using Centipede RTK Network for Land Surveying

Muhammad Ali Sammuneh<sup>a</sup>, Mojtaba Eslahi<sup>b</sup>, Rani EL Meouche<sup>c</sup> and Elham Farazdaghi<sup>d</sup>

Institut de Recherche en Construction (IRC), ESTP Paris, 94230 Cachan, France

**Keywords:** GNSS, Land Surveying, RTK, Centipede-RTK, Low-Cost GNSS.

**Abstract:** The question of using low-cost Multiband Global Navigation Satellite System GNSS receivers and antennas in land surveying is real and important. In France, mainly a collaborative Real Time Kinematic (RTK) network called Centipede covers the country providing the corrections open access in real time to the users. Furthermore, the low-cost interface application and software called SW Maps connect the Low-cost GNSS receiver to the Centipede-RTK network using a smartphone. The cost of surveying projects using all these elements is certainly economical. The main question here is the reliability of this package to perform continuous, stable, and reliable RTK land surveying. We test this capability by examining the differences in the RTK position of known control points with a series of measurements over different mount points. The results show that we can use this package for land surveying only with necessary validation and control by experimental users, as the indicators of Centipede RTK accuracy via the SW Maps interface are not representative.

## 1 INTRODUCTION

Low GNSS cost concept is attractive economically and it can offer an alternative to accomplish some kinds of engineering projects. Nevertheless, the main question is to examine the reliability of this package (Low-cost dual frequency receiver, low-cost antenna, open access Centipede-RTK network in France and free Android GIS application SW Maps on smartphone) to achieve land surveying in RTK in a continues, stable and reliable way. We explore this possibility by examining the differences in RTK position of known control points with a series of measurements over different mount points.

We start with the necessary definitions of technical terms and their abbreviations to ensure a good understanding of this work. A GNSS, or Global Navigation Satellite System, is a generic name for a group of artificial satellites consisting mainly of constellations from the United States (GPS), Russia (GLONASS), Europe (Galileo), and the China (Beidou) that transmit position and timing data from their high orbits (Teunissen and Montenbruck, 2017).

NTRIP (Network and Transport of RTCM via Internet Protocol) is a protocol for transmitting Real Time Kinematic (RTK) corrections over the Internet to Global Navigation Satellite System (GNSS) receivers. RTK, on the other hand is a technique for improving the accuracy of GNSS positioning using information from (GNSS-RTK base or a mount point) a fixed reference station whose position is well known (GNSS Science Support Centre fosters collaboration across scientific communities through the provision of GNSS science-based products and services., 2021). Typical nominal accuracy for RTK systems is 1 cm horizontally and 2 cm vertically (Seeber, 2003).

Centipede RTK is a network of shared, open-access GNSS RTK bases. The Centipede project aims to create a network of open RTK bases available to anyone in the coverage area. Public institutions, individuals, and private stakeholders (farmers or other public partners) extend the network. The objective of the project is to provide complete coverage of the metropolitan area. The French public research institute working for the coherent and sustainable development of agriculture, food and the

<sup>a</sup> https://orcid.org/0009-0008-4977-7386

<sup>b</sup> https://orcid.org/0000-0001-5005-1867

<sup>c</sup> https://orcid.org/0000-0001-5063-6638

<sup>d</sup> https://orcid.org/0000-0001-9935-1553

Sammuneh, M. A., Eslahi, M., El Meouche, R. and Farazdaghi, E.

Low-Cost GNSS Receivers Reliability Using Centipede RTK Network for Land Surveying.

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 211-218

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Proceedings Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
environment (INRAE) (“INRAE: research for Agriculture, Food and Environment,” n.d.), financially supports the project and since its launch in 2019, it has benefited from shared resources between research institutes, public bodies, farmers, and private companies (“Le Reseau Centipede RTK,” n.d.). SW Maps is a free Android GIS and mobile mapping application for collecting, presenting, and sharing geographic information (“SW Maps - GIS & Data Collector – Applications - Google Play,” n.d.). It allows users to perform high-precision GPS surveys using external RTK-compatible Bluetooth or USB serial receivers. It can be used with low-cost GNSS receivers and requires a USB-C male to USB-A male cable to work with an Android phone.

The current research presents studies on low-cost receivers and antennas that address their accuracy or stability. In addition, publications related to the Centipede collaborative network discuss the accuracy of this network using high-quality geodetic GNSS receivers and antennas. Nowadays several researches have been done on low-cost GNSS. In fact, we recently have access to low-cost GNSS receivers and low-cost antennas (LCRA). In addition, free smartphone applications serve as an interface to connect LCRA to the Centipede mount point (Sammuneh et al., 2023). We found it interesting to explore the possibility of securing land surveying in an economical and trustworthy manner. Our work differs from others in that we gather all the low-cost concepts and tools (receivers, antennas, correction network, interface, and software); and tried to answer this simple question: Could we trust the RTK results of LCRA within the Centipede network for reliable land surveying ?

There is an increasing number of studies on the accuracy of low-cost GNSS receivers and dual-frequency receivers (Jackson et al., 2018). A recent review, found that “Low-cost GNSS receivers generally exhibit lower observation quality compared to geodetic GNSS receivers in both open sky and urban conditions” and “Sub-centimeter accuracy can be achieved in the static relative method while a few centimeter accuracy is possible in RTK when open sky conditions are guaranteed (Stopar et al., 2024). However, for longer baselines and areas with obstructed views of the sky, low-cost GNSS receivers still can’t achieve the same positioning quality as geodetic GNSS receivers”. Studies on the performance of low-cost dual-frequency GNSS receivers and antennas for surveying in urban areas are the main subject of recent research (Hamza et al., 2023, 2021b, 2021a, 2020). There are many studies on the accuracy of RTK using LCRA in different

scenarios, including open-sky environments. (Bellone et al., 2016; Broekman and Gräbe, 2021; Cina and Piras, 2015). Semler et al. (2019) showed that 1 cm spatial accuracy is possible in open sky conditions using LCRA (Semler et al., 2019). Other researches discuss the limits of baseline distances, giving a limit of up to 20 km for short baselines, since beyond this distance the quality of positioning deteriorates due to ionospheric distortion (Caldera et al., 2016; Tsakiri et al., 2017). Moreover, when testing the kinematic performance of RTK in a non-urban area with a long baseline of about 30 km, the RTK solutions showed good concordance with the post-processed data, with less than 5% of the differences exceeding 3 cm in the planimetric component and 10 cm in the vertical component (Sanna et al., 2022).

However, Centipede is a network of mount points, each of which sends its own RTK corrections to the user but it is not NRTK. The Centipede website states that “This technology can be used, for example, to carry out naturalistic surveys (flora, fauna) with a high degree of localization accuracy, to carry out aerial photographic surveys (using drones) and to automate the driving of agricultural vehicles” (“Le Reseau Centipede RTK,” n.d.).

Thus, now the Centipede RTK databases are archived in RINEX (Receiver Independent Exchange Format) in the RENAG databases, which is a serious guarantee of durability for the network's data. Conversely, the Centipede databases are used for RENAG's daily analyses (“Le Reseau Centipede RTK,” n.d.).

## 2 MATERIALS AND METHODS

In our project, we use the Leica GNSS receivers & antenna, u-blox dual-frequency GNSS chip ZED-F9P Survey GNSS low-cost receiver (“ZED-F9P module,” 2023) and Calibrated Survey GNSS Multiband antenna (IP67) (“Calibrated Survey GNSS Tripleband + L-band antenna (IP67),” n.d.), SW maps android application, and smartphone. We use the GNSS calculations online service network of the IGN website to manage the Rinex files and to calculate control points coordinate (“Calculs GNSS Réseau en ligne | RGP,” n.d.). The centipede website is used to show the actual situation of the mount points.

In our case, we used two main modes: Static mode: Relative positioning to the phase in deferred time. The precision is of the order of
centimetres or even millimetres for high-quality receivers (Teunissen and Montenbruck, 2017).

RTK mode: Relative phase positioning in real-time. The precision is of the order of 5 cm. The data is sent via internet (Teunissen and Montenbruck, 2017).

The method of this study aims to have serial estimations of the coordinates of well-known Control points in RTK based on different mounting points going from the nearest one to 60 km distance. We start by using static mode with 3 hours of GNSS observation using a high quality receiver and antenna to generate a RINEX observation file with 30-sec frequencies. We then wait for 2 weeks to obtain the precise orbit file to calculate the final coordinates of the reference point REF1 ("Calculs GNSS Réseau en ligne | RGP," n.d.). Once the REF1 coordinates are fixed, we start using a Low-cost GNSS receiver and antenna to connect to the Centipede network via smartphone using the SW maps application. We start by creating a GIS project in SW Maps and allow only RTK fix quality features to be stored.

We examine the possibility of connecting the Centipede mount points and getting RTK fix quality from them by recording 3-4 minutes of RTK observations, with 5 seconds frequency for each mount point, within 60 km distance from our reference point. Then we collect the data related to each functional mount point within 30 minutes and 5 seconds frequency. After analysis, we focus on three mount points for the next data collection for one day with ten series of 30 minutes and 5 seconds frequency for each one of the three mount points to investigate their continuity, stability, and reliability. We examine three levels of planimetric accuracy, less than 5 cm, from 5 to 10 cm, and over 10 cm.

# 3 LOW-COST RELIABLE GNSS RECEIVERS DUE TO THE CENTIPEDE RTK NETWORK

## 3.1 Reference Point

We choose a reference point with an open sky view to make our measurements using (the signal) GNSS constellations without multipath effects. We call this point "REF1". We collect the observations using a LEICA GS10 dual frequency receiver and LEIAS10 Leica antenna ("Leica Viva GS10 et GS25 – Récepteurs GNSS de haute précision," n.d.). The measurements are made in static mode with 30 seconds frequency for 3 hours to generate the RINEX file of REF1. We use the IGN online site calculation

service to obtain the final position coordinates ("Calculs GNSS Réseau en ligne | RGP," n.d.). The calculations use Bernese GNSS software. It calculates vectors from 12 permanent GNSS stations (RGP) and REF1, using the precise orbits to determine the final coordinates of REF1. The estimated accuracy given by the calculation report is (North: 9 mm, East: 10 mm, Height: 23 mm), with final coordinates in UTM 31 N (E = 450714.314 m, N = 5404846.437 m).

## 3.2 Centipede Mount Points

In this section, we will mainly use the U-blox dual-frequency GNSS chip ZED-F9P Survey GNSS low-cost receiver & Calibrated Survey GNSS Multiband antenna connected to a smartphone by SW Map Android GIS application. In this part, we will discuss the possibility of contacting the mounting points of Centipede via the application within 60 km distance from the reference station REF1 to verify the *connectivity* and to ensure a fixed ambiguity solution (RTK Fixed). Then we will examine the *stability* of the observations for each mount point by observing the differences in behaviour over time (changes). Finally, we will concentrate on three stable mount points to test the accuracy for ten sets of measurements covering 6 hours of RTK observation to ensure the *reliability* of the mount station's diffused corrections.

### 3.2.1 Connectivity

The Centipede network covers the French territories but there are some gaps. For the practical use of this network and as we aim to propose a strategy, we will examine the connectivity and the differences in the plan components for near and far points within the limit of 60 km. The connection is made through the NTRIP settings in SW-Maps, and then we can indicate the type of solution (DGPS or RTK Fix), PDOP, HDOP, VDOP, satellite in view, satellite in use, the horizontal and vertical accuracy. However, the accuracy is given in meters. This does not help to ensure the user the accuracy needed for his work in land surveying. We are looking for an indicator that shows the level of accuracy in cm. However, we will verify in section 3.2.4, whether this indicator change has any impact on the results.

As mentioned in Table 1, we have mainly 14 mount points and we found that 12 mount points can be contacted giving RTK Fix solution except the GRIG mount point which gave DGPS RTK solution and is discarded from the test set. Table 1. 14 Centipede-RTK network mount points within 60 km of the reference point.
<table><caption>Table 1: 14 Centipede-RTK network mount points within 60 km of the reference point.</caption><thead><tr><th>Mount Point</th><th>Distance from Reference Point (KM)</th><th>Connectivity | RTK Fixed solution</th></tr></thead><tbody><tr><td>RICE</td><td>8</td><td>Yes|Yes</td></tr><tr><td>STME</td><td>9</td><td>No</td></tr><tr><td>OUIL</td><td>19</td><td>Yes|Yes</td></tr><tr><td>EN5G</td><td>20</td><td>Yes|Yes</td></tr><tr><td>EN5G2</td><td>20</td><td>Yes|Yes</td></tr><tr><td>LAUR</td><td>20</td><td>Yes|Yes</td></tr><tr><td>LNE1</td><td>25</td><td>No</td></tr><tr><td>CFDX</td><td>28</td><td>Yes|Yes</td></tr><tr><td>HBC77</td><td>40</td><td>Yes|Yes</td></tr><tr><td>GRIG</td><td>41</td><td>Yes|No</td></tr><tr><td>BARB</td><td>48</td><td>Yes|Yes</td></tr><tr><td>COND</td><td>49</td><td>Yes|Yes</td></tr><tr><td>SGC</td><td>51</td><td>Yes|Yes</td></tr><tr><td>GPTR</td><td>60</td><td>Yes|Yes</td></tr></tbody></table>

### 3.2.2 Planimetric Differences

In this work, we will focus only on horizontal or planimetric results. To estimate the accuracy of the measurements, we assume that the coordinates of reference REF1 are accurate and precise. Therefore, the 2D horizontal or planimetric differences $dP$ are considered as the accuracy levels to be discussed. It is given by difference of the components east $dE$ and north $dN$ of the observed coordinates $Obs(E, N, U)$ from the reference point $Ref(E, N, U)$:

$$dE = E_{Ref} - E_{Obs} \qquad (1)$$

$$dN = N_{Ref} - N_{Obs} \qquad (2)$$

$$dP = \sqrt{dE^2 + dN^2} \qquad (3)$$

We keep the term difference when talking about accuracy because it is more representative in our case with only one reference point. As Figure 1 shows, the 2D planimetric differences give an idea of the connectivity of the mount points, but also alert the results concerning the OUIL station for example. We expect the distant mount points to give high differences like COND-50KM which gives for some observations a 2D difference level of 20 cm. The OUIL station is 19 km away from our reference point and it shows a steady level of 15 cm 2D planimetric differences that exceed all the other mount points within 60 KM of distance. Mount points show except (OUIL and COND) in Figure 1, 2D planimetric differences reaching a maximum level of 7 cm for the RTK fix solution.

We see that for some mount points the behaviour is not stable giving a 5 cm level of difference and suddenly jumping to a 20 cm level COND-50 KM while. BARB-48 KM gives differences less than 5 cm level, but it changes for every 5 sec observation. Therefore, we need to verify the stability of the 2D planimetric differences with time for these eleven mount points over a longer observation period. It is quite interesting that the mount stations @50km and @60km show differences of about 5 to 7 cm.

Nevertheless, our sets of measurements in this section are intended to verify the connectivity.

<figure><img src="image_164.png" /><figcaption>Figure 1: The difference in 2D Planimetric coordinates of 11 mount points of Centipede Network within one minute per station with 5 seconds frequency.</figcaption></figure>

### 3.2.3 Stability

We collect observations for each mount point within five minutes with a five-second frequency. This choice is made to collect data within the period that the satellite configuration is likely to give the same positioning dilution of the precision PDOP for each data set, and to preserve the same metrology conditions for the troposphere and ionosphere. Figure 2 shows the difference in 2D planimetric coordinates of 11 mount points of Centipede Network within five minutes per station with 5 seconds frequency.

<figure><img src="image_165.png" /><figcaption>Figure 2: Difference in 2D Planimetric coordinates of 11 mount points of Centipede Network within five minutes per station with 5 seconds frequency.</figcaption></figure>

The poor accuracy of the OUIL mount point is again shown and it means that the corrections sent by this point are not good enough to be used for 2D topographic surveying with a 5 cm accuracy level. This confirms our suspicion about Centipede mount points and the need to verify their reliability before any surveying. Still talking about OUIL, the changes go from 20 to 16 cm which means that the rate of change is 4 cm within five minutes. Another interesting result comes from a distance of 20 km between two mount points on the same building,
ENSG (Ecole national des sciences geographies), called ENSG and ENSG2. The first provides a level of 4 cm difference while ENSG2 indicates 10 cm.

Therefore, one should use the first mount point rather than the second if they have the same distance from our reference point. The indicators in the SW-Maps interface are the same for both mount points. Beyond 20 km distance, the differences are quite above 5 cm level. HBC77@40km distance shows some exceptions to be below 5 cm level for some times. Remembering the results in (Sanna et al., 2022) about the performance of RTK in a non-urban area with a long baseline of about 30 km, the RTK solutions have shown a good concordance with post-processed data, with less than 5% of differences surpassing 3 cm in the planimetric component. Meanwhile, the BARB@48km mount point has a 6-10 cm level. COND@50km has quite troubling behaviour by giving less than a 5 cm level for the first two minutes and then suddenly it reaches a 15 cm level of difference. The difference then decreases slightly over time, but remains above the 5 cm level.

Therefore, the stability of this mount point again shows its chaotic behaviour. SEG@51km has more than a 15 cm difference level and sometimes some pics reach 50 cm. GPTR@60km has a stable 20 cm level of difference with one pic reaching 40 cm. In (Caldera et al., 2016; Tsakiri et al., 2017) a limit of 20 km for short baselines is given because more than this distance, the quality of positioning deteriorates due to the ionospheric bias. We focus on three mount points stations RICE@8km, LAUR@20km, and ENSG@20 km. As Figure 3 illustrates ENSG@20km has a mainly 5 cm level of difference within five minutes. RICE@8km is better than the 2 cm level, however, LAUR@20km is still below than the 3.5 cm level but with fluctuating behaviour.

<figure><img src="image_166.png" /><figcaption>Figure 3: Difference in 2D Planimetric coordinates of three mount points of Centipede Network within five minutes per station with 5 seconds frequency.</figcaption></figure>

### 3.2.4 Reliability

We need to extend the research for one day to study the behaviour of these three “short baselines” over time. We have ten sets of observations for each mount point. The observations are 5 minutes for RICE and then 5 minutes for LAURE and finally 5 minutes for ENSG within a 20 minute time span. We wait 20-30 minutes to restart the observations of the next set. We will consider nine sets to show the behaviour of the LAUR@20km mount point. Figure 4 shows sub-ten-centimeter level differences of all LAUR mount point sets. We can distinguish four sets with differences below the 5 cm level (3, 5, 6, and 8). The RTK measurements are for 5 minutes with 5 seconds frequency, which means that for these four sets the surveying work reaches the required level of 5 cm maximum of accuracy. Nevertheless, the other sets except set 2 go beyond the five centimeters level but below the seven centimeters level. Set 2 shows instability and the difference level reaches 10 cm.

<figure><img src="image_167.png" /><figcaption>Figure 4: 2D planimetric differences of nine sets of observations based on LAUR mount point.</figcaption></figure>

The LAUR mount point shows good results (<5cm) and bad results; therefore one can use it to periodically verify his measurements by comparing them with known points around his work. These conclusions of results are with 20 km distance from LAUR mount point. It is worth future research work to verify for <10 km distance whether the differences for all sets go lower than the five cm level of difference. LAUR mount point could be used for topographic or surveying works within a 20 km distance, with strict periodic verifications and controls on the site.