bluuebunny/crossref_metadata_2025 huggingface dataset:
Poaching of Encephalartos transvenosus, in the Limpopo Province, South Africa
Authors: Ndou Tshianeo M. , Stam Eduard M. , Tshisikhawe Milingoni P. , Alabi Mercy A. , Adeeyo Adeyemi O.
Published: December 2021

There is an established link between deforestation and negative hydrological effects which may affect watersheds. The number of the cycads Encephalartos transvenosus Stapf & Burtt Davy in South Africa is drastically reducing, and they are nearly extinct. Additionally, poaching and reduction in the population of cycads in reserves may impact negatively on watersheds in protected and buffer areas since the regeneration of this plant occurs over a long period. This research aimed to study the distributions of poached cycads, evaluate the factors responsible for poaching activity and suggest possible solutions towards conservation of E. transvenosus Stapf & Burtt Davy in some of the nature reserves in Limpopo province. Field observation was used to ascertain and collect locations of poached cycads. Data was obtained through the use of a questionnaire. Questionnaires were administered to rangers for the collection of data on causes of poaching, parts poached, measures taken and challenges of rangers in the nature reserves. Processing of the collected data was done by simple statistical analysis. The total population of rangers available in the reserves was used for the determination of the sample size of rangers by Slovin’s formula. Unemployment (46%) is the leading cause for poaching in Mphaphuli Nature Reserve, while trade is a leading factor in Modjadji and Lekgalameetse (37% and 60%) respectively. Debarked cycads amount to 14 (54%) while 12 of the total (26 poached cycads) were completely removed (46%). The most effective way of stopping the poaching of cycads as perceived by the rangers is by patrolling the nature reserves. The observed poaching of Encephalartos transvenosus is alarming since this plant has a low population size which is currently confined to the Limpopo Province in South Africa only. If poaching continues in the nature reserves, all cycads have the tendency of being removed. Therefore, there is a need to continually develop strategies for the conservation of cycads. The peculiar nature and challenges of each reserve have been established and unique solutions for these nature reserves in Limpopo Province have been proposed. Better conservation of cycad species can positively impact the hydrological process of the concerned area and better help the watersheds.
Hedging Effectiveness of Commodity Futures Contracts to Minimize Price Risk: Empirical Evidence from the Italian Field Crop Sector
Authors: Penone Carlotta , Giampietri Elisa , Trestini Samuele
Published: December 2021

Over the last years, farmers have been increasingly exposed to income risk due to the volatility of the commodities prices. Among others, hedging in futures markets (i.e., financial markets) represents an available strategy for producers to cope with income risks at farm level. To better understand the advantages of such promising tools, this paper aims at analyzing the hedging effectiveness for soybean, corn and milling wheat producers in Italy. Following the literature, three different methodologies (i.e., naïve, OLS, GARCH) are applied for the estimation of the hedge portfolio, then compared to an unhedged portfolio for assessing the income risk reduction. Findings confirm the hedging effectiveness of futures contracts for all the considered commodities, showing also that this effect increases with longer hedge horizons, and also showing better performances for the European exchange market (i.e., Euronext), compared to the North American counterpart.
Variations in Nocturnal Residual Layer Height and Its Effects on Surface PM2.5 over Wuhan, China
Authors: Ma Xin , Jiang Weicheng , Li Hui , Ma Yingying , Jin Shikuan , Liu Boming , Gong Wei
Published: December 2021

Large amounts of aerosols remain in the residual layer (RL) after sunset, which may be the source of the next day’s pollutants. However, the characteristics of the nocturnal residual layer height (RLH) and its effect on urban environment pollution are unknown. In this study, the characteristics of the RLH and its effect on fine particles with diameters <2.5 μm (PM2.5) were investigated using lidar data from January 2017 to December 2019. The results show that the RLH is highest in summer (1.55 ± 0.55 km), followed by spring (1.40 ± 0.58 km) and autumn (1.26 ± 0.47 km), and is lowest in winter (1.11 ± 0.44 km). The effect of surface meteorological factors on the RLH were also studied. The correlation coefficients (R) between the RLH and the temperature, relative humidity, wind speed, and pressure were 0.38, −0.18, 0.15, and −0.36, respectively. The results indicate that the surface meteorological parameters exhibit a slight correlation with the RLH, but the high relative humidity was accompanied by a low RLH and high PM2.5 concentrations. Finally, the influence of the RLH on PM2.5 was discussed under different aerosol-loading periods. The aerosol optical depth (AOD) was employed to represent the total amount of pollutants. The results show that the RLH has an effect on PM2.5 when the AOD is small but has almost no effect on PM2.5 when the AOD is high. In addition, the R between the nighttime mean RLH and the following daytime PM2.5 at low AOD is −0.49, suggesting that the RLH may affect the following daytime surface PM2.5. The results of this study have a guiding significance for understanding the interaction between aerosols and the boundary layer.
Extraction and Discrimination of MBT Anomalies Possibly Associated with the Mw 7.3 Maduo (Qinghai, China) Earthquake on 21 May 2021
Authors: Qi Yuan , Wu Lixin , Ding Yifan , Liu Yingjia , Chen Shuai , Wang Xiao , Mao Wenfei
Published: December 2021

Earthquakes are one of the most threatening natural disasters to human beings, and pre- and post-earthquake microwave brightness temperature (MBT) anomalies have attracted increasing attention from geosciences as well as remote sensing communities. However, there is still a lack of systematic description about how to extract and then discriminate the authenticity of seismic MBT anomalies. In this research, the first strong earthquake occurring near the northern edge of eastern Bayan Har block in nearly 20 years, the recent Mw 7.3 Maduo earthquake in Qinghai province, China on 21 May 2021, was selected as a case study. Based on the monthly mean background of MBT, the spatiotemporal features of MBT residuals with 10.65 GHz before and after the earthquake was firstly revealed. Referring to the spatial patterns and abnormal amplitudes of the results, four typical types of evident MBT positive residuals were obtained, and the time series of intensity features of each category was also quantitatively analyzed. Then, as the most influential factor on surface microwave radiation, air temperature, soil moisture and precipitation were analyzed to discriminate their contributions to these residuals. The fourth one, which occurred north to the epicenter after the earthquake, was finally confirmed to be caused by soil moisture reduction and thus ruled out as being related to seismicity. Therefore, the three retained typical MBT residuals with 10.65 GHz could be identified as possible anomalies associated with the Maduo earthquake, and were further analyzed collaboratively with some other reported abnormal phenomena related to the seismogenic process. Furthermore, through time series analysis, the MBT positive residuals inside the Bayan Har block were found to be more significant than that outside, and the abnormal behaviors of MBT residuals in the elevation range of 4000–5000 m reflected the shielding effect on microwave radiation from thawing permafrost on the plateau in March and April, 2021. This research provides a detailed technique to extract and discriminate the seismic MBT anomaly, and the revealed results reflect well the joint effect of seismic activity and regional coversphere environment on satellite-observed MBT.
Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series
Authors: Zhu Xiaolin , Helmer Eileen H. , Gwenzi David , Collin Melissa , Fleming Sean , Tian Jiaqi , Marcano-Vega Humfredo , Meléndez-Ackerman Elvia J. , Zimmerman Jess K.
Published: December 2021

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.
Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR
Authors: Agarwal Vivek , Kumar Amit , Gee David , Grebby Stephen , Gomes Rachel L. , Marsh Stuart
Published: December 2021

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities.
Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas
Authors: Hird Jennifer N. , Kariyeva Jahan , McDermid Gregory J.
Published: December 2021

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.
Assessment of Multi-Frequency PPP Ambiguity Resolution Using Galileo and BeiDou-3 Signals
Authors: Guo Jiang , Zhang Qiyuan , Li Guangcai , Zhang Kunlun
Published: December 2021

From network RTK to PPP-RTK, it is highly expected that high-precision positioning within a few minutes can be achieved with a sparse reference network. In this study, we investigate a rapid multi-frequency PPP convergence strategy based on Galileo E1/E5a/E6 and BeiDou-3 B1C/B2a/B3I signals, whose unambiguous wide-lane observables can efficiently assist in speeding up narrow-lane ambiguity resolution. Furthermore, frequency-specific biases existing on the third-frequency observables have been observed to slow down multi-frequency PPP-AR convergence. In this study, we partially mitigated their effects by estimating a second satellite clock for the third frequency of signals. We validated this approach with one month of data collected from 22 stations. On average, it took about 18 min for PPP wide-lane ambiguity resolution (PPP-WAR) to converge, while 32 min were required for ambiguity-float PPP. Compared with dual-frequency PPP-AR, which needed nearly 12 min to converge, multi-frequency PPP-AR required 6 min only. Once there were more than 10 satellites involved in PPP, the convergence could be achieved within 3 min on average. Meanwhile, 81% and 62% of multi-frequency PPP-AR solutions converged successfully within 5 and 1 min, respectively. Finally, we carried out a vehicle-borne experiment to validate this approach in a kinematic environment. Owing to frequent cycle slips during the movement of vehicle, it took 14 min for B1C/B2a/B3I and E1/E5a/E6 PPP-AR to obtain reliable positions, and 19 min for those using the other signal combinations B1C/B2a/B2b and E1/E5a/E5b, owning to higher noise. Overall, these results are promising for achieving high-precision PPP positioning globally within a few minutes if multi-frequency biases can be handled well in the data processing.
A New Mapping Function for Spaceborne TEC Conversion Based on the Plasmaspheric Scale Height
Authors: Wu Mengjie , Guo Peng , Zhou Wei , Xue Junchen , Han Xingyuan , Meng Yansong , Hu Xiaogong
Published: December 2021

The mapping function is crucial for the conversion of slant total electron content (TEC) to vertical TEC for low Earth orbit (LEO) satellite-based observations. Instead of collapsing the ionosphere into one single shell in commonly used mapping models, we defined a new mapping function assuming the vertical ionospheric distribution as an exponential profiler with one simple parameter: the plasmaspheric scale height in the zenith direction of LEO satellites. The scale height obtained by an empirical model introduces spatial and temporal variances into the mapping function. The performance of the new method is compared with the mapping function F&K by simulating experiments based on the global core plasma model (GCPM), and it is discussed along with the latitude, seasons, local time, as well as solar activity conditions and varying LEO orbit altitudes. The assessment indicates that the new mapping function has a comparable or better performance than the F&K mapping model, especially on the TEC conversion of low elevation angles.
Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment
Authors: Shelestov Andrii , Yailymova Hanna , Yailymov Bohdan , Kussul Nataliia
Published: December 2021

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries.
Accurate Instance Segmentation for Remote Sensing Images via Adaptive and Dynamic Feature Learning
Authors: Yang Feng , Yuan Xiangyue , Ran Jie , Shu Wenqiang , Zhao Yue , Qin Anyong , Gao Chenqiang
Published: December 2021

Instance segmentation for high-resolution remote sensing images (HRSIs) is a fundamental yet challenging task in earth observation, which aims at achieving instance-level location and pixel-level classification for instances of interest on the earth’s surface. The main difficulties come from the huge scale variation, arbitrary instance shapes, and numerous densely packed small objects in HRSIs. In this paper, we design an end-to-end multi-category instance segmentation network for HRSIs, where three new modules based on adaptive and dynamic feature learning are proposed to address the above issues. The cross-scale adaptive fusion (CSAF) module introduces a novel multi-scale feature fusion mechanism to enhance the capability of the model to detect and segment objects with noticeable size variation. To predict precise masks for the complex boundaries of remote sensing instances, we embed a context attention upsampling (CAU) kernel instead of deconvolution in the segmentation branch to aggregate contextual information for refined upsampling. Furthermore, we extend the general fixed positive and negative sample judgment threshold strategy into a dynamic sample selection (DSS) module to select more suitable positive and negative samples flexibly for densely packed instances. These three modules enable a better feature learning of the instance segmentation network. Extensive experiments are conducted on the iSAID and NWU VHR-10 instance segmentation datasets to validate the proposed method. Attributing to the three proposed modules, we have achieved 1.9% and 2.9% segmentation performance improvements on these two datasets compared with the baseline method and achieved the state-of-the-art performance.
Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection
Authors: Dong Longkai , Wang Chao , Tang Yixian , Zhang Hong , Xu Lu
Published: December 2021

The Coherent Pixels Technique Interferometry Synthetic Aperture Radar (CPT-InSAR) method of inverting surface deformation parameters by using high-quality measuring points possesses the flaw inducing sparse measuring points in non-urban areas. In this paper, we propose the Adaptive Coherent Distributed Pixel InSAR (ACDP-InSAR) method, which is an adaptive method used to extract Distributed Scattering Pixel (DSP) based on statistically homogeneous pixel (SHP) cluster tests and improves the phase quality of DSP through phase optimization, which cooperates with Coherent Pixel (CP) for the retrieval of ground surface deformation parameters. For a region with sparse CPs, DSPs and its SHPs are detected by double-layer windows in two steps, i.e., multilook windows and spatial filtering windows, respectively. After counting the pixel number of maximum SHP cluster (MSHPC) in the multilook window based on the Anderson–Darling (AD) test and filtering out unsuitable pixels, the candidate DSPs are selected. For the filtering window, the SHPs of MSHPC’ pixels within the new window, which is different compared with multilook windows, were detected, and the SHPs of DSPs were obtained, which were used for coherent estimation. In phase-linking, the results of Eigen decomposition-based Maximum likelihood estimator of Interferometric phase (EMI) results are used as the initial values of the phase triangle algorithm (PTA) for the purpose of phase estimation (hereafter called as PTA-EMI). The DSPs and estimated phase are then combined with CPs in order to retrievesurface deformation parameters. The method was validated by two cases. The results show that the density of measuring points increased approximately 6–10 times compared with CPT-InSAR, and the quality of the interferometric phase significantly improved after phase optimization. It was demonstrated that the method is effective in increasing measuring point density and improving phase quality, which increases significantly the detectability of the low coherence region. Compared with the Distributed Scatterer InSAR (DS-InSAR) technique, ACDP-InSAR possesses faster processing speed at the cost of resolution loss, which is crucial for Earth surface movement monitoring at large spatial scales.
Geospatial Approaches to Monitoring the Spread of Invasive Species of Solidago spp.
Authors: Koco Štefan , Dubravská Anna , Vilček Jozef , Gruľová Daniela
Published: December 2021

Global climate change influences plant invasion which spreads all over the Europe. Invasive plants are predominantly manifest negative impacts, which require increased attention not only from ecologists. The research examines the possibilities offered by geospatial technologies in mapping the spatial spread of invasive plants of the genus Solidago. Invasive plant population was investigated at two localities, Malý Šariš and Chminianska Nová Ves in Slovakia, as well as the mapping of the area by multispectral imaging to determine the spectral reflectance curve of the monitored plant species. Using spatial analyses in the geographic information system, we evaluated changes in the plant density in the two localities. Based on the obtained results, we found that the number of individuals (ramets) in the Malý Šariš is significantly increasing, while in the examined area of Chminianska Nová Ves, there is a decrease in the number of Solidago spp. in the last monitored year. At the same time, we can state that in the areas with the highest increase in the number of ramets, the highest plant density per hectare was also recorded. We can also say that due to the spectral proximity of the surrounding vegetation, the spectral resolution in four spectral bands is insufficient for the classification of multispectral records in the case of Solidago spp. and cannot replace the advantages of high spectral resolution hyperspectral imaging, which significantly refines the feature space for Solidago spp. and the surrounding vegetation.
Alteration Detection of Multispectral/Hyperspectral Images Using Dual-Path Partial Recurrent Networks
Authors: Li Jinlong , Yuan Xiaochen , Feng Li
Published: December 2021

Numerous alteration detection methods are designed based on image transformation algorithms and divergence of bi-temporal images. In the process of feature transformation, pseudo variant information caused by complex external factors will be highlighted. As a result, the error of divergence between the two images will be further enhanced. In this paper, we propose to fuse the variability of Deep Neural Networks’ (DNNs) structure flexibly with various detection algorithms for bi-temporal multispectral/hyperspectral imagery alteration detection. Specifically, the novel Dual-path Partial Recurrent Networks (D-PRNs) was proposed to project more accurate and effective deep features. The Unsupervised Slow Feature Analysis (USFA), Iteratively Reweighted Multivariate Alteration Detection (IRMAD), and Principal Component Analysis (PCA) were then utilized, respectively, with the proposed D-PRNs, to generate two groups of transformed features corresponding to the bi-temporal remote sensing images. We next employed the Chi-square distance to compute the divergence between two groups of transformed features and, thus, obtain the Alteration Intensity Map. Finally, threshold algorithms K-means and Otsu were, respectively, applied to transform the Alteration Intensity Map into Binary Alteration Map. Experiments were conducted on two bi-temporal remote sensing image datasets, and the testing results proved that the proposed alteration detection model using D-PRNs outperformed the state-of-the-art alteration detection model.
Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network
Authors: Ojogbane Sani Success , Mansor Shattri , Kalantar Bahareh , Khuzaimah Zailani Bin , Shafri Helmi Zulhaidi Mohd , Ueda Naonori
Published: December 2021

The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed human–computer interaction, which was a daunting proposition. To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. The proposed CNN has three parallel stream channels: the first is the high-resolution aerial imagery, while the second stream is the digital surface model (DSM). The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. Furthermore, the channel has eight group convolution blocks of 2D convolution with three max-pooling layers. The proposed model’s efficiency and dependability were tested on three different categories of complex urban building structures in the study area. Then, morphological operations were applied to the extracted building footprints to increase the uniformity of the building boundaries and produce improved building perimeters. Thus, our approach bridges a significant gap in detecting building objects in diverse environments; the overall accuracy (OA) and kappa coefficient of the proposed method are greater than 80% and 0.605, respectively. The findings support the proposed framework and methodologies’ efficacy and effectiveness at extracting buildings from complex environments.
Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?
Authors: Schratz Patrick , Muenchow Jannes , Iturritxa Eugenia , Cortés José , Bischl Bernd , Brenning Alexander
Published: December 2021

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.
A Precisely One-Step Registration Methodology for Optical Imagery and LiDAR Data Using Virtual Point Primitives
Authors: Yao Chunjing , Ma Hongchao , Luo Wenjun , Ma Haichi
Published: December 2021

The registration of optical imagery and 3D Light Detection and Ranging (LiDAR) point data continues to be a challenge for various applications in photogrammetry and remote sensing. In this paper, the framework employs a new registration primitive called virtual point (VP) that can be generated from the linear features within a LiDAR dataset including straight lines (SL) and curved lines (CL). By using an auxiliary parameter (λ), it is easy to take advantage of the accurate and fast calculation of the one-step registration transformation model. The transformation model parameters and λs can be calculated simultaneously by applying the least square method recursively. In urban areas, there are many buildings with different shapes. Therefore, the boundaries of buildings provide a large number of SL and CL features and selecting properly linear features and transforming into VPs can reduce the errors caused by the semi-discrete random characteristics of the LiDAR points. According to the result shown in the paper, the registration precision can reach the 1~2 pixels level of the optical images.
Analyzing Pixel-Level Relationships between Luojia 1-01 Nighttime Light and Urban Surface Features by Separating the Pixel Blooming Effect
Authors: Wu Ji , Zhang Zhi , Yang Xiao , Li Xi
Published: December 2021

Nighttime light (NTL) remote sensing data can effectively reveal human activities in urban development. It has received extensive attention in recent years, owing to its advantages in monitoring urban socio-economic activities. Due to the coarse spatial resolution and blooming effect, few studies can explain the factors influencing NTL variations at a fine scale. This study explores the relationships between Luojia 1-01 NTL intensity and urban surface features at the pixel level. The Spatial Durbin model is used to measure the contributions of different urban surface features (represented by Points-of-interest (POIs), roads, water body and vegetation) to NTL intensity. The contributions of different urban surface features to NTL intensity and the Pixel Blooming Effect (PIBE) are effectively separated by direct effect and indirect effect (pseudo-R2 = 0.915; Pearson correlation = 0.774; Moran’s I = 0.014). The results show that the contributions of different urban surface features to NTL intensity and PIBE are significantly different. Roads and transportation facilities are major contributors to NTL intensity and PIBE. The contribution of commercial area is much lower than that of roads in terms of PIBE. The inhibitory effect of water body is weaker than that of vegetation in terms of NTL intensity and PIBE. For each urban surface feature, the direct contribution to NTL intensity is far less than the indirect contribution (PIBE of total neighbors), but greater than the marginal indirect effect (PIBE of each neighbor). The method proposed in this study is expected to provide a reference for explaining the composition and blooming effect of NTL, as well as the application of NTL data in the urban interior.
Detecting Long-Term Deformation of a Loess Landslide from the Phase and Amplitude of Satellite SAR Images: A Retrospective Analysis for the Closure of a Tunnel Event
Authors: Zhu Yaru , Qiu Haijun , Liu Zijing , Wang Jiading , Yang Dongdong , Pei Yanqian , Ma Shuyue , Du Chi , Sun Hesheng , Wang Luyao
Published: December 2021

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.
Central Courtyard Feature Extraction in Remote Sensing Aerial Images Using Deep Learning: A Case-Study of Iran
Authors: Yazdi Hadi , Vukorep Ilija , Banach Marzena , Moazen Sajad , Nadolny Adam , Starke Rolf , Bazazzadeh Hassan
Published: December 2021

Central courtyards are primary components of vernacular architecture in Iran. The directions, dimensions, ratios, and other characteristics of central courtyards are critical for studying historical passive cooling and heating solutions. Several studies on central courtyards have compared their features in different cities and climatic zones in Iran. In this study, deep learning methods for object detection and image segmentation are applied to aerial images, to extract the features of central courtyards. The case study explores aerial images of nine historical cities in Bsk, Bsh, Bwk, and Bwh Köppen climate zones. Furthermore, these features were gathered in an extensive dataset, with 26,437 samples and 76 geometric and climatic features. Additionally, the data analysis methods reveal significant correlations between various features, such as the length and width of courtyards. In all cities, the correlation coefficient between these two characteristics is approximately +0.88. Numerous mathematical equations are generated for each city and climate zone by fitting the linear regression model to these data in different cities and climate zones. These equations can be used as proposed design models to assist designers and researchers in predicting and locating the best courtyard houses in Iran’s historical regions.
Performance Assessment of BDS-2/BDS-3/GPS/Galileo Attitude Determination Based on the Single-Differenced Model with Common-Clock Receivers
Authors: Wu Mingkui , Luo Shuai , Wang Wang , Liu Wanke
Published: December 2021

Global navigation satellite system (GNSS)-based attitude determination has been widely applied in a variety of fields due to its high precision, no error accumulation, low power consumption, and low cost. Recently, the emergence of common-clock receivers and construction of GNSS systems have brought new opportunities for high-precision GNSS-based attitude determination. In this contribution, we focus on evaluating the performance of the BeiDou regional navigation satellite system (BDS-2)/BeiDou global navigation satellite system (BDS-3)/Global Positioning System (GPS)/Galileo navigation satellite system (Galileo) attitude determination based on the single-differenced (SD) model with a common-clock receiver. We first investigate the time-varying characteristics of BDS-2/BDS-3/GPS/Galileo line bias (LB) with two different types of common-clock receivers. The results have confirmed that both the phase and code LBs are relatively stable in the time domain once the receivers have started. However, the phase LB is expected to change to an arbitrary value after each restart of the common-clock receivers. For the first time, it is also found that the phase LBs of overlapping frequencies shared by different GNSS systems are identical. Then, we primarily evaluated the performance of BDS-2/BDS-3/GPS/Galileo precise relative positioning and attitude determination based on the SD model with a common-clock receiver, using a static dataset collected at Wuhan. Experimental results demonstrated that, compared with the double-differenced (DD) model, the SD model can deliver a comparable root–mean–square (RMS) error of yaw but a significantly smaller RMS error of pitch, whether for BDS-2, BDS-3, GPS, or Galileo alone or a combination of them. The improvements of pitch accuracy are approximately 20.8–47.5% and 40.7–57.5% with single- and dual-frequency observations, respectively. Additionally, BDS-3 can deliver relatively superior positioning and attitude accuracy with respect to GPS and Galileo, due to its better geometry. The three-dimensional positioning and attitude (including yaw and pitch) accuracy for both the DD and SD models can be remarkably improved by the BDS-2, BDS-3, GPS, and Galileo combination with respect to a single system alone.
High-Precision Potential Evapotranspiration Model Using GNSS Observation
Authors: Zhao Qingzhi , Sun Tingting , Zhang Tengxu , He Lin , Zhang Zhiyi , Shen Ziyu , Xiong Si
Published: December 2021

Potential evapotranspiration (PET) can reflect the characteristics of drought change in different time scales and is the key parameter for calculating the standardized precipitation evapotranspiration index (SPEI). The Thornthwaite (TH) and Penman–Monteith (PM) models are generally used to calculate PET, but the precision of PET derived from the TH model is poor, and a large number of meteorological parameters are required to evaluate the PM model. To obtain high-precision PET with fewer meteorological parameters, a high-precision PET (HPET) model is proposed to calculate PET by introducing precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observation. The PET difference (DPET) between TH- and PM-derived PET was calculated first. Then, the relationship between the DPET and GNSS-derived PWV/temperature was analysed, and a piecewise linear regression model was calculated to fit the DPET. Finally, the HPET model was established by adding the fitted DPET to the initial PET derived from the TH model. The Loess Plateau (LP) was selected as the experiment area, and the statistical results show the satisfactory performance of the proposed HPET model. The averaged root mean square (RMS) of the HPET model over the whole LP area is 8.00 mm, whereas the values for the TH and revised TH (RTH) models are 34.25 and 12.55 mm, respectively, when the PM-derived PET is regarded as the reference. Compared with the TH and RTH models, the average improvement rates of the HPET model over the whole LP area are 77.5 and 40.5%, respectively. In addition, the HPET-derived SPEI is better than that of the TH and RTH models at different month scales, with average improvement rates of 49.8 and 23.1%, respectively, over the whole LP area. Such results show the superiority of the proposed HPET model to the existing PET models.
SAR Image Simulation of Complex Target including Multiple Scattering
Authors: Chiang Cheng-Yen , Chen Kun-Shan , Yang Ying , Zhang Yang , Zhang Tong
Published: December 2021

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features.
Changes in the Distribution Pattern of PM2.5 Pollution over Central China
Authors: Shen Lijuan , Hu Weiyang , Zhao Tianliang , Bai Yongqing , Wang Honglei , Kong Shaofei , Zhu Yan
Published: December 2021

The extent of PM2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which covers the lower flatlands in Hubei and Hunan provinces in central China, was found to be a high PM2.5 pollution region, with annual mean PM2.5 concentrations of 41–63 μg·m−3, which is larger than the values in YRD, SB, and PRD during 2014–2019, and high aerosol optical depth values (>0.8) averaged over 2000–2019 from the MODIS products. Heavy pollution events (HPEs) are frequently observed in the THB, with HPE-averaged concentrations of PM2.5 reaching up to 183–191 μg·m−3, which exceeds their counterparts in YRD, SB, and PRD for 2014–2019, highlighting the THB as a center of heavy PM2.5 pollution in central China. During 2014–2019, approximately 65.2% of the total regional HPEs over the THB were triggered by the regional transport of PM2.5 over Central and Eastern China (CEC). This occurred in view of the co-existing HPEs in the NCP and the THB, with a lag of almost two days in the THB-PM2.5 peak, which is governed by the strong northerlies of the East Asian monsoon (EAM) over CEC. Such PM2.5 transport from upstream source regions in CEC contributes 60.3% of the surface PM2.5 pollution over the THB receptor region. Hence, a key PM2.5 receptor of the THB in regional pollutant transport alters the distribution patterns of PM2.5 pollution over China, which is attributable to the climate change of EAMs. This study indicates a complex relationship between sources and receptors of atmospheric aerosols for air quality applications.
Thermographic Monitoring of Scum Accumulation beneath Floating Covers
Authors: Ma Yue , Rose Francis , Wong Leslie , Vien Benjamin Steven , Kuen Thomas , Rajic Nik , Kodikara Jayantha , Chiu Wing Kong
Published: December 2021

Large sheets of high-density polyethene geomembrane are used as floating covers on some of the wastewater treatment lagoons at the Melbourne Water Corporation’s Western Treatment Plant. These covers provide an airtight seal for the anaerobic digestion of sewage and allow for harvesting the methane-rich biogas, which is then used to generate electricity. There is a potential for scum to develop under the covers during the anaerobic digestion of the raw sewage by microorganisms. Due to the nature of the operating environment of the lagoons and the vast size (450 m × 170 m) of these covers, a safe non-contact method to monitor the development and movement of the scum is preferred. This paper explores the potential of using a new thermographic approach to identify and monitor the scum under the covers. The approach exploits naturally occurring variations in solar intensity as a trigger for generating a transient thermal response that is then fitted to an exponential decay law to determine a cooling constant. This approach is investigated experimentally using a laboratory-scale test rig. A finite element (FE) model is constructed and shown to reliably predict the experimentally observed thermal transients and cooling constants. This FE model is then set up to simulate progressive scum accumulation with time, using a specified scumberg geometry and a stepwise change in thermal properties. The results indicate a detectable change in the cooling constant at different locations on the cover, thereby providing a quantitative basis for characterising the scum accumulation beneath the cover. The practical application and limitations of these results are briefly discussed.