bluuebunny/crossref_metadata_2025 huggingface dataset:
Conventional subsoil irrigation techniques do not lower carbon emissions from drained peat meadows
Authors: Weideveld Stefan Theodorus Johannes , Liu Weier , van den Berg Merit , Lamers Leon Peter Maria , Fritz Christian
Published: June 2021

Abstract. The focus of current water management in drained peatlands is to facilitate
optimal drainage, which has led to soil subsidence and a strong increase in
greenhouse gas (GHG) emissions. The Dutch land and water authorities
proposed the application of subsoil irrigation (SSI) system on a large
scale to potentially reduce GHG emissions, while maintaining high biomass
production. Based on model results, the expectation was that SSI would
reduce peat decomposition in summer by preventing groundwater tables (GWTs)
from dropping below −60 cm. In 2017–2018, we evaluated the effects of SSI on GHG
emissions (CO2, CH4, N2O) for four dairy farms on drained
peat meadows in the Netherlands. Each farm had a treatment site with SSI
installation and a control site drained only by ditches (ditch water level
−60 / −90 cm, 100 m distance between ditches). The SSI system consisted of
perforated pipes −70 cm from surface level with spacing of 5–6 m to improve
drainage during winter–spring and irrigation in summer. GHG emissions were
measured using closed chambers every 2–4 weeks for CO2, CH4 and
N2O. Measured ecosystem respiration (Reco) only showed a small
difference between SSI and control sites when the GWT of SSI sites were
substantially higher than the control site (> 20 cm difference).
Over all years and locations, however, there was no significant difference
found, despite the 6–18 cm higher GWT in summer and 1–20 cm lower GWT in
wet conditions at SSI sites. Differences in mean annual GWT remained low
(< 5 cm). Direct comparison of measured N2O and CH4 fluxes
between SSI and control sites did not show any significant differences.
CO2 fluxes varied according to temperature and management events,
while differences between control and SSI sites remained small. Therefore,
there was no difference between the annual gap-filled net ecosystem exchange
(NEE) of the SSI and control sites. The net ecosystem carbon balance (NECB)
was on average 40 and 30 t CO2 ha−1 yr−1 in 2017 and 2018 on the
SSI sites and 38 and 34 t CO2 ha−1 yr−1 in 2017 and 2018 on the
control sites. This lack of SSI effect is probably because the GWT increase
remains limited to deeper soil layers (60–120 cm depth), which contribute
little to peat oxidation. We conclude that SSI modulates water table dynamics but fails to lower
annual carbon emission. SSI seems unsuitable as a climate mitigation
strategy. Future research should focus on potential effects of GWT
manipulation in the uppermost organic layers (−30 cm and higher) on GHG
emissions from drained peatlands.
Comment on K. Michaelian and A. Simeonov (2015) “Fundamental molecules of life are pigments which arose and co-evolved as a response to the thermodynamic imperative of dissipating the prevailing solar spectrum”
Authors: Björn Lars Olof
Published: June 2021

Abstract. This is a comment to: “Fundamental molecules of life are pigments which arose and co-evolved as a response to the thermodynamic imperative of dissipating the prevailing solar spectrum” by K. Michaelian and A. Simeonov, Biogeosciences, 12, 4913–4937, 2015. Michaelian and Simeonov formulate the leading thought in their article “The driving force behind the origin and evolution of life has been the thermodynamic imperative of increasing the entropy production of the biosphere through increasing the global solar photon dissipation rate”. I doubt that the reasoning that follows regarding the role of “pigments” (in which they include all substances able to absorb solar radiation) is correct.
Climate-driven desertification and its implications for the ancient Silk Road trade
Authors: Dong Guanghui , Wang Leibin , Zhang David Dian , Liu Fengwen , Cui Yifu , Li Guoqiang , Shi Zhilin , Chen Fahu
Published: June 2021

Abstract. The ancient Silk Road played a crucial role in cultural exchange and commercial trade between western and eastern Eurasia during the historical period. However, the exchanges were interrupted in the early 16th century CE, during the Ming dynasty. Various causes for the decline of the ancient Silk Road have been suggested. Unlike social factors, natural factors have not been adequately addressed. In this study, we use evidence from a sedimentary site (Xishawo, XSW) in Dunhuang oasis, together with analysis of
historical archives, to demonstrate the occurrence of extreme droughts and
desertification events in the Dunhuang area post ∼ 1450 CE,
which persisted at least for decades. The desertification may be closely
associated with the accessibility of the ancient Silk Road in the area,
which was responsible for a steep fall in the volume of trade, as well as
political chaos and mass migrations. Therefore, besides socio-economic
factors, climate change may have played an important role in trade exchange
between the Ming government and the West and may have even influenced the
rise and decline of the ancient Silk Road.
Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia
Authors: Wei Xueqiong , Widgren Mats , Li Beibei , Ye Yu , Fang Xiuqi , Zhang Chengpeng , Chen Tiexi
Published: June 2021

Abstract. Spatially explicit historical land cover datasets are essential not only for
simulations of climate and environmental dynamics but also for projections
of future land use, food security, climate, and biodiversity. However,
widely used global datasets are developed for continental- to global-scale
analysis and simulations. Their accuracy depends on the verification of more
regional reconstruction results. This study collects cropland area data of
each administrative unit (parish/municipality/county) in Scandinavia from
multiple sources. The cropland area data are validated, calibrated,
interpolated, and allocated into 1 km×1 km grid cells. Then, we
develop a dataset with spatially explicit cropland area from 1690 to 1999.
Results indicate that the cropland area increased from 1.82×106 ha to
6.71×106 ha from 1690 to 1950 and then decreased to 5.90×106 ha in
1999. Before 1810, cropland cover expanded in southern Scandinavia and
remained stable in northern Scandinavia. From 1810 to 1910, northern
Scandinavia experienced slight cropland expansion. The cropland area
increased rapidly in the southern part of the study area before changing
slightly. After 1950, the cropland areas began to decrease in most regions,
especially in eastern Scandinavia. When comparing global datasets with this
study, although the total Scandinavia cropland area is in agreement among
SAGE (Center for Sustainability and the Global Environment), HYDE (History Database of the Global Environment ) 3.2, PJ (Pongratz Julia), and this study, the spatial patterns show considerable
differences, except for in Denmark between HYDE 3.2 and this study. The
dataset can be downloaded from https://doi.org/10.1594/PANGAEA.926591 (Wei
et al., 2021).
The AntSMB dataset: a comprehensive compilation of surface mass balance field observations over the Antarctic Ice Sheet
Authors: Wang Yetang , Ding Minghu , Reijmer Carleen H. , Smeets Paul C. J. P. , Hou Shugui , Xiao Cunde
Published: June 2021

Abstract. A comprehensive compilation of observed records is needed
for accurate quantification of surface mass balance (SMB) over Antarctica,
which is a key challenge for calculation of Antarctic contribution to global
sea level change. Here, we present the AntSMB dataset: a new
quality-controlled dataset of a variety of published field measurements of
the Antarctic Ice Sheet SMB by means of stakes, snow pits, ice cores,
ultrasonic sounders, and ground-penetrating radar (GPR). The dataset
collects 3579 individual multi-year-averaged observations, 687 annually
resolved time series from 675 sites extending back over the past 1000 years, and
daily resolved records covering 245 years from 32 sites across the whole ice
sheet. These records are derived from ice cores, snow pits, stakes/stake
farms, and ultrasonic sounders. Furthermore, GPR multi-year-averaged
measurements are included in the dataset, covering an area of 22 025 km2. This is the first ice-sheet-scale compilation of SMB records at
different temporal (daily, annual, and multi-year) resolutions from multiple
types of measurement and is available at
https://doi.org/10.11888/Glacio.tpdc.271148 (Wang et al., 2021). The
database has potentially wide applications such as the investigation of
temporal and spatial variability in SMB, model validation, assessment of
remote sensing retrievals, and data assimilation. As a case of model
estimation, records of the AntSMB dataset are used to assess the performance
of ERA5 for temporal and spatial variability in SMB over Antarctica.
The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission
Authors: Guanter Luis , Bacour Cédric , Schneider Andreas , Aben Ilse , van Kempen Tim A. , Maignan Fabienne , Retscher Christian , Köhler Philipp , Frankenberg Christian , Joiner Joanna , Zhang Yongguang
Published: June 2021

Abstract. The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatio-temporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3-nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following Digital Object Identifier (DOI) https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104 (Guanter et al., 2021).
Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)
Authors: Román-Cascón Carlos , Lothon Marie , Lohou Fabienne , Hartogensis Oscar , Vila-Guerau de Arellano Jordi , Pino David , Yagüe Carlos , Pardyjak Eric R.
Published: June 2021

Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m sub-grid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.
Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States
Authors: Chen Xiaoyang , Zhang Yang , Wang Kai , Tong Daniel , Lee Pius , Tang Youhua , Huang Jianping , Campbell Patrick C. , Mcqueen Jeff , Pye Havala O. T. , Murphy Benjamin N. , Kang Daiwen
Published: June 2021

Abstract. As a candidate for the next-generation National Air Quality Forecast
Capability (NAQFC), the meteorological forecast from the Global Forecast System
with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be
applied to drive the chemical evolution of gases and particles described by
the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a
historical version of CMAQ, has been coupled with the North American Mesoscale
Forecast System (NAM) model in the current operational NAQFC. An experimental
version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with
CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the
National Oceanic and Atmospheric Administration (NOAA) to provide real-time
air quality forecasts over the contiguous United States (CONUS) since 2018. In
this work, comprehensive region-specific, time-specific, and categorical
evaluations are conducted for meteorological and chemical forecasts from the
offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows
good overall performance in forecasting meteorological variables with the
annual mean biases of −0.2 ∘C for temperature at 2 m,
0.4 % for relative humidity at 2 m, and 0.4 m s−1 for
wind speed at 10 m compared to the METeorological Aerodrome Reports
(METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts,
particularly in spring. Although the monthly accumulated precipitation
forecasts show generally consistent spatial distributions with those from the
remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly
precipitation forecasts compared to the Clean Air Status and Trends Network
(CASTNET) and METAR. While the forecast system performs well in forecasting
ozone (O3) throughout the year and fine particles with a diameter of
2.5 µm or less (PM2.5) for warm months (May–September),
it significantly overpredicts annual mean concentrations of
PM2.5. This is due mainly to the high predicted concentrations of
fine fugitive and coarse-mode particle components. Underpredictions in the
southeastern US and California during summer are attributed to missing
sources and mechanisms of secondary organic aerosol formation from biogenic
volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work
demonstrates the ability of FV3-based GFS in driving the air quality
forecasting. It identifies possible underlying causes for systematic region-
and time-specific model biases, which will provide a scientific basis for
further development of the next-generation NAQFC.
Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based coupled atmosphere–ocean modelling suite: atmospheric dataset
Authors: Denamiel Cléa , Pranić Petra , Ivanković Damir , Tojčić Iva , Vilibić Ivica
Published: June 2021

Abstract. In this evaluation study, the coupled atmosphere–ocean Adriatic Sea and Coast (AdriSC) climate model, which was implemented to carry out 31-year
evaluation and climate projection simulations in the Adriatic and northern Ionian seas, is briefly presented. The kilometre-scale AdriSC
atmospheric results, derived with the Weather Research and Forecasting (WRF) 3 km model for the 1987–2017 period, are then thoroughly
compared to a comprehensive publicly and freely available observational dataset. The evaluation shows that overall, except for the summer surface
temperatures, which are systematically underestimated, the AdriSC WRF 3 km model has a far better capacity to reproduce surface climate
variables (and particularly the rain) than the WRF regional climate models at 0.11∘ resolution. In addition, several spurious data have
been found in both gridded products and in situ measurements, which thus should be used with care in the Adriatic region for climate studies at
local and regional scales. Long-term simulations with the AdriSC climate model, which couples the WRF 3 km model with a 1 km ocean
model, might thus be a new avenue to substantially improve the reproduction, at the climate scale, of the Adriatic Sea dynamics driving the Eastern
Mediterranean thermohaline circulation. As such it may also provide new standards for climate studies of orographically developed coastal regions in
general.
MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data
Authors: Li Dawei , Liu Yudi , Chen Chaohui
Published: June 2021

Abstract. Eastern China is one of the most economically developed and
densely populated areas in the world. Due to its special geographical
location and climate, eastern China is affected by different weather systems,
such as monsoons, shear lines, typhoons, and extratropical cyclones. In the
near future, the rainfall rate becomes difficult to predict precisely due to
these systems. Traditional physics-based methods such as numerical weather
prediction (NWP) tend to perform poorly on nowcasting problems due to the
spin-up issue. Moreover, various meteorological stations are distributed in
this region, generating a large amount of observation data every day, which
have great potential for application to data-driven methods. Thus, it is
important to train a data-driven model from scratch that is suitable for the
specific weather situation of eastern China. However, due to the high degrees
of freedom and nonlinearity of machine learning algorithms, it is difficult
to add physical constraints. Therefore, with the intention of using various
kinds of data as a proxy for physical constraints, we collected three kinds
of data (radar, satellite, and precipitation data) in the flood season from
2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘.
The developed multisource data model (MSDM) combines the optical flow,
random forest, and convolutional neural network (CNN) algorithms. It treats
the precipitation nowcasting task as an image-to-image problem, which takes
radar and satellite data with an interval of 30 min as inputs and
predicts radar echo intensity with a lead time of 30 min. To reduce the
smoothing caused by convolutions, we use the optical flow algorithm to
predict satellite data in the following 120 min. The predicted radar
echoes from the MSDM together with satellite data from the optical flow
algorithm are recursively implemented in the MSDM to achieve a 120 min
lead time. The MSDM predictions are comparable to those of other baseline
models with a high temporal resolution of 6 min. To solve blurry image
problems, we applied a modified structural similarity (SSIM) index as a loss
function. Furthermore, we use the random forest algorithm with predicted
radar and satellite data to estimate the rainfall rate, and the results
outperform those of the traditional, nonlinear radar reflectivity factor and
rainfall rate (Z–R) relationships that use logarithmic functions. The
experiments confirm that machine learning with multisource data provides
more reasonable predictions and reveals a better nonlinear relationship
between radar echo and precipitation rate. Apart from developing complicated
machine learning algorithms, exploiting the potential of multisource data
will yield more improvements.
A climatology of tropical wind shear produced by clustering wind profiles from the Met Office Unified Model (GA7.0)
Authors: Muetzelfeldt Mark R. , Plant Robert S. , Clark Peter A. , Stirling Alison J. , Woolnough Steven J.
Published: June 2021

Abstract. Toward the goal of linking wind shear with the mesoscale organization of deep convection, a procedure for producing a climatology of tropical wind shear from the output of the Met Office Unified Model climate model is presented. Statistical information from wind profiles from tropical grid columns is used to produce a tractable number (10) of profiles that efficiently span the space of all wind profiles. Physical arguments are used to filter wind profiles that are likely to be associated with organized convection: only grid columns with substantial convective available potential energy (CAPE) and those with shear in the upper quartile are considered. The profiles are rotated so that their wind vectors at 850 hPa are aligned, in order to be able to group like profiles together, and their magnitudes at each level are normalized. To emphasize the effect of lower levels, where the organization effects of shear are thought to be strongest, the profiles above 500 hPa are multiplied by 14. Principal component analysis is used to truncate the number of dimensions of the profiles to seven (which explains 90 % of the variance), and the truncated profiles are clustered using a K-means clustering algorithm. The median of each cluster defines a representative wind profile (RWP). Each cluster contains information from thousands of wind profiles with different locations, times and 850 hPa wind directions. To summarize the clusters statistically, we interpret the RWPs as pseudo-wind profiles and display the geographic frequency, seasonal frequency and histograms of wind direction at 850 hPa for each cluster. Geographic patterns are evident, and certain features of the spatio-temporal distributions are matched to observed distributions of convective organization. The form of the RWPs is also matched to specific wind profiles from case studies of organized convection. By performing the analysis on climate-model output, we lay the foundations for the development of the representation of shear-induced organization in a convection parametrization scheme (CPS). This would use the same methodology to diagnose where the organization of convection occurs and modify the CPS in an appropriate manner to represent it. The procedure could also be used as a diagnostic tool for evaluating and comparing climate models.
Quantifying the effects of urban green space on water partitioning and ages using an isotope-based ecohydrological model
Authors: Gillefalk Mikael , Tetzlaff Dörthe , Hinkelmann Reinhard , Kuhlemann Lena-Marie , Smith Aaron , Meier Fred , Maneta Marco P. , Soulsby Chris
Published: June 2021

Abstract. The acceleration of urbanization requires sustainable, adaptive management strategies for land and water use in cities. Although the effects of buildings and sealed surfaces on urban runoff generation and local climate are well known, much less is known about the role of water partitioning in urban green spaces. In particular, little is quantitatively known about how different vegetation types of urban green spaces (lawns, parks, woodland, etc.) regulate partitioning of precipitation into evaporation, transpiration and groundwater recharge and how this partitioning is affected by sealed surfaces. Here, we integrated field observations with advanced, isotope-based ecohydrological modelling at a plot-scale site in Berlin, Germany. Soil moisture and sap flow, together with stable isotopes in precipitation, soil water and groundwater recharge, were measured over the course of one growing season under three generic types of urban green space: trees, shrub and grass. Additionally, an eddy flux tower at the site continuously collected hydroclimate data. These data have been used as input and for calibration of the process-based ecohydrological model EcH2O-iso. The model tracks stable isotope ratios and water ages in various stores (e.g. soils and groundwater) and fluxes (evaporation, transpiration and recharge). Green water fluxes in evapotranspiration increased in the order shrub (381±1mm)<grass(434±21mm)<trees(489±30 mm), mainly driven by higher interception and transpiration. Similarly, ages of stored water and fluxes were generally older under trees than shrub or grass. The model also showed how the interface between sealed surfaces and green space creates edge effects in the form of “infiltration hotspots”. These can both enhance evapotranspiration and increase groundwater recharge. For example, in our model, transpiration from trees increased by ∼ 50 % when run-on from an adjacent sealed surface was present and led to groundwater recharge even during the growing season, which was not the case under trees without run-on. The results form an important basis for future upscaling studies by showing that vegetation management needs to be considered within sustainable water and land use planning in urban areas to build resilience in cities to climatic and other environmental change.
3D AND HYPERSPECTRAL DATA INTEGRATION FOR ASSESSING MATERIAL DEGRADATION IN MEDIEVAL MASONRY HERITAGE BUILDINGS
Authors: Kolokoussis P. , Skamantzari M. , Tapinaki S. , Karathanassi V. , Georgopoulos A.
Published: June 2021

Abstract. Cultural Heritage (CH) is a domain which has been greatly affected by climate change in the past decades. At the same time Information and Communication Technologies (ICT) have been greatly exploited to contribute to the holistic documentation, to support conservation and preservation actions. In order to move further on from the interdisciplinary approach to the holistic approach on Cultural Heritage the fusion of data from various sensors is the next goal. This paper focuses on the exploitation and integration of close-range 3D and Hyperspectral data from four Cultural Heritage buildings of Rhodes in order to assess material degradation. The methodology and data processing for this integration are presented as well as the useful and promising results of this approach which lead to further analysis and future work. The research is conducted within the framework of an EU funded project.
FIRST ASSESSMENTS INTO THE USE OF COMMERCIAL-GRADE SOLID STATE LIDAR FOR LOW COST HERITAGE DOCUMENTATION
Authors: Murtiyoso A. , Grussenmeyer P. , Landes T. , Macher H.
Published: June 2021

Abstract. Heritage documentation has benefitted greatly from significant developments in sensor technology during the past two decades. Miniaturisation of sensors is also an important aspect in the development of low cost sensors, always interesting in heritage projects where budgetary constraints are often present. Among these sensors, the solid-state lidar has begun to attract attention, partly due to its integration in Apple Inc.’s latest version of the iPhone and iPad series. We hypothesise that this type of sensor will see a lot of use in the near future; however, the question remains whether they are sufficient for heritage documentation purposes. In this paper, results from the 2020 iPad Pro SSL point cloud will be assessed and compared to more traditional techniques for 3D scanning (photogrammetry and terrestrial laser scanning). While understandably the geometric quality of benchmark-level techniques such as these remain undeniably better, at least for the moment, the paper concludes that SSL sensors may nevertheless be sufficient for some lower-precision applications.
CALIBRATION AND VALIDATION OF THE INTEL T265 FOR VISUAL LOCALISATION AND TRACKING UNDERWATER
Authors: Appelt T. , van der Lucht J. , Bleier M. , Nüchter A.
Published: June 2021

Abstract. Localization and navigation for autonomous underwater vehicle (AUV) has always been a major challenge and many situations complex solutions had to be devised. One of the main approaches is visual odometry using a stereo camera. In this study, the Intel T265 fisheye stereo camera has been calibrated and tested to determine it’s usability for localisation and navigation under water as an alternative to more complex systems. Firstly the Intel T265 fisheye stereo camera was appropriately calibrated inside a water filled container. This calibration consisting of camera and distortion parameters got programmed onto the T265 fisheye stereo camera to take the differences between land and underwater usage into account. Successive the calibration, the accuracy and the precision of the T265 fisheye stereo camera were tested using a linear, a circular and finally a chaotic motion. This includes a review of the localisation and tracking of the cameras visual odometry compared to a ground truth provided by an OptiTrack V120:Trio to account for scaling, accuracy and precision. Also experiments to determine the usability with fast chaotic motions were performed and analysed. Finally, a conclusion concerning the applicability of the Intel T265 fisheye stereo camera, the challenges using this model, the possibilities for low cost operations and the main challenges for future work is conducted.
PHOTOGRAMMETRIC UNDERWATER AND UAS SURVEYS OF ARCHAEOLOGICAL SITES: THE CASE STUDY OF THE ROMAN SHIPWRECK OF TORRE SANTA SABINA
Authors: Calantropio A. , Chiabrando F. , Auriemma R.
Published: June 2021

Abstract. The 2020 underwater archaeological research in the inlet of Torre Santa Sabina – Baia dei Camerini (Municipality of Carovigno, Brindisi, Italy) represented the first phase of the pilot intervention of the Interreg Italia-Croatia UnderwaterMuse project. The project aims to enhance and make accessible the vast underwater heritage of the areas involved; this will be done by creating submerged archaeological parks and using virtual reality's narrative and communicative tools and platforms. During the 2020 campaign, different survey activities were carried out using several techniques and methodology to produce the area's multi-scale documentation. First of all, the entire stretch of coast was mapped with UASs (Uncrewed Aerial Systems) flights to reconstruct the coastal landscape in various phases. Furthermore, an underwater photogrammetric survey carried out by expert scuba divers has been achieved in the wreck's aft area (the site analyzed during the 2020 excavation activities). The fruitful synergy between the various actors involved and the support of the territory and the community has allowed the achievement of this campaign's objectives, preliminary to the broader and more articulated intervention foreseen for the following year.
BUNDLE ADJUSTMENT WITH POLYNOMIAL POINT-TO-CAMERA DISTANCE DEPENDENT CORRECTIONS FOR UNDERWATER PHOTOGRAMMETRY
Authors: Nocerino E. , Menna F. , Gruen A.
Published: June 2021

Abstract. Uncontrolled refraction of optical rays in underwater photogrammetry is known to reduce its accuracy potential. Several strategies have been proposed aiming at restoring the accuracy to levels comparable with photogrammetry applied in air. These methods are mainly based on rigours modelling of the refraction phenomenon or empirical iterative refraction corrections. The authors of this contribution have proposed two mitigation strategies of image residuals systematic patterns in the image plane: (i) empirical weighting of image observations as function of their radial position; (ii) iterative look-up table corrections computed in a squared grid. Here, a novel approach is developed. It explicitly takes into account the object point-to-camera distance dependent error introduced by refraction in multimedia photogrammetry. A polynomial correction function is iteratively computed to correct the image residuals clustered in radial slices in the image plane as function of the point-to-camera distance. The effectiveness of the proposed method is demonstrated by simulations that allow to: (i) separate the geometric error under investigation from other effects not easily modellable and (ii) have reliable reference data against which to assess the accuracy of the result.
INFLUENCE OF ILLUMINATION CHANGES ON IMAGE-BASED 3D SURFACE RECONSTRUCTION
Authors: Bruno N. , Giacomini A. , Roncella R. , Thoeni K.
Published: June 2021

Abstract. The paper investigates the influence of lighting conditions on image-based 3D surface reconstruction, with particular focus on periodic photogrammetric surveys for monitoring and 3D mapping applications. The analyses focus on the accuracy and completeness of each DSM and the daily and hourly repeatability of repeated photogrammetric surveys. Three test sites with rock slopes with a different orientation to the sun and different slope characteristics (slope, pattern, amount of outcropping elements that cast shadows) have been considered to ensure that results can give a general indication of the behaviours in different light conditions. In addition, a simulated virtual test site is included in the study to allow controlled image acquisition and evaluate the effect of the sun’s inclination on the DSM accuracy without influence of other weather conditions. The results show that, although there is an optimal time for the acquisitions, if particularly unfavourable light conditions are excluded, the accuracy reduction with time variation is always below 30%. The repeatability analyses by day and by time highlight a good consistence between DEMs belonging to the same day but acquired at different times and, also, between DEMs acquired at the same time but on different days. This suggests that reliable results can be obtained during continuous monitoring of, for instance, rock faces to identify rockfalls.
SCALE-AWARENESS FOR MORE ACCURATE OBJECT DETECTION USING MODIFIED SINGLE SHOT DETECTORS
Authors: Tsironis V. , Stentoumis C. , Lekkas N. , Nikopoulos A.
Published: June 2021

Abstract. Object detection performance is directly related to the apparent size of the object to be detected, thus most state-of-the-art algorithms dedicate different detection heads for each object size. In this work, we propose an end-to-end pipeline to adapt a single-shot object detector (SSD) to the underlying object size distribution of the target detection domain. Our contributions are the adjustments to the detector architecture and the introduction of a novel batch sampling method. To validate the effect of our method, we chose a task-specific highly specialized object detection and classification dataset of tomato fruits that apart from bounding box information, it also contains class information for three ripening stages of each tomato fruit.More specifically, the major motivation and contributions are discussed in relation to the recent bibliography. Next, an extensive analysis of our pipeline is presented, where the concept of scale alignment is thoroughly presented along with the novel sampling method. Following the results of a series of experiments, we conclude that our pipeline significantly improves over the “off-the-shelf” base single-shot detector and its detection performance is comparable to more elaborate algorithms, especially if we measure detection performance slightly disregarding box localization. Lastly, we include a stratified ablation study in the closing sections where we measure the impact of each step along our proposed SSD adaptation pipeline.
AI4GEO: A DATA INTELLIGENCE PLATFORM FOR 3D GEOSPATIAL MAPPING
Authors: Brunet P.-M. , Lassalle P. , Baillarin S. , Vallet B. , Le Bris A. , Romeyer G. , Le Besnerais G. , Weissgerber F. , Foulon G. , Gaudissart V. , Triquet C. , Darques M. , Souille G. , Gabet L. , Ferrero C. , Huynh T.-L. , Lavergne E.
Published: June 2021

Abstract. The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives.
BIDIRECTIONAL REFLECTANCE DISTRIBUTION FUNCTION (BRDF) OF MIXED PIXELS
Authors: Kizel F. , Vidro Y.
Published: June 2021

Abstract. Hyperspectral imaging is crucial for a variety of land-cover mapping and analyzing tasks. The available large number of reflected light measurements along a wide range of wavelengths allows for distinguishing between different materials under various conditions. Though, several effects bear an undesired variability within hyperspectral images and increase the complexity of interpreting such data. Two of the most significant effects in this regard are the BRDF and the spectral mixture. Due to the first, the acquisitions geometrical and viewing conditions influences the measured spectral signature of a surface to a large extent. On the other hand, because of the typical low spatial resolution of remotely sensed images, each pixel can contain more than one material. Despite much research addressing either the BRDF effect and ways to correct it or the spectral unmixing, too few works considered these two effects' mutual influence. In this work, we study the BRDF of mixed pixels and present preliminary insights of testing a strategy to correct its undesired impact on the data by incorporating the EMs fractions within an unmixing-based semi-empirical correction model. Experimental results using real laboratory data acquired under controlled conditions clearly show the significant improvement of the corrected reflectance results through the proposed model.
SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES
Authors: Lumban-Gaol Y. A. , Ohori K. A. , Peters R. Y.
Published: June 2021

Abstract. Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3 m to 1.94 m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15 m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.
ROCKY SHORELINE EXTRACTION USING A DEEP LEARNING MODEL AND OBJECT-BASED IMAGE ANALYSIS
Authors: Bengoufa S. , Niculescu S. , Mihoubi M. K. , Belkessa R. , Abbad K.
Published: June 2021

Abstract. In the context of the increasing anthropogenic influence on the coastal areas that are subject to high climate variability, the main challenge is to understand its current dynamics and to predict its future evolution. Therefore, monitoring of the shoreline kinematics is a key factor for the coastal erosion assessment and an essential feature for the sustainable management of these naturally vulnerable areas.This work focuses on the detection and extraction of the shoreline, basing on a specific remote sensing methodology using Very High Resolution (VHR) optical images. Indeed, an integrated approach based on a Deep Learning model, which is the Convolutional Neural Network (CNN) and Object Based Image Analysis (OBIA) has been developed. This study aims to evaluate the methodological contribution of this integrated approach for the (semi)-automatic extraction of the rocky shoreline, for which the botanical indicator has been chosen. Therefore the upper limit of black marine lichen has been detected and extracted as the target shoreline. It is the first indication of a (semi)-automatic detection of such a complex type of shoreline.The classification results derived from the combined CNN model and OBIA methods had achieved a high overall accuracy of 0.94. The extracted shoreline have been compared to a shoreline of reference derived from a traditional method that is a manual digitizing. The distances between the two shorelines has been calculated in order to assess the accuracy of the extraction method. This comparison revealed that 76% of the extracted shoreline lies within 1 m, and 35% lies within 0.5 m of reference one. Therefore, the CNN model integrated to OBIA was successfully shown to be a good method for shoreline extraction and could offer an immediate insight regarding rocky shoreline position, providing an alternative to its monitoring.
INVESTIGATION OF THE CHANGES OF LAKE SURFACE TEMPERATURES AND AREAS: CASE STUDY OF BURDUR AND EGIRDIR LAKES, TURKEY
Authors: Aslan N. , Koc-San D.
Published: June 2021

Abstract. The world's average surface temperature has been increasing in recent decades. This situation is expected to affect aquatic systems and lakes are one of the most important aquatic systems. The main aims of this study are to examine Lake Surface Water Temperature (LSWT) and area changes of Burdur and Egirdir lakes located in the West Mediterranean Region (TR61) of Turkey for the years 1998, 2008 and 2018 using Landsat satellite images. For this purpose, initially, Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) images were generated and the lake shorelines were extracted by thresholding these images. Then, the LSWT values were obtained by using Landsat thermal images. Finally, the area and LSWT changes of Burdur and Egirdir lakes between the years 1998-2008-2018 and the relationships of these parameters with each other were analysed. The obtained results showed that the lake boundaries could be semi-automatically extracted with overall accuracy values higher than 95%. In 20-year time period it was also observed that the Burdur Lake area decreased significantly, while the Egirdir Lake area decreased slightly. When the LSWT values were analysed, it can be stated that the LSWT values increased in both lakes during this time period. The amount of increase in LSWT values was about 2.2 °C for Burdur Lake, while about 1.3 °C for Egirdir Lake.
JOINT LAND COVER AND CROP TYPE MAPPING USING MULTI-TEMPORAL SENTINEL-2 DATA FROM VARIOUS ENVIRONMENTAL ZONES IN GREECE
Authors: Karakizi C. , Kandylakis Z. , Vaiopoulos A. D. , Karantzalos K.
Published: June 2021

Abstract. In this work, we elaborate on the gained insights from various classification experiments towards detailed land cover mapping over four representative regions of different environmental characteristics in Greece. In particular, the proposed methodology exploits Sentinel-2 data at an annual basis, for the joint classification of 35 land cover and crop type classes. A number of pre-processing steps were employed on the satellite data, in order to address atmospheric and geometric effects, as well as clouds and pertinent shadows. Several classification set-ups were designed and performed using either time series of spectral features or temporal features. The latter consisted of statistical metrics, derived from the spectral time series, and therefore were significantly reduced in dimension. Experiments using the Random Forest algorithm were performed by building several per-tile models, as well as cross- regional models based on training data from all considered regions/tiles. Overall classification accuracy rates exceeded 90% for most experiments. Further analysis on the experimental results highlighted that crop types were classified more accurately when using the spectral time series features, compared to the temporal ones. Classification accuracy for non-crop classes proved much less affected by the type of employed features. The inclusion of auxiliary data layers was beneficial in all cases, both for overall and for per-class accuracy metrics. Qualitative evaluation on the predicted maps further affirmed the efficiency of the developed methodology.
SPATIAL TEMPORAL MAPPING OF SPREAD OF WATER HYACINTH IN WINUM GULF, LAKE VICTORIA
Authors: Nyawacha S. O. , Meta V. , Osio A.
Published: June 2021

Abstract. Water hyacinth (Eichhornia crassipes) is an invasive hydro plant that invaded the waters of Lake Victoria and has since been spreading rapidly affecting the socio-economic livelihood of the community around the Lake. The weed's rapid spread is due to various anthropogenic activities in the surrounding environment among them being the eutrophication of the lake waters.This study aims at using remote sensing applications and presenting the results of the analysis of the water hyacinth Normalized Difference Vegetation Index (NDVI), water extent, and analysis of correlation with the water quality over time from Sentinel 2 satellite imagery in January 2017 to January of 2021. The analysis aims at understanding the vegetation growth coverage in the five years and sets the basis of monthly predictive modelling of the behavior of water hyacinth. Predictive modelling applies historical statistical data while trying to use trend analysis in predicting the future behavior of a phenomenon. This study also seeks to answer the research question of the role of suspended sediments and dissolved minerals in abating the spread of and growth of water hyacinth.