Remote Sensing Information Research Articles

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.

Preface: ISPRS Geospatial Week 2023

Abstract. The International Society for Photogrammetry and Remote Sensing (ISPRS) Geospatial Week 2023 (GSW’2023) is a combination of 29 workshops organized by the ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2023 is held from 2–7 September 2023, at the Semiramis Cairo Hotel, on the magnificent river Nile, Egypt, and is convened by the Arab Academy for Science, Technology & Maritime Transport (AASTMT) acting as the local organizer. The GSW’2023 is the first in Africa and the Middle East. The conference was chaired by Prof. Naser EL-Sheimy and Prof. Ismail Abdelgafhar (President of AASTMT) as the Honorary Chair and host of the conference under the Auspices of H.E. Professor Mostafa Madbouly, Prime Minister of the Arab Republic of Egypt. The GSW’2023 is the fifth edition, after, Antalya Turkey in 2013, La Grande Motte France in 2015, Wuhan China in 2017, and Enschede The Netherlands in 2019. The following 29 workshops provide state of the art and future trends in geospatial, sensors, photogrammetry, remote sensing, and spatial information sciences technologies in their applications in many industries and economic sectors: Cultural Heritage Visualization and Virtual Restoration SpACE – Spectral Remote Sensing in the era of AI, Cloud and Edge Computing Youth Presentation Forum Openness in Geospatial and Remote Sensing Precision GNSS: Technology Advances and Applications for Navigation and Mapping Photogrammetric 3D Reconstruction for Geo-Applications (PhotoGA 2023) Geospatial Data Analytics for Physical Geography Impact Assessment on Environment, Health andSociety Intelligent Systems in Sensor Web and Internet of Things Underwater Mapping Workshop: Geospatial techniques for underwater documentation, mapping andmonitoring SO&C: Sensor orientation and calibration for mapping and navigation purposes Smart Forests – Forest ecosystem assessment and monitoring using Remote Sensing, ArtificialIntelligence, and Robotics Satellite Remote Sensing and Its Applications Advanced Data Preparation and Data Management for Geospatial and Remote Sensing Scenarios Laser Scanning 2023 NGC of AV: Navigation, Guidance and Control of Autonomous Vehicles ISSDQ 2023- Artificial Intelligence and Uncertainty Modeling in Spatial Analysis Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences GeoHB 2023: Geo-Spatial Computing for Understanding Human Behaviours GI4SDGs: The Geospatial Information and SDG Nexus SARcon 2023 – SAR constellations and applications Digital Construction CrowdMapping: Crowdsourcing for Global Mapping Indoor 3D IAMS – Intelligent and autonomous mapping systems AI-PC: AI-based Point Cloud and Image Understanding UAV-based mapping with imaging and LiDAR systems: challenges, data processing, and applications 3DS Smart Cities – 3D Sensing for Smart Cities Robotics for Mapping – SLAM approaches for mobile mapping and robot intelligence MMT and HD Maps – Mobile Mapping Technologies and HD Maps Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV for mapping, laser scanning, Semantics3D, SARcon, ISSDQ hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping, smart forest and smart cities to global mapping studies and planetary mapping. The conference also includes for the first-time few workshops organized by other working groups from sister organizations and the ISPRS youth forum. In total 172 full papers and 423 extended abstracts were submitted by authors from 68 countries. 1400 reviews have been delivered by 283 reviewers. A total of 137 full papers have been accepted for the volume X-1/W1-2023 of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals). Another 260 papers are published in volume XLVIII-1/W2-2023 of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives). The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2023. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives. The editors would like to extend their great appreciation to the President of AASTMT Prof. Ismail Abdel Ghafar for his great support in hosting the ISPRS GSW’2023. We also would like to extend our thanks to the members of the local organizing committee for great effort in making GSW’2023 conference a success, namely, Prof. Amr Ali Hanafi, Prof. Ahmed Shaker, Research Eng. Ayman Elzagh, Dr. Mengchi Ai, Dr. Adel Moussa, Dr. Yiran Luo, Mr. Yasser Elrashidi, Eng. Nagy K. Aly, Prof. Amira Zaki, Dr. Radwa Osman, Eng. Mohamed ElQurashi and Mr. Mohamed Abdallah Mahmoud. We hope you enjoy reading the proceedings. Editor: Prof. Naser El-Sheimy, Geospatial Week Director 2023, General Chair (University of Calgary, Canada)Co-Editors, The Arab Academy for Science, Technology & Maritime Transport:Prof. Alaa Abdelwahed Abdelbary, Chair Local Organizing CommitteeProf. Nashwa El-BendaryProf. Yahya Mohasseb

Preface: ISPRS Geospatial Week 2023

Abstract. The International Society for Photogrammetry and Remote Sensing (ISPRS) Geospatial Week 2023 (GSW’2023) is a combination of 29 workshops organized by the ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2023 is held from 2–7 September 2023, at the Semiramis Cairo Hotel, on the magnificent river Nile, Egypt, and is convened by the Arab Academy for Science, Technology & Maritime Transport (AASTMT) acting as the local organizer. The GSW’2023 is the first in Africa and the Middle East. The conference was chaired by Prof. Naser EL-Sheimy and Prof. Ismail Abdelgafhar (President of AASTMT) as the Honorary Chair and host of the conference under the Auspices of H.E. Professor Mostafa Madbouly, Prime Minister of the Arab Republic of Egypt. The GSW’2023 is the fifth edition, after, Antalya Turkey in 2013, La Grande Motte France in 2015, Wuhan China in 2017, and Enschede The Netherlands in 2019. The following 29 workshops provide state of the art and future trends in geospatial, sensors, photogrammetry, remote sensing, and spatial information sciences technologies in their applications in many industries and economic sectors: Cultural Heritage Visualization and Virtual Restoration SpACE – Spectral Remote Sensing in the era of AI, Cloud and Edge Computing Youth Presentation Forum Openness in Geospatial and Remote Sensing Precision GNSS: Technology Advances and Applications for Navigation and Mapping Photogrammetric 3D Reconstruction for Geo-Applications (PhotoGA 2023) Geospatial Data Analytics for Physical Geography Impact Assessment on Environment, Health andSociety Intelligent Systems in Sensor Web and Internet of Things Underwater Mapping Workshop: Geospatial techniques for underwater documentation, mapping andmonitoring SO&C: Sensor orientation and calibration for mapping and navigation purposes Smart Forests – Forest ecosystem assessment and monitoring using Remote Sensing, ArtificialIntelligence, and Robotics Satellite Remote Sensing and Its Applications Advanced Data Preparation and Data Management for Geospatial and Remote Sensing Scenarios Laser Scanning 2023 NGC of AV: Navigation, Guidance and Control of Autonomous Vehicles ISSDQ 2023- Artificial Intelligence and Uncertainty Modeling in Spatial Analysis Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences GeoHB 2023: Geo-Spatial Computing for Understanding Human Behaviours GI4SDGs: The Geospatial Information and SDG Nexus SARcon 2023 – SAR constellations and applications Digital Construction CrowdMapping: Crowdsourcing for Global Mapping Indoor 3D IAMS – Intelligent and autonomous mapping systems AI-PC: AI-based Point Cloud and Image Understanding UAV-based mapping with imaging and LiDAR systems: challenges, data processing, and applications 3DS Smart Cities – 3D Sensing for Smart Cities Robotics for Mapping – SLAM approaches for mobile mapping and robot intelligence MMT and HD Maps – Mobile Mapping Technologies and HD Maps Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV for mapping, laser scanning, Semantics3D, SARcon, ISSDQ hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping, smart forest and smart cities to global mapping studies and planetary mapping. The conference also includes for the first-time few workshops organized by other working groups from sister organizations and the ISPRS youth forum. In total 172 full papers and 423 extended abstracts were submitted by authors from 68 countries. 1400 reviews have been delivered by 283 reviewers. A total of 137 full papers have been accepted for the volume X-1/W1-2023 of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals). Another 260 papers are published in volume XLVIII-1/W2-2023 of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives). The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2023. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives. The editors would like to extend their great appreciation to the President of AASTMT Prof. Ismail Abdel Ghafar for his great support in hosting the ISPRS GSW’2023. We also would like to extend our thanks to the members of the local organizing committee for great effort in making GSW’2023 conference a success, namely, Prof. Amr Ali Hanafi, Prof. Ahmed Shaker, Research Eng. Ayman Elzagh, Dr. Mengchi Ai, Dr. Adel Moussa, Dr. Yiran Luo, Mr. Yasser Elrashidi, Eng. Nagy K. Aly, Prof. Amira Zaki, Dr. Radwa Osman, Eng. Mohamed ElQurashi and Mr. Mohamed Abdallah Mahmoud. We hope you enjoy reading the proceedings. Editor: Prof. Naser El-Sheimy, Geospatial Week Director 2023, General Chair (University of Calgary, Canada)Co-Editors, The Arab Academy for Science, Technology & Maritime Transport:Prof. Alaa Abdelwahed Abdelbary, Chair Local Organizing CommitteeProf. Nashwa El-BendaryProf. Yahya Mohasseb

Application of Remote Sensing and Geographic Information System in Identification of Urban Growth nodes: A Case of Surat City, India

With the passage of time, the city's growth behavior will not change unless and until the government intervenes, and thus its identity will shift from monocentric to polycentric to meet the needs of citizens. As a result, this study is being conducted to identify emerging growth nodes within a selected area of Surat City, as well as their growth drivers over a 30-year period. Quantified built-up area within a patch size of 1km x 1km was used to compute patch density at five-year intervals from 1991 to 2021. In addition, the spatial changes that occurred within patches over the same time period were examined. Both analyses aid in determining the emerging growth nodes over a 30-year period. From 1991 to 2021, the city was driven by socioeconomic criteria such as land price, availability of good health and educational facilities, water and sewerage networks, fire stations, proximity factors such as proximity to major roads, bridges, bus stations, metro, railway stations, airport, environmental factors such as the development of riverfront and linear park, bio-diversity park, and government interventions in terms of Town Planning Schemes. This study thus aids urban planners and decision-makers in selecting which growth nodes to plan for new development and type of development, what to connect, and what to protect in the years to come.

Geostatistical modelling of child undernutrition in developing countries using remote-sensed data: evidence from Bangladesh and Ghana demographic and health surveys

Childhood chronic undernutrition, known as stunting, remains a critical public health problem globally. Unfortunately while the global stunting prevalence has been declining over time, as a result of concerted public health efforts, there are areas (notably in sub-Saharan Africa and South Asia) where progress has stagnated. These regions are also resource-poor, and monitoring progress in the fight against chronic undernutrition can be problematic. We propose geostatistical modelling using data from existing demographic surveys supplemented by remote-sensed information to provide improved estimates of childhood stunting, accounting for spatial and non-spatial differences across regions. We use two study areas–Bangladesh and Ghana–and our results, in the form of prevalence maps, identify communities for targeted intervention. For Bangladesh, the maps show that all districts in the south-eastern region are identified to have greater risk of stunting, while in Ghana the greater northern region had the highest prevalence of stunting. In countries like Bangladesh and Ghana with limited resources, these maps can be useful diagnostic tools for health planning, decision making and implementation.

Assessing Spatio-Temporal Change and Dynamics of Forest Ecosystem Succession Using Patch Analysis

Aim of study: This study focuses on creating a secondary forest succession (SFS) map between 1972 and 2014 according to the Clementsian theory based on land cover, assessing the spatio-temporal pattern of forest succession change, and determining the factors affecting the forest ecosystem.
 Area of study: This study was conducted at the Çermik Forest Enterprise (FE) in Diyarbakır city, located in the Southeastern Anatolia Region of Türkiye.
 Material and methods: Clementsian theory, Remote Sensing (RS), and Geographical Information System (GIS) were used to generate the SFS map. Patch Analyst 4.0 was used to determine changes in spatiotemporal patterns with landscape indices.
 Main results: The total forested area increased from 32405.1 ha (13% of the study area) in 1972 to 45054.7 ha (18% of the study area) in 2014, with a net increase of 12649.6 ha. It was determined that the progressive succession area was 87736.7 ha, the regressive succession area was 39216.5 ha, and the unchanged succession area was approximately 129989.6 ha. The number of patches increased over a 42-year period.
 Research highlights: The forest ecosystem was more fragmented, with patches becoming more irregular, complex, and edgy

Sub-Watershed Prioritization for Erosion Susceptibility based On Drainage Morphometric Characters

Watershed located in a Hilly-plain environment is more susceptible for soil erosion. Loss of fertile soil in agricultural land hampers the life of the farmers of the region. Identification of the vulnerable areas and devising strategies to minimize the damage caused due to soil erosion are important to address the problems faced by the agrarian society. In our current work, drainage morphometric parameters were evaluated to assess the soil erosion susceptibility of the four sub-watersheds (SW01, SW02, SW03 and SW04) of the Yagachi watershed located in the Hemavathi river basin in Karnataka State using Remote Sensing (RS) and Geographical Information System (GIS). Seventeen morphometric parameters namely Area, Stream Order, Mean Bifurcation Ratio, Drainage Density, Stream Frequency, Texture Ratio, Infiltration Number, Compactness Co-Efficient, Length of Overland Flow, Elongation Ratio, Circulatory Ratio, Form Factor, Relief Ratio, Shape Factor, Basin Relief, Dissection Index and Ruggedness Number were considered. The priority rank and category for each sub-watershed were assigned based on Compound Factor (CF) value. The sub-watersheds with the lowest CF value are most susceptible to erosion and need highest priority for the soil conservation measures. Based on CF values, the sub-watersheds were categorized into 4 priority groups: ‘Very High’ priority (<2.00), ‘High’ priority (2.01–2.50), ‘Moderate’ priority (2.51–3.00) and ‘Low’ priority (≥3). Out of 4 sub-watersheds, SW03 falls in ‘Very High’ priority category in terms of erosion susceptibility and needs appropriate soil conservation measures in order to arrest fertile soil erosion and increase agricultural produce output.

Comparative analysis of gridded rainfall datasets over the Bagmati river basin, India

Abstract In this study, three high-resolution gridded rainfall datasets, viz., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) have been collected, analyzed, and compared against the ground-based observed rain gauge datasets of Indian Meteorological Department (IMD) of the Bagmati river basin from 2001 to 2014 in the Bihar State of India. Comparison analyses were performed at daily, monthly, seasonal, and annual time scales. Various statistical parameters, contingency tests, trend analysis, and rainfall anomaly index were used for comparison of datasets. Though MERRA-2 had the highest probability of detection (POD) and lowest false alarm ratio (FAR), analysis showed that IMERG data were closely matching with the observed data, whereas MERRA-2 and PERSIANN underestimated the extreme values. For the monthly scale, again IMERG had the most optimal Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency (NSE) values. IMERG also performed well in detecting rainfall trends and identifying wet and dry years. Overall, IMERG was the most suitable dataset at all time scales for future studies in the basin.

Mapping Groundwater Prospective Zones Using Remote Sensing and Geographical Information System Techniques in Wadi Fatima, Western Saudi Arabia

Integration of remote sensing (RS) and GIS methods has allowed for the identification of potential water resource zones. Here, climatic, ecological, hydrologic, and topographic data have been integrated with microwave and multispectral data. Sentinel-2, SRTM, and TRMM data were developed to characterize the climatic, hydrologic, and topographic landscapes of Wadi Fatima, a portion of western Saudi Arabia that drains to the Red Sea. The physical characteristics of Wadi Fatima’s catchment area that are essential for mapping groundwater potential zones were derived from topographic data, rainfall zones, lineaments, and soil maps through RS data and GIS techniques. Twelve thematic factors were merged with a GIS-based knowledge-driven approach after providing a weight for every factor. Processing of recent Sentinel-2 data acquired on 4 August 2023 verified the existence of a zone of vegetation belonging to promising areas of groundwater potential zones (GPZs). The output map is categorized into six zones: excellent (10.98%), very high (21.98%), high (24.99%), moderate (21.44%), low (14.70%), and very low (5.91%). SAR CCD derived from Sentinel-1 from 2022 to 2023 showed that the parts of no unity are in high-activity areas in agricultural and anthropogenic activities. The model predictions were proven with the ROC curves with ground data, existing wells’ locations, and the water-bearing formations’ thickness inferred from geophysical data. Their performance was accepted (AUC: 0.73). The outcomes of the applied methodologies were excellent and important for exploring, planning, managing, and sustainable development of resources of water in desert areas. The present study successfully provided insights into the watershed’s hydrologic, climatic, vegetated variation, and terrain database information using radar, optical, and multi-temporal InSAR data. Furthermore, the applied multi-criteria overlay technique revealed promising areas for groundwater abstraction, which can be applied elsewhere in various environmental situations.

Using RS and GIS Techniques to Assess and Monitor Coastal Changes of Coastal Islands in the Marine Environment of a Humid Tropical Region

Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Utilizing satellite imagery from the LANDSAT series with a spatial resolution of 30 m, the analysis spanned the years from 1973 to 2019. The Digital Shoreline Analysis System (DSAS) tool, integrated into the ArcGIS software, was employed to monitor and analyze shoreline shifts, encompassing erosion and accretion. Various statistical parameters, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), were utilized to evaluate these changes. Additionally, the study aimed to discern the root causes of shoreline modifications in the study area, encompassing disturbances and the construction of new structures on these islands. The results conclusively demonstrated the substantial impact endured by these coastal islands, with accretion on both sides leading to the creation of new landmasses. This manuscript effectively illustrates that these islands have experienced marine transgression, notably evidenced by accretion. Anthropogenic activities were identified as the primary drivers behind the observed shoreline changes, underscoring the need for careful management and sustainable practices in these fragile coastal ecosystems.

Identification of Urban Heat Island Effect on Land Use Land Cover Changes

The effect of urban heat island (UHI), characterized by higher temperatures in urban areas compared to surrounding rural areas, is a significant consequence of rapid urbanization. As cities continue to grow and attract more people, the UHI effect intensifies due to increased heat-absorbing surfaces and reduced green spaces. This can lead to various environmental and health issues, specifically affecting vulnerable populations. This study investigates the phenomenon of UHIs in the Kalutara district of Sri Lanka and examines the relationship between Land Use Land Cover (LULC) changes, urbanization, and the intensity of UHIs. Landsat satellite images from 1991, 2001, 2011, and 2021 were analyzed using Remote Sensing and Geographic Information System (GIS) techniques to create land Surface Temperature (LST) maps and LULC maps. The results show a significant expansion of urban areas and a reduction in green areas within the Kalutara district over the study period, accompanied by an increase in the intensity of UHIs. Regression analysis indicates a negative correlation between the percentage of green areas and LST, highlighting the cooling effect of vegetation. Conversely, a positive correlation is found between LST and the percentage of urban areas. The research demonstrates the spread of UHIs from limited urban centers in 1991 to larger urban areas and adjacent regions in 2021. The findings emphasize the need for sustainable land management practices, preservation of green areas, and effective urban planning strategies to mitigate the adverse effects of UHIs.

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong/RSE_Cross-city.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method.

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km2), moderate (76.44%; 2274.4 km2), and high (11.267%; 345.943 km2), in sequential sequence. These categories define the model's output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study's findings to better inform future regulatory initiatives.

Evaluation of 12 precipitation products and comparison of 8 multi-model averaging methods for estimating precipitation in the Qilian Mountains, Northwest China

Mountains are the water towers of the world, so it is critical to obtain accurate precipitation data for mountainous areas. Due to the complex topography of high mountainous areas, precipitation ground stations are sparse and unevenly distributed in such areas, so precipitation products such as remote sensing and reanalysis products are used to obtain gridded precipitation data for these areas. However, no single precipitation product performs best in all areas of mountainous regions. Therefore, this study first evaluated the performance of 12 precipitation products in estimating precipitation in the Qilian Mountains at the station scale and sub-basin scale, and then compared the performance of precipitation estimates for the Qilian Mountains generated by 8 multi-model averaging methods. The evaluation results for 29 meteorological stations in the Qilian Mountains showed that the China Meteorological Forcing Dataset product was the best-performing precipitation product, while the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record product was the worst-performing precipitation product. The evaluation results for 18 sub-basins showed that at these sub-basins, the WorldClim was the best-performing precipitation product, while the High Asia Refined analysis was the worst-performing precipitation product. Thus, station-scale evaluations may not necessarily be applicable to the basin scale. Multi-model averaging methods effectively improved the accuracy of precipitation estimates both at station scale and at sub-basin scale. The Granger-Ramanathan variant C was the best multi-model averaging method for estimating precipitation at station scale. As the Granger-Ramanathan methods allow negative weights, they are not recommended to interpolate the Granger-Ramanathan weight values of stations to grids. The Bayesian model averaging (BMA) was found to be the most suitable multi-model averaging method for estimating precipitation in the Qilian Mountains by interpolation of weight values of stations to grids. The precipitation estimates generated by BMA show that the mean annual precipitation in the Qilian Mountains from 2001 to 2018 was approximately 336.1 mm, and the annual precipitation during this period increased linearly by 2.4 mm per year.

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods.

Change Detection and Spatial Variability in Dal Lake and Nigeen Lake using Remote Sensing and Geographical Information System

Change Detection and Spatial Variability in Dal Lake and Nigeen Lake using Remote Sensing and Geographical Information System

Geo-Spatial Assessment of Groundwater Potential Zones in Birnin-Kudu Local Government Area Jigawa State, Nigeria

The study attempted to identify the groundwater potential zones in Birnin Kudu Local Government Area of Jigawa State, Nigeria using Remote Sensing and Geographic Information System techniques. Groundwater is an important source for water supply, considering its availability, quality, cost and time effective to develop, and simple accessibility. The study used thematic maps like Rainfall, Lineament, Geology, Soil, DEM Soil, Drainage and Land Use Land Cover map as GIS layers in geo-database. The rankings/weights of factor contributing to groundwater prospects in the study area were evaluated by pair wise comparison using Analytical Hierarchy Process (AHP). Weightages of the factors influencing groundwater prospects zones in the study area shows that rainfall is one of the major contributor, and was weighed as the highest contributor with (34%) followed by lineament (24%) and geology was weighed (14%). The least groundwater contributor in the study area is the land use land cover with (2%). The study showed that the very high groundwater potential areas occupy 59.92 km2 (2.89%) of the area, the high potential zones constitute about 527.52km2 (25.45%) of the study area and the moderate potential area has the highest area of 602.69km2 (29.07%). The low and very low potentials occupy an area of about 528.13 km2 (25.48%) and 354.75 km2 (17.11%) respectively. The study concluded that the study area has an abundance.

Spatiotemporal Evaluation of Satellite-Based Precipitation Products in the Colorado River Basin

Abstract Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in the Upper Colorado River basin (UCRB) for the period 2000–20. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3–4 mm day−1 differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November–April) than in the dry season (May–October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50%–60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features.

Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed

This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.