Abstract Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to accessibility, satellite remote sensing has become a critical and cost-effective tool for acquiring this information in a temporal context. The methods that are commonly used include supervised and unsupervised

Remote sensing-based investigation of ice flow dynamics of Polar Record glacier (PRG) during December–April of 2016-2019 has been conducted in this work. Using offset tracking method on the Sentinel-1 Synthetic Aperture Radar (SAR) images, we estimated the glacier ice flow velocity. Ice flow velocity near the glacier terminus indicated higher velocity during January and subsequently showed lower values

ABSTRACT The relationship between soil properties and environmental covariates is mostly nonlinear for human impacted areas. Herein, we proposed a nonlinear model for mapping soil organic carbon (SOC) and total nitrogen (TN) in a typical human impacted area (a small watershed of Poyang Lake, China), namely radial basis function neural network combined with agricultural land use (RBFNN_ALU). The results

Abstract Earthquake hazards cause changes in landforms, economic losses, and human casualties. Seismic Vulnerability Mapping (SVM) is key information to prevent and predict the damage of earthquakes. The purpose of this study is to train and compare the results of the Classification Tree Analysis (CTA) learner model with three Gini, Entropy, Ratio split algorithms, and Fuzzy ARTMAP (FAM) model by the

The development of a new Very High-Resolution (VHR) satellite sensor provides opportunities for mapping more details. The building extraction process is a challenging problem due to the misclassification between buildings and similar spectral objects. The main aim of this study is to introduce an approach to building rooftops border extraction. In this approach, a new index to highlight building areas

Abstract Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks

Abstract The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbor (KNN), boosted regression tree (BRT), and logitboost (LB) with the base classifier adabag (AB) for flood susceptibility mapping (FSM). The proposed models were implemented in the central west coast of India, which

Abstract Despite wider applications for Unmanned Aerial Vehicle (UAV) in aquatic remote sensing, frequent sun glint in UAV acquisition often results in significant data gaps. Much research exists in the development of sun glint correction methods for airborne and satellite imagery to generate accurate coral habitat maps. Conversely, little is known about an appropriate glint correction method that

Abstract Wetland, an important carbon pool on the earth, is of great significance for human beings and the environment. In this study, we modeled the wetland NPP using Carnegie–Ames–Stanford Approach (CASA) and time series with high spatial and temporal resolution generated by Landsat 8 and Sentinel-2 images. Firstly, the downscaled Landsat 8 data (10 m) combined with Sentinel-2 data were utilized

Abstract Northern Pakistan is a rugged mountainous area that is seismically active, high gradients, disintegrated lithology, and glaciers in the high peaks. District Ghizer lies among the most vulnerable areas and experience landslides every year due to different causative factors. This study has carried out to prepare a detailed landslide inventory and to develop a susceptibility model for the area

ABSTRACT This work presents an analytical framework that includes slope and rainfall variability with value of the critical area in defining a threshold for channel initiation processes for the whole river basin. The study overcomes the existing limitation of application of constant threshold for extracting channels throughout the basin by proposing slope and rainfall dependent variable threshold.

Abstract Edge enhancement techniques have been widely used in mapping geologic features, such as faults, contacts and dikes. The universal disadvantages of previously presented filters are that they cannot balance signals caused by geological structures located at different depths or they bring false edges or the images obtained from these filters have low resolution. We introduce a filter SF that

Abstract The 2014-2016 El Niño Southern Oscillation (ENSO) caused a prolonged marine heatwave that led to widespread coral bleaching and mortality across the Indo-pacific coral reefs. Prediction of coral bleaching and assessment of bleaching impact on corals is vital for reef ecosystem functioning and services. Wherein, advanced satellite remote sensing approach to determine and quantify the thermal

Abstract Accurately estimating the shrubland aboveground biomass (AGB) of the desert steppe and understanding its dynamic changes are vitally important for studying the regional carbon cycle and sustainable use of grassland resources. Synthetic aperture radar (SAR) data are seldom applied to estimate shrubland AGB, particularly in arid and semiarid desert steppe landscapes. The main objective of this

Abstract This study aims to quantify the areal variation of glacial lakes in the coastal ice-free areas of King George Island (KGI) and Nelson Island (NI) from 1986 to 2020. Glacial lake and glacial area fluctuations are estimated using spaceborne remote sensing data and annual mean near-surface air temperature data from station observations and ERA-Interim reanalysis were analyzed. The area of glacial

Abstract The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear

Abstract Multispectral indices are useful to improve the knowledge of plant organic functionality. Geographically weighted regression (GWR), multispectral data from unmanned aerial vehicles (UAVs) and individual tree attributes were used in combination to generate forest parameters in an even-aged orchard of Pinus arizonica Engelm. The NDVI index was the best indicator of vegetation vigour, correlated

Abstract The unplanned and uncontrolled urbanization of Indian cities has put them under different ecological and environmental threats. Urban heat island (UHI) is one such critical ecological hazard, whereby an urban area is experiencing higher land surface temperature (LST) as compared to the surrounding rural area. In the present study, the relationship of LST and surface urban heat island (SUHI)

Abstract Understanding spatiotemporal dynamics of cotton cultivation patterns is an essential input to cotton farming management, production prediction and policy formulations. Thus, this study demonstrates cotton cultivation dynamics by constructing their change trajectories using multi-temporal Landsat and Sentinel-2 images. Then we reclassified the trajectories into four change trends, namely permanent

ABSTRACT Main aim of the paper is to emphasise the advantage of 1D and 2D hydrodynamic models coupling in simulating flood inundations using high resolution Digital Terrain Models. The developed flood early warning model was calibrated and validated thoroughly at critical locations using historic observed discharge data and point rainfall data of about 350 stations. The flood inundation simulation

Abstract Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cover products due to dense-cloud-contaminated and/or technical issues. This study proposes a novel approach (Ensemble Learning Updating Classifier/ELUC), which can be applied with various classification

Abstract The coastal zone is an extremely volatile environment and is constantly changing. We assessed short- and long-term shoreline changes in the Ganjam district of Odisha on the eastern coast of India from 1990 − 2019 using Landsat satellite imagery and the Digital Shoreline Analysis System (DSAS) tool in the Geographic Information System. In addition, we have also projected the likely future coastline

Abstract A comprehensive integrated framework was designed to categorize individual streams and watersheds based on their temporal variation, and to determine the key drivers controlling watershed hydrology in the Qorveh-Dehgolan Basin. Four stream networks were extracted from multi-temporal Pan-Sharpened Landsat 7 and Landsat 8 imageries. Nineteen geomorphometric indices are measured and classified

Abstract The 2014-2016 El Niño Southern Oscillation (ENSO) caused a prolonged marine heatwave that led to widespread coral bleaching and mortality across the Indo-pacific coral reefs. Prediction of coral bleaching and assessment of bleaching impact on corals is vital for reef ecosystem functioning and services. Wherein, advanced satellite remote sensing approach to determine and quantify the thermal

Abstract Accurately estimating the shrubland aboveground biomass (AGB) of the desert steppe and understanding its dynamic changes are vitally important for studying the regional carbon cycle and sustainable use of grassland resources. Synthetic aperture radar (SAR) data are seldom applied to estimate shrubland AGB, particularly in arid and semiarid desert steppe landscapes. The main objective of this

Abstract This study aims to quantify the areal variation of glacial lakes in the coastal ice-free areas of King George Island (KGI) and Nelson Island (NI) from 1986 to 2020. Glacial lake and glacial area fluctuations are estimated using spaceborne remote sensing data and annual mean near-surface air temperature data from station observations and ERA-Interim reanalysis were analyzed. The area of glacial

Abstract The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear

Abstract Multispectral indices are useful to improve the knowledge of plant organic functionality. Geographically weighted regression (GWR), multispectral data from unmanned aerial vehicles (UAVs) and individual tree attributes were used in combination to generate forest parameters in an even-aged orchard of Pinus arizonica Engelm. The NDVI index was the best indicator of vegetation vigour, correlated

Abstract The unplanned and uncontrolled urbanization of Indian cities has put them under different ecological and environmental threats. Urban heat island (UHI) is one such critical ecological hazard, whereby an urban area is experiencing higher land surface temperature (LST) as compared to the surrounding rural area. In the present study, the relationship of LST and surface urban heat island (SUHI)

Abstract Understanding spatiotemporal dynamics of cotton cultivation patterns is an essential input to cotton farming management, production prediction and policy formulations. Thus, this study demonstrates cotton cultivation dynamics by constructing their change trajectories using multi-temporal Landsat and Sentinel-2 images. Then we reclassified the trajectories into four change trends, namely permanent

ABSTRACT Main aim of the paper is to emphasise the advantage of 1D and 2D hydrodynamic models coupling in simulating flood inundations using high resolution Digital Terrain Models. The developed flood early warning model was calibrated and validated thoroughly at critical locations using historic observed discharge data and point rainfall data of about 350 stations. The flood inundation simulation

Abstract Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cover products due to dense-cloud-contaminated and/or technical issues. This study proposes a novel approach (Ensemble Learning Updating Classifier/ELUC), which can be applied with various classification

Abstract The coastal zone is an extremely volatile environment and is constantly changing. We assessed short- and long-term shoreline changes in the Ganjam district of Odisha on the eastern coast of India from 1990 − 2019 using Landsat satellite imagery and the Digital Shoreline Analysis System (DSAS) tool in the Geographic Information System. In addition, we have also projected the likely future coastline

Abstract A comprehensive integrated framework was designed to categorize individual streams and watersheds based on their temporal variation, and to determine the key drivers controlling watershed hydrology in the Qorveh-Dehgolan Basin. Four stream networks were extracted from multi-temporal Pan-Sharpened Landsat 7 and Landsat 8 imageries. Nineteen geomorphometric indices are measured and classified

Numerous studies utilizing unmanned aerial systems (UASs) have been conducted using the structure from motion technique to study regions/objects of interest for various geoscience-related studies. To provide higher model accuracy, ground control points (GCPs) are typically used to aid the image analysis. However, in cases where GCPs are difficult to set or see, such an approach cannot be used. This

Abstract Building recognition is a core task for urban image classification (mapping), especially in optical high-resolution imagery. Convolutional Neural Networks (CNNs) have recently achieved unprecedented performance in the automatic recognition of objects (e.g. buildings, roads, or trees) in high-resolution imagery. Although these results are promising, questions remain about generalizability.

This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline

Abstract NASA’s Suomi National Polar-orbiting Partnership (NPP), Visible Infra-red Imaging Radiometer Suite (VIIRS) data utilized to detect night time lights (NTL) radiances and fishing boat pixels using VIIRS boat detection (VBD) approach. Study attempted for first time in Indian waters to detect night time fishing boats during 2017 along with chlorophyll and sea surface temperature (SST). Night-time

Abstract In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media.This feature is used for the estimation of biophysical parameters and monitoring of deforestation.Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic

Groundwater scarcity is one of the most concerning issues in arid and semi-arid regions. In this study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial neural network (ANN), reduced-error pruning trees (REPTree), radial basis function (RBF), M5P and random forest (RF) with particle swarm optimization (PSO) for delineating

Abstract Earlier location-based accessibility analysis by car mode utilizes network travel distance (NTD) or static travel time (STT) as travel impedances. A recent trend in the literature that considers impedance as dynamic travel time (DTT) emerged, allowing examining continuous accessibility patterns. This research aims to evaluate the measured spatial-temporal change in accessibility for the different

Abstract The present work intended to model the vegetation health index (VHI) of rabi crop and explored its time series change (1990–2019) at pixel scale using Landsat satellite imageries. Identification of consistency level of rabi crop was another objective. VHI was estimated by integrating crop condition index (CCI) and temperature condition index (TCI). The CCI and TCI were derived from normalized

Abstract Spectroscopy is a fast, non-destructive, and cheap method, which has been widely used in the estimation of soil organic matter (SOM) concentration. This study presented a methodology to estimate and map SOM content by crop canopy reflectance spectra combining with land parameters in the Yarlung Zangbo River (YZR) basin. The reflectance spectra of the oat canopy were collected in the field

Abstract The present study aims to estimate the trends in daytime land surface temperature (LST) and its relationship with other parameters such as elevation and land use/land cover (LULC) over the Ganga River Basin (GRB) during 2001–2019. The study examined monthly daytime clear-sky LST, elevation, LULC, snow cover area (SCA), clear-sky days, and aerosols data of the GRB. Annual mean clear-sky daytime

Abstract To reduce poverty, it is important to obtain accurate information on poverty conditions in a timely manner. Previous studies indicated that nighttime light products are helpful for poverty estimation. However, there exist no studies that have investigated the potential of LuoJia1-01, a new-generation nighttime light satellite with a much finer resolution (∼130 m), for analyzing poverty. In

Abstract The ecological evolution and relevant factors in semi-arid and arid regions is a research focus on climate changes. One such region, the mountain-oasis-desert coupling system in Xinjiang, China, is sensitive to global climate changes because it has a fragile ecological environment. This study attempted to investigate the temporal-spatial variations of surface soil moisture, vegetation cover

Abstract Damage of fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) on sorghum from the farmers’ fields of southern India was assessed using space-borne data. Comparison of the Sentinel-2A satellite data of pre and post infestation periods revealed reduction in Leaf Area Index (LAI) in the infested fields. Groundtruth data confirmed that FAW infestation reduced LAI by 49.7%, biomass by 32.5%

Abstract The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method

Abstract The advent of Sentinel-1 SAR data with high temporal and medium spatial resolutions along with its being unaffected by presence of cloud provided opportunities for using remote sensing in mapping LULCs with high temporal detail. The objective of this study is to explore the possibility of applying Sentinel-1 data together with OBIA and machine learning in classifying LULC in a complex agricultural

Abstract Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrineh Rood watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy

Abstract Glacial Lake Outburst Floods (GLOFs) are a recurring hazard in the Himalayas, posing significant threat to downstream communities. The North Sikkim district of India, comprising the upper reaches of the Teesta River in the Eastern Himalayas, has experienced past GLOF events. The identification of lakes susceptible to this phenomenon is therefore paramount. Using multi-temporal satellite images

Abstract Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrating multiple cognitive features and manual cartographic experiences. Taking building center points as nodes, adjacent buildings were organized as a graph in which cognitive variables including size

Abstract Hydro-climatic (HC) models have complex environments due to the integration of hydrological processes and climate indices for the assessment of historical and future scenarios. The approximation of HC models leads to a major uncertainty in the selection of optimal methods for processing, enhancement, and assessment. The present work developed a User-Friendly Interface (UI) in the R programming

Abstract This study tests the applicability of three resampling methods (i.e. bootstrapping, random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines

ABSTRACT This study evaluates and compares three machine learning models: K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for landslide susceptibility mapping for part of areas in Rangamati District, Bangladesh. The performance of these methods has been assessed by employing statistical methods such as the area under the curve (AUC) for success rate (SR) and prediction

Abstract The study involves developing a framework of research methodology for enhancing the capability of spaceborne integrated DInSAR and PSI approach for detection, monitoring, and analysis of land surface deformation in the underground mining environment. First component of the framework involves pre-feasibility assessment of the integrated approach followed by selecting suitable InSAR data sets

Abstract This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training

Abstract Accurate and reliable estimation of Above Ground Biomass (AGB) in tropical forests is much needed for net carbon assessments. The aim of the study is to determine the uncertainty in biomass estimation in terms of field plot size, shape and location error using field plot and remote sensing data in tropical dry deciduous forests of India. Detailed tree measurements and location mapping are

Abstract This study explores the impact on land surface temperature due to the spatial clustering of urban landforms with normalized difference vegetation index, normalized difference water index and dry bare-soil index. In order to determine the contribution of different land use/land cover classes in affecting the land surface temperature, the contribution index was used for summer and winter seasons

Abstract Increasing numbers of wildfires have resulted in severe post-fire effects on the environment. Understanding post-fire recovery is important for post-fire management. However, an in-depth evaluation of the spatiotemporal dynamics of burn degree is rarely reported around the world, which is not conducive to accurate post-fire management. This study aimed to assess the changes in the degree of