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

Abstract Artificial-intelligence and machine-learning algorithms are gaining the attention of researchers in the field of groundwater modeling. This study explored and assessed a new approach based on Gini, Entropy, and Ratio based classification trees to predict spatial patterns of groundwater potential in a mountainous region of Iran. To do this, a springs inventory was undertaken, and 362 springs

Abstract Although optical remote sensing has been widely used to monitor vegetation characteristics, its use on aboveground vegetation water storage (AVWS) is quite scare. Therefore, we combined the Landsat 8 OLI and Sentinel-1 imageries to quantify AVWS using generalized linear regression (GLM), artificial neural network (ANN) and random forest (RF) with the linkage of field observations in Mao Country

Abstract The specific impact of ecological environment quality at a regional scale due to the rubber plantations expansion is still unclear in Xishuangbanna, Yunnan province, China. First, we used a pixel and phenology-based multiple normalization approach to map rubber plantations over six time periods during 1995-2018 with the available high-quality Landsat imagery. Second, a pixel-based optimized

Abstract Building objects is one of the principal features that are essential for updating the geospatial database. Extracting building features from high-resolution imagery automatically and accurately is challenging because of the existence of some obstacles in these images, such as shadows, trees, and cars. Although deep learning approaches have shown significant improvements in the results of image

ABSTRACT Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009-2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composite crop-coefficient was introduced to address bulk AWD from mixed agricultural landscape. Significant spatio-temporal variation of AWD was observed over India. The decadal mean of annual AWD was found

Abstract This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in the northwest of the Antarctic Peninsula. Sentinel-1A Synthetic Aperture Radar (SAR) data were used to estimate fraction images needed for subpixel analysis of passive microwave data acquired by

Abstract Disposal and accumulation of agricultural and sanitary wastewater in poorly engineered ponds in the Mut region, Dakhla Oasis of Egypt is a concern due to their poor water quality; significant expansion over a large spatial area, and their consequential impact on the environment. GIS and remote sensing were useful tools for determining spatiotemporal and the impact of wastewater ponds and land

Abstract The present study attempts to structural and tectonic interpretation around the Laccadive ridge in the Western Indian Ocean using EGM2008 Bouguer gravity data. The different derivatives of EGM2008 Bouguer gravity data indicate that the tectonic boundaries are predominantly confined in the N-S and NNW-SSE directions followed by E-W and NE-SW trends. The Euler deconvolution result suggests the

Abstract Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs. Thus, an accurate assessment of reservoir risk for certain areas is mandatory for the improvement of reservoir flash-flood forecasting and warnings. In this context, the

High-resolution unmanned aerial vehicle (UAV) remote sensing images have super-high ground resolution. Although they provide complete and detailed surface observation data for various engineering applications, the extraction of information from complex and diverse surface scenes is challenging. Characterising surface targets with bright colours and different shapes using samples with fixed sizes and

Abstract The riparian communities of the Zou basin of West Africa rely heavily on drinking water supplied by fractured aquifer systems. This study aims to provide accurate fracture maps and derived products (fracture density, coincidence map and cross-points) using Landsat 8 (visible and infrared bands) and PALSAR DEM datasets with borehole data collected from the national integrated database. Digital

Abstract This study explored the utilization of rotation forests (RTF) and extreme gradient boosting (XGBoost) machine learning algorithms (MLAs) to classify drought damage in commercial forests in KwaZulu-Natal (KZN). These algorithms were trained using information obtained from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation and conditional drought indices. The results

Abstract This study adopts an integrated approach using the geographic information system (GIS) and remote sensing techniques for structural mapping in inaccessible zone of the internal segment of North-Western Rif belt, Morocco. The Principal Component Analysis (PCA), Optimal Index Factor (OIF), band ratios and directional filtering are applied to Landsat 8 OLI (Operational Land Imager) image for

Abstract The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled

ABSTRACT In the Arctic, the impacts of global warming are strong and the rate of ice melting is increasingly accelerating due to warmer temperatures relative to snow accumulation. With recent advances in unmanned air vehicle (UAV) technology and image processing techniques, field measurements for cryosphere studies have gained significance today. In the current report, an attempt is being made to measure

In recent years, the problem of urban traffic congestion has become increasingly prominent. In this study, an intelligent traffic signal model based on open source road traffic information was proposed. We used real-time traffic data provided by Gaode open platform to model urban traffic congestion of direction road segments, single intersections and adjacent multiple intersections. And based on these

Abstract Determining the source edges is a frequently requested task in the analysis of potential fields. However, the edge detection methods have some drawbacks or shortcomings, for example, blurred responses to weak amplitude anomalies, the estimated peaks over the edges are low in gradient or bring out overestimated edges in the resulting maps. In this paper, a new method based on an arcsine function

Abstract This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using analytic hierarchy process (AHP) model based on multi-criteria analysis in a Geographic information system (GIS) platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate (max temperature, rainfall, wind speed,

Abstract The Himalayan glaciers significantly contribute to the largest river systems like the Indus, Ganga, and the Brahmaputra. The change in glacial area and mass can affect the mountain community and people living in the Indo-Gangetic plain. The present study adopted the geodetic method to estimate the elevation change and mass budget of 61 glaciers in the Alaknanda Basin, using the satellite data

Abstract Some indicators for evaluating heavy metal pollution have been proposed. However, a threshold is lacking to judge if the heavy metal concentration has reached the extent that is harmful to plant health. In order to get the threshold, aerial hyperspectral images were obtained. The spatial patterns of six soil heavy metals (Cu, Zn, As, Sn, Cr and Cd) were obtained by establishing random forest

Abstract Overall health condition of the vegetation is obtained by combining satellite data derived moisture and thermal stresses present in vegetation condition index (VCI) and thermal condition index (TCI), respectively and improves the accuracy of drought classification. Although vegetation health index fuses the information present in VCI and TCI, the relative contribution of each index depends

Abstract This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil

ABSTRACT Monitoring of landslides by computing the displacements gain importance in recent studies due to the accuracy in its measurements. Kerala-Karnataka landslides of August 2018, has led to severe loss of life and damages to both states. The study emphasizes the contribution of three major landslide-driving factors viz., rainfall intensity, land-use/land-cover and slope, towards the occurrence

Abstract Vertical striping present in hyperspectral images are quite common in Hyperion images. The removal of these stripes turns out to be an important step of noise correction along with atmospheric correction and other artifact correction. Often these processes are done on radiometric values. We have proposed a novel approach to correct these vertical stripes using wavelet transformation and singular

Abstract One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the

Abstract The novel coronavirus (COVID-19) pandemic is a public health emergency of international concern that caused disaster consequences all over the world. To mitigate and manage the pandemic, GIS-Based spatial analysis has been used in a key role. To this aim, many GIS-based maps are produced by many national and international institutions. However, the produced maps are prepared by conventional

Abstract Spatially distributed air temperature (Ta) data are essential for environmental studies. Ta data are collected from meteorological stations of sparse distribution. This problem can be overcome by using remotely sensed datasets at different scales. This study used land-based temperature measurements and satellite data for estimating Ta distribution over the United Arab Emirates. Land-based

Abstract Among natural hazards, land subsidence (LS) is considered to be one of the most significant natural disasters which occur in populated and inhabited regions of Iran. The Varamin aquifer, as one of the regions near the capital of Iran, where this geo-hazard has occurred widely and extensively, requires conducting comprehensive researches on LS. In this research, for LS risk assessment, the

Abstract Defining watersheds with enough topographical features is crucial when large infrastructure projects are concerned. German Aerospace Center (DLR) recently announced the dissemination of the 90 m variant of the original global TanDEM-X 12 m Digital elevation model (DEM). It was announced as produced with a special averaging technique, which would even surpass the precision of the 12 m DEM.

Abstract In this study, the spatial pattern of land use refers to the sequence of land use intensity (LUI) in the sea-land direction. To explore the correlation between the port shoreline and the order of land use intensity in the vertical direction from the port shoreline, spatial relationships were introduced into association rule mining models to establish spatial association models that reflected

Abstract In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namely Bagging-MLPC, Dagging-MLPC, Decorate-MLPC which are a combination Multi-layer Perceptron Neural Network Classifier (MLPC) and Bagging, Dagging, and Decorate ensemble methods, respectively. The

Abstract The use of remote sensing for forest health monitoring has increased in popularity over the years, with improved quality in spatial and spectral resolutions. However, satellite revisit times are generally too low to detect real-time changes at a pace fast enough to aid in early management actions. There exists a need to employ higher resolution monitoring to quantify the damage caused by biotic

Abstract The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide

Abstract Microwave remote sensing using time-series synthetic aperture radar interferometry (InSAR) techniques has the potential of retrieving the land subsidence velocity efficiently. The time series InSAR technique like Persistent SAR Interferometry (PSI) can estimate the land subsidence at a high accuracy of nearly in mm scale. The major limitation of these InSAR methods is that they measure land

Driver drowsiness causes many road accidents, and preparing a risk map of these accidents with spatial criteria and data mining algorithms highlights accident points well. In this study, accidents risk caused by driver drowsiness in Qazvin province, Iran, was modeled using decision tree (DT), random forest (RF), and support vector regression (SVR) algorithms in GIS environment. Seven spatial criteria

Abstract The aim of the study is to compare four recent gradient boosting algorithms named as Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) for modelling landslide susceptibility (LS). In the first step of the study, the geodatabase including landslide inventory map and landslide conditioning factors

Abstract This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for groundwater potential (GP) mapping in the governorate of Tafillah, Jordan. Twelve topographical, hydrological and geological factors were considered. The mapping process was done with and without

Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis