Abstract In this article, GIS, remote sensing (RS) and fuzzy AHP are used to develop a methodology to delineate the groundwater potential zones. GIS-RS technique is used to prepare the different thematic maps that influence the groundwater potentiality. Fuzzy AHP method is used to assign weights and fuzzy ratings to different thematic layers and their attributes classes, respectively. After assigning

Abstract In this paper, the novelty of Artificial Neural Networks (ANN) model and GIS platform for the delineation of groundwater potential zones were compared in Fincha Catchment, Abay Basin, Ethiopia. LULC, rainfall, soil, geology, drainage density, lineament density, and geomorphologic units were used as key factors in both models. Weights were generated in ANN and Analytical Hierarchy Process (AHP)

Abstract COVID-19 poses a major threat to global health care systems, and the recent surge in mortality rates confirms the importance of timely access to care. The capacity of medical service providers is reflected both in the spatial accessibility of medical institutions and in the spatial scope of their services. Therefore, this study aims to investigate the spatial scope of services and spatial

Abstract This research aims to analyze the use of deep semantic segmentation to detect eucalyptus afforestation areas using Sentinel-2 images. The study compared six architectures (U-net, DeepLabv3+, FPN, MANet, PSPNet, LinkNet) with four encoders (ResNet-101, ResNeXt-101, Efficient-net-b3, and Efficient-net-b7), using 10 spectral bands. Even though the differences were not large among the different

Abstract In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method

This study performed flood risk assessment in urbanized areas using geospatial and remotely sensed data for 1990–2005–2020 periods using the linear unmixing model (LUM), random forests (RF), and support vector machines (SVM). The urban mapping results verified with reference data indicated close agreement, with the overall accuracies and Kappa coefficients higher than 88.9% and 0.78, respectively.

Abstract Sustainable management of biodiversity benefit from cost-effectively multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species (n = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A

Abstract Landslide hazard is usually incorporated into land-use planning as susceptibility zoning. Multiple-variable models have been widely used for susceptibility zoning due to their advantage to use different performance techniques to improve their prediction capacity. In Chile, the incorporation of landslide hazard into land-use planning instruments lacks a frame of reference that defines the susceptibility

The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P), and REPTree (RF-REPTree), with three statistical models, i.e., weight

Abstract Moderate Resolution Imaging Spectroradiometer (MODIS) products were used in this study which covers a period of 2000 − 2014. Comparison of the SCA derived from MODIS and Landsat NDSI showed a significant correlation, the resultant coefficient of determination (R2) was 0.85. For analyzing the trend in the SCA, the Mann-Kendall test was applied; the results show an increasing non-significant

Abstract Glacier ice-thickness measurement and its distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier evolution under the climate changing scenario. Yet, substantial gaps in ice thickness knowledge exist for the Himalayan glaciers. The present study provides a long-term assessment (1965-2016)

Abstract Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System

Abstract The present study deals with Landsat data for the change analysis and flood mapping using water class extraction. Among the various available bands, the most suitable band combination is identified using class separability analysis and a novel water index (WI2020) is proposed for the extraction of water. The proposed index is able to separate the water class from other land features and performs

ABSTRACT Unmanned Aerial Vehicle (UAV) based remote sensing is one of the modern techniques for crop management, which has been used in this study for biomass and Nitrogen (N) level estimations for Stevia rebaudiana, a medicinal crop used as an alternative to sugar as a natural sweetener. Different levels of nitrogen treatments were given to S. rebaudiana and the crops were harvested for biomass estimation

Abstract Identification of groundwater potential zones (GWPZ) is as equally important as determining the most effective groundwater mapping models for efficient production of reliable GWPZ maps. In this study, relative frequency ratio (RF) with predictor value and analytic hierarchy process (AHP) models, including two RF-AHP hybrid models (HM-A and HM-B) were applied and compared to evaluate their

Abstract The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species

Abstract Air Pollution plagues urbanised areas and enhances mankind’s vulnerability to the COVID-19 virus. Background: According to W.H.O, Kolkata (India) ranks 25th amongst 1100 cities in the world. Curbs on movement reduced this pollution during COVID-19 lockdown. Resumption of activities nullified this. Objectives: This study analyses the trend of air pollutants, their interrelationships with PM2

Abstract The repository of soil by water at a national and basin scale was estimated using the RUSLE empirical model which is the first of its kind in Bhutan. The annual soil loss is estimated and categorized into five categories; very low (<200 t/yr), low (200-400 t/yr), moderate (400-600 t/yr), high (600-800 t/yr), and very high (>800 t/yr). Sakteng and Jaldakha basins contributed the highest soil

Abstract Hydrological modelling of Ar Rub Al Kahli over a very regional scale using the weight of evidence based on pixel calculation and several pieces of evidences is timely consuming. This study aims to modify an approach, which calls a weight of hydrological evidence (WoHE) approach. The approach is based on calculating the logarithmic ratio of the total areas of distribution good (event) and distribution

Abstract Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy c-means based adaptive neuro fuzzy system (ANFIS-FCM), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree), in estimating

The accurate extraction of Built-Up Areas (BUAs) is of great significance for analysing urban spatial evolution patterns. In this study, we proposed a new method for high-precision extraction of BUA based on multi-source remote sensing data. Firstly, Built-Up Area Extraction Index (BAEI) was used to preliminarily identify the BUA based on Landsat 8 imagery. Secondly, the Support Vector Machine (SVM)

Dams must provide human development with economic viability, social equitability, and be environmentally sustainable However, environmental and social impacts are often negative and seldom considered in the design of these projects. We made a multitemporal analysis classifying 26 satellite images of several Mexican hydroelectric power stations between 1972 and 2016 to evaluate the effect of dams on

Abstract The dual polarimetric study including degree of polarization ( mL) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel – 1A and ALOS - 2 satellite data were utilized for dual polarimetric radar vegetation index (DPRVI), polarimetric radar vegetation index (PRVI) and radar vegetation index (RVI) computation and simulation of backscattering

Abstract This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and

Abstract Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although

Abstract This paper presents a novel approach for estimating wheat yields based on estimated abundances of endmembers attributed to photosynthetic pigments, using Landsat 8 images acquired during maximum greenness. Endmembers within the wheat pixels are found using an unmixing algorithm then they are further optimized to maximize the predictive power of the abundances for the yields. Similarity of

Abstract All the supervised classification methods need sufficient and efficient samples, which are commonly labeled by visual inspection. In this study, to resolve the issues of insufficient training samples and time-consuming, a novel method for detailed and automated LULC classification by LC_Type1 of MCD12Q1 IGBP schemes in the GEE cloud platform was proposed based on the RF and CART classifiers

Abstract In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model

Abstract Is it true that rural areas, like urban areas, experience temperature changes over time? The aim of this mission was to find the season-wise trend associated with land surface thermal alteration based on satellite imagery from the last 30 years using the least square regression method, as well as to determine the stimulus of LST for the years 2027 and 2037 using Artificial Neural Network and

Abstract Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vital input to crop-growth models. Mustard experienced increase as it advanced in phenology but for wheat, decreased due to absorption. A significant relationship at initial crop stages (height <150 cm

Abstract Classification of fields into management units based on soil variability and fertility is important for spatial crop planning. The present study was conducted in Chukanagallu subwatershed (97 Km2), Koppal district of Northern Karnataka Plateau, India to map the soil fertility management units and to analyse the suitability of soil for different crops. Random forest regression and classification

ABSTRACT Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment

Abstract MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São

Abstract Wetlands are highly productive systems that act as habitats for a variety of fauna and flora. Despite their ecohydrological relevance, wetland systems are severely under threat from global environmental changes and anthropogenic activities. This results in the disturbance of plant species composition, spatial distribution, productivity, diversity, and their ability to offer goods and services

ABSTRACT Forest roads are the primary infrastructure facilities of forestry activities. Identifying the forest roads that can be used after a disaster is very important for management of disasters such as fire, flood, and landslide as well as for strengthening of the forest road infrastructure. In this context, forest road extraction from orthophoto images has become a hot research topic in the field

Abstract Seagrass habitats are crucial marine ecosystem component that affects the biophysical and chemical environment, providing nursery beds to various invertebrates, juvenile fish, and dugongs. This study focuses on mapping and monitoring changes in seagrass total above-ground biomass (STAGB) within the coastline of Peninsula Malaysia. The retrieval of the substrate leaving radiance was realized

Abstract The data employed in urban gain modeling classes are often imbalanced, negatively affecting the accuracy of traditional and standard data mining and machine learning models. This study presents a new framework on the basis of clustering-based modeling and ensemble models to deal with the class imbalance problem in urban gain modeling. The random forest (RF), artificial neural network (ANN)

ABSTRACT The present study investigated heavy metal (HM) concentrations in the water and sediment of the Kunhar River and its tributaries, Kaghan valley, Northern Pakistan. The highest mean concentration was observed for Fe 316 ± 47.4 µg/L and the lowest for Cd 2.45 ± 0.31 µg/L upstream water of this study. The highest mean hazard quotient value of 0.43 was calculated for children through water consumption

Abstract Kolong River is recognized as an immensely degraded and flow deficit river of India. Installation of the Hatimura embankment as an ad-hoc flood control measure had restricted the entry of water into the river channel from its source thus converting it into a flow deprived polluted river. Significance of this work lies in the identification of the impacts of the Hatimura embankment on the planform

ABSTRACT The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in

Abstract Combining Landsat-8 and Sentinel-2 images is an effective approach to obtain high spatiotemporal resolution data for Earth observation and remote sensing modeling. The differences between Landsat-8 and Sentinel-2 products, such as the reflectance at the top of atmosphere (TOA) and land surface, should be compared and evaluated to make sure they are spectrally consistent. Their consistency

ABSTRACT Groundwater is more prone to contamination due to its extensive usage. Different methods are applied to study vulnerability of groundwater including widely used DRASTIC method, SI and GOD. This study proposes a novel method of mapping groundwater vulnerability using machine learning algorithms. In this study, point extraction method was used to extract point values from a grid of 646 points

Abstract Glacier dynamics and mass balance observations in Himalayan-Karakoram (H-K) region are important to monitor the effect of climatic perturbations over the highest cryospheric region of the world. Such studies can be accomplished by employing satellite based remote sensing technique as field measurements are difficult to obtain in these inaccessible region. In the present study an attempt was

Abstract Idukki district faced adverse mishappenings during the 2018 Kerala landslides due to incessant torrential rainfall. This study emphasizes developing an efficient and accurate ANN model to integrate the data, process and generate landslide susceptibility maps. Fifteen conditioning factors that influence landslides' occurrence opted in the study constitutes 49 input neurons to the ANN model

Abstract The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and change categories than is typically attempted with Landsat data. Two dates of Landsat imagery (1990 and 2015) were utilized with surface reflectance, Vegetation-Impervious-Soil (V-I-S) fractions, grey-level

Abstract The flood in Kashmir valley has a long history and is considered as recurrent phenomena, though the flood occurred in 2014 was extremely severe due to its extent, intensity and impact on mankind. To understand the variation of this precarious climatic condition, the long-term variability of precipitation over Kashmir valley was analysed over a period of 1901-2018 on an annual, seasonal, monthly

Abstract Atmospheric correction (ATC) of radiance image data is a preliminary and necessary procedure to reach a coherent unsupervised classification. Though ATC results in removal of noise artefacts related to path radiance, loss of some data is inherent by the process. The unsupervised principal component analysis-based classification (PCABC) was harnessed in this paper using radiance data that bypass

Abstract In this study, Hölder Exponent (HE), Variance (var) and densely populated range are measured to extract meaningful features from the high resolution (HR) images. These extracted features are considered here as the reduced representation of high resolution (HR) images. Five-layer simplified Artificial Neural Network (ANN) architecture configured and trained using the reduced representation

Abstract The Red Sea is a conspicuous water body that hosts important coral reef ecosystems. This semi-closed sea is exposed to warming either by climate change or by anthropogenic stressors. The desalination plants along the Red Sea coast of Saudi Arabia dispose tremendous effluents of hot water from cooling systems, which is expected to impact the neighboring coral reef patches. The main objectives

Abstract Hyperspectral remote sensing is frequently used to monitor chlorophyll content, an important characteristic for assessing photosynthetic ability, health and defence against a variety of degenerative diseases. To obtain hyperspectral data, field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have been

Abstract Suspended sediment concentration (SSC) is one of the primary reasons with respect to watersheds or river basins, which must be assessed in a correct manner so that it will help decision makers to make right decisions regarding hydraulic structure, flash-flood, flood-mitigation of the basin. Present research evaluated efficacy of a hybrid model integrating Support Vector Machine with Whale

Abstract The present work tried to develop multi-parametric statistical and machine learning models of wetland water richness at two phases in the pre-dam period and at one phase in the post-dam period. Eight models four each in statistical and machine learning approaches including six relevant parameters have been developed. The performance of the EBF among statistical models and REPtree among machine

Abstract Historical exploration of flash flood events and producing flash flood susceptibility maps are crucial steps for decision makers in disaster management. In this paper, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) were implemented to create a flash flood susceptibility

Abstract Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological

ABSTRACT The objective was to compare the evapotranspiration of cotton (Gossypium sp. L.) estimated by the SEBAL model and the FAO-56 method, throughout the phenological cycle of the plant on eight fields located in the upper area of the Rio das Mortes basin, State of Mato Grosso – Brazil. Images from the Landsat 8 satellite were used under the Geographic Information Systems environment through the

ABSTRACT The present study focusses on the estimation of ET using Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model over the Sal (Shorea robusta Gaertn.f) forest in the subtropical Doon Valley, India for the dry periods of 2016-18. Parameterization of METRIC model was done for surface temperature (Ts), momentum roughness length (zom), and surface albedo (α)

ABSTRACT Differential morphological profile (DMP) and differential attribute profile (DAP) are effective tools for automated building extraction from very high-resolution (VHR) images. Compared to the DMP, DAP provides a more flexible way to model image features not only related to the scale of regions, but also any measure (structural, spatial, and spectral) that can be evaluated in the region. The

ABSTRACT Himalayan glaciers are distinct by their surface characteristics such as debris-cover, supra/proglacial lakes, ice-cliff, and tributaries' contributions, thus complicating their surface velocity pattern and their response towards climate warming. While remote sensing and modelled surface velocity estimation are valuable on a larger scale, in-situ high-resolution data is crucial to validate

ABSTRACT In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created