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

Abstract This study aims to assess land use dynamics of Southwestern coastal Bangladesh using multi-temporal Landsat images. Future trend was done by CA-ANN model. Hybrid image classification method and estimation of land surface temperature along with field survey were accomplished. This study revealed that vegetation decreased significantly (11.75%) and converted mostly into wetlands whereas built-up

Abstract The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change and human interventions. Hazard mapping is essential for local policymaking for prevention, planning the mitigation actions, and also adaptation to extremes. This study proposes novel predictive models for susceptibility mapping for flood and erosion. Furthermore, this

The present study adopts a multi-sensor approach that utilizes optical remote sensing and Synthetic Aperture Radar (SAR) data complimentarily for coastal bathymetry estimation. The depths between 0-15m are derived from Landsat 8 OLI imageries, by using the machine learning approach of Support Vector Regression (SVR); and high accuracies with RMSEs ranging between 0.17m -0.95m are obtained for the chosen

North China Plain is the largest agricultural production center in China and wheat-maize rotation is a widespread cultivation practice in this area. As gross primary production (GPP) is a proxy of land productivity, research on its spatial-temporal dynamics helps understand the variation of grain production in wheat-maize rotation. Here, Moderate Resolution Imaging Spectroradiometer (MODIS) data and

The accurate and rapid acquisition of the soil salt content and spatial distribution is of great significance for effectively monitoring cropland soil salinization. Using OLI/Landsat-8 images as data sources, shortwave infrared bands (SWIR1, SWIR2) are introduced to the traditional vegetation and salinity indices, and the enhanced indices are put forward to obtain an improved inversion effect. After

Soil erosion and the subsequent sedimentation are the major problem in Ethiopia. We assessed the impact of rainfall-induced soil erosion and prioritize the sub-watersheds using RUSLE and SYI models. We procured mean annual precipitation, Abay Basin soil map, Digital Elevation Model (DEM) and LANDSAT 8 OLI/TIRS, 2017 datasets. Results show that annual estimated soil loss was <270 t ha−1 yr−1 and ≤120

Hyperspectral image (HSI), acquired in narrow and contiguous bands, contains redundant information in neighbouring bands. In order to overcome the processing overhead of this data like Hughes phenomenon, dimension of HSI is reduced either by using feature extraction or selection technique. Most of the existing dimensionality reduction (DR) methods depend on the user input to provide the reduced set

Differential SAR interferometry (DInSAR) techniques is extensively utilized over decades to monitor and detect surface distortion. Interferometric SAR results obtained from Sentinel 1 data experiences phase delay due to interference with the atmospheric water vapour and pressure and DEM is considered as one of the important parameter in topographic phase removal. In the present study an attempt was

Monitoring and determining landslides in dam reservoirs is very crucial as it is one of the main factors of dam failures in the world. Coruh river basin is one of the most important river basin in the Northeast part of Turkey which accompanies five big dams. Although PSInSAR method is a powerful remote sensing technique which can measure and monitor displacements of the Earth’s surface over time, its

The data fusion technique driven with unmixing of a spectrum of homogenous regions of Low Spatial Hyperspectral (LSH) and High Spatial Multispectral (HSM) remote sensing images is attempted to produce High Spatial Hyperspectral (HSH) data. The objective of the present study is to analyse the quality of hyperspectral data fusion with spectral unmixing technique. The resultant HSH data shows new prospects

Earth Observation (EO) employed often in synergy with simulation process models provides a promising direction for acquiring information on key parameters required to understand the Earth’s system physical mechanisms and interactions. The objective of this study is two-fold: First, it explores the combined use of EO data from the Advanced Along-Track Scanning Radiometer (AATSR) with the SimSphere land

Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modeling. This study aims to use remote sensing (RS) and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical based Empirical Bayesian Kriging Regression (EBKR) method was used and performance was compared with the artificial

Long-term MODIS time series Land Surface Temperature (LST) observations over coal fire affected areas were utilized for detection and characterization of coal fire as a function of its temporal intensity variation (e.g., growing, temporally consistent or diminishing) on annual basis in Jharia coalfield. LST pixel time series (LPTS) vectors were generated for selected coal fire and non-coal fire locations

Forest roads are a basic necessity in forestry policies and should be planned by considering many factors. This study aims to generate optimum forest road routes and to compare them with current forest roads. Firstly, FRNSM has been produced according to AHP, using nine factors for the study area. Then, risk statuses of the current forest roads are examined. According to results, 35 percent of the

The present study assesses the seasonal variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and nineteen Landsat images of pre-monsoon, monsoon, post-monsoon, and winter seasons from 1988 to 2019. The results show that the highest LST is found in the bare lands and built-up areas, whereas

For 43 years, multi-dated satellite imagery is used to examine shifts in shorelines and potential location on shorelines for Dr. Abdul Kalam Island, in the Bay of Bengal, India. A Normalized Difference Water Index with object-based classification system and automatic shoreline extraction tool has been used to retrieve the shoreline automatically. In order to calculate the rates of shoreline change

The generation of a very accurate and precise Digital Elevation Model (DEM) is one of the main objectives of remote sensing applications. Synthetic-Aperture Radar (SAR) interferometry is one of the most common methods for DEM generation. Moreover, one of the most recent Sentinel-1 missions is SAR imagery. Sometimes due to a variety of errors such as interferogram phase error, DEMs based on SAR interferometry

Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years and food security has become a major issue. For a country like India, with a huge population to cater, the problem becomes more serious. Since the inception of remote sensing in scientific agriculture management, optical remote sensing along with field data has been used for soil health monitoring

Soil erosion is a serious concern in most of the Indian watersheds and needs a thorough study. Every year, the Brahmaputra River carries millions of tons of sediment, and the shape of the world’s largest river island Majuli is changing. But due to different activities like urbanization, deforestation, agricultural practices, etc., there is a change in soil loss rate in different parts of the watershed

In the present study, a new spectral index named as Normalized Ratio Urban Index (NRUIms) has been proposed for multi-sensor Landsat satellite data. The new index along with other existing indices, has been used as additional feature for image classification to assess Land Use Land Cover (LULC) changes for Dehradun, India. It has been found that use of indices, increases separability between different

The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The task detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct

The use of satellite remote sensing imagery for mineral exploration is a fast and low-cost approach for indicating high potential zones. Exploration of iron skarn mineralization in metamorphic regions is challenging during the fieldwork campaign. In this study, Landsat-7 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery were used for mapping alteration zones

Urban light rail transit systems have a significant potential to alter future urban development. We developed a new cellular automata model (CACG) based on conjugate gradients, and applied it to 1) simulate historical urban development at Jinhua of China, and 2) project future development scenarios incorporating the effect of future light rail transit stations (LRTS). The model produced a realistic

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable

The distribution of urban built-up surfaces is a key indicator of the level of urbanization, which originates from several ecological effects. In this study, three new spectral indices i.e. New Impervious Index (NII), Road Detection Index (RDI) and New Roof Extraction Index (NREI) have been developed for detection of built-up (Level-1), road and roof surfaces (Level-2), respectively, followed by a

Sentinel-1 provides improved temporal and spatial resolutions in comparison with previous satellite missions. Unlike the change detection method that assumes the time-invariance of surface roughness at a coarse resolution, this study spatially inverted roughness and soil moisture from Sentinel-1 backscatter. Although it is important at high-resolution, local measurements of surface roughness are not

Soil moisture is an important variable in the agriculture system. Likewise, accurate information on soil moisture is needed for the effective modeling of many hydrological and climatological processes. Synthetic Aperture Radar (SAR) operates with the competence to acquire data in any weather condition, has been proved to be sensitive to surface soil moisture. This study has attempted to establish simple

Uganda continues to experience high-refugee population influx that is beyond the established settlement capacities. Little information is available on the present and future impacts of settling refugees and host-communities on landuse changes and vegetation degradation. This study used Sentinel-2 images (20m) of 2016-2019 and Dinamica Ego-software to determine the current and future spatial areal-extent

In this study, a comparative analysis of three satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B43 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS V2) with ground-measured Indian Meteorological Department (IMD)

In this study, we have used visible near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of the Operational Land Imager (OLI) sensor of Landsat 8 satellite to delineate promising areas for iron exploration in the parts of Madhya Pradesh, India. OLI bands resampled laboratory reflectance spectra of Banded Iron Formation (BIF) were used as the reference to identify OLI spectral bands suitable