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

The present study aims to estimate the spatio-temporal variability of gross primary productivity (GPP) in moist and dry deciduous plant functional types (PFTs) of northwest Himalayan foothills of India using remote sensing-based Temperature-Greenness (TG) model and to study the response of GPP to environmental variables. TG model was implemented in Google Earth Engine platform using Moderate Resolution

Obtaining accurate information on the fractional cover of non-photosynthetic vegetation (NPV) (fNPV) on grasslands is essential for monitoring soil erosion risk, assessing grassland productivity and managing grassland ecosystems. However, few studies have monitored fNPV in the Xilingol grassland region (XGR) and its spatiotemporal variations based on Moderate Resolution Imaging Spectroradiometer (MODIS)

Tea plantations expansion has occurred in the major tea planting area of Yunnan province, China, however, it is a big challenge to extract and map the distribution of tea plantations due to their non-obvious phenological characteristics in the tropical and subtropical regions. Firstly, we demonstrated the pruning phenological phase of tea plantations by using field hyperspectral data. Secondly, we

This study aims to investigate the ability of remote sensing sensors to map subsurface fault zones with the support of hydrological information collected from boreholes. We first extract linear features crosscut and drain the study area and its adjoining mountainous areas from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), the Landsat 8 images and a set of slope and shaded relief maps

Glacier facies or Radar glacier zones (RGZs) of the surge-type glaciers of the Karakoram Himalaya were classified based on the differences in the backscatter values utilizing multi-temporal SAR data from Sentinel-1 satellite of the years 2018 and 2019. Distinct zones of glaciers such as dry snow zones, percolation zones, wet snow zone, bare ice zone, and debris-covered area can be discernible on the

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including

The hyperspectral image such as AVIRIS-NG provides a lot of spectral information and fine resolution that identify and discriminate similar objects based on their spectral reflectance. This study reveals a comparative analysis of multiple classification algorithms for AVIRIS-NG data, before and after Minimum Noise Fraction (MNF) transformation for Land Use Land Cover classification. Applied distance

In this research work, non-linearity in data has been handled by incorporating kernel with the Modified Possibilistic c-Means (MPCM) algorithm. Nine different types of kernel function have been proposed to classify nine different classes and have non-linearity among them. Gaussian has been identified as the best performing kernel at an optimized fuzzified value m = 1.5 with an overall accuracy 92.45%

Regular monitoring and precise mapping of urban environment is required by various applications. In this paper, a new method is proposed, in which different combinations of feature bands have been utilized for extraction of built-up surfaces, sub-surfaces and materials in different levels (Level-1, 2 and 3) using AVIRIS-NG hyperspectral imagery of Jodhpur, Rajasthan region of India. Features identified

A new approach, developed for coastal aquifers, consists of determination of salt-freshwater interface as well as obtaining map of groundwater vulnerability, for which a number of methods have been provided, including index method and numerical modeling. In the previous studies, the common vulnerability methods such as DRASTIC haven’t took the coastal regions into account, or that method of GALDIT

This study evaluates the vulnerability of aquifers to the nitrate concentration variations. Prediction of nitrate concentration using MT3DMs modeling for a period of 10 years. The results showed that most of the wells have faced concentration increment, where it is exacerbated in the lower areas of agricultural and urban lands. The prediction of nitrate concentration zoning map obtained from the model

The sustainable management of natural resources requires critical understanding of land use and land cover changes and how these changes impact natural resources and rural livelihoods. This study examined the impacts of LULC changes on natural resources and rural livelihoods of Central Malawi. The study used an integrated approach combining remote sensing, household surveys consisting of structured

Land surface temperature is a significant source of energy budget and climate information, contributing to various environmental and biophysical processes. This research includes comparing the LSTs retrieved from the ASTER sensor using the Reference Channel method, the Emissivity Normalization method (NOR), TES method, and Retrieving LSE by taking the proportion of vegetation cover coupled with NDVI

Global Land Cover (GLC) maps have been extensively used as background data for numerous applications such as land-use planning, climate change monitoring, and urban planning. While countries in Europe and North America have greatly developed land cover maps, several developing countries in Asia and Africa lack such vital data sources. GlobeLand30’s 30m resolution dataset has the potential to meet the

In the last few years, the agricultural sector in Syria has suffered from major problems related to land degradation. To cope with this problem, a land suitability assessment has become an essential tool for sustainable land use management. The present research qualitatively evaluated the suitability of land in the Al-Yarmouk Basin (S-Syria) for rainfed winter wheat (Triticum aestivum) cultivation

Mediterranean islands are characterized by high biodiversity and cultural value. As the human demand for natural resources increases, the need for assessing the future landscape is prerequisite. This study aimed to simulate the future landscape composition and structure of the Ionian Islands (Western Greece). To do so, we integrated the Future Land Use Model and Markov Chain analysis, taking into consideration

Biodiversity of benthic habitats is among the highest of all ecological communities. This study was conducted to model benthic habitat biodiversity indices using a remote sensing approach in optically shallow waters in Karimunjawa Islands-Indonesia. These islands have a wide variety of benthic environments. Two multispectral imagers, namely Sentinel-2A and Landsat 8 OLI, were used. A series of statistical

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used for mapping lithological units and hydrothermal alteration zones associated with Pb-Zn mineralization in the Kerman–Kashmar Tectonic Zone (KKTZ), Iran. The visible near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) bands of ASTER were used to

Soil salt information from unified soil salinity content (SSC) inversion models based on all samples is insufficient for accurate regional SSC monitoring. Here, a method of building SSC inversion models for different salinization grades is proposed, combined with unmanned aerial vehicle (UAV) and Sentinel-2A images. According to different salinization grades, three groups of samples (mild (M), medium-severe

Crop classification is an integral part of crop monitoring and production forecasting, which in turn helps in making a strategic decision for food security. Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of

Soil moisture is essential for water resources management, yet accurate information of soil moisture has been a challenge. The major goal was to parametrize the Modified Water Cloud Model (MWCM). The Sentinel-1A data of winter wheat crop was collected of two weeks. Concurrently, in-situ soil moisture data was collected using Time Domain Reflectometer (TDR). A parametric scheme was used for the retrieval