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

Numerous remote sensing indices have been introduced to investigate the drought. Hence, the purpose of this study was to develop a Comprehensive Remote Sensing Drought Index (CRSDI) using the strengths of the previous Remote Sensing Drought Indices (RSDIs). Eleven mostly used RSDIs were used to develop CRSDI. Aras and Karkheh basins were studied as representative of Western Iran. Vegetation indices

Essential forest ecosystem services can be assessed by better understanding the diversity of vegetation, specifically those of Mediterranean region. A species level classification of maquis would be useful in understanding vegetation structure and dynamics, which would be an indicator of degradation or succession in the region. Although remote sensing was regularly used for classification in the region

Drought has become a more frequent phenomenon under changing climatic conditions, particularly in Sub Saharan Africa. This study tested the utility of a newly proposed Temperature-Vegetation Water Stress Index (T-VWSI) in detecting drought severity using Landsat data for the years 2008, 2012, 2016 and 2018. This index was created using both NDVI and LST to detect drought severity within the region

Accurate understanding of the scene of ship ports is important in a broad range of military and civilian applications, such as maritime management, maritime safety, fisheries management, maritime situational awareness (MSA), and ocean traffic surveillance. Semantic segmentation, which implements pixel-level classification, can achieve an exhaustive analysis of ship ports. However, in the earlier time

This study proposes a new approach to establish a multi-parameter risk mapping method by employing the K-Means clustering technique. Accordingly, spatial assessment of Arsenic (As), Nitrate (NO3), and Total Dissolved Solids (TDS) were carried out based on the type of land use to estimate contamination potential in an aquifer. Since risk mapping is always associated with the occurrence probability of

This study aimed to simulate and assess forest cover and LULC changes between 2019 and 2039 using the Cellular Automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous

Worldwide COVID-19 is playing havoc, and in India, the situation is similarly bleak. Due to the higher population density and weak health sector, the Indian government’s approach to curb the spread of this deadly virus, has been the imposition of tougher lockdowns. Perhaps, isolating and quarantining the infected and asymptomatic people is the only strategy that seems to work right now. Local administrations

The reason behind the landslides in Darjeeling Himalaya varies from place to place. Seepage of rainwater through fragile lithological structure initiates shallow landslides, while modification of slope due to unscientific anthropogenic activities led to the occurrence of deep-seated landslides. Besides this, this region does not possess any proper landslide inventory at the micro-scale. All of these

Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were

The monitoring of pre-failure movements and their evolution over time is necessary to assess the predisposing and triggering factors. This research focusses on assessing terrain displacements in the Idukki district (Kerala, India) over a year before the deadly disaster, which occurred in August 2018; this disaster was claimed to be a dreadful one as it took off hundreds of human lives and properties

Building footprint maps are useful for urban planning, infrastructural development, population estimation and disaster management. Remote-sensing data such as high-resolution satellite imagery, digital elevation models, and LIDAR (Light Detection and Ranging) can be used for preparation of building-footprint maps. However, due to limited availability of historical LIDAR datasets, and due to higher

Drought is one of the costliest natural disasters, effecting many aspects of human livelihoods. This study assessed drought conditions in Cambodia from MODIS data during 2000 to 2019 using the Vegetation Health Index (VHI). The results verified with the satellite soil moisture indicated close agreement between these two datasets. The drought areas were concentrated in the country’s central plains,

Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting

Synthetic aperture radar (SAR) data has been an alternative for monitoring ground targets, especially in areas with cloud cover. This study evaluates the potential of Sentinel-1A attributes for mapping land use and land cover (LULC) in a region of the Brazilian Amazon, using two different machine learning classifiers: Random Forest (RF) and Support Vector Machine (SVM). Different scenarios were used

Change Vector Analysis in Posterior Probability Space (CVAPS), which compares two posterior probability maps of different time periods, has been introduced recently as an effective method for change detection of satellite images. CVAPS is based on the length of change and its direction in a Posterior Probability (PP) space. However, CVAPS is prone to similar direction cosine values, a source of error

With the increase of the world's population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower

Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used a as a proxy of passenger flow to dynamically explore and evaluate the

Some areas in Iran are affected by the consequences of subsidence. This study aimed to compare the performance of the Analytical Hierarchy Process and Certain Factor models in subsidence susceptibility mapping in the Neyshabur Aquifer. At first, 60% of subsidence areas were randomly selected for building subsidence susceptibility and the remaining 40% were used to validate the models. Maps of effective

In this study, the potential of phenological indicators derived from Sentinel-2A (S2) time series were evaluated to explore the key variables that allow identifying both cropland and crop types. Based on the derived S2 phenological metrics and fitted vegetation indices (VI), ten feature sets were developed and assessed to discriminate different crop types via Random Forest (RF) classifier. The comparison

Continuous monitoring of snow cover area (SCA) in space and time is a vital input to estimate the snow/glacier melt runoff, glacial mass balance and other hydrological studies. The present study aims to find out the spatio-temporal variation of SCA and inter-relationship between snow accumulation and topography in the Upper Ganga River Basin (UGRB) including Bhagirathi, Alaknanda, Mandakini and Pindar

This study used the Landsat 8 OLI satellite image and the supervised classification method to estimate uneven-aged forest stand parameters and land use/cover. The spatial success of classification was also investigated. The overall success rates and Kappa values of the classification were, respectively, 74.7% and 0.75 for actual structural type, 84.6% and 0.80 for crown closure, and 88.35% and 0.81

This paper evaluates the capability of visible-near-infrared (VIS-NIR) spectroscopy to estimate soil organic carbon (SOC) at multiple depths including 0-15, 15-40, 40-60, and 60-80 cm. Four modeling algorithms, namely partial least squares regression(PLSR), principal component regression(PCR), support vector regression(SVR), and Random forest(RF) were calibrated to process the spectroscopy data. Overall

Release of snow avalanche from a mountain slope depends on various parameters such as snow cover, terrain and meteorological conditions of the region. The precise information of avalanche occurrence in terms of its location and extent is essentially important for hazard mapping and for avalanche occurrence feedback. In the present study, various techniques have been explored for automatic detection

This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables . The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation

This study employs geographic information system (GIS) and remote-sensing techniques to predict drainage morphometry parameters and hydrological processes. A new approach, named genetic landscape evolution (GLE), is used to model drainage based on optimal waterway network states to predict future drainage patterns and morphometry. Following the theoretical balance between the forces of uplift and sedimentation

Reflectance spectroscopy can determine the nature and geothermal gradient of metamorphic assemblage. This contemplates that the spectral identification of primary mineral phase is insufficient in ascertaining the nature and gradient of metamorphism, thus, role of associated mineral phases needs to be incorporated. A series of algorithms, statistical distance mapping, spectral simulation of metamorphic

The considerable increase in the contamination load and the diversity of urban, agricultural, and industrial contaminants more than ever have necessitated the need to understand the process of changes and predict the quality of water resources. In this regard, simulations are considered as important tools. In this research, the water quality of the Dez River (southwest of Iran) was simulated using

Despite the fact that wildland fires have always been an integral part of many ecosystems, their increased frequency and intensity have reinforced the need of fire managers for updated and highly accurate information associated with the spatial distribution of forest fuels. In 2015, a fuel type mapping method was developed in the framework of the “National Observatory of Forest Fires (NOFFi)” project

The present study attempts a conjugate analysis of satellite-based remote sensing, gravity, and magnetic data for evaluation of structures and tectonic for new insights on Kimberlite emplacement around the Bundelkhand Craton. Initially, Landsat Operational Land Imager (OLI) data has been enhanced using high-pass filtering for the delineation of lineaments. A combined lineament density map has been

The article addresses the void in developing analytical methods concerning to design urban configurations that could reduce fire risks, and, thus, could help in achieving sustainable goals. A novel algorithm is developed to generate alternative Urban Built Form (UBF) models that could be less susceptible to fire compared to the existing built-form. Fire susceptibility of a generated UBF is predicted

Drought is a recurring natural disaster, which significantly affects the economy and environment of diverse geographical area worldwide. Therefore, it is imperative to develop a new technique that enables comprehensive identification and monitoring of drought over a large spatial extent. In this study, a new drought index called Combined Terrestrial Evapotranspiration Index (CTEI) is developed by using

Mapping of the major structures in the Zuru schist belt and preliminary evaluation of distribution of the locations of gold mineralization relative to these structures was achieved using remote sensing and field data. On the basis of interpretation of remote sensing and field data, four deformational episodes are identified. The first two deformational episodes are ductile in nature, with the first