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

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR

In order to improve the cross-domain applicability of road segmentation, a feature transfer based adversarial domain adaptation method is presented for cross-domain road extraction. The presented method consists of two main parts, a feature transfer network and an adversarial domain adaption network. The feature transfer network transforms the images of the source domain into the feature space of the

Land surface temperature (LST) is an important indicator of the earth’s environmental analysis which is significantly affected by changes in land use/land cover (LULC). The Landsat 5, 7 and 8 satellite data of January and September of the years 1991, 2001, 2011 and 2016 was used to study the impact of LULC changes on LST and its effect on crop yield (rice and wheat) in SBS Nagar, Ludhiana and Bathinda

In this study, we propose a modified thresholds method for the determination of land surface emissivity (LSE) for snow covered mountainous areas. The conventional Normalized Differenced Vegetation Index (NDVI) thresholds method (NDVITHM) does not discriminate the snow covered pixels with soil pixels in assigning the LSE based on NDVI thresholds. In the proposed approach, we incorporate different thresholding

Mapping urban structures is a vital prerequisite for urban planners to enhance their database for a liveable city dedicated to sustainable development. Therefore, it is significant to measure urban grey and green structures at the scale of local districts to understand the urban structure and residential needs for urban ecosystem services. For a detailed analysis we exploit digital orthophotos (DOP)

High budget transport infrastructure projects in the Istanbul Metropolitan Area endeavour to increase the efficiency of the transportation system. Among those investments; the bus rapid transit system – Istanbul Metrobus Line (IML) – is the most popular one that serves one million trips per day. Yet, the performance and impact of IML are not quantitatively assessed. Hence in this study, a high-resolution

The Nile Delta is located very close to the Suez Canal, the main route for oil transport in the world, makes it prone to pollution from any accidental oil spills in the Mediterranean Sea. The coast of the Nile Delta is generally arcuate and highly exposed to waves and currents. The present study attempted to perform the environmental sensitivity of the shoreline to oil slicks. Six variables were incorporated

Fore-warning plays an essential role for effective drought mitigation planning. A new approach, i.e. fractional wetness (fw), has been proposed towards fore-warning of early season agricultural drought over Indian region. The optimum soil moisture triggers the subsequent crop sowing activities that has been conceptualized in this approach. Shortwave Angle Slope Index (SASI) images derived from time-series

The application of multivariate statistical analysis (MSA) in analyzing drainage basin morphometry, in conjunction with seismic events (focal mechanism) and associated tectonic influences, has been discussed less in the framework of existing geomorphometric studies. Hence, in this study, an integrated approach was designed combining the geomorphometric analysis with MSA, focal mechanism and associated

This study investigates spatio-temporal paradigm of the impact of road networks on urban ecosystem dynamics (UED) by analysing the composition and configuration of land use land cover (LULC) and land surface temperatures (LST) derived from Landsat data for the years 2000 and 2017. The results indicate a positive association between road network and built-up land showing higher concentration of built-up

We use remotely sensed data to estimate species diversity in Blouberg Nature Reserve (BNR) in the Limpopo province, South Africa to understand the state of biodiversity since communities’ involvement in conservation initiatives. To achieve this objective, Landsat series data collected before and after community involvement in biodiversity conservation were used in conjunction with selected diversity

(2020). Geocarto International’s on-going long-term mission to publish research on Earth Observations and GIS: our notable contributions to Sustainability Development Goals of the United Nations 2030 Agenda for Sustainable Development. Geocarto International. Ahead of Print.

Detailed investigation on texture and mineralogy of beach sediments helps to understand their nature of deposition and potential targets of strategic mineral deposits. The beach sediments from the coast of Thiruvananthapuram, the southernmost district of Kerala, India, have been studied to understand the variation in grain size by using the spectral indices as derived from the visible-NIR-TIR bands

Rainfall and temperature are the key factors responsible for vegetation condition, health and growth. This research work analysis spatiotemporal phenomena in between SPI, LST and VIs in summer session from 2000 to 2018 in European Russia. This study used MODIS, NDVI, LST and TRMM data. The negative SPI values represent increasing drought events with reducing rainfall and vice-versa. Numerical consequences

The present study investigates the capability of two metaheuristic optimization approaches, namely the Butterfly Optimization Algorithm (BOA) and the Genetic Algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive

The article deals with the length differences between georeference surfaces within the state coordinate system of Serbia. It’s an analysis starting from the topography, through geoid and ellipsoid, to the map projection. During the literature review, analyses of the length changes from topography to the state map projection for the geographic territory of Serbia were not found. The set of initial data

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation

Mapping of regolith is significant to identify and discriminate geomorphic expression, regolith evolution, weathering histories and assistance in clarification of relationships between parent and surface materials. In this research work, the hydrothermally altered regolith of Western Jahajpur Belt, Bhilwara, India is studied using ASTER and AVIRIS imageries by PCA, decorrelation stretching supervised

Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1

In this study, remote sensing-based algorithm, METRIC is used to estimate evapotranspiration (ET) over a mountainous watershed in the lesser Himalayas in Uttarakhand, India. ET estimates of METRIC are compared with the ET values determined by using FAO Penman-Monteith equation and crop coefficients. In its original form, METRIC underestimated the ET values considerably with mean absolute error (MAE)

Ulva prolifera blooms have occurred every summer in the South Yellow Sea since 2008. U. prolifera blooms appeared in the area near the Jiangsu shoal and then were driven to the Shandong Peninsula by the prevailing summer winds. The blooms covered a maximum area of more than 50000 km2 and had an aggregate estimated biomass of 3.64 × 105 tons. Large number of U. prolifera dissipation in the natural degradation

Hyperspectral images are efficient tools for discriminating different types of earth's surface materials. Spectral features traditionally perform classification of hyperspectral images, but different studies have proved the efficiency of spatial features as complementary information in increasing the classification accuracy. The fractal geometry can be regarded as a potent tool for spatial data modeling

Mountain ecosystems of the Himalaya have warmed more rapidly in recent decades than other areas of the globe. Landscape-level delineation of treeline position and its dynamics can give insight regarding climatic variability. Landsat-2 Multispectral Scanner (MSS), Resourcesat-2 Linear Imaging Self Scanning Sensor (LISS-III) and National Oceanic and Atmospheric Administration- Advanced Very High Resolution

Spatial impacts of highway projects on biodiversity of North-Eastern Himalaya remains largely unexplored. Usually a number of ecological criteria are required in biodiversity impact assessment. However, a wide set of such criteria can be overwhelming for the decision-makers to assess the viability of such projects. SBIAM uses landscape metrics and experts’ opinion to create a single composite biodiversity

Surface Soil Moisture (SSM) is a key parameter of global energy and water cycle, and knowing its spatiotemporal variability is of key importance in an array of research topics and practical applications alike. Recent developments in Earth Observation (EO) have indicated that SSM can be retrieved from different regions of electromagnetic spectrum, and numerous approaches have been proposed to facilitate

Land cover change analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series data for the period 2005–2014. MODIS EVI data coupled with Quality Assessment Science Data Sets (QASDS) was de-noised with Savitzky–Golay filter while enhancing quality and preserving the temporal profile of EVI. Wavelet transform (WT) based approach along

A robust geodetic framework comprising Terrestrial Laser Scanner (TLS), Global Navigation Satellite Systems (GNSS), Robotic Total Station (RTS) and Multi-temporal InSAR (MT-InSAR) was employed first in India to investigate a landslide-prone Sirobagarh region, Uttarakhand, at different spatial extents, and to evaluate the relationship amongst the displacement estimates obtained from the applied surveying

Development of synthetic aperture radar (SAR) based satellites provide remedies to major geohazards under critical situations around the world. Sentinel 1A mission with the revisit period of 12 days helps to serve the objective in an effective manner. With the availability of interferometric wide (IW) Swath mode, Sentinel 1A mission is capable of monitoring the surface deformation using interferometric

In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset

In this study, we have developed five spatially explicit ensemble predictive machine learning models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai Province, Vietnam. In the model studies, Random Subspace (RSS) was used as the ensemble learner with Best First Decision Tree (BFT), Functional Tree (FT), J48 Decision Tree (J48DT), Naïve Bayes Tree (NBT) and Reduced Error

A growing number of wildfires around the world have been resulting in severe post-fire effects. Understanding the accurate distribution of the wildfire burn severity is particularly important for post-fire management. The differenced Normalized Burn Ratio (dNBR) and its relative dNBR (RdNBR) are the two main indices for burn severity assessment. In China, little information is available on the capabilities

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in

The aim of this study was to estimate the aboveground biomass (AGB) using Landsat 8 Operational Land Imager (OLI) satellite image in Araç Forest Enterprise. The ground data obtained from ground measurements in 317 sample plots were used to accomplish this purpose. The AGB for each sample plot was calculated using this data. The original band brightness, vegetation indices and texture feature values

Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have

Geospatial techniques and Multi-Criteria Decision Analysis (MCDA) play a crucial role in the planning and management of land and water resources. GIS-based MCDA technique ‘Catastrophe theory’ has been recently proposed for evaluating groundwater potential. However, the major limitation of ‘Catastrophe theory’ is that only quantitative factors/thematic layers can be used for assessing groundwater potential

Accurate soil organic matter (SOM) estimation could provide critical information to understand soil organic carbon sequestration, soil fertility, and the global carbon cycle. A nearest-neighbourhood autoregressive moving average (NN-ARMA) modelling technique along with linear regression has been used to predict localized soil SOM variation based on topographical characteristics and vegetation indices

We used Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data for deriving automated layered igneous intrusion maps by implementing the support vector machine (SVM) algorithm. We proposed a spectral analysis based approach to identify a set of optimum input spectral bands for deriving SVM-based maps of layered rocks of Sittampundi Layered Complex, India. We used

This work comprises combining the passive radiometer (SMAP) and active scatterometer (ASCAT) remotely sensed surface soil moisture datasets by employing cumulative distributive frequency matching algorithm using second-order polynomial regression for the Indo-Gangetic basin by keeping GLDAS-NOAH as the reference dataset for the period 2015–2016. In order to evaluate the quality, utility and applicability