Flood susceptibility mapping is essential for characterizing flood risk zones and planning mitigation approaches. Using a multi-criteria decision support system, this study investigated susceptible region in Bihar, India. It used combination of the analytical hierarchy process (AHP) geographic information system (GIS)/remote sensing (RS) with cloud computing API on Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric,...

Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic topographical factors, while this research expands scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), machine learning (ML) methodologies identifying fire-prone areas STR their...

A radio-controlled unmanned helicopter-based low-altitude remote sensing (LARS) platform was used to acquire quality images of high spatial and temporal resolution in order estimate yield total biomass a rice crop (Oriza sativa L.). Fifteen field plots with five N treatments (0, 33, 66, 99, 132 kg ha-1) having three replicates each were arranged randomized complete block design for estimating as function applied N. Images obtained by image acquisition sensors mounted on the LARS operating at...

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, study aims to advance the field by developing an innovative approach Groundwater zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out integrating various primary factors, hydrologic, soil permeability, morphometric, terrain distribution, anthropogenic influences, incorporating twenty-seven...

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, decision tree (ML) models. A total 400 flood nonflood locations acted as target variables hazard zoning map. All operative in this study tested using variance inflation factor (VIF) values...

Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped 26 subcriteria. A total of 986 orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area 277953.7 ha farmland. The novelty our research is integration Google Earth Engine cloud ML models, namely random forest, support vector machine,...

Though soil nutrients play important roles in maintaining fertility and crop growth, their estimation requires direct sampling followed by laboratory analysis incurring huge cost time. In this research work, were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) Support Vector Machine Model (SVMR) model through principal component analysis. Two hundred samples collected from Tarekswar, Hooghly, West Bengal, India to predict...

Crop selections and rotations are very important in optimising land labour productivities, enhancing higher cropping intensities, producing better crop yield. A suitability analysis system based on the analytical hierarchy process (AHP) technique coupled with Geographic Information System (GIS) software environment can be a unique tool for selection. The AHP-GIS was used rotation decisions, rice-jute (Kharif season) potato-lentil (Rabi crops Hooghly District, West Bengal, India. study area...

A low-altitude remote sensing (LARS) system with an unmanned radio-controlled helicopter platform was used to acquire high-quality images of land and crop properties higher spatial temporal resolution. It is vital visualize the relationship LARS-based parameters, such as nutrient levels, etc. Five N-treatment (0, 33, 66, 99 132 kg ha-1) rates three replications each were arranged in a randomized manner for testing LARS image acquisition system. Images taken by unit operated at height 20 m...

New generation energy sources is very much essential in Indian and global context. The available renewable have to be optimized give maximum biofuel returns. selective techniques been imprisoned on the research institutes University laboratories. major includes Algae, Jatropha oil vegetable oils, cellulosic materials, corn sugarcane etc. under surveillance since late 1990s. Major drawback so far for are continuous flow of from a single one. Overestimation potential oil, as potent source has...

A total land suitability analysis was carried out through FuzzyAHP technique for rice and potato crops in West Bengal, India. Around 21 most relevant crop parameters were selected classified under five primary criteria, such as terrain distribution parameter, static soil available nutrient, agriculture practice local variation parameter the study. The factors NDVI SAVI values estimated from Sentinel 2B images "SNAP" toolbox software environment, whereas nutrients standard laboratory methods....

The presence of weeds, bare spots, and variation in fruit yield within wild blueberry fields emphasizes the need for mapping site-specific application agrochemicals. An automated monitoring system (AYMS) consisting a digital color camera, differential global positioning system, custom software, ruggedized laptop computer was developed mounted on specially designed Farm Motorized Vehicle (FMV) real-time mapping. Two were selected central Nova Scotia to evaluate performance AYMS. Calibration...

Abstract Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects entire Earth daily, with resolution 3.7 m. from satellite, along vegetation indices, geo-environmental data, and soil parameters, were utilized analysed using machine learning models enhance accuracy predicting total biomass rice yield at field...