Abstract In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral

ABSTRACT The economic and cultural importance of lupin has been identified in regions such as the alpine zone of Ecuador, where per capita consumption reaches 0.4 kg per month. However, fungal pathogens affect lupin cultivation, causing considerable crop losses. In this study, Lupinus mutabilis Sweet plants started from seeds were analysed using remote sensing techniques, and different physical disinfection

Abstract To examine the effects of historical land-use change and static land-use state on the modeling, we established three cellular automata (CA) models using the spatial autoregressive model (SAR). The models are CASAR-Cha based on the change data, CASAR-Sta based on the start-state data, and CASAR-End based on the end-state data. The models that considered five different neighborhood sizes (from

Abstract Land degradation and desertification have recently become a critical problem in Ethiopia. Accordingly, identification of land degradation vulnerable zonation and mapping was conducted in Wabe Shebele River Basin, Ethiopia. Precipitation derived from Global Precipitation Measurement Mission (GMP), the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized difference vegetation index

Abstract Yellowwood or Podocarpus (spp.) holds the esteemed biodiversity status, as key forest species in the mist-belt Afromontane forests of southern Africa. The podocarps are listed as endangered species owing to extensive logging. The forest species support large communities of plants and birds, attributing to the maintenance of biodiversity. Therefore, there is a need to understand the condition

Abstract One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X

Abstract Unmanned Aerial Vehicle (UAV) may provide us super resolution data, however, they are often captured with artefacts and distortion. To investigate impact of distortion on the coral reef information, UAV surveys were deployed using multispectral sensor over two reefs in Bidong Island (Malaysia). Band-specific analysis of distortion revealed five different types of distorted images from the

Abstract In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia’s coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed

Abstract Classification is the main field of hyperspectral data processing. To date, many methods are introduced to increase the accuracy of image classification. In recent years, various convolutional neural network models are proposed for hyperspectral image classification. This study puts forward a multiscale structure of convolutional neural networks that use several patches of different sizes

Abstract Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common due to monsoon precipitation with short duration

Abstract Accurate mapping of coarse cereals envisaged maize to be discriminated from co-existing Kharif crops using multiplatform, multiparametric SAR and Optical data in various polarization combination from Sentinel-1 and RADARSAT-2 in synergism with Sentinel-2, under machine learning algorithms viz. support vector machine (SVM) and random forest (RF) and knowledge-based Decision Tree (DT). SVM over-estimates

Abstract Mapping of carbonatites and related mineral deposits has occupied prominent place in mineral resource exploration programs given their potential to host valuable concentrations of critical metals such as rare earth elements and niobium. Based on spectral characteristics of most indicative minerals for these rocks, a mapping approach was developed using Advanced Spaceborne Thermal Emission

Abstract Hyperspectral remote sensing is opening new avenues for multitude of urban applications. This paper extends target detection method for extraction of engineered surfaces or urban targets, particularly roads and roofs. The study involves application of deep learning using AVIRIS-NG data. In pre-processing, generating ground reference image using Vertex Component Analysis (VCA) is done instead

Abstract Vector GIS represents shorelines as polylines that show the boundaries between land and water. This article compares two methods to measure how boundaries move among time points. The Area method converts the polylines at various time points into polygons of either land or water. The Area method measures temporal change as loss or gain of land areas. The Transect method requires subjective

Abstract Commercial forest species discrimination is valuable for optimal management of commercial forests. Therefore, second-order image texture combinations computed from a 0.5 m WorldView-2 pan-sharpened image integrated with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were used to discriminate commercial forest species. The

Abstract Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process

Abstract Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders’ land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated

ABSTRACT This study examines the watersheds in the Bastora river basin and builds a geographic model within ARC GIS software. The work was divided into three phases. The first phase was to obtain the results of 14 morphometric parameters to determine priorities, by using AHP model to extract the relative weights of the parameters. Then, these weights are adopted in building (SDI, TOPSIS and SAW) models

Abstract Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of Fuzzy Logic algorithm with the following four machine learning models: Classification and Regression Tree, Deep Learning

ABSTRACT In this study, high resolution (HR) images, GIS and Deep convolution neural network (DCNN) are used for assessment of traffic congestion. A DCNN architecture comprises of one convolution layer, two pooling layers and a five-layer fully connected neural network evaluated for identifying vehicles in a movable windows in HR images. A simple mathematical method is followed for changing the scale

Abstract Terrestrial groundwater dependent vegetation (TGDV) are crucial ecosystems which provide important goods and services such as carbon sequestration, habitat, water purification and aesthetic benefits in semi-arid environments. Global climate change and anthropogenic effects on surface water resources have led to increased competing claims on groundwater resources to meet an exponential water

Abstract Among natural disasters, drought hits almost half of the world every year, regardless of the climatic zones. Identifying drought vulnerability regions is fundamental to plan and adopt mitigation measures. Here we apply a multi-criteria-based machine learning technique that integrates spatial data for preparing drought vulnerability map of different categories. We adopted remote sensing tools

Abstract Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability

Abstract In this article, GIS, remote sensing (RS) and fuzzy AHP are used to develop a methodology to delineate the groundwater potential zones. GIS-RS technique is used to prepare the different thematic maps that influence the groundwater potentiality. Fuzzy AHP method is used to assign weights and fuzzy ratings to different thematic layers and their attributes classes, respectively. After assigning

Abstract In this paper, the novelty of Artificial Neural Networks (ANN) model and GIS platform for the delineation of groundwater potential zones were compared in Fincha Catchment, Abay Basin, Ethiopia. LULC, rainfall, soil, geology, drainage density, lineament density, and geomorphologic units were used as key factors in both models. Weights were generated in ANN and Analytical Hierarchy Process (AHP)

Abstract COVID-19 poses a major threat to global health care systems, and the recent surge in mortality rates confirms the importance of timely access to care. The capacity of medical service providers is reflected both in the spatial accessibility of medical institutions and in the spatial scope of their services. Therefore, this study aims to investigate the spatial scope of services and spatial

Abstract This research aims to analyze the use of deep semantic segmentation to detect eucalyptus afforestation areas using Sentinel-2 images. The study compared six architectures (U-net, DeepLabv3+, FPN, MANet, PSPNet, LinkNet) with four encoders (ResNet-101, ResNeXt-101, Efficient-net-b3, and Efficient-net-b7), using 10 spectral bands. Even though the differences were not large among the different

Abstract In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method

This study performed flood risk assessment in urbanized areas using geospatial and remotely sensed data for 1990–2005–2020 periods using the linear unmixing model (LUM), random forests (RF), and support vector machines (SVM). The urban mapping results verified with reference data indicated close agreement, with the overall accuracies and Kappa coefficients higher than 88.9% and 0.78, respectively.

Abstract Sustainable management of biodiversity benefit from cost-effectively multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species (n = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A

Abstract Landslide hazard is usually incorporated into land-use planning as susceptibility zoning. Multiple-variable models have been widely used for susceptibility zoning due to their advantage to use different performance techniques to improve their prediction capacity. In Chile, the incorporation of landslide hazard into land-use planning instruments lacks a frame of reference that defines the susceptibility

The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P), and REPTree (RF-REPTree), with three statistical models, i.e., weight

Abstract Moderate Resolution Imaging Spectroradiometer (MODIS) products were used in this study which covers a period of 2000 − 2014. Comparison of the SCA derived from MODIS and Landsat NDSI showed a significant correlation, the resultant coefficient of determination (R2) was 0.85. For analyzing the trend in the SCA, the Mann-Kendall test was applied; the results show an increasing non-significant

Abstract Glacier ice-thickness measurement and its distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier evolution under the climate changing scenario. Yet, substantial gaps in ice thickness knowledge exist for the Himalayan glaciers. The present study provides a long-term assessment (1965-2016)

Abstract Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System

Abstract The present study deals with Landsat data for the change analysis and flood mapping using water class extraction. Among the various available bands, the most suitable band combination is identified using class separability analysis and a novel water index (WI2020) is proposed for the extraction of water. The proposed index is able to separate the water class from other land features and performs

ABSTRACT Unmanned Aerial Vehicle (UAV) based remote sensing is one of the modern techniques for crop management, which has been used in this study for biomass and Nitrogen (N) level estimations for Stevia rebaudiana, a medicinal crop used as an alternative to sugar as a natural sweetener. Different levels of nitrogen treatments were given to S. rebaudiana and the crops were harvested for biomass estimation

Abstract Identification of groundwater potential zones (GWPZ) is as equally important as determining the most effective groundwater mapping models for efficient production of reliable GWPZ maps. In this study, relative frequency ratio (RF) with predictor value and analytic hierarchy process (AHP) models, including two RF-AHP hybrid models (HM-A and HM-B) were applied and compared to evaluate their

Abstract The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species

Abstract Air Pollution plagues urbanised areas and enhances mankind’s vulnerability to the COVID-19 virus. Background: According to W.H.O, Kolkata (India) ranks 25th amongst 1100 cities in the world. Curbs on movement reduced this pollution during COVID-19 lockdown. Resumption of activities nullified this. Objectives: This study analyses the trend of air pollutants, their interrelationships with PM2

Abstract The repository of soil by water at a national and basin scale was estimated using the RUSLE empirical model which is the first of its kind in Bhutan. The annual soil loss is estimated and categorized into five categories; very low (<200 t/yr), low (200-400 t/yr), moderate (400-600 t/yr), high (600-800 t/yr), and very high (>800 t/yr). Sakteng and Jaldakha basins contributed the highest soil

Abstract Hydrological modelling of Ar Rub Al Kahli over a very regional scale using the weight of evidence based on pixel calculation and several pieces of evidences is timely consuming. This study aims to modify an approach, which calls a weight of hydrological evidence (WoHE) approach. The approach is based on calculating the logarithmic ratio of the total areas of distribution good (event) and distribution

Abstract Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy c-means based adaptive neuro fuzzy system (ANFIS-FCM), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree), in estimating

The accurate extraction of Built-Up Areas (BUAs) is of great significance for analysing urban spatial evolution patterns. In this study, we proposed a new method for high-precision extraction of BUA based on multi-source remote sensing data. Firstly, Built-Up Area Extraction Index (BAEI) was used to preliminarily identify the BUA based on Landsat 8 imagery. Secondly, the Support Vector Machine (SVM)

Dams must provide human development with economic viability, social equitability, and be environmentally sustainable However, environmental and social impacts are often negative and seldom considered in the design of these projects. We made a multitemporal analysis classifying 26 satellite images of several Mexican hydroelectric power stations between 1972 and 2016 to evaluate the effect of dams on

Abstract The dual polarimetric study including degree of polarization ( mL) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel – 1A and ALOS - 2 satellite data were utilized for dual polarimetric radar vegetation index (DPRVI), polarimetric radar vegetation index (PRVI) and radar vegetation index (RVI) computation and simulation of backscattering

Abstract This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and

Abstract Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although

Abstract This paper presents a novel approach for estimating wheat yields based on estimated abundances of endmembers attributed to photosynthetic pigments, using Landsat 8 images acquired during maximum greenness. Endmembers within the wheat pixels are found using an unmixing algorithm then they are further optimized to maximize the predictive power of the abundances for the yields. Similarity of

Abstract All the supervised classification methods need sufficient and efficient samples, which are commonly labeled by visual inspection. In this study, to resolve the issues of insufficient training samples and time-consuming, a novel method for detailed and automated LULC classification by LC_Type1 of MCD12Q1 IGBP schemes in the GEE cloud platform was proposed based on the RF and CART classifiers

Abstract In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model

Abstract Is it true that rural areas, like urban areas, experience temperature changes over time? The aim of this mission was to find the season-wise trend associated with land surface thermal alteration based on satellite imagery from the last 30 years using the least square regression method, as well as to determine the stimulus of LST for the years 2027 and 2037 using Artificial Neural Network and

Abstract Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vital input to crop-growth models. Mustard experienced increase as it advanced in phenology but for wheat, decreased due to absorption. A significant relationship at initial crop stages (height <150 cm

Abstract Classification of fields into management units based on soil variability and fertility is important for spatial crop planning. The present study was conducted in Chukanagallu subwatershed (97 Km2), Koppal district of Northern Karnataka Plateau, India to map the soil fertility management units and to analyse the suitability of soil for different crops. Random forest regression and classification

ABSTRACT Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment

Abstract MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São

Abstract Wetlands are highly productive systems that act as habitats for a variety of fauna and flora. Despite their ecohydrological relevance, wetland systems are severely under threat from global environmental changes and anthropogenic activities. This results in the disturbance of plant species composition, spatial distribution, productivity, diversity, and their ability to offer goods and services

ABSTRACT Forest roads are the primary infrastructure facilities of forestry activities. Identifying the forest roads that can be used after a disaster is very important for management of disasters such as fire, flood, and landslide as well as for strengthening of the forest road infrastructure. In this context, forest road extraction from orthophoto images has become a hot research topic in the field

Abstract Seagrass habitats are crucial marine ecosystem component that affects the biophysical and chemical environment, providing nursery beds to various invertebrates, juvenile fish, and dugongs. This study focuses on mapping and monitoring changes in seagrass total above-ground biomass (STAGB) within the coastline of Peninsula Malaysia. The retrieval of the substrate leaving radiance was realized

Abstract The data employed in urban gain modeling classes are often imbalanced, negatively affecting the accuracy of traditional and standard data mining and machine learning models. This study presents a new framework on the basis of clustering-based modeling and ensemble models to deal with the class imbalance problem in urban gain modeling. The random forest (RF), artificial neural network (ANN)