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Estimation of Aboveground Biomass from PolSAR and PolInSAR using Regression-based Modelling Techniques Geocarto Int. (IF 3.789) Pub Date : 2021-01-21 R. Mukhopadhyay; S. Kumar; H. Aghababaei; A. Kulshrestha
Abstract In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media.This feature is used for the estimation of biophysical parameters and monitoring of deforestation.Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic
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Proposing receiver operating characteristic-based sensitivity analysis with introducing swarm optimized ensemble learning algorithms for groundwater potentiality modelling in Asir region, Saudi Arabia Geocarto Int. (IF 3.789) Pub Date : 2021-01-21 Javed Mallick; Swapan Talukdar; Majed Alsubhi; Mohd. Ahmed; Abu Reza Md Towfiqul Islam; Shahfahad; Viet Thanh Nguyen
Groundwater scarcity is one of the most concerning issues in arid and semi-arid regions. In this study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial neural network (ANN), reduced-error pruning trees (REPTree), radial basis function (RBF), M5P and random forest (RF) with particle swarm optimization (PSO) for delineating
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Impedance measures in evaluating accessibility change Geocarto Int. (IF 3.789) Pub Date : 2021-01-14 Wasim Shoman; Hande Demirel
Abstract Earlier location-based accessibility analysis by car mode utilizes network travel distance (NTD) or static travel time (STT) as travel impedances. A recent trend in the literature that considers impedance as dynamic travel time (DTT) emerged, allowing examining continuous accessibility patterns. This research aims to evaluate the measured spatial-temporal change in accessibility for the different
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Modelling rabi crop health in flood plain region of India using time-series Landsat data Geocarto Int. (IF 3.789) Pub Date : 2021-01-13 Swades Pal; Pallabi Chowdhury; Swapan Talukdar; Rajesh Sarda
Abstract The present work intended to model the vegetation health index (VHI) of rabi crop and explored its time series change (1990–2019) at pixel scale using Landsat satellite imageries. Identification of consistency level of rabi crop was another objective. VHI was estimated by integrating crop condition index (CCI) and temperature condition index (TCI). The CCI and TCI were derived from normalized
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Estimating and Mapping of Soil Organic Matter Content in a Typical River Basin of the Qinghai-Tibet Plateau Geocarto Int. (IF 3.789) Pub Date : 2021-01-12 Qing Yu; Hongwei Lu; Wei Feng; Tianci Yao
Abstract Spectroscopy is a fast, non-destructive, and cheap method, which has been widely used in the estimation of soil organic matter (SOM) concentration. This study presented a methodology to estimate and map SOM content by crop canopy reflectance spectra combining with land parameters in the Yarlung Zangbo River (YZR) basin. The reflectance spectra of the oat canopy were collected in the field
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Estimation of spatio-temporal variability in land surface temperature over the Ganga River Basin using MODIS data Geocarto Int. (IF 3.789) Pub Date : 2021-01-11 Suraj Mal; Seema Rani; Pyarimohan Maharana
Abstract The present study aims to estimate the trends in daytime land surface temperature (LST) and its relationship with other parameters such as elevation and land use/land cover (LULC) over the Ganga River Basin (GRB) during 2001–2019. The study examined monthly daytime clear-sky LST, elevation, LULC, snow cover area (SCA), clear-sky days, and aerosols data of the GRB. Annual mean clear-sky daytime
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Poverty estimation at the county level by combining LuoJia1-01 nighttime light data and points of interest Geocarto Int. (IF 3.789) Pub Date : 2021-01-11 Jinyao Lin; Shuyi Luo; Yiqin Huang
Abstract To reduce poverty, it is important to obtain accurate information on poverty conditions in a timely manner. Previous studies indicated that nighttime light products are helpful for poverty estimation. However, there exist no studies that have investigated the potential of LuoJia1-01, a new-generation nighttime light satellite with a much finer resolution (∼130 m), for analyzing poverty. In
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Temporal-spatial Variations of Vegetation Cover and Surface Soil Moisture in the Growing Season across the Mountain-Oasis-Desert System in Xinjiang, China Geocarto Int. (IF 3.789) Pub Date : 2021-01-11 Liu Yang; Wenyu Wei; Tingting Wang; Lanhai Li
Abstract The ecological evolution and relevant factors in semi-arid and arid regions is a research focus on climate changes. One such region, the mountain-oasis-desert coupling system in Xinjiang, China, is sensitive to global climate changes because it has a fragile ecological environment. This study attempted to investigate the temporal-spatial variations of surface soil moisture, vegetation cover
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Detecting the invasive fall armyworm pest incidence in farm fields of southern India using Sentinel-2A satellite data Geocarto Int. (IF 3.789) Pub Date : 2021-01-07 Mathyam Prabhakar; Kodigal A. Gopinath; Nakka Ravi Kumar; Merugu Thirupathi; Uppu Sai Sravan; Golla Srasvan Kumar; Gutti Samba Siva; Guddad Meghalakshmi; Sengottaiyan Vennila
Abstract Damage of fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) on sorghum from the farmers’ fields of southern India was assessed using space-borne data. Comparison of the Sentinel-2A satellite data of pre and post infestation periods revealed reduction in Leaf Area Index (LAI) in the infested fields. Groundtruth data confirmed that FAW infestation reduced LAI by 49.7%, biomass by 32.5%
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A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; Case study: Tehran-Qazvin freeway Geocarto Int. (IF 3.789) Pub Date : 2021-01-07 Reza Sanayei; Alireza Vafaeinejad; Jalal Karami; Hossein Aghamohammadi Zanjirabad
Abstract The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method
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Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta Geocarto Int. (IF 3.789) Pub Date : 2021-01-06 Luan Hong Pham; Lien T. H. Pham; Thanh Duc Dang; Dung Duc Tran; Toan Quang Dinh
Abstract The advent of Sentinel-1 SAR data with high temporal and medium spatial resolutions along with its being unaffected by presence of cloud provided opportunities for using remote sensing in mapping LULCs with high temporal detail. The objective of this study is to explore the possibility of applying Sentinel-1 data together with OBIA and machine learning in classifying LULC in a complex agricultural
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Optimization of Statistical and Machines Learning Hybrid Models for Groundwater Potential Mapping Geocarto Int. (IF 3.789) Pub Date : 2021-01-04 Peyman Yariyan; Mohammadtaghi Avand; Ebrahim Omidvar; Quoc Bao Pham; Nguyen ThiThuy Linh; John P. Tiefenbacher
Abstract Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrineh Rood watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy
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Inventory and GLOF hazard assessment of glacial Lakes in the Sikkim Himalayas, India Geocarto Int. (IF 3.789) Pub Date : 2021-01-03 Nazimul Islam; Priyank Pravin Patel
Abstract Glacial Lake Outburst Floods (GLOFs) are a recurring hazard in the Himalayas, posing significant threat to downstream communities. The North Sikkim district of India, comprising the upper reaches of the Teesta River in the Eastern Himalayas, has experienced past GLOF events. The identification of lakes susceptible to this phenomenon is therefore paramount. Using multi-temporal satellite images
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A graph deep learning approach for urban building grouping Geocarto Int. (IF 3.789) Pub Date : 2020-12-29 Xiongfeng Yan; Tinghua Ai; Min Yang; Xiaohua Tong; Qian Liu
Abstract Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrating multiple cognitive features and manual cartographic experiences. Taking building center points as nodes, adjacent buildings were organized as a graph in which cognitive variables including size
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Flexible user interface for machine learning techniques to enhance the complex geospatial hydro-climatic models with future perspective Geocarto Int. (IF 3.789) Pub Date : 2020-12-28 Venkatesh Budamala; Amit Baburao Mahindrakar
Abstract Hydro-climatic (HC) models have complex environments due to the integration of hydrological processes and climate indices for the assessment of historical and future scenarios. The approximation of HC models leads to a major uncertainty in the selection of optimal methods for processing, enhancement, and assessment. The present work developed a User-Friendly Interface (UI) in the R programming
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Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility Geocarto Int. (IF 3.789) Pub Date : 2020-12-22 Moslem Borji Hassangavyar; Hadi Eskandari Damaneh; Quoc Bao Pham; Nguyen Thi Thuy Linh; John Tiefenbacher; Quang-Vu Bach
Abstract This study tests the applicability of three resampling methods (i.e. bootstrapping, random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines
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Landslide Susceptibility Mapping in Three Upazilas of Rangamati Hill District Bangladesh: Application and Comparison of GIS-based Machine Learning Methods Geocarto Int. (IF 3.789) Pub Date : 2020-12-17 Yasin Wahid Rabby; Md Belal Hossain; Joynal Abedin
ABSTRACT This study evaluates and compares three machine learning models: K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for landslide susceptibility mapping for part of areas in Rangamati District, Bangladesh. The performance of these methods has been assessed by employing statistical methods such as the area under the curve (AUC) for success rate (SR) and prediction
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Improving the Capability of Integrated DInSAR and PSI approach for Better Detection, Monitoring, and Analysis of Land Surface Deformation in Underground Mining Environment Geocarto Int. (IF 3.789) Pub Date : 2020-12-17 Mohammad Soyeb Alam; Dheeraj Kumar; R S Chatterjee
Abstract The study involves developing a framework of research methodology for enhancing the capability of spaceborne integrated DInSAR and PSI approach for detection, monitoring, and analysis of land surface deformation in the underground mining environment. First component of the framework involves pre-feasibility assessment of the integrated approach followed by selecting suitable InSAR data sets
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Detection of Diseased Pine Trees in Unmanned Aerial Vehicle Images by using Deep Convolutional Neural Networks Geocarto Int. (IF 3.789) Pub Date : 2020-12-17 Gensheng Hu; Yanqiu Zhu; Mingzhu Wan; Wenxia Bao; Yan Zhang; Dong Liang; Cunjun Yin
Abstract This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training
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Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest Geocarto Int. (IF 3.789) Pub Date : 2020-12-17 T Mayamanikandan; Suraj Reddy; Rakesh Fararoda; Kiran Chand Thumaty; M S S Praveen; G Rajashekar; C S Jha; I C Das; G. Jaisankar
Abstract Accurate and reliable estimation of Above Ground Biomass (AGB) in tropical forests is much needed for net carbon assessments. The aim of the study is to determine the uncertainty in biomass estimation in terms of field plot size, shape and location error using field plot and remote sensing data in tropical dry deciduous forests of India. Detailed tree measurements and location mapping are
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Quantifying the Effects of Urban Land Forms on Land Surface Temperature and Modeling the Spatial Variation Using Machine Learning Geocarto Int. (IF 3.789) Pub Date : 2020-12-15 Vikas Kumar Rana; Tallavajhala Maruthi Venkata Suryanarayana
Abstract This study explores the impact on land surface temperature due to the spatial clustering of urban landforms with normalized difference vegetation index, normalized difference water index and dry bare-soil index. In order to determine the contribution of different land use/land cover classes in affecting the land surface temperature, the contribution index was used for summer and winter seasons
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Assessing the post-fire recovery in the southeast coast of China during the early period Geocarto Int. (IF 3.789) Pub Date : 2020-12-11 Longyan Cai; Min Wang
Abstract Increasing numbers of wildfires have resulted in severe post-fire effects on the environment. Understanding post-fire recovery is important for post-fire management. However, an in-depth evaluation of the spatiotemporal dynamics of burn degree is rarely reported around the world, which is not conducive to accurate post-fire management. This study aimed to assess the changes in the degree of
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Assessment of Gini, Entropy, and Ratio based classification trees for groundwater potential modeling and prediction Geocarto Int. (IF 3.789) Pub Date : 2020-12-11 Omid Rahmati; Mohammadtaghi Avand; Peiman Yarian; John P. Tiefenbacher; Ali Azareh; Dieu Tien Bui
Abstract Artificial-intelligence and machine-learning algorithms are gaining the attention of researchers in the field of groundwater modeling. This study explored and assessed a new approach based on Gini, Entropy, and Ratio based classification trees to predict spatial patterns of groundwater potential in a mountainous region of Iran. To do this, a springs inventory was undertaken, and 362 springs
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Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries Geocarto Int. (IF 3.789) Pub Date : 2020-12-11 X. R. Luo; S. D. Li; L. Liu; W. N. Yang; Y. H. Zhang; G. Chen; S. Y. Qiu; Q. L. Tang; X. L. Tang
Abstract Although optical remote sensing has been widely used to monitor vegetation characteristics, its use on aboveground vegetation water storage (AVWS) is quite scare. Therefore, we combined the Landsat 8 OLI and Sentinel-1 imageries to quantify AVWS using generalized linear regression (GLM), artificial neural network (ANN) and random forest (RF) with the linkage of field observations in Mao Country
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Ecological environment quality assessment of Xishuangbanna rubber plantations expansion (1995-2018) based on Multi-temporal Landsat imagery and RSEI Geocarto Int. (IF 3.789) Pub Date : 2020-12-11 Yuan Xiong; Weiheng Xu; Shaodong Huang; Chao Wu; Fei Dai; Leiguang Wang; Ning Lu; Weili Kou
Abstract The specific impact of ecological environment quality at a regional scale due to the rubber plantations expansion is still unclear in Xishuangbanna, Yunnan province, China. First, we used a pixel and phenology-based multiple normalization approach to map rubber plantations over six time periods during 1995-2018 with the available high-quality Landsat imagery. Second, a pixel-based optimized
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An Ensemble Architecture of Deep Convolutional Segnet and Unet Networks for Building Semantic Segmentation from High-resolution Aerial Images Geocarto Int. (IF 3.789) Pub Date : 2020-12-01 Abolfazl Abdollahi; Biswajeet Pradhan; Abdullah M. Alamri
Abstract Building objects is one of the principal features that are essential for updating the geospatial database. Extracting building features from high-resolution imagery automatically and accurately is challenging because of the existence of some obstacles in these images, such as shadows, trees, and cars. Although deep learning approaches have shown significant improvements in the results of image
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A Baseline Estimate of Regional Agricultural Water Demand from GEO-LEO Satellite Observations Geocarto Int. (IF 3.789) Pub Date : 2020-12-01 Indrani Choudhury; Bimal Kumar Bhattacharya
ABSTRACT Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009-2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composite crop-coefficient was introduced to address bulk AWD from mixed agricultural landscape. Significant spatio-temporal variation of AWD was observed over India. The decadal mean of annual AWD was found
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Spectral Linear Mixing Model applied to data from passive microwave radiometers for sea ice mapping in the Antarctic Peninsula Geocarto Int. (IF 3.789) Pub Date : 2020-12-01 Fernando Luis Hillebrand; Ulisses Franz Bremer; Marcos Wellausen Dias de Freitas; Juliana Costi; Cláudio Wilson Mendes Júnior; Jorge Arigony-Neto; Jefferson Cardia Simões
Abstract This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in the northwest of the Antarctic Peninsula. Sentinel-1A Synthetic Aperture Radar (SAR) data were used to estimate fraction images needed for subpixel analysis of passive microwave data acquired by
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Geoenvironmental assessment of the Mut wastewater ponds in the Dakhla Oasis, Egypt Geocarto Int. (IF 3.789) Pub Date : 2020-12-01 Wael F Galal; Mahmoud H Darwish
Abstract Disposal and accumulation of agricultural and sanitary wastewater in poorly engineered ponds in the Mut region, Dakhla Oasis of Egypt is a concern due to their poor water quality; significant expansion over a large spatial area, and their consequential impact on the environment. GIS and remote sensing were useful tools for determining spatiotemporal and the impact of wastewater ponds and land
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Structural and tectonic interpretation of EGM2008 gravity data around the Laccadive ridge in the Western Indian Ocean: An implication to continental crust Geocarto Int. (IF 3.789) Pub Date : 2020-12-01 Ujjawal Kumar; Satya Narayan; Sanjit Kumar Pal
Abstract The present study attempts to structural and tectonic interpretation around the Laccadive ridge in the Western Indian Ocean using EGM2008 Bouguer gravity data. The different derivatives of EGM2008 Bouguer gravity data indicate that the tectonic boundaries are predominantly confined in the N-S and NNW-SSE directions followed by E-W and NE-SW trends. The Euler deconvolution result suggests the
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Reservoir Risk Modelling Using a Hybrid Approach Based on the Feature Selection Technique and Ensemble Methods Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 Junnan Xiong; Quan Pang; Weiming Cheng; Nan Wang; Zhiwei Yong
Abstract Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs. Thus, an accurate assessment of reservoir risk for certain areas is mandatory for the improvement of reservoir flash-flood forecasting and warnings. In this context, the
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Multi-resolution classification network for high-resolution UAV remote sensing images Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 Ming Cong; Jiangbo Xi; Ling Han; Junkai Gu; Ligong Yang; Yiting Tao; Miaozhong Xu
High-resolution unmanned aerial vehicle (UAV) remote sensing images have super-high ground resolution. Although they provide complete and detailed surface observation data for various engineering applications, the extraction of information from complex and diverse surface scenes is challenging. Characterising surface targets with bright colours and different shapes using samples with fixed sizes and
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Fracture aquifers identification in the Zou basin (West Africa) using Remote sensing and GIS Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 Francis E. Oussou; Nicaise Yalo; Christopher E. Ndehedehe; Joseph Oloukoi; Abdoukarim Alassane; Moussa Boukari; Vinel G.H. Gbèwézoun
Abstract The riparian communities of the Zou basin of West Africa rely heavily on drinking water supplied by fractured aquifer systems. This study aims to provide accurate fracture maps and derived products (fracture density, coincidence map and cross-points) using Landsat 8 (visible and infrared bands) and PALSAR DEM datasets with borehole data collected from the national integrated database. Digital
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Comparing rotation forests and extreme gradient boosting for monitoring drought damage on KwaZulu-Natal commercial forests Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 M. N. M. Buthelezi; R. T. Lottering; S.T. Hlatshwayo; K. Y. Peerbhay
Abstract This study explored the utilization of rotation forests (RTF) and extreme gradient boosting (XGBoost) machine learning algorithms (MLAs) to classify drought damage in commercial forests in KwaZulu-Natal (KZN). These algorithms were trained using information obtained from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation and conditional drought indices. The results
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Integrating remote sensing, GIS and in-situ data for structural mapping over a part of the NW Rif belt, Morocco Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 Omar Skakni; Rachid Hlila; Amin Beiranvand Pour; Manuel Martín Martín; Ali Maate; Soufian Maate; Aidy M Muslim; Mohammad Shawkat Hossain
Abstract This study adopts an integrated approach using the geographic information system (GIS) and remote sensing techniques for structural mapping in inaccessible zone of the internal segment of North-Western Rif belt, Morocco. The Principal Component Analysis (PCA), Optimal Index Factor (OIF), band ratios and directional filtering are applied to Landsat 8 OLI (Operational Land Imager) image for
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Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique Geocarto Int. (IF 3.789) Pub Date : 2020-11-19 Syaza Rozali; Zulkiflee Abd Latif; Nor Aizam Adnan; Yousif Hussin; Alan Blackburn; Biswajeet Pradhan
Abstract The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled
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Detecting Short-term Surface Melt over Vestre Broggerbreen, Arctic glacier using indigenously developed Unmanned Air Vehicles Geocarto Int. (IF 3.789) Pub Date : 2020-11-12 M Geetha Priya; Krishna Venkatesh; Lohit Shivanna; Suresh Devaraj
ABSTRACT In the Arctic, the impacts of global warming are strong and the rate of ice melting is increasingly accelerating due to warmer temperatures relative to snow accumulation. With recent advances in unmanned air vehicle (UAV) technology and image processing techniques, field measurements for cryosphere studies have gained significance today. In the current report, an attempt is being made to measure
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A new intelligent traffic signal model based on open source road information Geocarto Int. (IF 3.789) Pub Date : 2020-11-12 Chaode Yan; Ziwei Pan; Bo Kong; Keru Chen; Ziwei Li; Muhammad Waseem Boota; Xiao Liu
In recent years, the problem of urban traffic congestion has become increasingly prominent. In this study, an intelligent traffic signal model based on open source road traffic information was proposed. We used real-time traffic data provided by Gaode open platform to model urban traffic congestion of direction road segments, single intersections and adjacent multiple intersections. And based on these
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A new high resolution filter for source edge detection of potential field data Geocarto Int. (IF 3.789) Pub Date : 2020-11-12 Luan Thanh Pham; Ahmed M. Eldosouky; Erdinc Oksum; Saada Ahmed Saada
Abstract Determining the source edges is a frequently requested task in the analysis of potential fields. However, the edge detection methods have some drawbacks or shortcomings, for example, blurred responses to weak amplitude anomalies, the estimated peaks over the edges are low in gradient or bring out overestimated edges in the resulting maps. In this paper, a new method based on an arcsine function
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Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region Geocarto Int. (IF 3.789) Pub Date : 2020-11-12 Narayan Kayet; Khanindra Pathak; Abhisek Chakrabarty; Subodh Kumar; V.M. Chowdary; C. P. Singh
Abstract This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using analytic hierarchy process (AHP) model based on multi-criteria analysis in a Geographic information system (GIS) platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate (max temperature, rainfall, wind speed,
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Glacier mass loss in the Alaknanda basin, Garhwal Himalaya on a decadal scale Geocarto Int. (IF 3.789) Pub Date : 2020-11-05 S N Remya; Anil V Kulkarni; Tajdarul Hassan Syed; Harish Chandra Nainwal
Abstract The Himalayan glaciers significantly contribute to the largest river systems like the Indus, Ganga, and the Brahmaputra. The change in glacial area and mass can affect the mountain community and people living in the Indo-Gangetic plain. The present study adopted the geodetic method to estimate the elevation change and mass budget of 61 glaciers in the Alaknanda Basin, using the satellite data
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Use of hyperspectral imagery to detect affected vegetation and heavy metal polluted areas: Sheng-li mining area, China Geocarto Int. (IF 3.789) Pub Date : 2020-11-02 Xingchen Yang; Shaogang Lei; Yibo Zhao; Wei Cheng
Abstract Some indicators for evaluating heavy metal pollution have been proposed. However, a threshold is lacking to judge if the heavy metal concentration has reached the extent that is harmful to plant health. In order to get the threshold, aerial hyperspectral images were obtained. The spatial patterns of six soil heavy metals (Cu, Zn, As, Sn, Cr and Cd) were obtained by establishing random forest
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Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data Geocarto Int. (IF 3.789) Pub Date : 2020-11-02 Anjana N. J. Kukunuri; Deepak Murugan; Dharmendra Singh
Abstract Overall health condition of the vegetation is obtained by combining satellite data derived moisture and thermal stresses present in vegetation condition index (VCI) and thermal condition index (TCI), respectively and improves the accuracy of drought classification. Although vegetation health index fuses the information present in VCI and TCI, the relative contribution of each index depends
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Retrospective analysis and version improvement of the satellite-based drought composite index. A Semi-arid Tensift-Morocco application Geocarto Int. (IF 3.789) Pub Date : 2020-11-01 Ismaguil Hanadé Houmma; Loubna El Mansouri; Rachid Hadria; Anas Emran; Abdelghani Chehbouni
Abstract This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil
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Contribution of SAR-Driven Displacement Measurement in Assessing the Triggering Factors of Rainfall-Induced Landslides Geocarto Int. (IF 3.789) Pub Date : 2020-11-01 Jacinth Jennifer Jesudasan; Subbarayan Saravanan
ABSTRACT Monitoring of landslides by computing the displacements gain importance in recent studies due to the accuracy in its measurements. Kerala-Karnataka landslides of August 2018, has led to severe loss of life and damages to both states. The study emphasizes the contribution of three major landslide-driving factors viz., rainfall intensity, land-use/land-cover and slope, towards the occurrence
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Vertical Stripe correction in Hyperion image using wavelet transformation and Singular Value Decomposition (SVD) Geocarto Int. (IF 3.789) Pub Date : 2020-10-30 Ankur Dixit; Shefali Agarwal
Abstract Vertical striping present in hyperspectral images are quite common in Hyperion images. The removal of these stripes turns out to be an important step of noise correction along with atmospheric correction and other artifact correction. Often these processes are done on radiometric values. We have proposed a novel approach to correct these vertical stripes using wavelet transformation and singular
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Artificial Intelligence Techniques in Extracting Building and Tree Footprints Using Aerial Imagery and LiDAR Data Geocarto Int. (IF 3.789) Pub Date : 2020-10-30 Saeideh Sahebi Vayghan; Mohammad Salmani; Neda Ghasemkhani; Biswajeet Pradhan; Abdullah Alamri
Abstract One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the
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Mapping the Global Spatio-Temporal Dynamics of Covid-19 Outbreak Using Cartograms During the First 150 Days of the Pandemic Geocarto Int. (IF 3.789) Pub Date : 2020-10-30 Mustafa Yalcin
Abstract The novel coronavirus (COVID-19) pandemic is a public health emergency of international concern that caused disaster consequences all over the world. To mitigate and manage the pandemic, GIS-Based spatial analysis has been used in a key role. To this aim, many GIS-based maps are produced by many national and international institutions. However, the produced maps are prepared by conventional
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Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates Geocarto Int. (IF 3.789) Pub Date : 2020-10-28 Abduldaem S. Alqasemi; Mohamed E. Hereher; Ayad M. Fadhil Al-Quraishi; Hakim Saibi; Ala Aldahan; Abdelgadir Abuelgasim
Abstract Spatially distributed air temperature (Ta) data are essential for environmental studies. Ta data are collected from meteorological stations of sparse distribution. This problem can be overcome by using remotely sensed datasets at different scales. This study used land-based temperature measurements and satellite data for estimating Ta distribution over the United Arab Emirates. Land-based
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Subsidence risk assessment based on a novel hybrid form of a tree-based machine learning algorithm and an index model of vulnerability Geocarto Int. (IF 3.789) Pub Date : 2020-10-27 Ghazaleh Mohebbi Tafreshi; Mohammad Nakhaei; Razyeh Lak
Abstract Among natural hazards, land subsidence (LS) is considered to be one of the most significant natural disasters which occur in populated and inhabited regions of Iran. The Varamin aquifer, as one of the regions near the capital of Iran, where this geo-hazard has occurred widely and extensively, requires conducting comprehensive researches on LS. In this research, for LS risk assessment, the
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Questioning the effects of raster-resampling and slope on the precision of TanDEM-X 90 m Digital Elevation Model Geocarto Int. (IF 3.789) Pub Date : 2020-10-22 Arif Oguz Altunel
Abstract Defining watersheds with enough topographical features is crucial when large infrastructure projects are concerned. German Aerospace Center (DLR) recently announced the dissemination of the 90 m variant of the original global TanDEM-X 12 m Digital elevation model (DEM). It was announced as produced with a special averaging technique, which would even surpass the precision of the 12 m DEM.
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Mining the association rules between port shoreline and land utilization intensity: A case study in the coastal zone of Kuala Lumpur, Malaysia Geocarto Int. (IF 3.789) Pub Date : 2020-10-16 Jinfeng Yan; Ruiming Xiao; Fenzhen Su; Tian Wang; Jinbiao Bai; Feixue Jia; Jiaxue Du; Shiyi Zhao
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
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Rainfall Induced Landslide Susceptibility Mapping Using Novel Hybrid Soft Computing Methods Based on Multi-layer Perceptron Neural Network Classifier Geocarto Int. (IF 3.789) Pub Date : 2020-10-16 Mehebub Sahana; Binh Thai Pham; Manas Shukla; Romulus Costache; Do Xuan Thu; Rabin Chakrabortty; Neelima Satyam; Huu Duy Nguyen; Tran Van Phong; Hiep Van Le; Subodh Chandra Pal; G Areendran; Kashif Imdad; Indra Prakash
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
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A Spatio-temporal analysis of Baboon Damage using Sentinel-2 imagery and Extreme Gradient Boosting Geocarto Int. (IF 3.789) Pub Date : 2020-10-16 Regardt Ferreira; Kabir Peerbhay; Josua Louw; Ilaria Germizhuizen; Andrew Morris; Riyad Ismail
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
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Assessing and mapping landslide susceptibility using different machine learning methods Geocarto Int. (IF 3.789) Pub Date : 2020-10-16 Osman Orhan; Suleyman Sefa Bilgilioglu; Zehra Kaya; Adem Kursat Ozcan; Hacer Bilgilioglu
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
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An Approach for Multi-Dimensional Land Subsidence Velocity Estimation using Time-Series Sentinel-1 SAR Datasets by applying Persistent Scatterer Interferometry Technique Geocarto Int. (IF 3.789) Pub Date : 2020-10-07 Shubham Awasthi; Kamal Jain; Vishal Mishra; Ajeet Kumar
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
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Spatial modeling of accidents risk caused by driver drowsiness with data mining algorithms Geocarto Int. (IF 3.789) Pub Date : 2020-10-06 Farbod Farhangi; Abolghasem Sadeghi-Niaraki; Ali Nahvi; Seyed Vahid Razavi-Termeh
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
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Comparative Analysis of Gradient Boosting Algorithms for Landslide Susceptibility Mapping Geocarto Int. (IF 3.789) Pub Date : 2020-10-05 Emrehan Kutlug Sahin
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
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Novel Hybrid Models Combining Meta-heuristic Algorithms with Support Vector Regression (SVR) for Groundwater Potential Mapping Geocarto Int. (IF 3.789) Pub Date : 2020-10-02 A'kif Al-Fugara; Mohammad Ahmadlou; Rania Shatnawi; Saad AlAyyash; Rida Al-Adamata; Abdel Al-Rahman Al-Shabeeb; Sangeeta Soni
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
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A new method to detect targets in hyperspectral images based on principal component analysis Geocarto Int. (IF 3.789) Pub Date : 2020-10-02 Shahram Sharifi Hashjin; Safa Khazai
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
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