Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(7), P. 1135 - 1135

Published: April 2, 2020

Rapid and uncontrolled population growth along with economic industrial development, especially in developing countries during the late twentieth early twenty-first centuries, have increased rate of land-use/land-cover (LULC) change many times. Since quantitative assessment changes LULC is one most efficient means to understand manage land transformation, there a need examine accuracy different algorithms for mapping order identify best classifier further applications earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive (Fuzzy ARTMAP), spectral angle mapper (SAM) Mahalanobis distance (MD) were examined. Accuracy was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation root mean square error (RMSE). Results coefficient show that all classifiers similar level minor variation, but RF algorithm has highest 0.89 MD (parametric classifier) least 0.82. addition, visual cross-validation (correlations between normalised differentiation water index, vegetation index built-up are 0.96, 0.99 1, respectively, at 0.05 significance) comparison other adopted. Findings from literature also proved ANN classifiers, although non-parametric like SAM (Kappa 0.84; area under (AUC) 0.85) better consistent than algorithms. Finally, review concludes classifier, among examined it necessary test morphoclimatic conditions future.

Language: Английский

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The effects of changing land use and flood hazard on poverty in coastal Bangladesh

Land Use Policy, Journal Year: 2020, Volume and Issue: 99, P. 104868 - 104868

Published: July 1, 2020

Language: Английский

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Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(9), P. 1977 - 1977

Published: April 20, 2022

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, management of the Earth. With advent cloud computing platforms, time series feature extraction techniques, machine learning classifiers, new opportunities arising in more accurate large-scale LULC mapping. In this study, we aimed at finding out how two composition methods spectral–temporal metrics extracted from satellite can affect ability a classifier produce maps. We used Google Earth Engine (GEE) platform create cloud-free Sentinel-2 (S-2) Landsat-8 (L-8) over Tehran Province (Iran) as 2020. Two methods, namely, seasonal composites percentiles metrics, were define four datasets based on series, vegetation indices, topographic layers. The random forest was classification identifying most variables. Accuracy assessment results showed that S-2 outperformed L-8 overall class level. Moreover, comparison indicated percentile both series. At level, improved performance related their better about phenological variation different classes. Finally, conclude methodology GEE an fast way be

Language: Английский

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Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(21), P. 3539 - 3539

Published: Oct. 28, 2020

Garlic and winter wheat are major economic grain crops in China, their boundaries have increased substantially recent decades. Updated accurate garlic maps critical for assessing impacts on society the environment. Remote sensing imagery can be used to monitor spatial temporal changes croplands such as maize. However, our knowledge, few studies focusing area mapping. Here, we proposed a method coupling active passive satellite identification of both Northern China. First, (Sentinel-2 Landsat-8 images) extract (garlic wheat) with high accuracy. Second, applied (Sentinel-1 distinguish from wheat. Third, generated map by above two classification results. For evaluation classification, overall accuracy was 95.97%, kappa coefficient 0.94 eighteen validation quadrats (3 km 3 km). The user’s producer’s accuracies 95.83% 95.85%, respectively; wheat, these 97.20% 97.45%, respectively. This study provides practical exploration targeted crop mixed planting areas using multisource remote data.

Language: Английский

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Spatial-temporal pattern of land use conflict in China and its multilevel driving mechanisms

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 801, P. 149697 - 149697

Published: Aug. 20, 2021

Language: Английский

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Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh

Ecological Indicators, Journal Year: 2021, Volume and Issue: 126, P. 107612 - 107612

Published: March 30, 2021

Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due global environmental change. Many studies employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in existing literature. Thus, coupling bagging, random forest (RF), subspace (RSS), their ensemble model with multi-temporal datasets within GIS environment makes it possible Teesta River Basin (TRB), Bangladesh. The number patch (NP), edge density (ED), largest index (LPI), contagion (%) (CONTAG), aggregation (AI), perimeter area ratio (P/A ratio), class (CA), percentage landscape (PLAND), (PD), total (TE), shape (LSI) core (TCA) were matrices, which derived from maps using FRAGSTATS software. machine learning-based sensitivity models, such as decision tree support vector machine-based feature selection techniques implemented explore influence parameters modeling. results showed water bodies barren land substantially decreased by (6.21%), (14.59%) respectively while built-up areas increased 1.45% 2010 2019. Results revealed dominance agricultural has been human interference elevated TRB. However, twelve class-level matrices used delineate zone aid RF, RSS algorithms. images models validated kappa coefficient under curve (AUC) receiver operating characteristics (ROC). validation outcomes depicted three bagging (AUC = 0.864), RF 0.819), 0.859), 0.912) good capability appraise probability, highest precision level among models. Nearly 49% (1789 km2) was high very potential requires protected direct measures. analysis patches significantly influenced model, least sensitive parameter

Language: Английский

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Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan

Geology Ecology and Landscapes, Journal Year: 2021, Volume and Issue: 7(1), P. 46 - 58

Published: May 18, 2021

The aim of this research was to assess the land use/land cover (LULC) changes and its impact on surface temperature (LST) using remote-sensing (RS) technique in district Khanewal, Punjab, Pakistan. Data were pre-processed ERDAS imagine 15 Arc GIS 10.4 software for layer stacking, mosaicking, sub-setting Landsat images. After pre-processing, supervised classification scheme applied years 1980, 2000, 2020, which explains maximum likelihood algorithm identify LULC observed study area. "Built-up area" 1980 occupied 1.75% but build-up area increased (5.27%) compared 2020. Vegetation decreased by 4.12% from 2020 Khanewal. It that there has been a rapid change vegetation LST values 0.50°C due increasing East West direction Maximum minimum normalized difference index (NDVI) 0.72 −0.2 regression line produced definitive explanation, showing strong negative correlation with NDVI LST. outcomes indicated dramatic transformation took place Khanewal regarding decrease greenness increase population density, urban growth, other infrastructural developments. Thus, these results will be used regional planning managing agriculture coming environmental changes.

Language: Английский

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Spatiotemporal dynamics of wetlands and their driving factors based on PLS-SEM: A case study in Wuhan

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 806, P. 151310 - 151310

Published: Oct. 29, 2021

Globally, wetlands have been severely damaged due to natural environment and human activities. Understanding the spatiotemporal dynamics of their driving forces is essential for effective protection. This study proposes a research framework explore interaction between activities its impact on wetland changes, by introducing Partial Least Squares Structural Equation Modeling (PLS-SEM) Geographically Weighted Regression (GWR) model, then applying methodology in Wuhan, typical city China. The validity reliability evaluation indicated that PLS-SEM model reasonable. results showed area Wuhan decreased 10.98% 1990-2018 four obvious direct pathways influence were found. Positive soil terrain conditions are conducive maintaining wetlands, while rapid urbanization drastically reduce distribution wetlands. It remarkable climate gradually shifting from positive negative. Furthermore, potential indirect affecting shown enhance negative distribution, impacts weaken soil's impact. provides quantitative determining causes loss; it can also be applied other cities or regions, which more measures protect

Language: Английский

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Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 78, P. 103119 - 103119

Published: June 17, 2022

Language: Английский

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Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

Emerging Science Journal, Journal Year: 2023, Volume and Issue: 7(2), P. 428 - 444

Published: Feb. 14, 2023

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing different classification methods, datasets, satellite images, ancillary dataset types available. However, using just low spatial resolution visible bands remotely sensed images will not provide good accuracy. Remotely thermal data contains valuable to monitor investigate CD LU/LC. So, it needs involve datasets for better outcomes. Fusion plays a big role map CD. Therefore, this study aims find out refining method estimating accurate LU/LC patterns by investigating integration effectiveness (a) adopting noise removal model, (b) resampling, (c) image fusion, combining integrating between Grim Schmidt spectral (GS) method, (d) applying Mahalanobis distances (MH), Maximum likelihood (ML) artificial neural network (ANN) classifiers on captured from Landsat-8 TIRS OLI system, these were operational imager (OLI) infrared (TIRS) sensors 2015 2020 generate about twelve LC maps. (e) The comparison was made among all classifiers' results. results reveal that ANN technique integrated combined has highest compared rest applied approaches. obtained overall 96.31% 98.40%, kappa coefficients (0.94) (0.97) years 2020, respectively. ML classifier obtains MH approach. fusion improve 5%–6% proposed than spatial-resolution alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PDF

Language: Английский

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