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

Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in th...

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Autores principales:	Swapan Talukdar, Kutub Uddin Eibek, Shumona Akhter, Sk Ziaul, Abu Reza Md. Towfiqul Islam, Javed Mallick
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Land-use and land cover Fragmentation probability Machine learning algorithms, Land use transitional matrix Sensitivity analysis Teesta River basin Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/f76fae7efb014f07846f6ccdd3aad824
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spelling	oai:doaj.org-article:f76fae7efb014f07846f6ccdd3aad8242021-12-01T04:49:28ZModeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh1470-160X10.1016/j.ecolind.2021.107612https://doaj.org/article/f76fae7efb014f07846f6ccdd3aad8242021-07-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21002776https://doaj.org/toc/1470-160XLand-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in the existing literature. Thus, the coupling of bagging, random forest (RF), random subspace (RSS), and their ensemble model with multi-temporal datasets within the GIS environment makes it possible to model the fragmentation probability of LULC in the Teesta River Basin (TRB), Bangladesh. The number of patch (NP), edge density (ED), largest patch index (LPI), contagion index (%) (CONTAG), aggregation index (AI), perimeter area ratio (P/A ratio), the class area (CA), percentage of landscape (PLAND), patch density (PD), total edge (TE), largest shape index (LSI) and total core area (TCA) were the landscape and class matrices, which were derived from the LULC maps using FRAGSTATS software. The machine learning-based sensitivity models, such as decision tree and support vector machine-based feature selection techniques were implemented to explore the influence of the parameters for fragmentation probability modeling. The results showed that water bodies and barren land were substantially decreased by (6.21%), and (14.59%) respectively while the built-up areas increased by 1.45% from 2010 to 2019. Results revealed that the dominance of the agricultural area has been increased as human interference has been elevated in the TRB. However, twelve class-level and landscape matrices were used to delineate the fragmentation probability zone with the aid of bagging, RF, and RSS algorithms. LULC images and fragmentation probability models were validated using the kappa coefficient and the area under curve (AUC) of the receiver operating characteristics (ROC). The validation outcomes depicted that the three models such as bagging (AUC = 0.864), RF (AUC = 0.819), RSS (AUC = 0.859), and ensemble model (AUC = 0.912) have a good capability to appraise the fragmentation probability, and ensemble model has the highest precision level among three models. Nearly 49% (1789 km2) of the LULC was under a high to very high fragmentation potential zone that requires to be protected with direct measures. The results of sensitivity analysis showed that the number of patches significantly influenced the fragmentation probability model, while the largest patch index was the least sensitive parameter for modeling.Swapan TalukdarKutub Uddin EibekShumona AkhterSk ZiaulAbu Reza Md. Towfiqul IslamJaved MallickElsevierarticleLand-use and land coverFragmentation probabilityMachine learning algorithms,Land use transitional matrixSensitivity analysisTeesta River basinEcologyQH540-549.5ENEcological Indicators, Vol 126, Iss , Pp 107612- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Land-use and land cover Fragmentation probability Machine learning algorithms, Land use transitional matrix Sensitivity analysis Teesta River basin Ecology QH540-549.5
spellingShingle	Land-use and land cover Fragmentation probability Machine learning algorithms, Land use transitional matrix Sensitivity analysis Teesta River basin Ecology QH540-549.5 Swapan Talukdar Kutub Uddin Eibek Shumona Akhter Sk Ziaul Abu Reza Md. Towfiqul Islam Javed Mallick Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
description	Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in the existing literature. Thus, the coupling of bagging, random forest (RF), random subspace (RSS), and their ensemble model with multi-temporal datasets within the GIS environment makes it possible to model the fragmentation probability of LULC in the Teesta River Basin (TRB), Bangladesh. The number of patch (NP), edge density (ED), largest patch index (LPI), contagion index (%) (CONTAG), aggregation index (AI), perimeter area ratio (P/A ratio), the class area (CA), percentage of landscape (PLAND), patch density (PD), total edge (TE), largest shape index (LSI) and total core area (TCA) were the landscape and class matrices, which were derived from the LULC maps using FRAGSTATS software. The machine learning-based sensitivity models, such as decision tree and support vector machine-based feature selection techniques were implemented to explore the influence of the parameters for fragmentation probability modeling. The results showed that water bodies and barren land were substantially decreased by (6.21%), and (14.59%) respectively while the built-up areas increased by 1.45% from 2010 to 2019. Results revealed that the dominance of the agricultural area has been increased as human interference has been elevated in the TRB. However, twelve class-level and landscape matrices were used to delineate the fragmentation probability zone with the aid of bagging, RF, and RSS algorithms. LULC images and fragmentation probability models were validated using the kappa coefficient and the area under curve (AUC) of the receiver operating characteristics (ROC). The validation outcomes depicted that the three models such as bagging (AUC = 0.864), RF (AUC = 0.819), RSS (AUC = 0.859), and ensemble model (AUC = 0.912) have a good capability to appraise the fragmentation probability, and ensemble model has the highest precision level among three models. Nearly 49% (1789 km2) of the LULC was under a high to very high fragmentation potential zone that requires to be protected with direct measures. The results of sensitivity analysis showed that the number of patches significantly influenced the fragmentation probability model, while the largest patch index was the least sensitive parameter for modeling.
format	article
author	Swapan Talukdar Kutub Uddin Eibek Shumona Akhter Sk Ziaul Abu Reza Md. Towfiqul Islam Javed Mallick
author_facet	Swapan Talukdar Kutub Uddin Eibek Shumona Akhter Sk Ziaul Abu Reza Md. Towfiqul Islam Javed Mallick
author_sort	Swapan Talukdar
title	Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
title_short	Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
title_full	Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
title_fullStr	Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
title_full_unstemmed	Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh
title_sort	modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the teesta river basin, bangladesh
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f76fae7efb014f07846f6ccdd3aad824
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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

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