CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China

The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious confl...

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Main Authors: Le’an Qu, Zhenjie Chen, Manchun Li
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/11/20/5657
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spelling doaj-eda41bb63280405ba120342bcc2302302020-11-25T01:39:23ZengMDPI AGSustainability2071-10502019-10-011120565710.3390/su11205657su11205657CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, ChinaLe’an Qu0Zhenjie Chen1Manchun Li2School of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaThe periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%; (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of “salt-and-pepper” noise; and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.https://www.mdpi.com/2071-1050/11/20/5657land use changecart-rf classificationtime-series dataspatial-temporal-logical filterjiangsu
collection DOAJ
language English
format Article
sources DOAJ
author Le’an Qu
Zhenjie Chen
Manchun Li
spellingShingle Le’an Qu
Zhenjie Chen
Manchun Li
CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
Sustainability
land use change
cart-rf classification
time-series data
spatial-temporal-logical filter
jiangsu
author_facet Le’an Qu
Zhenjie Chen
Manchun Li
author_sort Le’an Qu
title CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
title_short CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
title_full CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
title_fullStr CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
title_full_unstemmed CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China
title_sort cart-rf classification with multifilter for monitoring land use changes based on modis time-series data: a case study from jiangsu province, china
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2019-10-01
description The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%; (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of “salt-and-pepper” noise; and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.
topic land use change
cart-rf classification
time-series data
spatial-temporal-logical filter
jiangsu
url https://www.mdpi.com/2071-1050/11/20/5657
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