Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas r...

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Main Authors: Lihong Zhu, Xiangnan Liu, Ling Wu, Yibo Tang, Yuanyuan Meng
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/10/1234
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spelling doaj-8505fd1e7d29402f9ae04a7ca3b8b1f42020-11-25T01:36:36ZengMDPI AGRemote Sensing2072-42922019-05-011110123410.3390/rs11101234rs11101234Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat ImageryLihong Zhu0Xiangnan Liu1Ling Wu2Yibo Tang3Yuanyuan Meng4School of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaTracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998&#8722;2018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km<sup>2</sup> was converted to lake and 499.9 km<sup>2</sup> was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region.https://www.mdpi.com/2072-4292/11/10/1234cropland change patternsLandTrendr algorithmLandsat time seriesGoogle Earth EngineDongting LakeChina
collection DOAJ
language English
format Article
sources DOAJ
author Lihong Zhu
Xiangnan Liu
Ling Wu
Yibo Tang
Yuanyuan Meng
spellingShingle Lihong Zhu
Xiangnan Liu
Ling Wu
Yibo Tang
Yuanyuan Meng
Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
Remote Sensing
cropland change patterns
LandTrendr algorithm
Landsat time series
Google Earth Engine
Dongting Lake
China
author_facet Lihong Zhu
Xiangnan Liu
Ling Wu
Yibo Tang
Yuanyuan Meng
author_sort Lihong Zhu
title Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
title_short Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
title_full Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
title_fullStr Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
title_full_unstemmed Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
title_sort long-term monitoring of cropland change near dongting lake, china, using the landtrendr algorithm with landsat imagery
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-05-01
description Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998&#8722;2018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km<sup>2</sup> was converted to lake and 499.9 km<sup>2</sup> was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region.
topic cropland change patterns
LandTrendr algorithm
Landsat time series
Google Earth Engine
Dongting Lake
China
url https://www.mdpi.com/2072-4292/11/10/1234
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