Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method

In China, a nation facing farmland scarcity, accurate mapping of winter fallow arable lands is crucial for enhancing crop rotation and land use efficiency. The Dynamic Threshold (DT) method commonly used in phenology and winter fallow land studies often employs empirically set subjective thresholds....

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Main Authors: Xiangyi Wang, Yingbin He, Yan Zha, Huicong Chen, Yongye Wang, Xiuying Wu, Jiong Ning, Anran Feng, Shengnan Han, Shanjun Luo
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:GIScience & Remote Sensing
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Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2024.2333587
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author Xiangyi Wang
Yingbin He
Yan Zha
Huicong Chen
Yongye Wang
Xiuying Wu
Jiong Ning
Anran Feng
Shengnan Han
Shanjun Luo
author_facet Xiangyi Wang
Yingbin He
Yan Zha
Huicong Chen
Yongye Wang
Xiuying Wu
Jiong Ning
Anran Feng
Shengnan Han
Shanjun Luo
author_sort Xiangyi Wang
collection DOAJ
description In China, a nation facing farmland scarcity, accurate mapping of winter fallow arable lands is crucial for enhancing crop rotation and land use efficiency. The Dynamic Threshold (DT) method commonly used in phenology and winter fallow land studies often employs empirically set subjective thresholds. This approach tends to uniformly apply a single threshold across diverse vegetation types and geographical regions, neglecting the variations in physiological traits and spatial heterogeneity. This study developed a multitemporal overlapping area minimization threshold method (MOAMT). This approach constructed Normalized difference vegetation index (NDVI) probability density functions for both winter fallow and non-winter fallow arable land across a time series to attain a statistically significant threshold. We use MOAMT and DT methods for winter fallow arable land extraction in southern China, respectively. And then, comparison of identification accuracy between MOAMT and DT has been implemented by using confusion matrix method. Compared to the DT method, MOAMT exhibits better performance. The mapping of winter fallow arable lands highlighted their predominant distribution in the middle and lower reaches of the Yangtze River (MLR-YR) Basin, covering approximately 20.8 million hectares, with potential for development. This study will provide information support for the optimization of planting layouts in China, offering new opportunities for further increasing grain production. While in the sense of research approach, this study indicates the superiority of threshold obtained through statistical probability calibration over empirical universal thresholds for classification accuracy. We advocate for prioritizing threshold calibration in future applications of threshold classification methods.
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publishDate 2024-12-01
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series GIScience & Remote Sensing
spelling doaj-art-28ee1a8b8bce4b1498aec6ad4b8b60792025-08-20T02:31:26ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2333587Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold methodXiangyi Wang0Yingbin He1Yan Zha2Huicong Chen3Yongye Wang4Xiuying Wu5Jiong Ning6Anran Feng7Shengnan Han8Shanjun Luo9State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaSchool of Environmental Science and Engineering, Tiangong University, Tianjin, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaSchool of Environmental Science and Engineering, Tiangong University, Tianjin, ChinaSchool of Environmental Science and Engineering, Tiangong University, Tianjin, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaAerospace Information Research institute, Henan Academy of Sciences, Zhengzhou, ChinaIn China, a nation facing farmland scarcity, accurate mapping of winter fallow arable lands is crucial for enhancing crop rotation and land use efficiency. The Dynamic Threshold (DT) method commonly used in phenology and winter fallow land studies often employs empirically set subjective thresholds. This approach tends to uniformly apply a single threshold across diverse vegetation types and geographical regions, neglecting the variations in physiological traits and spatial heterogeneity. This study developed a multitemporal overlapping area minimization threshold method (MOAMT). This approach constructed Normalized difference vegetation index (NDVI) probability density functions for both winter fallow and non-winter fallow arable land across a time series to attain a statistically significant threshold. We use MOAMT and DT methods for winter fallow arable land extraction in southern China, respectively. And then, comparison of identification accuracy between MOAMT and DT has been implemented by using confusion matrix method. Compared to the DT method, MOAMT exhibits better performance. The mapping of winter fallow arable lands highlighted their predominant distribution in the middle and lower reaches of the Yangtze River (MLR-YR) Basin, covering approximately 20.8 million hectares, with potential for development. This study will provide information support for the optimization of planting layouts in China, offering new opportunities for further increasing grain production. While in the sense of research approach, this study indicates the superiority of threshold obtained through statistical probability calibration over empirical universal thresholds for classification accuracy. We advocate for prioritizing threshold calibration in future applications of threshold classification methods.https://www.tandfonline.com/doi/10.1080/15481603.2024.2333587Winter fallow arable landsmulti-temporal overlapped area minimization threshold method (MOAMT)dynamic threshold (DT)GEEsamples
spellingShingle Xiangyi Wang
Yingbin He
Yan Zha
Huicong Chen
Yongye Wang
Xiuying Wu
Jiong Ning
Anran Feng
Shengnan Han
Shanjun Luo
Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
GIScience & Remote Sensing
Winter fallow arable lands
multi-temporal overlapped area minimization threshold method (MOAMT)
dynamic threshold (DT)
GEE
samples
title Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
title_full Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
title_fullStr Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
title_full_unstemmed Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
title_short Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method
title_sort mapping winter fallow arable lands in southern china by using a multi temporal overlapped area minimization threshold method
topic Winter fallow arable lands
multi-temporal overlapped area minimization threshold method (MOAMT)
dynamic threshold (DT)
GEE
samples
url https://www.tandfonline.com/doi/10.1080/15481603.2024.2333587
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