Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua
The amount of cultivated land per capita in China is relatively low, and the phenomenon of non-cultivated land (NCL) in recent years has negatively impacted the stability of grain production in China. In this study, long-time series images obtained via satellite remote sensing were used to monitor s...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9852424/ |
| _version_ | 1811308712084635648 |
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| author | Sen Zhang Hebing Zhang Xiaohe Gu Jinbao Liu Ziyan Yin Qian Sun Zhonghui Wei Yuchun Pan |
| author_facet | Sen Zhang Hebing Zhang Xiaohe Gu Jinbao Liu Ziyan Yin Qian Sun Zhonghui Wei Yuchun Pan |
| author_sort | Sen Zhang |
| collection | DOAJ |
| description | The amount of cultivated land per capita in China is relatively low, and the phenomenon of non-cultivated land (NCL) in recent years has negatively impacted the stability of grain production in China. In this study, long-time series images obtained via satellite remote sensing were used to monitor spatio-temporal changes in NCL at the county scale. Seven-phase images were acquired from 1990 to 2020 (every five years) using medium-resolution Landsat MSS, TM, ETM+, and Sentinel MSI. Vegetation indices and texture features were extracted for all images. Terrain features such as slope, aspect and elevation were extracted from the DEM data. Combining vegetation index features, texture features, terrain features and multispectral bands, the image classification was performed using the random forest (RF) algorithm. The indices of classification accuracy assessment indices included overall accuracy (OA) and multiclass F-scores (F<sub>m</sub>). Zonal statistics were used to calculate the area of cultivated land in towns for 1990 and 2020, and to create grades for the reduction of cultivated land. Finally, indicators including land use dynamic degree (LUDD), land use type change (LUTC) and land use change rate (LUCR) were adopted to reflect the spatio-temporal of NCL in the study area. The results show that RF classification algorithm achieves accurate and efficient land use extraction. The OA were greater than 86%, and the F<sub>m</sub> were over 0.88. The cultivated land area in the study area showed decreasing trend. From 1990 to 2020, the ratio of cultivated land decreased from 59.75% to 50.21%. Meanwhile, the dynamic degree of cultivated land increased annually. The conversion of cultivated land into construction land was dominant, accounting for 31.84% of the total change in cultivated land over the past 30 years. This study also reveals that NCL is highly related to local economic and land-use policies. Multi-source remote sensing data have been used to quantitatively analyse the spatio-temporal changes in cultivated land conversion, providing a reference for relevant land management departments to master cultivated land use changes and adjust land management policies. |
| first_indexed | 2024-04-13T09:28:11Z |
| format | Article |
| id | doaj.art-d799994b537b4ffa9257827c684d5176 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:28:11Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d799994b537b4ffa9257827c684d51762022-12-22T02:52:22ZengIEEEIEEE Access2169-35362022-01-0110845188453410.1109/ACCESS.2022.31976509852424Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in XinghuaSen Zhang0https://orcid.org/0000-0002-3932-0306Hebing Zhang1Xiaohe Gu2https://orcid.org/0000-0002-7102-1939Jinbao Liu3Ziyan Yin4Qian Sun5Zhonghui Wei6Yuchun Pan7School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaInstitute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Company Ltd., Xi’an, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaThe amount of cultivated land per capita in China is relatively low, and the phenomenon of non-cultivated land (NCL) in recent years has negatively impacted the stability of grain production in China. In this study, long-time series images obtained via satellite remote sensing were used to monitor spatio-temporal changes in NCL at the county scale. Seven-phase images were acquired from 1990 to 2020 (every five years) using medium-resolution Landsat MSS, TM, ETM+, and Sentinel MSI. Vegetation indices and texture features were extracted for all images. Terrain features such as slope, aspect and elevation were extracted from the DEM data. Combining vegetation index features, texture features, terrain features and multispectral bands, the image classification was performed using the random forest (RF) algorithm. The indices of classification accuracy assessment indices included overall accuracy (OA) and multiclass F-scores (F<sub>m</sub>). Zonal statistics were used to calculate the area of cultivated land in towns for 1990 and 2020, and to create grades for the reduction of cultivated land. Finally, indicators including land use dynamic degree (LUDD), land use type change (LUTC) and land use change rate (LUCR) were adopted to reflect the spatio-temporal of NCL in the study area. The results show that RF classification algorithm achieves accurate and efficient land use extraction. The OA were greater than 86%, and the F<sub>m</sub> were over 0.88. The cultivated land area in the study area showed decreasing trend. From 1990 to 2020, the ratio of cultivated land decreased from 59.75% to 50.21%. Meanwhile, the dynamic degree of cultivated land increased annually. The conversion of cultivated land into construction land was dominant, accounting for 31.84% of the total change in cultivated land over the past 30 years. This study also reveals that NCL is highly related to local economic and land-use policies. Multi-source remote sensing data have been used to quantitatively analyse the spatio-temporal changes in cultivated land conversion, providing a reference for relevant land management departments to master cultivated land use changes and adjust land management policies.https://ieeexplore.ieee.org/document/9852424/Non-agricultural cultivated landlong-time seriesrandom forestspatio-temporal changeremote sensing |
| spellingShingle | Sen Zhang Hebing Zhang Xiaohe Gu Jinbao Liu Ziyan Yin Qian Sun Zhonghui Wei Yuchun Pan Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua IEEE Access Non-agricultural cultivated land long-time series random forest spatio-temporal change remote sensing |
| title | Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua |
| title_full | Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua |
| title_fullStr | Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua |
| title_full_unstemmed | Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua |
| title_short | Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua |
| title_sort | monitoring the spatio temporal changes of non cultivated land via long time series remote sensing images in xinghua |
| topic | Non-agricultural cultivated land long-time series random forest spatio-temporal change remote sensing |
| url | https://ieeexplore.ieee.org/document/9852424/ |
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