Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine
Rubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the eco...
| Main Authors: | , , , , |
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| Format: | Article |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9531343/ |
| _version_ | 1818920582275661824 |
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| author | Yuchen Li Chenli Liu Jun Zhang Ping Zhang Yufei Xue |
| author_facet | Yuchen Li Chenli Liu Jun Zhang Ping Zhang Yufei Xue |
| author_sort | Yuchen Li |
| collection | DOAJ |
| description | Rubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the economy, and the environment. However, existing optical images are greatly limited by frequent cloud cover, which seriously affects the accuracy of rubber plantation area extraction. To overcome this issue, we used dense Landsat time series stacks based on Google Earth Engine, combined phenological features, and applied random forest algorithms to monitor rubber plantations in Xishuangbanna from 1987 to 2020. The results showed that <xref ref-type="disp-formula" rid="deqn1">1</xref>) the phenological characteristics of rubber plantations in Xishuangbanna indicated that the leaf-off period lasts from late December to mid-February of the following year, while the leaf-on of rubber plantations occurred in other months; <xref ref-type="disp-formula" rid="deqn2">2</xref>) the overall accuracy and kappa coefficient values ranged from 0.82 to 0.96 and 0.76 to 0.95, respectively, showing that the extraction accuracy of rubber plantation information can meet the accuracy requirement; <xref ref-type="disp-formula" rid="deqn3">3</xref>) the rubber plantation area in Xishuangbanna increased between 1987 and 2020 from 7.05×10<sup>4</sup> to 47.78×10<sup>4</sup> hm<sup>2</sup>. The peak rubber plantation area occurred in 2015 (49.60×10<sup>4</sup> hm<sup>2</sup>) followed by a downward trend; <xref ref-type="disp-formula" rid="deqn4">4</xref>) spatially, the rubber plantation is mainly distributed in Jinghong City and Mengla County, while less abundant in Menghai County. Overall, this article is expected to contribute to the rapid and accurate mapping of rubber plantations in large-scale applications and analysis. |
| first_indexed | 2024-12-20T01:24:02Z |
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| id | doaj.art-d7c0c2a39f5c401d83d199abde975958 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-20T01:24:02Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-d7c0c2a39f5c401d83d199abde9759582022-12-21T19:58:18ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01149450946110.1109/JSTARS.2021.31107639531343Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth EngineYuchen Li0Chenli Liu1https://orcid.org/0000-0001-6389-8792Jun Zhang2https://orcid.org/0000-0002-1278-6077Ping Zhang3Yufei Xue4School of Earth Sciences, Yunnan University, Kunming, ChinaState Key Laboratory of Grassland Agro-Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaRubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the economy, and the environment. However, existing optical images are greatly limited by frequent cloud cover, which seriously affects the accuracy of rubber plantation area extraction. To overcome this issue, we used dense Landsat time series stacks based on Google Earth Engine, combined phenological features, and applied random forest algorithms to monitor rubber plantations in Xishuangbanna from 1987 to 2020. The results showed that <xref ref-type="disp-formula" rid="deqn1">1</xref>) the phenological characteristics of rubber plantations in Xishuangbanna indicated that the leaf-off period lasts from late December to mid-February of the following year, while the leaf-on of rubber plantations occurred in other months; <xref ref-type="disp-formula" rid="deqn2">2</xref>) the overall accuracy and kappa coefficient values ranged from 0.82 to 0.96 and 0.76 to 0.95, respectively, showing that the extraction accuracy of rubber plantation information can meet the accuracy requirement; <xref ref-type="disp-formula" rid="deqn3">3</xref>) the rubber plantation area in Xishuangbanna increased between 1987 and 2020 from 7.05×10<sup>4</sup> to 47.78×10<sup>4</sup> hm<sup>2</sup>. The peak rubber plantation area occurred in 2015 (49.60×10<sup>4</sup> hm<sup>2</sup>) followed by a downward trend; <xref ref-type="disp-formula" rid="deqn4">4</xref>) spatially, the rubber plantation is mainly distributed in Jinghong City and Mengla County, while less abundant in Menghai County. Overall, this article is expected to contribute to the rapid and accurate mapping of rubber plantations in large-scale applications and analysis.https://ieeexplore.ieee.org/document/9531343/Google Earth Engine (GEE)phenologyrandom forest (RF)rubber plantationXishuangbanna |
| spellingShingle | Yuchen Li Chenli Liu Jun Zhang Ping Zhang Yufei Xue Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Google Earth Engine (GEE) phenology random forest (RF) rubber plantation Xishuangbanna |
| title | Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine |
| title_full | Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine |
| title_fullStr | Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine |
| title_full_unstemmed | Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine |
| title_short | Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine |
| title_sort | monitoring spatial and temporal patterns of rubber plantation dynamics using time series landsat images and google earth engine |
| topic | Google Earth Engine (GEE) phenology random forest (RF) rubber plantation Xishuangbanna |
| url | https://ieeexplore.ieee.org/document/9531343/ |
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