An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6188 |
| _version_ | 1827637082740228096 |
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| author | Xiaojing Xue Caiyong Wei Qin Yang Lingwen Tian Lihong Zhu Yuanyuan Meng Xiangnan Liu |
| author_facet | Xiaojing Xue Caiyong Wei Qin Yang Lingwen Tian Lihong Zhu Yuanyuan Meng Xiangnan Liu |
| author_sort | Xiaojing Xue |
| collection | DOAJ |
| description | Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems. |
| first_indexed | 2024-03-09T15:54:22Z |
| format | Article |
| id | doaj.art-48b85273449f406c98d65ebdd15053a3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-48b85273449f406c98d65ebdd15053a32023-11-24T17:45:34ZengMDPI AGRemote Sensing2072-42922022-12-011424618810.3390/rs14246188An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time SeriesXiaojing Xue0Caiyong Wei1Qin Yang2Lingwen Tian3Lihong Zhu4Yuanyuan Meng5Xiangnan Liu6School of Information Engineering, China University of Geoscience, Beijing 100083, ChinaSchool of Information Engineering, China University of Geoscience, Beijing 100083, ChinaSchool of Information Engineering, China University of Geoscience, Beijing 100083, ChinaSchool of Information Engineering, China University of Geoscience, Beijing 100083, ChinaSchool of Information Engineering, China University of Geoscience, Beijing 100083, ChinaInstitute of Ecology, College of Urban and Environmental Sciences and Key Laboratory for Earth Surface Processes, Peking University, Beijing 100871, ChinaSchool of Information Engineering, China University of Geoscience, Beijing 100083, ChinaLarge-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems.https://www.mdpi.com/2072-4292/14/24/6188planted forests mappingshapelet-based classificationimage segmentationobject-level time seriesforestry eco-engineering projects |
| spellingShingle | Xiaojing Xue Caiyong Wei Qin Yang Lingwen Tian Lihong Zhu Yuanyuan Meng Xiangnan Liu An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series Remote Sensing planted forests mapping shapelet-based classification image segmentation object-level time series forestry eco-engineering projects |
| title | An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series |
| title_full | An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series |
| title_fullStr | An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series |
| title_full_unstemmed | An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series |
| title_short | An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series |
| title_sort | object and shapelet based method for mapping planted forest dynamics from landsat time series |
| topic | planted forests mapping shapelet-based classification image segmentation object-level time series forestry eco-engineering projects |
| url | https://www.mdpi.com/2072-4292/14/24/6188 |
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