A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China
Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. Th...
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MDPI AG
2018-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/10/12/1899 |
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| author | Zhenlong Cheng Dengsheng Lu Guiying Li Jianqin Huang Nibedita Sinha Junjun Zhi Shaojin Li |
| author_facet | Zhenlong Cheng Dengsheng Lu Guiying Li Jianqin Huang Nibedita Sinha Junjun Zhi Shaojin Li |
| author_sort | Zhenlong Cheng |
| collection | DOAJ |
| description | Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems. |
| first_indexed | 2024-04-11T16:09:52Z |
| format | Article |
| id | doaj.art-a7c27d98f5784104a838ce3ffaa7f7d1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:09:52Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a7c27d98f5784104a838ce3ffaa7f7d12022-12-22T04:14:43ZengMDPI AGRemote Sensing2072-42922018-11-011012189910.3390/rs10121899rs10121899A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, ChinaZhenlong Cheng0Dengsheng Lu1Guiying Li2Jianqin Huang3Nibedita Sinha4Junjun Zhi5Shaojin Li6State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, ChinaFujian Provincial Key Laboratory for Subtropical Resources and Environment, Fujian Normal University, Fuzhou 350007, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaLin’An Meteorological Bureau, Hangzhou 311300, ChinaIncreasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.https://www.mdpi.com/2072-4292/10/12/1899soil erosion riskhickory plantationsrandom forestsubtropical mountainous regionremote sensingGIS |
| spellingShingle | Zhenlong Cheng Dengsheng Lu Guiying Li Jianqin Huang Nibedita Sinha Junjun Zhi Shaojin Li A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China Remote Sensing soil erosion risk hickory plantations random forest subtropical mountainous region remote sensing GIS |
| title | A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China |
| title_full | A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China |
| title_fullStr | A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China |
| title_full_unstemmed | A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China |
| title_short | A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China |
| title_sort | random forest based approach to map soil erosion risk distribution in hickory plantations in western zhejiang province china |
| topic | soil erosion risk hickory plantations random forest subtropical mountainous region remote sensing GIS |
| url | https://www.mdpi.com/2072-4292/10/12/1899 |
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