A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery
Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenologica...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2045 |
| _version_ | 1797503020234178560 |
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| author | Xinyi Li Chen Sun Huimin Meng Xin Ma Guanhua Huang Xu Xu |
| author_facet | Xinyi Li Chen Sun Huimin Meng Xin Ma Guanhua Huang Xu Xu |
| author_sort | Xinyi Li |
| collection | DOAJ |
| description | Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions. |
| first_indexed | 2024-03-10T03:44:31Z |
| format | Article |
| id | doaj.art-00287a5faf17413384cc93d11be9959f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:31Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-00287a5faf17413384cc93d11be9959f2023-11-23T09:09:43ZengMDPI AGRemote Sensing2072-42922022-04-01149204510.3390/rs14092045A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite ImageryXinyi Li0Chen Sun1Huimin Meng2Xin Ma3Guanhua Huang4Xu Xu5Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaChinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, ChinaWater Resources Research Institute of Inner Mongolia, Hohhot 010051, ChinaChinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, ChinaChinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, ChinaUpdated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions.https://www.mdpi.com/2072-4292/14/9/2045land cover classificationcrop mappingphenologyfragmented agricultural landscapesSentinel-2 satellite |
| spellingShingle | Xinyi Li Chen Sun Huimin Meng Xin Ma Guanhua Huang Xu Xu A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery Remote Sensing land cover classification crop mapping phenology fragmented agricultural landscapes Sentinel-2 satellite |
| title | A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery |
| title_full | A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery |
| title_fullStr | A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery |
| title_full_unstemmed | A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery |
| title_short | A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery |
| title_sort | novel efficient method for land cover classification in fragmented agricultural landscapes using sentinel satellite imagery |
| topic | land cover classification crop mapping phenology fragmented agricultural landscapes Sentinel-2 satellite |
| url | https://www.mdpi.com/2072-4292/14/9/2045 |
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