Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm
Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/23/4762 |
| _version_ | 1797507266698543104 |
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| author | Panpan Wei Weiwei Zhu Yifan Zhao Peng Fang Xiwang Zhang Nana Yan Hao Zhao |
| author_facet | Panpan Wei Weiwei Zhu Yifan Zhao Peng Fang Xiwang Zhang Nana Yan Hao Zhao |
| author_sort | Panpan Wei |
| collection | DOAJ |
| description | Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification. |
| first_indexed | 2024-03-10T04:46:06Z |
| format | Article |
| id | doaj.art-16d91b313e6a45d681bdac67e4e2e158 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T04:46:06Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-16d91b313e6a45d681bdac67e4e2e1582023-11-23T02:55:58ZengMDPI AGRemote Sensing2072-42922021-11-011323476210.3390/rs13234762Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF AlgorithmPanpan Wei0Weiwei Zhu1Yifan Zhao2Peng Fang3Xiwang Zhang4Nana Yan5Hao Zhao6Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaAfrica has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.https://www.mdpi.com/2072-4292/13/23/4762KenyaRFE-RF algorithmgrasslandrandom forest classifierGEE |
| spellingShingle | Panpan Wei Weiwei Zhu Yifan Zhao Peng Fang Xiwang Zhang Nana Yan Hao Zhao Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm Remote Sensing Kenya RFE-RF algorithm grassland random forest classifier GEE |
| title | Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm |
| title_full | Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm |
| title_fullStr | Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm |
| title_full_unstemmed | Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm |
| title_short | Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm |
| title_sort | extraction of kenyan grassland information using proba v based on rfe rf algorithm |
| topic | Kenya RFE-RF algorithm grassland random forest classifier GEE |
| url | https://www.mdpi.com/2072-4292/13/23/4762 |
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