Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China
Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/15/2/288 |
| _version_ | 1797298240938311680 |
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| author | Lizhi Liu Qiuliang Zhang Ying Guo Yu Li Bing Wang Erxue Chen Zengyuan Li Shuai Hao |
| author_facet | Lizhi Liu Qiuliang Zhang Ying Guo Yu Li Bing Wang Erxue Chen Zengyuan Li Shuai Hao |
| author_sort | Lizhi Liu |
| collection | DOAJ |
| description | Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U<sup>2</sup>-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km<sup>2</sup>, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment. |
| first_indexed | 2024-03-07T22:32:52Z |
| format | Article |
| id | doaj.art-abf758fdcbf6467685715170d1b5513e |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-07T22:32:52Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-abf758fdcbf6467685715170d1b5513e2024-02-23T15:16:51ZengMDPI AGForests1999-49072024-02-0115228810.3390/f15020288Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, ChinaLizhi Liu0Qiuliang Zhang1Ying Guo2Yu Li3Bing Wang4Erxue Chen5Zengyuan Li6Shuai Hao7Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, ChinaInformation about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U<sup>2</sup>-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km<sup>2</sup>, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.https://www.mdpi.com/1999-4907/15/2/288coniferous forestssemi-aridmulti-classifier fusionGaofen-1Sentinel-1Google Earth Engine |
| spellingShingle | Lizhi Liu Qiuliang Zhang Ying Guo Yu Li Bing Wang Erxue Chen Zengyuan Li Shuai Hao Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China Forests coniferous forests semi-arid multi-classifier fusion Gaofen-1 Sentinel-1 Google Earth Engine |
| title | Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China |
| title_full | Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China |
| title_fullStr | Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China |
| title_full_unstemmed | Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China |
| title_short | Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China |
| title_sort | mapping coniferous forest distribution in a semi arid area based on multi classifier fusion and google earth engine combining gaofen 1 and sentinel 1 data a case study in northwestern liaoning china |
| topic | coniferous forests semi-arid multi-classifier fusion Gaofen-1 Sentinel-1 Google Earth Engine |
| url | https://www.mdpi.com/1999-4907/15/2/288 |
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