Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province
The acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We comb...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24012457 |
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| author | Yicheng Li Zhuo Wu Linglong Zhu Xiaocheng Huang Jianhong Mo |
| author_facet | Yicheng Li Zhuo Wu Linglong Zhu Xiaocheng Huang Jianhong Mo |
| author_sort | Yicheng Li |
| collection | DOAJ |
| description | The acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We combined six commonly land cover datasets and forest age, canopy height, above-ground biomass, and tree species distribution datasets to reconstruct 30 m spatially accurate forest refinement dataset (FRD) for Guangdong Province. In addition, the distribution characteristics of forest structure and function were evaluated using forest morphological spatial pattern analysis. The results show that the overall accuracy of FRD of the Guangdong Province in 2020 reached 86.07 %. Forest types in the Guangdong Province were mainly dominated by evergreen needle-leaf forests. Tsuga chinensis, Red cedar, and Pinus sylvestris were more commonly planted. Older and taller trees were found in northern and eastern Guangdong. In addition, forest above-ground biomass (AGB) was larger in the coastal areas of northern and western Guangdong. The core and perforation had the oldest age and the highest tree height, and the islet had the lowest for all forest structure and function indicators. Based on multi-source datasets, this study contributes to a better understanding of the attributes characterizing the structure and function of forests. The refined dataset and research framework will effectively enhance forest management efficiency and policy making, as well as provide case references for research on climate change response, forest conservation and biodiversity assessment. |
| format | Article |
| id | doaj-art-313066f8eeda4b51b9dc5b1f0c380a11 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-313066f8eeda4b51b9dc5b1f0c380a112024-12-16T05:35:02ZengElsevierEcological Indicators1470-160X2024-12-01169112788Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong ProvinceYicheng Li0Zhuo Wu1Linglong Zhu2Xiaocheng Huang3Jianhong Mo4School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China; Corresponding author at: School of Geography and Remote Sensing, Guangzhou University, No. 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaGuangdong Blueroc Ocean Technology Company Limited, Zhuhai 519075, ChinaThe acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We combined six commonly land cover datasets and forest age, canopy height, above-ground biomass, and tree species distribution datasets to reconstruct 30 m spatially accurate forest refinement dataset (FRD) for Guangdong Province. In addition, the distribution characteristics of forest structure and function were evaluated using forest morphological spatial pattern analysis. The results show that the overall accuracy of FRD of the Guangdong Province in 2020 reached 86.07 %. Forest types in the Guangdong Province were mainly dominated by evergreen needle-leaf forests. Tsuga chinensis, Red cedar, and Pinus sylvestris were more commonly planted. Older and taller trees were found in northern and eastern Guangdong. In addition, forest above-ground biomass (AGB) was larger in the coastal areas of northern and western Guangdong. The core and perforation had the oldest age and the highest tree height, and the islet had the lowest for all forest structure and function indicators. Based on multi-source datasets, this study contributes to a better understanding of the attributes characterizing the structure and function of forests. The refined dataset and research framework will effectively enhance forest management efficiency and policy making, as well as provide case references for research on climate change response, forest conservation and biodiversity assessment.http://www.sciencedirect.com/science/article/pii/S1470160X24012457Data fusionMulti-source dataMorphological spatial pattern analysis |
| spellingShingle | Yicheng Li Zhuo Wu Linglong Zhu Xiaocheng Huang Jianhong Mo Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province Ecological Indicators Data fusion Multi-source data Morphological spatial pattern analysis |
| title | Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province |
| title_full | Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province |
| title_fullStr | Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province |
| title_full_unstemmed | Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province |
| title_short | Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province |
| title_sort | innovative reconstruction and evaluation of forest refinement datasets by combining multi source data a case study of guangdong province |
| topic | Data fusion Multi-source data Morphological spatial pattern analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24012457 |
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