Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna
Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/7/1276 |
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| author | Yong Wu Guanglong Ou Tengfei Lu Tianbao Huang Xiaoli Zhang Zihao Liu Zhibo Yu Binbing Guo Er Wang Zihang Feng Hongbin Luo Chi Lu Leiguang Wang Weiheng Xu |
| author_facet | Yong Wu Guanglong Ou Tengfei Lu Tianbao Huang Xiaoli Zhang Zihao Liu Zhibo Yu Binbing Guo Er Wang Zihang Feng Hongbin Luo Chi Lu Leiguang Wang Weiheng Xu |
| author_sort | Yong Wu |
| collection | DOAJ |
| description | Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as <i>Pinus kesiya</i> var. <i>langbianensis</i>, oak, <i>Hevea brasiliensis</i>, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of <i>P. kesiya</i> var. <i>langbianensis</i> forests was estimated with the highest accuracy, followed by <i>H. brasiliensis</i>, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies. |
| first_indexed | 2024-04-24T10:35:21Z |
| format | Article |
| id | doaj.art-2835f50b5af94b788a3f622e544ceb88 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:35:21Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2835f50b5af94b788a3f622e544ceb882024-04-12T13:25:51ZengMDPI AGRemote Sensing2072-42922024-04-01167127610.3390/rs16071276Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in XishuangbannaYong Wu0Guanglong Ou1Tengfei Lu2Tianbao Huang3Xiaoli Zhang4Zihao Liu5Zhibo Yu6Binbing Guo7Er Wang8Zihang Feng9Hongbin Luo10Chi Lu11Leiguang Wang12Weiheng Xu13Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaYunnan Institute of Forest Inventory and Planning, Kunming 650051, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaKey Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, ChinaImproving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as <i>Pinus kesiya</i> var. <i>langbianensis</i>, oak, <i>Hevea brasiliensis</i>, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of <i>P. kesiya</i> var. <i>langbianensis</i> forests was estimated with the highest accuracy, followed by <i>H. brasiliensis</i>, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.https://www.mdpi.com/2072-4292/16/7/1276lowland tropical forestaboveground biomassLandsat 8 OLISentinel 2Astratification model |
| spellingShingle | Yong Wu Guanglong Ou Tengfei Lu Tianbao Huang Xiaoli Zhang Zihao Liu Zhibo Yu Binbing Guo Er Wang Zihang Feng Hongbin Luo Chi Lu Leiguang Wang Weiheng Xu Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna Remote Sensing lowland tropical forest aboveground biomass Landsat 8 OLI Sentinel 2A stratification model |
| title | Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna |
| title_full | Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna |
| title_fullStr | Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna |
| title_full_unstemmed | Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna |
| title_short | Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna |
| title_sort | improving aboveground biomass estimation in lowland tropical forests across aspect and age stratification a case study in xishuangbanna |
| topic | lowland tropical forest aboveground biomass Landsat 8 OLI Sentinel 2A stratification model |
| url | https://www.mdpi.com/2072-4292/16/7/1276 |
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