气候变化领域集成服务门户(CCPortal): Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

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DOI	10.3390/rs16071276
	Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna
	Wu, Yong; Ou, Guanglong; Lu, Tengfei; Huang, Tianbao; Zhang, Xiaoli; Liu, Zihao; Yu, Zhibo; Guo, Binbing; Wang, Er; Feng, Zihang; Luo, Hongbin; Lu, Chi; Wang, Leiguang; Xu, Weiheng
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	7
卷号	16 期号:7
英文摘要	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 Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, 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 x 7 perform best for AGB sensitivity, followed by 5 x 5, with 3 x 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 P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, 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.
英文关键词	lowland tropical forest; aboveground biomass; Landsat 8 OLI; Sentinel 2A; stratification model
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001201121500001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304330
作者单位	Southwest Forestry University - China; Southwest Forestry University - China; Southwest Forestry University - China
推荐引用方式 GB/T 7714	Wu, Yong,Ou, Guanglong,Lu, Tengfei,et al. Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna[J],2024,16(7).
APA	Wu, Yong.,Ou, Guanglong.,Lu, Tengfei.,Huang, Tianbao.,Zhang, Xiaoli.,...&Xu, Weiheng.(2024).Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna.REMOTE SENSING,16(7).
MLA	Wu, Yong,et al."Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna".REMOTE SENSING 16.7(2024).