Climate Interprets Saturation Value Variations Better Than Soil and Topography in Estimating Oak Forest Aboveground Biomass Using Landsat 8 OLI Imagery-Reference-Cited by-同舟云学术

Climate Interprets Saturation Value Variations Better Than Soil and Topography in Estimating Oak Forest Aboveground Biomass Using Landsat 8 OLI Imagery

Published:2024-04-10 Issue:8 Volume:16 Page:1338
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Wu Yong¹²^ORCID,Ou Guanglong¹²^ORCID,Huang Tianbao¹²,Zhang Xiaoli¹²,Liu Chunxiao¹²,Liu Zhi¹²,Yu Zhibo¹²,Luo Hongbin¹²^ORCID,Lu Chi¹²^ORCID,Shi Kaize³,Wang Leiguang¹⁴,Xu Weiheng¹⁴^ORCID

Affiliation:

1. Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China

2. Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China

3. Yunnan Institute of Forest Inventory and Planning, Kunming 650051, China

4. Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650224, China

Abstract

The optical saturation problem is one of the main factors causing uncertainty in aboveground biomass (AGB) estimations using optical remote sensing data. It is critical for the improvement in AGB estimation accuracy to clarify the relationships between environmental factors and the variations in optical saturation values (OSVs). In this study, we obtained the OSVs for 20 districts and clarified the individual, interactive, and comprehensive effects of climate, soil, and topographical factors on the OSV variations. The results are as follows: (1) the range of the OSVs was from 104 t/hm2 to 182 t/hm2 for the 20 districts; (2) the soil factor had the greatest (−0.635) influence on the OSVs compared to climate and topography; (3) the highest interaction effect (−0.833) was between climate and soil; (4) the comprehensive effect of the three environmental factors on the OSVs was obvious, and the correlation coefficient was 0.436. Moreover, the mean temperature of the coldest quarter (MCQMean) had the highest effect on the OSVs. The results indicate that environmental factors significantly affect the variation in OSVs through their individual, interactive, and comprehensive effects. Our findings provide a valuable reference for reducing the uncertainty caused by spectral saturation in AGB estimations using optical remote sensing data.

Funder

the Key Research and Development Program of Yunnan Province, China

Publisher

MDPI AG

Link

https://www.mdpi.com/2072-4292/16/8/1338/pdf

Reference84 articles.

1. Chen, G., Zhang, X., Liu, C., Liu, C., Xu, H., and Ou, G. (2022). Error Analysis on the Five Stand Biomass Growth Estimation Methods for a Sub-Alpine Natural Pine Forest in Yunnan, Southwestern China. Forests, 13.

2. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems;Lu;Int. J. Digit. Earth,2016

3. Hu, T., Zhang, Y., Su, Y., Zheng, Y., Lin, G., and Guo, Q. (2020). Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote Sens., 12.

4. Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations;Zhou;Remote Sens. Environ.,2020

5. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing;Zolkos;Remote Sens. Environ.,2013