Comparison of variable extraction methods using surface field data and its key influencing factors: A case study on aboveground biomass of Pinus densata forest using the original bands and vegetation indices of Landsat 8

Zhi Liu; Yong Wu; Xiaoli Zhang; Meng Li; Chunxiao Liu; Wenfang Li; Mengni Fu; Sitong Qin; Qinling Fan; Hongbin Luo; Chi Lu; Guanglong Ou

Ecological Indicators (Dec 2023)

Comparison of variable extraction methods using surface field data and its key influencing factors: A case study on aboveground biomass of Pinus densata forest using the original bands and vegetation indices of Landsat 8

Zhi Liu,
Yong Wu,
Xiaoli Zhang,
Meng Li,
Chunxiao Liu,
Wenfang Li,
Mengni Fu,
Sitong Qin,
Qinling Fan,
Hongbin Luo,
Chi Lu,
Guanglong Ou

Affiliations

Zhi Liu: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Yong Wu: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Xiaoli Zhang: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Meng Li: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Chunxiao Liu: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Wenfang Li: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Mengni Fu: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Sitong Qin: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Qinling Fan: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Hongbin Luo: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Chi Lu: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China
Guanglong Ou: Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China; Corresponding author at: Southwest Forestry University, Kunming 650233, China.

Journal volume & issue: Vol. 157
p. 111307

Abstract

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Insufficient sample data is a challenge when estimating forest aboveground biomass (AGB) using large-scale remote sensing. Extracting remote sensing information from sub-compartments could rectify such defects, but the corresponding method, its accuracy, and influencing factors still need to be clarified. We combined Landsat 8 data with a Pinus densata forest sub-compartment to extract remote sensing information that matched the sample plots. Six sub-compartment based methods, including the centroid point extraction method, and the minimum, mean, maximum, majority, and median statistic extraction methods were used to extract sub-compartment remote sensing information and compare the differences between each method and the true values. For each method, structural equation modelling (SEM) was used to explore the effect of sub-compartment topography, shape, and forest stand factors on the extraction error. Mean statistic was the best extraction method, with the highest consistency index, and the lowest mean relative error, between the extracted and true values. All three factors affected extraction accuracy, with forest stand being the dominant one. When sub-compartment data are sufficient, but sample plots are insufficient, it is an effective extrapolation method for large-scale AGB estimation.

Published in Ecological Indicators

ISSN: 1470-160X (Print); 1872-7034 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-indicators

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