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Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms
Open AccessArticle

Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

1
Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2
College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
3
Central South Inventory and Planning Institute of National Forestry and Grassland Administration, Changsha 410014, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 45; https://doi.org/10.3390/f11010045
Received: 10 December 2019 / Revised: 22 December 2019 / Accepted: 25 December 2019 / Published: 31 December 2019
To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation. View Full-Text
Keywords: aboveground biomass; Landsat 8 OLI; seasonal images; stepwise regression; map quality; subtropical forest aboveground biomass; Landsat 8 OLI; seasonal images; stepwise regression; map quality; subtropical forest
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Li, C.; Li, M.; Liu, J.; Li, Y.; Dai, Q. Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest. Forests 2020, 11, 45.

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