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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R2 = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha−1, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R2 = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors.

Details

Title
Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics
Author
Sun, Zhibin 1   VIAFID ORCID Logo  ; Qian, Wenqi 2 ; Huang, Qingfeng 3 ; Lv, Haiyan 2 ; Yu, Dagui 2 ; Ou, Qiangxin 3 ; Lu, Haomiao 2 ; Tang, Xuehai 3   VIAFID ORCID Logo 

 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; [email protected] 
 School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036, China; [email protected] (W.Q.); [email protected] (Q.H.); [email protected] (H.L.); [email protected] (D.Y.); [email protected] (Q.O.); [email protected] (H.L.) 
 School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036, China; [email protected] (W.Q.); [email protected] (Q.H.); [email protected] (H.L.); [email protected] (D.Y.); [email protected] (Q.O.); [email protected] (H.L.); Anhui Dabie Mountains Forest Ecosystem Research Station, National Forestry and Grassland Administration, Jinzhai 237300, China 
First page
1066
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2637787582
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.