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FOREST RESOURCES WANAGEMENT ›› 2021, Vol. 0 ›› Issue (6): 83-89.doi: 10.13466/j.cnki.lyzygl.2021.06.014

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Research on Estimation of Aboveground Biomass and Scale Conversion for Pinus densata Mast

TANG Jinhao1(), ZHANG Jialong1(), CHEN Liye2, CHENG Tao3   

  1. 1. Faculty of Forestry,Southwest Forestry University,Kunming 650224,China
    2. Zhijiang Dong Autonomous County Forestry Bureau,Huaihua,Hunan 419100,China
    3. National Geomatics Center of China,Beijing 100830,China
  • Received:2021-09-02 Revised:2021-10-28 Online:2021-12-28 Published:2022-01-12
  • Contact: ZHANG Jialong E-mail:TJH@swfu.edu.cn;jialongzhang@swfu.edu.cn

Abstract:

In order to study the impact of scaling up of remote sensing images for forest biomass estimation,this paperused the Nearest Neighbor method,Bilinear Interpolation,Cubic Convolution Interpolation,Local Average method and Pixel Aggregation method to convert original Landsat-8,Sentinel-2A and Spot-7 images to low spatial resolution images in Shangri-La City. Then,this paper combined the aboveground biomass data of Pinus densata Mast. obtained from field surveys and the real image of target scale to establish the biomass estimation models of Random Forest (RF) and Gradient Boosted Regression Trees (GBRT) respectively,and compared the modeling effects of the two models. The results showed that: after being scaled up by the Nearest Neighbor method,the estimation accuracy (P) of RF and GBRT modeling based on Spot-7 images were 76.65% and 75.55%,respectively.,the P values of the two models based on Sentinel-2 images were 81.78% and 72.74% respectively,which were better than the other four scale conversion methods;The estimation accuracy of the RF estimation (81.78%~63.94%) of the image constructed by the five scale conversion methods was better than the estimation accuracy of GBRT (75.55%~61.03%). Sentinel-2A images were more suitable for scaling up to estimate forest biomass. The study results can provide reference for the selection of scale conversion methods and biomass estimation models.

Key words: multisource remote sensing data, aboveground biomass, scale conversion, random forests, gradient boosting regression tree

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