Dongling Wang, Qingtai Shu, Qiang Wang, Hongbin Luo, Keren Wang. The Spatial Scale Effecton Analysis on Remote Sensing Estimation Model of Pinus densata Above-biomass Based on Partial Least Squares Regression[J]. Journal of Southwest Forestry University, 2020, 40(4): 87-93. DOI: 10.11929/j.swfu.201910021
Citation: Dongling Wang, Qingtai Shu, Qiang Wang, Hongbin Luo, Keren Wang. The Spatial Scale Effecton Analysis on Remote Sensing Estimation Model of Pinus densata Above-biomass Based on Partial Least Squares Regression[J]. Journal of Southwest Forestry University, 2020, 40(4): 87-93. DOI: 10.11929/j.swfu.201910021
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The Spatial Scale Effecton Analysis on Remote Sensing Estimation Model of Pinus densata Above-biomass Based on Partial Least Squares Regression

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  • Aiming at the uncertainty of scale of optical image in forest biomass remote sensing estimation. In this study, taking Pinus densata forest in Shangri-La as the research object, based on SPOT-5 image, combining with 92 sample plots of angle gauge measurement to build partial least squares regression models under different observation scales, and to analyzed the accuracy of models under different observation scales.The results show that the upper limit of the reasonable observation scale of the plot is 70 m, the optimal spatial scale for biomass estimation is 50 m, at this time, the RMSE of the PLS model is the smallest, which is 20.001 9 t/hm2, and R2, P reach the maximum, they are 0.505 8 and 66.20%, respectively.
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