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Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data

  • role： First author第一作者
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

  • Email：zhangtingwei@swfu.edu.cn
  • Introduction：张庭苇， 研究方向为林业微波遥感。E-mail： zhangtingwei@swfu.edu.cn
ZHANG Tingwei,  
  • role： Corresponding author通信作者
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

  • Email：zhangwf@swfu.edu.cn
  • Introduction：张王菲， 研究方向为林业微波遥感。E-mail： zhangwf@swfu.edu.cn
ZHANG Wangfei*,  
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

ZHANG Yongxin,  
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

HUANG Guoran

résumé

Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) has been widely used in forest height inversion. Accurate evaluation of the uncertainty caused by model input parameters, model assumptions, stand structure, and site conditions can improve the accuracy of forest height inversion with PolInSAR technology. In practical application, the study on uncertainty of forest height inversion is as important as of forest height estimation methods. Quantification of global carbon stocks based on forest biomass calculations usually requires reducing the error in biomass estimates through forest height. The uncertainty of forest height may be attributed to model input parameters, model assumptions, observed data, and forest scene factors. However, comprehensive collaborative impact analyses on the uncertainty of forest height inversion results are few. On this basis, the uncertainty of forest height inversion should be studied using PolInSAR technique. We initially analyze the uncertainty caused by the input parameters of the RVoG (Random Volume over Ground) model based on the Bayesian model using the simulated L-band full PolInSAR data, and then prior knowledge (value of the forest height in the imaging) is applied to fix the extinction of the RVoG model. Subsequently,we inversed the forest height. The results show that a priori knowledge can greatly reduce canopy height uncertainties in some cases. On this basis, we combine the RVoG model and Bayesian framework, use L-band simulated PolInSAR data, and comprehensively explore the uncertainties that result from the input parameters of the RVoG model, model hypothesis, observation value, changes in forest tree species, forest density, surface properties, ground moisture content, and other factors in the process of forest height inversion. The research results indicated that: (1) prior knowledge can reduce the uncertainty of the forest height inversion (by fix the extinction value) with RVoG model and L-band PolInSAR data. (2) The forest height inversion results are greatly affected by forest tree species, and the inversion results on the uncertainty of coniferous forest are lower than those of broad-leaved forest. (3) The change in forest stand density has a significant influence on the uncertainty of the forest height inversion results. The higher density indicates lower uncertainty, especially in the pure coniferous forest. When the forest density is small, the uncertainty of the forest height retrieved by the RVoG model is large. When the forest stand density increases from 150 plants/hm² to 1200 plants/hm², the uncertainty decreased to approximately 67.5%. (4) The change in surface roughness has a positive correlation with the uncertainty of the forest height inversion results, the greater roughness indicates higher uncertainty. (5) The uncertainty caused by ground moisture content is smaller than those by the other factors and can be ignored.

mots-clés

PolInSAR;RVoG;forest height;Tree Species;Forest Stand Density;Surface roughness;Ground Moisture Content

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