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Scientia Silvae Sinicae ›› 2022, Vol. 58 ›› Issue (2): 13-22.doi: 10.11707/j.1001-7488.20220202

• Frontier & Focus: Topic of forest carbon sequestration • Previous Articles     Next Articles

A Method of Estimation Aboveground Biomass of Sparse Tree-Shrub Using Optical Remote Sensing

Yonglei Shi1,2,Zhihui Wang2,*,Shiming Li3,Chunyi Li1,Peiqing Xiao2,Pan Zhang2,Xiaoge Chang1   

  1. 1. School of Surveying and Land Information Engineering, Henan Polytechnic University Jiaozuo 454003
    2. Key Laboratory of Soil and Water Conservation in the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research Zhengzhou 450003
    3. Research Institute of Forest Resource Information Techniques, CAF Beijing 100091
  • Received:2021-06-24 Online:2022-02-25 Published:2022-04-26
  • Contact: Zhihui Wang


Objective: Three stratification schemes were used to construct canopy coverage-aboveground biomass model of tree-shrub based on UAV data and aboveground biomass models of tree-shrub based on spectral indices of Lansat8 OLI data. Differences among stratification schemes in the precision of the aboveground tree-shrub biomass model were investigated to provide a scientific and theoretical basis for high-precision estimation of aboveground tree-shrub biomass of plantations in dryland based on remote sensing data. Method: A total of 102 plots were surveyed in Mu Us Sandy Land, information on vegetation coverage of tree, shrub and herbaceous were obtained from the high-resolution UAV images using object-oriented machine learning algorithm, and then the canopy coverage-aboveground biomass models of tree-shrub were developed with three stratification schemes (non- stratification, stratification-based two vegetation types of tree and shrub, stratification-based five tree species). Based on landsat-8 OLI images, 6 spectral indices (NDVI, RVI, MSAVI, TCG, NDMI, NIRv) were used to interpret the herbaceous coverage in combination with UAV images. Different spectral index-aboveground tree-shrub biomass models were developed using the 3 stratification schemes (non-stratification, stratification using plots with and without herbaceous vegetation, stratification using plots with three herbaceous coverage level). Finally, the precision of estimation was compared among the models with different stratification schemes. Results: The canopy coverage-aboveground tree-shrub biomass model without stratification has the worst robustness (R2=0.22, n=102) and the lowest estimation precision (RMSE=14.98 t·hm-2). The estimation errors were reduced by 50.32% and 61.1% for the models with stratification by 2 vegetation types of tree and shrub (RMSE=7.44 t·hm-2) and by 5 tree species (RMSE=5.82 t·hm-2), respectively. In the spectral index-aboveground tree-shrub biomass models, NIRv had the highest precision in estimation of aboveground tree-shrub biomass (the average RMSE of the 3 stratification schemes was 7.25 t·hm-2), and NDVI had the lowest precision (the average RMSE of the 3 stratification schemes was 9.43 t·hm-2). The explanatory ability of different spectral indices to variation of aboveground biomass of sparse tree-shrub was ranked as follows: NIRv > NDMI > TCG > MSAVI > RVI > NDVI. The effect of woody vegetation types on precision of spectral index-aboveground tree-shrub biomass model was less than that of the canopy coverage-aboveground tree-shrub biomass model. The RMSE of the spectral index-aboveground tree-shrub biomass models was reduced by 8.13%~16.62% considering the background of herbaceous coverage, and the sensitivity of precision to such background was ranked as follow: NIRv > TCG > NDVI > MSAVI > RVI > NDMI. Conclusion: The high spatial resolution remote sensing of UAV can be used to obtain prior knowledge of tree species types and herbaceous vegetation information for sparse tree-shrub mixed forest. The woody vegetation types have a great influence on the precision of canopy coverage-aboveground biomass model in sparse tree-shrub mixed forest, and at least the two vegetation types of tree and shrub should be distinguished to ensure the estimation precision of the method to meet the practical application requirements. The stratification model schemes of NIRv-aboveground biomass model derived from Landsat-8 OLI satellite data considering herbaceous vegetation coverage is suitable for remote sensing estimation of aboveground tree-shrub biomass in a large area.

Key words: sparse tree-shrub, aboveground biomass, stratification-based model, spectral index, canopy coverage

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