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Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests

May 1, 2018

Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using k nearest neighbor (kNN) methods and evaluated at different scales. The performance of 630 kNN models based on different distance metrics, k values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.

Citation Information

Publication Year 2018
Title Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests
DOI 10.1139/cjfr-2017-0346
Authors Qinglong Zhang, Hong S. He, Yu Liang, Todd Hawbaker, Paul D. Henne, Jinxun Liu, Shengli Huang, Zhiwei Wu, Chao Huang
Publication Type Article
Publication Subtype Journal Article
Series Title Canadian Journal of Forest Research
Index ID 70201610
Record Source USGS Publications Warehouse
USGS Organization Geosciences and Environmental Change Science Center