Hong S. He Yu Liang Todd Hawbaker Paul D. Henne Jinxun Liu Shengli Huang Zhiwei Wu Chao Huang Qinglong Zhang 2018 <p><span>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&nbsp;</span><i>k</i><span>&nbsp;nearest neighbor (</span><i>k</i><span>NN) methods and evaluated at different scales. The performance of 630&nbsp;</span><i>k</i><span>NN models based on different distance metrics,&nbsp;</span><i>k</i><span>&nbsp;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 &lt; 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.</span></p> application/pdf 10.1139/cjfr-2017-0346 en Canadian Science Publishing Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests article