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.

Wall-to-wall tree-lists information (lists of species and diameter for every tree) at a regional scale is required for managers to assess forest sustainability and design effective forest management strategies. Currently, the k-nearest neighbors (kNN) method and the Weibull diameter distribution function have been widely used for estimating tree lists. However, the kNN method usually relies on a large number of field inventory plots to impute tree lists, whereas the Weibull function relies on strong correlations between stand attributes and diameter distribution across large regions. In this study, we developed a framework to estimate wall-to-wall tree lists over large areas based on a limited number of forest inventory plots. This framework integrates the ability of extrapolating diameter distribution from Weibull and kNN imputation of wall-to-wall forest stand attributes from Moderate Resolution Imaging Spectroradiometer (MODIS). We estimated tree lists using this framework in Chinese boreal forests (Great Xing’an Mountains) and evaluated the accuracy of this framework. The results showed that the passing rate of the Kolmogorov–Smirnov (KS) test for Weibull diameter distribution by species was from 52% to 88.16%, which means that Weibull distribution could describe the diameter distribution by species well. The imputed stand attributes (diameter at breast height (DBH), height, and age) from the kNN method showed comparable accuracy with the previous studies for all species. There was no significant difference in the tree density between the estimated and observed tree-lists. Results suggest that this framework is well-suited to estimating the tree-lists in a large area. Our results were also ecologically realistic, capturing dominant ecological patterns and processes.

Climate change is exacerbating urban heatwave, posing critical public health risks that are amplified by accelerating urbanization trends. The multilevel spatiotemporal interactions among environmental factors, heatwave characteristics, and public sentiment re-main unclear, especially in urban agglomerations. This study introduces an innovative analytical framework combining Geographical Convergent Cross Mapping (GCCM) for bidirectional causal inference with Bayesian Generalized Linear Mixed Models (GLMMs) for multilevel effect quantification. By systematically integrating a decade (2014-2023) of multi-source remote sensing data, environmental indicators, and 3.2 million heat-perception social media posts across five major Chinese urban agglomerations, this study explore dynamic spatiotemporal relationships between environment, heatwaves, and emotions. Results reveal pronounced asymmetric causality, with environmental im-pacts on heatwaves significantly exceeding heatwave feedback to environments. Intensity indicators (HWA, HWM) show strongest responses to environmental factors, frequency indicators (HWF, HWday) demonstrate moderate-to-strong responses, while duration indicators (HWD) exhibit slightly lower causality. Standardized effect sizes rank environ-mental factors as: Albedo (0.87), FVC (0.79), NDTI (0.75), water bodies (0.57), NTL (0.62) and Elevation (0.35), confirming the dominant influence of surface properties over hydro-logical and topographic factors. Heatwaves and public emotional responses show significant nonlinear relationships with regional variations. As heatwave intensity in-creases, all five emotional dimensions rise with distinct patterns: heat perception responds most directly, health concerns accelerate during moderate-to-high intensity events, outdoor activity preferences decrease significantly during moderate events, and energy consumption concerns peak during high-intensity heatwaves. Each urban agglomeration exhibits unique emotional response patterns due to spatial and geographical differences. These findings provide critical insights into urban thermal environment mechanisms and their societal impacts, highlighting the necessity of region-specific strategies for urban planning and climate adaptation.