Although the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (>95% quantile) and low (<median), respectively. This study provides a critical insight into the relationship between tree canopy height and predictive abilities of random forest models when taking account of spatial non-stationarity. Conclusions indicate that a trade-off approach based on the actual need of project should be taken when selecting an optimal model integrating both local and global effects in modeling attributes such as canopy height from remotely sensed data.

尽管随机森林算法已广泛应用于遥感数据以预测森林的特征（例如树冠高度），但是在建模过程中空间非平稳性的影响常常被忽略。先前的研究提出了解决局部尺度上空间变异的方法，但是很少有人探索建模树冠层高度中残差的空间自相关模式或研究冠层高度与模型性能之间的关系。通过结合光探测与测距（LiDAR）和Landsat数据集，我们使用了空间加权地理随机森林（GRF）和传统随机森林（TRF）方法来预测复杂山区的混合干旱森林林地中的树冠高度。TRF和GRF模型之间的比较表明，后者可以降低预定义的极端残差，从而使模型性能相对更强。此外，模型性能与实际冠层高度变化程度之间的关系在不同的高度分位数内可能会发生很大的变化。当相应的树冠高度分别为高（> 95％分位数）和低（<中位数）时，这两种模型都可能会低估和高估。这项研究在考虑空间非平稳性的情况下，对树冠高度与随机森林模型的预测能力之间的关系提供了重要的见解。