Estimating canopy cover in artificial forests using high spatial resolution GF-1 and ZY-3 images: across-sensor and across-site comparison

Abstract

ABSTRACT Canopy cover is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. However, estimation of forest canopy cover from high spatial resolution images is an arduous task. To evaluate the utility of high spatial resolution images for estimation of canopy cover of artificial forests, the performance of canopy cover estimation models across sensors and across sites was evaluated. Choosing Wangyedian Forest Farm and Gaofeng Forest Farm as experimental areas, based on Chinese high spatial resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images, three models, namely multiple linear regression (MLR), generalized additive model (GAM), and Random Forest (RF) were established. The capabilities of the two data sources and three model types to estimate canopy cover of the forests were compared and the portability of the three models across sensors and sites was analysed. The ranges of the root mean square error (RMSE) and relative root mean square error (rRMSE) of the MLR, GAM, and RF models for each study area established from GF-1 and ZY-3 were 0.0632–0.1205 and 9.98–19.93%, respectively. GF-1 showed better performance than ZY-3 when using the same model type. Whether the objective was across sensors or across sites transplantation, the accuracy of the model decreased with transplantation of the model. The MLR model was non-significant when transplanted across sites and model migration failed, whereas across sensors and across sites transplantation of RF and GAM models was successful. The three evaluation indices of transplanted GAM models were significantly superior to those of transplanted RF and MLR models. Thus, the GAM model showed strong portability and the highest model stability.