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The landscape-level estimation of nitrogen will constitute a significant input in global change models. High resolution broadband satellite technology is a promising solution to the limitation of spatial coverage. This study sought to identify an optimal spatial resolution for estimating foliar nitrogen in dry miombo woodlands. The contrast texture measure, calculated at increasing window size on Sentinel-2 imagery, was related to field measured nitrogen via random forest regression models. Our results show that the optimum size for estimating nitrogen is 50m. This result is significant in understanding the scale at which dynamics in foliar nitrogen concentration can be remotely measured. [ABSTRACT FROM AUTHOR]

Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively. [ABSTRACT FROM AUTHOR]