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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]

In the context of climate change, large-scale net primary productivity (NPP) estimation and its impact feature variables are drawing more and more attention. Traditional process-based and empirical models are limited by their model structure and input variable design. The available field measurement data are limited by their small coverage. We propose to train an NPP calculation model using random forest and quantify the influence of multiple meteorological features on NPP. The calculated NPP correlates well with the MODIS product (correlation coefficient higher than 0.8). The importance rankings of multiple features are related to the local economy and development strategy of research areas. In developed areas, vegetation indexes are the most important, while in developing areas, land classification type influences NPP the most. The experiments suggest random forest is promising for estimating NPP and useful in analysing the impact features in terms of global change. [ABSTRACT FROM AUTHOR]