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Abstract: Forest land is the largest carbon storage of the terrestrial ecosystem for its giant biomass and plays a very important and irreplaceable role in mitigating and adapting to the global climate change. Much attention has been paid to its function and role in alleviating and adapting to the global climate change, Poyang Lake watershed as one of the most important wetland nature reserve in the world is also one of the regions where forest land is most densely distributed in China. The authors have studied the spatial dynamics of the forest land in this region during the past 20 years based on panel data of four periods (1988, 1995, 2000, 2005), and comprehensively characterized its changing patterns under the macroscopic context of rapid economic growth, social change and technical progress. A simultaneous equations model was established and the significance ranking of various factors was obtained in order to analyze the driving mechanism of the spatial-temporal process of the forest land. Three scenarios, i.e. economic priority, ecologic conservation and constant scenarios were designed according to the driving mechanism and trends of economic, policy and technical progress. The module functions of Dynamic of Land System (DLS) were employed and extended to develop possible scenarios of spatial-temporal explicit expression of the forest land. [ABSTRACT FROM AUTHOR]

• Twelve machine learning algorithms are compared to construct a retrieval model. • The TN concentration is retrieved using the UAV Hyper Spectral Remote Sensing data. • Spatial distribution of TN concentration is retrieved with high resolution. • Providing scientific references for water quality monitoring and management. Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir. [ABSTRACT FROM AUTHOR]