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Intro -- Contents -- 1 Research Progress of the Grassland Carbon Cycle and Grassland Degradation in China -- 1.1 General Situation of Grassland in China -- 1.2 Related Definitions and Model Formulation -- 1.2.1 Basic Process of Carbon Cycle -- 1.2.2 Observation and Simulation of NPP and Carbon Sinks in Grassland Ecosystem -- 1.3 Literature Review -- 1.3.1 Overview of Grassland Degradation -- 1.3.2 Grassland Degradation Monitoring Method -- 1.3.3 Driving Factors of Grassland Degradation -- 1.3.4 Ecological Restoration Project -- 1.4 Problems in Carbon Sinks Accounting and Grassland Degradation Research -- 1.4.1 Studies on the Driving Mechanism of Grassland NPP in China Are Few -- 1.4.2 Research on the Grassland Carbon Sinks Evaluation in China's National Scale Area Few -- 1.4.3 Lack of Remote Sensing Monitoring and Driving Mechanism Quantitative Assessment of Grassland Degradation in China -- References -- 2 Grassland Coverage Inter-Annual Variations and Its Coupling Relation with Hydrothermal Factors in China -- 2.1 Introduction -- 2.2 Methodology -- 2.2.1 Normalized Difference Vegetation Indices (NDVI) -- 2.2.2 Meteorological Data -- 2.2.3 Land-Cover Products -- 2.2.4 Estimation of Grass Coverage and Its Accuracy Validation -- 2.2.5 Grassland Vegetation Coverage Trend Analysis -- 2.2.6 Correlation Analysis of Grass Coverage and Climate Factors -- 2.3 Implementations and Comparisons -- 2.3.1 Spatial Distribution of Long-Term Mean Grass Coverage -- 2.3.2 Temporal Changes of Grass Coverage in China -- 2.3.3 Correlation Analysis of Grass Coverage and Temperature, Precipitation -- 2.3.4 Comparisons -- 2.4 Conclusions -- References -- 3 Spatial-Temporal Dynamics of Grassland Net Primary Productivity in China and Its Response to Climate Factors -- 3.1 Introduction -- 3.2 Data Source and Processing -- 3.2.1 GIMMS NDVI Data and Processing.

AbstractPlant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.

This document contains the draft Chapter 4 of the IPBES Global Assessment on Biodiversity and Ecosystem Services. Governments and all observers at IPBES-7 had access to these draft chapters eight weeks prior to IPBES-7. Governments accepted the Chapters at IPBES-7 based on the understanding that revisions made to the SPM during the Plenary, as a result of the dialogue between Governments and scientists, would be reflected in the final Chapters.IPBES typically releases its Chapters publicly only in their final form, which implies a delay of several months post Plenary. However, in light of the high interest for the Chapters, IPBES is releasing the six Chapters early (31 May 2019) in a draft form. Authors of the reports are currently working to reflect all the changes made to the Summary for Policymakers during the Plenary to the Chapters, and to perform final copyediting.