Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI_soil and VI_veg, with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

Satellite remote sensing has been used in many fields but less commonly for monitoring grazing intensity (GI). In this study, grassland above-ground biomass (AGB) was simulated by multispectral reflectance derived from an artificial neural network (ANN) to investigate GI. One-way analysis of variance (ANOVA) and frequency histograms from sample plots were used to determine the thresholds of AGB for four levels of GI characterized as ungrazed, lightly grazed, moderately grazed and heavily grazed. The distribution of GI in the Xilingol steppe region of Inner Mongolia, China, was then mapped from the remotely sensed grassland AGB. This study could be used as a guide for management interventions and grassland restoration.

The use of remotely sensed data to estimate and monitor the gross primary production (GPP) of an ecosystem on regional scales is an important method in climate change research. Under the unremitting efforts of scientists, many successful remote-sensing-based GPP models have been developed for various vegetation types and regions. However, in practice, some models have been applied to a wide variety of ecosystems, and the suitability of a particular model for the environment under consideration has seldom been taken into account. Due to ecosystem diversity and climatic and environmental variation, it is often difficult to find a model that is suitable for a specific vegetation region. In this article, a new method is proposed for estimating the GPP of alpine vegetation, known as the alpine vegetation model (AVM). The accuracy of the AVM in estimating the GPP was compared to that of four other models: the vegetation photosynthesis model (VPM), eddy covariance–light use efficiency (EC–LUE) model, temperature and greenness (TG) model, and vegetation index (VI) model. The results demonstrated that the AVM displays superior accuracy in estimating the GPP of alpine vegetation. We also found that there is information redundancy in the input variables of these four models, which may account for their lower accuracy in estimating the GPP. In addition, the GPP estimates using the enhanced vegetation index are affected more in the case of low rather than high GPP by the influence of senesced grass during the early and late grassland growing season.

Grassland as a distinct landscape type sustains biological and cultural diversity. However, its use at an unprecedentedly high intensity has had a disastrous impact on grassland ecosystems, with degeneration occurring nationwide. Traditional management policy, drafted according to the stocking rate from livestock statistics and/or in situ assessment, seems to have played no role in alleviating this trend. A major cause is the failure of decision making to consider spatial heterogeneity in the impact of grazing on grassland. To address this issue, high temporal resolution moderate-resolution imaging spectroradiometer data were used to develop a new indicator, named the grazing intensity index (GII), for quantifying grazing intensity (GI). The new index was compared with two traditional approaches and the results demonstrated that GII could substitute for both of them to quantify GI.

Keywords: Urban sprawl, Ecological environment, Beijing, Multi-source data, Multiscale With urban population growing and urban sprawling, urban ecological environment problems appear. Study on spatiotemporal characteristics of urban sprawl and its impact on ecological environment is useful for ecological civilization construction. Although a lot of work has been conducted on urban sprawl and its impact on ecological environment, resolution of images to extract urban boundary was relatively coarse and most studies only focused on certain indicators of ecological environment, rather than comprehensive evaluation of urban ecological environmental impact. In this study, high-resolution remote sensing images of Beijing from aerial photography in 2002 and 2013 respectively are employed to extract urban boundary with manual interpretation. Fractional Vegetation Coverage (FVC), Water Density (WD), Impervious Surfaces Coverage (ISC), Net Primary Production (NPP), and Land Surface Temperature (LST) are adopted to represent ecological environment. The ecological environment indicators are measured with some general algorithms by combining Landsat images, GIS data and metrological data of 243 day, 2001 and 244 day, 2013. In order to evaluate the impact of urban sprawl on ecological environment, pseudo changes due to metrological variation and other noise in this time period are removed after images calibration. The impact of urban sprawl on ecological environment is evaluated at different scales of urban extent, Beijing ring road and watershed. Results show that Beijing had been undergoing a rapid urbanization from 2002 to 2013, with urban area increase from 600 square kilometres to 987 square kilometres. All ecological environment indicators except LST became terrible in urban sprawl region, with carbon reduction of approximate 40508 tons. The Beiyun River watershed of Beijing degraded seriously since ISC increased to 0.59. Gratifyingly, ecological environment indicators including NDVI, NPP, and LST inside of 4^th Ring Road became well.