Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China’s Statistical Yearbooks (R² of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.

Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2.000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods.

Grassland degradation intensifies human-environment conflicts and adversely affects local residents’ livelihoods. To reduce grassland degradation in Inner Mongolia, China, the government has enforced (since 1998) a series of grassland conservation and management policies that restrict the use of grasslands. To ease the impact on the residents’ livelihoods, the national and regional governments have offered a series of top-down arrangements to stimulate sustainable use of the grasslands. Simultaneously, local households spontaneously developed bottom-up countermeasures. To determine the effects of these processes, we interviewed members of 135 households using a mix of qualitative and quantitative methods. We analyzed the effects on household dependence on local grasslands and on perceptions of the future of grassland use. Our findings show that the implementation of the grassland conservation policies significantly affected household livelihoods, which in turn affected household use of natural assets (primarily the land), their agricultural assets (farming and grazing activities) and their financial assets (income and consumption), resulting in fundamental transformation of their lifestyles. The households developed adaptation measures to account for the dependence of their livelihood on local ecosystems by initializing strategies, such as seeking off-farm work, leasing pasture land, increasing purchases of fodder for stall-fed animals and altering their diet and fuel consumption to compensate for their changing livelihoods.