Hongyan Wang

Land degradation dynamic in the first decade of twenty-first century in the Beijing–Tianjin dust and sandstorm source region

Land degradation dynamic assessment is very important to understand the effectiveness of ecological engineering and provide decision support for future restorations. Taking the Beijing–Tianjin dust and sandstorm source region (BTSSR) as the study area, the land degradation dynamic in the first decade of twenty-first century is assessed by utilizing the time series trends of net primary production (NPP) and modified rain use efficiency (RUE), respectively, and their sensitivities and performances are evaluated through the validation. The results shows that (1) a widespread degradation in the BTSSR is identified from 2000 to 2010, with a proportion of 52.7xa0% by using NPP, and 65.2xa0% when using RUE; (2) the proposed RUE is effective in eliminating high correlations between NPP and precipitation, and overcame the problem that the RUE assumption often breaks down at the pixel scale, through incorporating the land degradation assessment division; and (3) the RUE is preferred for land degradation assessment with a higher accuracy (93.5xa0%), compared with NPP (81xa0%), which can provide valuable insights into the land degradation assessment in the arid and semi-arid regions.

Dynamic and dry/wet variation of climate in the potential extent of desertification in China during 1981–2010

The potential extent of desertification in China (PEDC), composed by arid, semi-arid and dry sub-humid regions, was re-determined using a high spatial resolution meteorological dataset (1981–2010) and by applying the Thornthwaite method, which was recommended by the UNCCD for division of dryland zones (extremely arid, arid, semi-arid, dry sub-humid zones). The dynamic and the dry/wet trends in climatic variation in the general PEDC during 1981–2010 were statistically analyzed using several different methods. The newly calculated area of the general PEDC is approximately 3.222xa0×xa0106xa0km2, accounting for 33.6xa0% of China’s terrestrial. However, the results of a decadal spatial analysis indicated that the actual reduction appeared in the western region, where the general PEDC shrank to lower altitude areas. Meanwhile, the eastern border of the general PEDC was expanded to the east and southeast. Over three decades, the lower mountainous areas in northwestern Qinghai-Tibetan Plateau have experienced an obviously alternating process of drought–wetness–drought, the semi-arid and dry sub-humid regions in the east of the Helan Mountains as well as the Junggar Basin have experienced an alternating process of wetness–drought–wetness, and the vast arid region in the west of the Helan Mountains has experienced a process of drought–drought–wetness. In the most recent decade, most areas, except for the major western mountainous regions, have been experiencing a wet trend, which is favorable for combating desertification.

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

Effects of water and heat on growth of winter wheat in the North China Plain

The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March.

Land degradation assessment by applying relative rue in Inner Mongolia, China, 2001–2010

Land degradation in Inner Mongolia, China is much severe. Remote sensing application on land degradation assessment can provide scientific basis for land degradation prevention in the study area. In this paper, land degradation was assessed by applying two improved relative Rain Use Efficiency (RUE) indicators based on time series MODIS NDVI data and high-resolution meteorological data from 2001 to 2010. The results show that 76.74% land of the whole study area with good or unusually good condition, it indicates that the most areas have normal or good vegetation production capacity. The unusually degraded and degraded lands account for 11.94% of the study area, especially they are less degraded lands distributing in Beijing and Tianjin sandstorm source region within the Inner Mongolia, it indicates that some ecological engineering projects implemented in this area have achieved significantly for restoration of degraded ecosystems in recent 10 years.

Assessment of land degradation using time series trends analysis of vegetation indictors in Beijing-Tianjin dust and sandstorm source region

Assessment of land condition is a basic prerequisite for finding the degradation of a territory under climatic and human pressures leading to desertification. The temporal change in vegetation productivity is a key indicator of land degradation. In this paper, taking the Beijing-Tianjin dust and sandstorm source region (BTDSSR) as a case, the annual-maximum normalized difference vegetation index (NDVI_max) and net primary production (NPP) dynamic trends during 2001-2010 were analyzed, with the Mann-Kendall test and the Correlation Analysis method. The results showed that the two vegetation indicators (NDVI_max and NPP) had shown a downward trend with the two methods in the past 10 years and the land was in the degradation. For the analysis of the two vegetation indicators (NDVI_max and NPP), it indicated a decreasing trend in 66.60% and 70.26% of the study area according to the Mann-Kendall test and in 59.53% and 64.35% of the study area according to the correlation analysis method. However, the change trends were not significant, the significant trends at the 95% confidence level accounted for only a small proportion. Analysis of NDVI_max and NPP series showed a significant decreasing trend in 6.79% and 12.3% with the Mann-Kendall test. The NDVI_max and NPP change trends showed obvious positive link with the precipitation in the study area.

NPP variation and its respond to precipitation change in potential extent of desertification in China during 2001–2010

The Net Primary Production (NPP) variation and its respond to precipitation change were analyzed in the general potential extent of desertification in China (PEDC) based on time series MODIS NDVI data and high-resolution meteorological data from 2001 to 2010. The results indicated that only 2.8% of general PEDC was significantly decreased and 6.8% of total area was significantly increased. So there are no clear evidences to verify that the annual NPP in general PEDC has greatly decreased or increased during last decade. 23.2% in NPP decreased areas and 23.1% in NPP increased areas are significantly correlated with precipitation. These indicated the vegetation degradation within general PEDC was possibly caused mainly by human activities, and the projects implemented for combating desertification in some areas have played an important role in vegetation rehabilitation.

Evaluation of potential crop productivity based on remote sensing and agro-ecological zones around the world

Abstract Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983–2011 and the gap between RPP and HP.

Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-8 OLI image in a karst environment

In the karst regions of southwest China, rocky desertification is one of the most serious problems in land degradation. The bedrock exposure rate is an important index to assess the degree of rocky desertification in karst regions. Because of the inherent merits of macro-scale, frequency, efficiency, and synthesis, remote sensing is a promising method to monitor and assess karst rocky desertification on a large scale. However, actual measurement of the bedrock exposure rate is difficult and existing remote-sensing methods cannot directly be exploited to extract the bedrock exposure rate owing to the high complexity and heterogeneity of karst environments. Therefore, using unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data for Xingren County, Guizhou Province, quantitative extraction of the bedrock exposure rate based on multi-scale remote-sensing data was developed. Firstly, we used an object-oriented method to carry out accurate classification of UAVimages. From the results of rock extraction, the bedrock exposure rate was calculated at the 30 m grid scale. Parts of the calculated samples were used as training data; other data were used for model validation. Secondly, in each grid the band reflectivity of Landsat-8 OLI data was extracted and a variety of rock and vegetation indexes (e.g., NDVI and SAVI) were calculated. Finally, a network model was established to extract the bedrock exposure rate. The correlation coefficient of the network model was 0.855, that of the validation model was 0.677 and the root mean square error of the validation model was 0.073. This method is valuable for wide-scale estimation of bedrock exposure rate in karst environments. Using the quantitative inversion model, a distribution map of the bedrock exposure rate in Xingren County was obtained.

A simple assessment approach for winter wheat loss risk impacted by water stress

Abstract In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about −931 kg/ha, which was higher than that in China (−570 kg/ha) and the United States (−367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods.