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The gradual degradation of grasslands on a global scale goes hand‐in‐hand with significant challenges for agriculture and animal husbandry development. Numerous relevant policies and projects have been implemented to protect and restore Chinese ecosystems, but it is still unclear to what degree grassland ecosystems can be recovered. In view of this, we constructed an advanced local net production scaling (ALNS) method by replacing the classification method by self‐organizing feature maps (SOFM) and tailoring the ideal state evaluation method in the LNS method. The ALNS method is used to analyze differences within grassland ecosystems, explore the ideal state of grassland ecosystems, and define degradation as the degree to which the actual state deviates from the ideal state, representing the degree to which grassland ecosystems can be recovered. It thereby quantifies and assesses the overall degradation of such systems in Inner Mongolia. Based on the results, more than 98.5% of the total grassland area failed to reach the ideal state, with the highest levels in the northeast with DN (degraded net primary productivity) values exceeding 200 gc/(m²·yr), followed by the midlands with DN values from 50 to 200 gc/(m²·yr) and the southwest with DN values between 0 and 150 gc/(m²·yr). The ALNS method can efficiently assess grassland ecosystem degradation and can be used to indicate the deviation degrees from ideal states, facilitating the development of protection and restoration programs for grassland ecosystems.

Fine-scale population gridded datasets are of great significance in emergency response, resource allocation, and traffic planning. Many studies have developed fine-scale population spatialization m...

Urban parks have been considered as an effective measure to mitigate the urban heat island (UHI) effects. Many studies have investigated the impacts of shape, size and interior components on the cold island effect (CIE) of parks, while little attention has been given to the impact of neighboring buildings. Thus, taking twenty-two parks in Beijing as samples, this study investigated the impacts of the neighboring building on the CIE of central parks. The results showed that the average land surface temperature (LST) of parks are 30.98 &deg, C in summer and &minus, 1.10 &deg, C in winter. Parks have a strong CIE in summer, and average cold island footprint (CIF) and LST difference are 0.15 km2 and 2.01 &deg, C higher than that in winter. The components of the building in the CIF of parks are dominated by middle-rise building (MRB), followed by low-rise building (LRB), and high-rise building (HRB) is the least dominant. The percentage of landscape (PLAND) and landscape shape index (LSI) of MRB, and perimeter area fractal dimension (PAFRAC) of LRB are significantly related to CIF in summer and winter. This study could extend scientific understanding of the impacts of neighboring buildings on the CIE of central parks, and could guide urban planners in mitigating the UHI effects through the rational allocation of buildings.

Urban parks can effectively mitigate the urban heat island (UHI) effect. Many studies have investigated the relationship between the shape, size, interior components and cooling effect of the park, little attention have been given to explore the relationship between land surface temperature (LST) of central park and buildings in the neighboring areas. This study has explored the effect of the neighboring building on LST of central park, taking Beijing as the study area. The results showed that the cold island footprint of the park in summer was larger than that in winter (with an average area of 0.15 km2 larger). The components of building in cold island footprint of the park were dominated by middle-rese building (MRB). LSI of MRB and AREA_SD of LMB were identified as the key explanatory variables in summer and winter, respectively, which could explained 16.8% and 13.9% of the variance in the park’s LST. This study could extend scientific understanding of the effect of building on park’s LST, and could provide guidance to urban planners on how to mitigate the UHI effects through the rational allocation of buildings.

Yield gridded datasets are essential for agricultural land management, food security and harmonious human&ndash, land relationships. Many studies have developed yield spatialization models that are based on cropland areas. However, crop planting areas, phenological dates, and net primary production (NPP) have received minimal attention. This study proposes a novel method to simulate winter wheat yields in China from 2000 to 2015 using crop phenological datasets, phenological observations, and NPP. The results showed that the NPP in the growing season and statistical yield showed a significant positive correlation (R2 = 0.93, p &lt, 0.01). The mean prediction error of the gridded yield dataset was 12.01%. The relative errors of the gridded yield dataset for approximately half of the samples were between &minus, 10% and 10%. Furthermore, the yield distribution was high in the east and low in the west. The high yield was primarily concentrated in the North China Plain, while low yield was observed in eastern Gansu, central Shanxi, southern Hebei, and eastern Sichuan. From 2000 to 2015, the yield mainly showed an increasing trend in the study area, with the average rate of 0.17 t ha-1 yr-1, especially in the North China Plain. This study suggests that NPP is a key indicator to evaluate the yield of winter wheat. Furthermore, this method can be used to generate gridded yield maps along with providing credible and fundamental data for climate change and sustainable agricultural development.

Ecological projects are an important and vital method to help ecosystem adaptation and restoration in response to the environment change and human interference. The accurate and objective assessment of ecological projects will assist ecosystem management and adaption. This study took the central Tibetan Plateau as the study area, where a series of ecological projects has been implemented since 2005 to prevent grassland degradation by protecting and restoring the grasslands. Our aim is to explore where and to what extent the ecological projects influenced the grassland variation, using SPOT NDVI-based residual trend as an indicator. The results indicated that before the projects (between 1998 and 2004), human-induced degradation characterized the grassland. However, a general grassland restoration was detected after the projects from 2005 to 2012. Moreover, over 60% of project plots had positive trends in the NDVI residuals. From the spatial patterns, project-induced restoration was detected in the western and northern regions, such as Maduo, Dongde and Xinghai counties. For the eastern regions, the human-induced degradation has been generally mitigated and yet not reversed after the projects. Our results indicated that ecological protection and restoration projects in the central Tibetan Plateau have mitigated the grassland degradation and even reversed the degradation in some areas, and also suggested that the NDVI-based residual trend is a useful indicator for assessing the effectiveness of the ecological projects in alpine regions. (C) 2015 Elsevier B.V. All rights reserved.

Rainfall and land use conversion are important factors influencing soil erosion. Differing from the stationary land use type, land use conversion is a process of dynamic change usually spanning several decades. Soil erosion modeling using historic land use data offers an opportunity to study the impacts of actual land use conversion on soil erosion. However, rainfall has been taken into account only in a few studies because its spatio-temporal distribution often varies greatly over a long period of time. In this article, the erosivity index was used to quantify the erosive force of rainfall. Based on entropy theory, erosivity distribution of study area during the 1988 to 2013 period was analyzed. Two extreme zones, Zone I and Zone II, with highest and lowest annual erosivity, respectively, were identified. The intensity of soil erosion among land use types are: farmland > orchard > grass/open forest > shrub > forest. Zone I has severer soil erosion than Zone II. The effectiveness of land use conversion for reducing soil erosion varies between different erosivity zones. Farmland–forest conversion is considered as the first choice to reduce soil erosion when land use conversion is implemented. The conversions of farmland–grassland and farmland–open forest can be used as important supplements to conversion of farmland–forest in low erosivity zone of the study area, although they do not perform well under higher erosivity situation.

Terrain, an important limiting factor in land use change, strongly influences agricultural productivity. This study investigated the topographical characteristics of agricultural potential productivity of cropland transformations in China during the first decade of the 21st century, and explored the impacts of cropland transformations with different terrains on agricultural potential productivity as it relates to China’s national food supply. The results showed the production potential of cropland loss was much greater than that of cropland gain in most regions, and topographical characteristics of croplands losses and gains actually impacted the agricultural potential productivity in China. The losses in agricultural potential productivity was serious in regions with good terrain conditions, where the slope is lower than 5°, and the elevation is below 100 m, especially in the middle and lower Yangtze region and the Huang-Huai-Hai region. The situation for cropland loss with better quality, and expanded the worse was severe in China, and this is expected to negatively influence sustainable agricultural development in the future. This study suggests that researchers focus not only on the total area of expanded cropland but also on cropland quality, such as the different terrain conditions of the croplands, to avoid a loss in national agricultural productivity caused by the process of cropland transformation.

As elicitors of terrestrial system change (e.g., land use transformation) through the introduction of anthropogenic causes, the spatial patterns and levels of roads might be more detrimental to the long-term health of ecosystems at a large scale than the road paving itself. This paper reveals the relationship between soil erosion and roads from a large-scale perspective in Jiangxi Province, China. Temporal and spatial distribution characteristics of artificial and natural drive factors of soil erosion alongside roads were addressed. It was found that, from 1990 to 2010, Jiangxi Province experienced an obvious reduction in soil erosion (the mean annual soil erosion rate decreased from 930.8 t·km−2·a−1 to 522.0 t·km−2·a−1), which was positively correlated with road density (p < 0.01). The maximum soil erosion reduction occurred at a distance of 0–1 km from the village roads. The order of soil erosion effects of the four levels of roads is: Village road > county road > provincial/national road. We emphasize that studying the indirect roles of roads in soil erosion is strongly dependent on a comprehensive consideration of historical policy and the economic development stage in a study area. This paper highlights the indirect role of village roads in soil erosion evolution.

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Black soils, originally characterized by a deep, dark A-horizon, are widespread in the Northeast Plain of China and have been one of the most fertile agricultural resources in the country. However, more than a half-century of intensified management degraded its productivity, mainly with the loss of the dark-coloured A-horizon by rainfall erosion. Using the Revised Universal Soil Loss Equation (RUSLE), the rainfall erosion losses of black soils in YuShu and DeHui counties of Jilin Province were estimated. The rate of loss of thickness of the A-horizon of black soils and the time over which the A-horizons of some black soils in the region might be lost were evaluated. The results showed that about 4–45 t ha−1 topsoil could have been lost each year under corn (Zea mays L.) production. Soybean (Glycine max L. Merr) production would double the losses. Soil losses were directly related to soil type, tillage practices and crop grain yields. The thickness of the A-horizon of black soils in the region decreased at rates of 0ċ5–4ċ5 mm yr−1, depending on soil type and management practices. Corn production may have resulted in an annual loss of 8ċ3 million tonnes of topsoil from black soils alone in Jilin Province; soybean production could have greatly increased this loss. Traditional intensified farming can accelerate the degradation of black soils; conservation tillage has great potential to prevent rainfall erosion losses for the same soils. Accordingly, to preserve and restore the productivity of black soils, conservation tillage is appropriate and should be adopted in Jilin. Copyright © 2003 John Wiley & Sons, Ltd.

The Southwest China Karst, the largest continuous karst zone in the world, has suffered serious rock desertification due to the large population pressure in the area. Recent trend analyses have indicated general greening trends in this region. The region has experienced mild climate change, and yet significant land use changes, such as afforestation and reforestation. In addition, out-migration has occurred. Whether climate change or human-induced factors, i.e., ecological afforestation projects and out-migration have primarily promoted forest restoration in this region was investigated in this study, using Guizhou Province as the study area. Based on Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data, we found general greening trends of the forest from 2000 to 2010. About 89% of the forests have experienced an increase in the annual NDVI, and among which, about 41% is statistically significant. For the summer season, more than 65% of the forests have increases in summer NDVI, and about 16% of the increases are significant. The strongest greening trends mainly occurred in the karst areas. Meanwhile, annual average and summer average temperature in this region have increased and the precipitation in most of the region has decreased, although most of these changes were not statistically significant (p > 0.1). A site-based regression analysis using 19 climate stations with minimum land use changes showed that a warming climate coupled with a decrease in precipitation explained some of the changes in the forest NDVI, but the results were not conclusive. The major changes were attributed to human-induced factors, especially in the karst areas. The implications of an ecological afforestation project and out-migration for forest restoration were also discussed, and the need for further investigations at the household level to better understand the out-migration–environment relationship was identified.

Ascertaining the relationships between sediment transport processes and vegetation cover is essential for watershed soil and water conservation. However, it is not easy to realize this target on a large scale. In this study, the location-weighted landscape contrast index (LCI) based on the “source-sink” theory of ecological processes was introduced to unravel the response of sediment yield to vegetation cover changes in the Poyang Lake drainage area (the largest freshwater lake in China). A modified location-weighted landscape contrast index (mLCI) was proposed to improve the accuracy and efficiency of the LCI. The average vegetation fraction of the study area significantly increased from 46.2% in Period I (1992–1994) to 76.5% in Period II (2004–2006) and then slightly decreased to 73.2% in Period III (2011–2013). From Period I to Period II, the area-specific sediment yield (ASY) sharply decreased by 55%. In Period II, the “source” vegetation patches were found further away from the water bodies, and were more likely to be located on gentler slopes than in Period I. From Period II to Period III, the ASY sharply increased by 83%. In Period III, “source” patches were found closer to the water bodies than in Period II, whereas the “sink” patches were found further away from the water bodies. The high statistical correlation between LCIs/mLCIs and ASY indicated a sensitive response of the sediment yield to vegetation cover changes that significantly altered the sediment transport processes in the study area. The ASY was better correlated with the mLCI than with the LCI. Three key harnessing sub-watersheds of the Ganjiang watershed were identified by calculating the mLCIs: Qingfengshan, Yuanhe, and Jinjiang. This study supported watershed ecological management in the Poyang Lake drainage area and provided a methodology reference for future sediment transport process studies.

The Khingan Mountain region, the most important and typical natural foci of tick-borne encephalitis (TBE) in China, is the largest and northernmost forest area and the one more sensitive to climate change. Taking this region as the study area, we investigated the spatio-temporal dynamics of deciduous broadleaf forest (DBF) and its phenology changes in relation to climate change and elevation. Based on MODIS Enhanced Vegetation Index (EVI) time series over the period of 2001 to 2009, the start-of-season (SOS), length-of-season (LOS) and another two vegetation variables (seasonal amplitude (SA) and integrated EVI (SI)) were derived. Over the past decade, the DBF in Khingan Mountains has generally degraded and over 65% of DBF has experienced negative SA and SI trends. Earlier trends in SOS and longer trends in LOS for DBF were observed, and these trends were mainly caused by climate warming. In addition, results from our analysis also indicated that the effects of temperature on DBF phenology were elevation dependent. The magnitude of advancement in SOS and extension in LOS with temperature increase significantly increased along a raising elevation gradient.