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Journal of Geographical Sciences >

2017 , Vol. 27 >Issue 4: 387 - 402

DOI: https://doi.org/10.1007/s11442-017-1383-7

Research Articles

Status of land use intensity in China and its impacts on land carrying capacity

  • YAN Huimin , 1, 2 ,
  • LIU Fang , 1, * ,
  • LIU Jiyuan 1 ,
  • XIAO Xiangming 3, 4 ,
  • QIN Yuanwei 3
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  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
  • 4. Institute of Biodiversity Science, Fudan University, Shanghai 200433, China
*Corresponding author: Liu Fang (1984-), PhD, specialized in land use change. E-mail:

Author: Yan Huimin (1974-), PhD, specialized in land use change. E-mail:

Received date: 2016-08-20

  Accepted date: 2016-10-12

  Online published: 2017-04-20

Supported by

National Key Research and Development Program of China, No.2016YFC0503500, No.2016YFC0503700

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

Land use intensity quantifies the impacts of human activities on natural ecosystems, which have become the major driver of global environmental change, and thus it serves as an essential measurement for assessing land use sustainability. To date, land-change studies have mainly focused on changes in land cover and their effects on ecological processes, whereas land use intensity has not yet received the attention it deserves and for which spatially-explicit representation studies have only just begun. In this paper, according to the degree and reversibility of surface disturbance by human activities, there are four main classes of land use intensity: artificial land, semi-artificial land, semi-natural land, and natural land. These were further divided into 22 subclasses based on key indicators, such as human population density and the cropping intensity. Land use intensity map of China at a 1-km spatial resolution was obtained based on satellite images and statistical data. The area proportions of artificial land, semi-artificial land, semi-natural land, and natural land were 0.71%, 19.36%, 58.93%, and 21%, respectively. Human and economic carrying capacity increased with the increase of land use intensity. Artificial land supports 24.58% and 35.62% of the total population and GDP, using only 0.71% of the total land, while semi-artificial land supported 58.24% and 49.61% of human population and GDP with 19.36% of China’s total land area.

Key words： land use intensity; land carrying capacity; classification system; spatial pattern

Cite this article

YAN Huimin , LIU Fang , LIU Jiyuan , XIAO Xiangming , QIN Yuanwei . Status of land use intensity in China and its impacts on land carrying capacity[J]. Journal of Geographical Sciences, 2017 , 27(4) : 387 -402 . DOI: 10.1007/s11442-017-1383-7

1 Introduction

Land use change has become an important driver of biodiversity change and the stability of ecosystem services (Blüthgen et al., 2012; Laliberté et al., 2010; Verburg et al., 2013). Importantly, however, land use change encompasses two aspects: changes in land cover and land use intensity (Erb et al., 2014). Current research in land use science has mainly focused on land cover change and its impacts on processes such as climate change (Lambin et al., 2000, 2001), biodiversity maintenance (Allan et al., 2014), and ensuring food security (Verburg et al., 2013). Changes in land use intensity - i.e., those subtle but important differences within the same category or unit class of land cover - can have profound impacts on carbon, nitrogen, and water cycles, and on biodiversity and ecological services too, but they have been overlooked until a decade ago (Burney et al., 2010; FAOSTAT, 2011; Green et al., 2005; Sala et al., 2000). Additionally, due to human population growth and increased food consumption that together exacerbate the scarcity of land resources (Dai and Zhu, 2013), the sustainable intensification of land use has become a prime pathway to achieve long-term food and environmental security in China (Foley et al., 2011; Kuemmerle et al., 2013). However, it still remains impossible to accurately quantify the impacts of land use changes on ecological changes and to evaluate the land carrying capacity based solely on land cover maps. The classification methods should be developed to measure the intensity of land use, while these methods should be robust enough to quantify changes in land use intensity in terms of its spatial and temporal patterns, landscape structure, and reversibility of change (Ellis et al., 2010; van Asselen and Verburg, 2012; Verburg et al., 2009; Erb et al., 2014).
Limited land resources and continued population growth, coupled to their related increases in land-based products and services, will ultimately lead to land use intensification (Jiang et al., 2013). Population pressure (Boserup, 1965) and the resulting market incentives it generates (Shriar, 2005; Stone, 2001) will stimulate improvement in the output efficiency of existing land resources by modifying key agricultural practices, such as watering regimes, and agronomic inputs of fertilization and pesticide. These driving factors of land use intensification will in turn determine the spatial differentiation of land use intensity. Therefore, it is sensible to envision that a robust land use classification system should be constructed based not only on the increased demands upon the land but also on agricultural management. Ellis and Ramankutty (2008) developed the first global land use intensity map by combining maps of land cover, irrigation distribution, and population density. Later, Van Asselen and Verburg (2012) classified and mapped land systems at a global scale based on agricultural areas and natural vegetation. Similarly, Václavík et al. (2013) mapped global land system based on compiled datasets of land use intensity and of prevailing environmental and socioeconomic conditions. Nevertheless, our understanding of the spatial patterns of land use intensity in most regions remains weak (Erb et al., 2013; Kuemmerle et al., 2013). In particular, detailed monitoring datasets and spatially explicit maps are required to characterize land use intensity (Zhu and Sun, 2014). Unfortunately, current land use intensity datasets are only available at a coarse resolution (10 km), thus precluding the detailed information needed on the spatial heterogeneity of land use intensity. Additionally, most research into cropland use intensity is performed by integrating information on patterns of irrigation and livestock population density, neglecting the important characterization of the intensity of the cropping process itself.
At the turn of this century, China embarked on new paths of urbanization and industrialization, and underwent a transition from land market formation to ensuring cultivated land protection and ecological development (Xu et al., 2003). Therefore, land use intensity in China presented new features in this period: accelerated urbanization led to the urban encroachment on farmland (Liu et al., 2010); regional development strategies such as “Grand Western Development Program,” “Revitalization of Northeast China,” and “Rise of Central China” accelerated labor migration (Liu et al., 2010), and the resulting decreased farmer labor would leave more extensively used agricultural areas (Xie and Jiang, 2016); the implementation of national agricultural supporting policies would encourage farmers to grow grain crops (Song and Ouyang, 2012) for more intensive farmland use (Jiang et al., 2013); national key forestry and ecological protection projects gained new forest, while large areas of native forests were converted into cash crops (Li et al., 2007); “Returning Grazing Land to Grassland Project” had restored grassland ecosystem to a certain extent (Zhang et al., 2015), but the livestock and forage balance management policy would promote the conversion from native grassland to artificial grassland, leading to more intensive use of grassland (Xu, 2014). Hence, a clear and detailed understanding of land use intensity in China for the year 2000 is a primary task to achieve harmony among ecological, economic, and social systems, which will provide a baseline reference for evaluating future change and its sustainability. However, current research in China often targets cropland and built-up land, leaving other land types less studied. Household surveys and field questionnaires at provincial and regional scales (Hao and Li, 2011; Hua et al., 2013) may provide detailed representations of land use intensity, but they are not suitable over a broad geographical area because they are labor- and time-intensive. Instead, for such large-scale analysis, the agricultural censuses available at the provincial and county scales have been widely used to map land use intensity without detailed spatial variations (Chen and Li, 2009; Li and Fang, 2014; Liu and Li, 2006; Wu and Qu, 2007; Yao et al., 2014; Zhang et al., 2005). So far, however, mapping land use intensity at a fine spatial resolution with national coverage has not been reported in China.
By integrating land use datasets with spatially explicit indicators, we first developed a land use intensity classification system and then mapped land use intensity in China at a 1-km spatial resolution. Second, the spatial differentiation of land use intensity was characterized at three relevant scales of interest: national, regional, and provincial. Third, and finally, the linkages between land use intensity and land carrying capacity were explored and examined. The study provides the methodology and data basis for a trajectory dynamics analysis of land use intensity as driven by economic development and ecological protection, and thereby offers timely guidance for sustainable land management.

2 Data and methodology

2.1 Data sources and processing

Spatially explicit indicators crucial for measuring land use intensity included the following: land use, irrigated and rain-fed croplands, cropping intensity, human population density, and livestock population density. For the linkage analysis between land use intensity and land carrying capacity, in addition to human population density, the gross domestic product (GDP) and net primary productivity (NPP) were also applied.
2.1.1 Land use
The land use map for 2000 was obtained from China’s National Land Use/Cover Dataset (NLCD). The dataset used a mapping scale of 1:100,000, and it was generated from the manual digitalization of Landsat TM/ETM images acquired in 1999/2000, which were cross validated through extensive field survey datasets (Liu et al., 2014). A grid fraction dataset at a 1-km spatial resolution was obtained by calculating the area fractions of each land use type.
2.1.2 Irrigated and rain-fed croplands
This dataset came from Global Food Security-Support Analysis Data (GFSAD1000 V1.0, http://geography.wr.usgs.gov/science/croplands/), and was derived at the nominal 1-km scale based on current global cropland products having reliable data quality (Friedl et al., 2010; Pittman et al., 2010; Thenkabail et al., 2009, 2011; Yu et al., 2013). The dataset was shown to be applicable to our study through a correlated analysis with statistical data at the county scale.
2.1.3 Cropping intensity
Cropping intensity refers to the crop frequency for a given cropland area on a per year basis, which is important to properly characterize the levels of cropland use intensity. National cropping intensity in 2000 derived from MODIS data using the peak detect method, which integrated the datasets of smoothed EVI time series (500 m, 8-day intervals) and agro-meteorological and phenology records (Yan et al., 2008, 2010, 2014).
2.1.4 Human population density
Human population has been widely used to indicate the intensity of human-environment interactions (Boserup, 1965; Ellis and Ramankutty, 2008). In this paper, human population density was used to quantify land use intensity in forests by overlaying a human population density map to a forest map. County-level human population density was applied to measure the intensity of water use. The gridded population density database of China in 2000 was provided by Data Center for Resources and Environmental Science, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). The grid cell (1 km2) represented the total population within a given square kilometer. Furthermore, the gridded Population of the World (GPW) (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3) was used to fill in the missing data for Taiwan. Following the study done by Ellis and Ramankutty (2008), human population density was divided into three levels: a high population density (> 100 persons/km2), a low population density (1-100 persons/km2), and negligible population density (< 1 person/km2).
2.1.5 Livestock population density
Differences in livestock population density can help distinguish grassland use intensity. The livestock population density dataset came from Gridded Livestock of the World (GLW), at a spatial resolution of 1 km (Robinson et al., 2014). The database contained the global distribution maps for the main species of livestock, which was created by integrating multi-source datasets. The measure of Tropical Livestock Units (TLU) was used to aggregate different livestock types and sizes (Petz et al., 2014). We classified TLU into three levels based on a Jenks natural break optimization technique: a high population density (>10 TLU/km2), a low population density (1-10 TLU/km2), and negligible population density (<1 TLU/km2).
2.1.6 Net primary productivity
National NPP data in 2000 at a spatial resolution of 500 m was simulated using the Vegetation Photosynthesis Model (VPM). The remote sensing-based light use efficiency model had been developed by Xiao et al. (2004a, 2004b); it used MODIS data and flux observation data. The data was resampled to a fixed scale of 1 km for spatial consistency and used with the land use intensity results.
2.1.7 Gross domestic product
The gridded GDP map of China in 2000 was provided by the Data Center for Resources and Environmental Science, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). The 1-km grid cell represented the total GDP within a given square kilometer.

2.2 Land use intensity classification system

As defined by Anderson et al. (1976), the ideal land use intensity classification system should meet the following criteria: (1) be applicable over extensive areas; (2) have comprehensive classes covering the whole area; (3) have hierarchical classes allowing subcategories aggregation and categories disaggregation; (4) use a classification procedure repeatable by different operators at any time; (5) have a dominant class in each grid cell.
Land use intensity measures the impact of human activities on natural ecosystems, so its associated metrics should reflect the spatial pattern, temporal intensity, landscape structure, and degree of reversibility of human effects. According to the degree and reversibility of land surface disturbance by human activities, we divided land use into four main classes (Table 1): (1) artificial land, i.e., impervious surface area with irreversible effects; (2) semi-artificial land, characterized by surface soil that is frequently disturbed; (3) semi- natural land, characterized by occasionally disturbed surface soil but frequently disturbed vegetation; (4) natural land, with no human disturbance of the surface soil and native vegetation. These four classes were further divided into 22 subclasses. Artificial land was divided into two categories: urban vs. village and others. Semi-artificial land consisted of triple- cropping paddy, double-cropping paddy, single-cropping paddy, irrigated triple-cropping dryland, irrigated double-cropping dryland, irrigated single-cropping dryland, rain-fed triple-cropping dryland, rain-fed double-cropping dryland, rain-fed single-cropping dryland, and fallow land according to their combined cropping intensity and water conditions. Semi-natural land was classified into three levels (high intensity, low intensity, and natural) each for forest, grassland, and water body types according to local human population density. Natural land referred to unused land including sandy land, Gobi, salina, wetland, bare soil, and bare rock.
Table 1 Land use intensity classification system
Class Subclass Description
Artificial land (Built-up land) Urban Dense built environments with very high population density
Village and others Rural settlements, factories, and transportation facilities with high population but fragmented landscape
Semi-artificial land (Cropland) Triple-cropping paddy Cropland mainly for triple paddy rice
Double-cropping paddy Cropland mainly for double paddy rice
Single-cropping paddy Cropland mainly for single paddy rice
Irrigated triple-cropping dryland Dryland mainly for irrigated triple crop
Irrigated double-cropping dryland Dryland mainly for irrigated double crop
Irrigated single-cropping dryland Dryland mainly for irrigated single crop
Rain-fed triple-cropping dryland Rain-fed dryland with triple cropping
Rain-fed double-cropping dryland Rain-fed dryland with double cropping
Rain-fed single-cropping dryland Rain-fed dryland with single cropping
Fallow land Cropland left idle during the growing season
Semi-natural land (Forest, Grassland, Water body) High intensity forest Forest with high human population density (>100 persons/km2)
Low intensity forest Forest with low human population density (1-100 persons/km2)
Natural forest Forest with negligible human population density (>1 person/km2)
High intensity grassland Grassland with high livestock population density (>10 TLU/km2)
Low intensity grassland Grassland with low livestock population density (1-10 TLU /km2)
Natural grasslandl water Grassland with negligible livestock population density (<1 TLU /km2)
High intensity water body Water body located in county with high human population density (>100 persons/km2)
Low intensity water body Water body located in county with low human population density (1-100 persons/km2)
Natural water body Water body located in county with negligible human population density (<1 person/km2)
Natural land (Unused land) Unused land Sandy land, Gobi, salina, wetland, bare soil and bare rock

3 Results

3.1 Spatial patterns of land use intensity in China

Spatial patterns of land use intensity at the national, provincial and county scales were analyzed to characterize the spatial differentiation of land use intensity.
3.1.1 Land use intensity at the national scale
In China, semi-natural land covered the majority China’s land surface, at 58.93%, followed by natural land (20.99%) and semi-artificial land (19.36%), while artificial land covered the least proportion (0.71%). Urban, as well as village and others, both covered a similar proportion of artificial land area. Considering semi-artificial land, rain-fed single-cropping dryland covered the greatest area proportion (34.69%), followed by irrigated single-cropping
dryland (18.29%). Among different land cover types of semi-natural land, the highest intensity occurred under water body, for which area proportions of high and low intensity levels were 32.17% and 43.21%, respectively. Ranked second in intensity after water body was grassland, for which area proportions of high and low intensity were 23.46% and 34.54%, respectively. Among forest types, natural forest accounted for the largest area proportion (56.09%), followed by low (24.83%) and high (24.83%) intensity forest.
Spatial patterns in land use intensity showed that, as indicated by the boundary of Hu Line, the southeastern part with its denser human population was more intensively used than the northwestern part with its sparse population (Figure 1). Statistics on land use intensity classes were performed every 1°, in both latitudinal and longitudinal directions. In the latitudinal direction, the area proportion of natural land between 31.5°N and 39.5°N latitude accounted for over 20%, and reached its peak value between 35.5°N and 37.5°N latitudes wherein the Taklimakan desert was located. In the longitudinal direction, a transition in the dominant land use intensity class was from natural and semi-natural land to semi-natural and semi-artificial land with the increment of longitude.
Figure 1 Land use intensity map of China in 2000. Ecological Regions include Northeast Region (I), Inner Mongolia and the Great Wall Region (II), Huang-Huai-Hai Region (III), Loess Plateau Region (IV), Middle and Lower Reaches of the Yangtze River Region (V), Southwest Region (VI), South China Region (VII), Gan-Xin Region (VIII) and Qinghai-Tibet Region (IX).
3.1.2 Land use intensity at regional scale
Land use intensity classes showed remarkable spatial differentiation across different Ecological Regions (henceforth refer to both Table 2 and Figure 2). Artificial land was mainly distributed in Huang-Huai-Hai Region, Middle and Lower Reaches of the Yangtze River Region, South China Region, and Northeast Region. In particular, Huang-Huai-Hai Region had the largest area of built-up land, while Middle and Lower Reaches of the Yangtze River Region and Sichuan Basin had a higher intensity, with urban area accounting for >70% of the built-up area. Semi-artificial land was primarily located in Huang-Huai-Hai Region, Middle and Lower Reaches of the Yangtze River Region, and Northeast Region. Middle and Lower Reaches of the Yangtze River Region, South China Region, and Sichuan Basin had the highest cropland use intensity wherein paddy rice farming accounted for 71.29%, 45.70%, and 30.42% of cropland area in use, respectively. Cropland with multiple cropping practices accounted for 58.11%, 64.35%, and 44.57% of the area in these regions, respectively. Beyond these regions, Huang-Huai-Hai Region had 76% sum of irrigated dryland and paddy rice, and comparable proportions of single- and double-cropped land which added up to 98.40% of the total cropland.
Figure 2 Area proportions of the 22 land use intensity subclasses distributed in 9 ecological regions of China
Table 2 Proportions of land area under four land use intensity classes in 9 ecological regions of China (%)
Regions Artificial land Semi-artificial land Semi-natural land Natural land
Northeast Region 13.20 17.88 10.12 2.20
Inner Mongolia and the Great Wall Region 5.42 8.74 10.22 2.87
Huang-Huai-Hai Region 35.57 19.22 1.09 0.11
Loess Plateau Region 5.10 8.81 4.23 0.07
Middle and Lower Reaches of the Yangtze River Region 15.98 18.81 10.80 0.10
Southwest Region 4.64 14.65 12.99 0.03
South China Region 13.62 6.15 6.56 0.02
Gan-Xin Region 5.78 5.07 12.89 70.93
Qinghai-Tibet Region 0.70 0.66 31.09 23.67
Irrigated dryland and paddy rice added up to 46.02% of the total cropland in Northeast Region, in which 13.40% of the area was single-cropping paddy rice. Where dominated by the single-cropping practice, Inner Mongolia and the Great Wall Region, Qinghai-Tibet Region, Loess Plateau Region, and Gan-Xin Region had the lowest cropland use intensity with rain-fed dryland accounting for 86.94%, 72.92%, 68.86%, and 63.08% of the total cropland, respectively. Qinghai-Tibet Region had the largest area of semi-natural land, followed by Southwest Region, and Gan-Xin (Gansu-Xinjiang) Region. Huang-Huai-Hai Region had the highest proportion of high intensity level of semi-natural land (55.94%), while Northeast Region and Qinghai-Tibet Region had lower intensity with natural land covering the largest area proportion.
Forest was mainly distributed in the Northeast Region, Middle and Lower Reaches of the Yangtze River Region, Southwest Region, and South China Region. However, Middle and Lower Reaches of the Yangtze River Region and Southwest Region had the highest intensity, with high and low intensity forest covering 63.47% and 62.41% of the total forest area, respectively. South China Region ranked second, with high and low intensity forest together accounting for 52.92%. The Northeast Region was dominated by natural forest, which accounted for 78.28% of the total forest area found in this region. Grassland use intensity decreased in the following order: Inner Mongolia and the Great Wall Region, Gan-Xin Region, and Qinghai-Tibet Region. Inner Mongolia and the Great Wall Region had the largest proportion of high intensity grassland, which accounted for 38.01% of total grassland in this region. Natural land covered the largest proportion (58.51%) in Qinghai-Tibet Region, with high intensity grassland distributed in local area. Water body in Middle and Lower Reaches of the Yangtze River Region had the highest land use intensity because of its dense human population; meanwhile, Huang-Huai-Hai Region was found to have high pressure on its water resources. Natural land was concentrated in Gan-Xin Region and Qinghai-Tibet Region, both of which have sparse human populations. Not surprisingly, Gan-Xin Region had the largest natural land because it contains a vast desert.
3.1.3 Land use intensity at the provincial scale
Significant spatial variation in land use intensity was also observed among the provinces in China (Figure 3). Considering artificial land, Shandong province had the largest area proportion of it followed by Jiangsu province, Hebei province, Guangdong province, and Henan province, which had proportions of 12.03%, 8.87%, 8.85%, 7.84%, and 7.07%, respectively. The area of semi-artificial land in provinces of Heilongjiang, Henan, Sichuan, Shandong,
Inner Mongolia, and Hebei exceeded 10 × 104 km2 in each case. Semi-natural land was widely distributed in Tibet, Inner Mongolia, and Xinjiang with corresponding area proportions of 18.36%, 12.82%, and 10.05% of total semi-natural land in China. Notably, 50.05% of natural land in China was located in Xinjiang, followed by Inner Mongolia and then Tibet. Artificial land exceeded 10% in each of the following: Macao, Hong Kong, Shanghai, Tianjin and Beijing. The area proportions of semi-artificial land in provinces of Jiangsu, Shandong, and Henan each exceeded 70%, while those of Tianjin, Hebei and Anhui each exceeded 50%. Semi-natural land covered most of the total area in Tibet, at 85.41%. The 17 provinces with an area proportion of semi- natural land exceeding 60% were mainly distributed in southern and western China. Natural land in Xinjiang accounted for 61.63%, which was much higher than seen for the other Chinese provinces.
Figure 3 Area statistics on the land use intensity classes in each province of China
Provinces were divided into three categories according to their geographical locations: Eastern, Central, and Western (see Fang and Wang, 2015). The land use intensity in the Eastern provinces was the highest, followed by Central and Western provinces. The total area with semi-natural and semi-artificial land in both Eastern and Central provinces together amounted to 96%. In contrast, the Western provinces were mainly featured with semi-natural land and natural land, which together added up to 90% of the total area (Figure 4). In terms of the land use intensity subclasses (Figure 5), the Eastern provinces had average proportions of urban, villages and others, paddy rice, irrigated cropland, and high intensity forest that exceeded counterparts in Central and Western provinces. The area proportion of irrigated dryland was greater than that of paddy rice in Central provinces, whereas the unused land covered the largest proportion of area in Western provinces, followed by grassland. Finally, the area proportion of high intensity grassland in Western China exceeded that found in the Eastern and Central provinces.
Figure 4 Proportions of the four land use intensity classes in Eastern, Central, and Western China
Figure 5 Proportions of the land use intensity subclasses in Eastern, Central, and Western China (diamonds are means, error bars depict min-max range)

3.2 Linking China’s land use intensity to its land carrying capacity

In order to explore the relationship between land use intensity and land carrying capacity, indicators such as population carrying capacity, economic carrying capacity, supporting capacity, and land use efficiency were selected for this study. Population density and gross domestic product (GDP) represent the size of human and economy. Net primary productivity (NPP) indicates the supporting capacities of natural systems. The ratio of GDP to NPP quantifies land use efficiency.
Spearman rank correlation analysis showed that land use intensity was positively correlated with population carrying capacity as well as the economic carrying capacity (r=0.902, p<0.01; r=0.876, p<0.01). In terms of land use intensity classes (Figures 6a and 6b, respectively), artificial land had the highest carrying capacity for both human population and economy, with average values of 4755.20 persons/km2 and 5423.53×104 yuan/km2,respectively. Artificial land supported 24.58% and 35.62% of the total population density and GDP, respectively, though it directly used only 0.71% of the total land area of China. The average population and economic carrying capacity of semi-artificial land was lower than that of artificial land. Although it accounted for just 19.36% of the total area in China, semi-artificial land supported 58.24% and 49.61% of the human population and GDP output, respectively. Population and economic carrying capacity of semi-natural land ranked third, with average values of 39.23 persons/km2 and 26.25×104 yuan/km2, supporting 16.89% and 14.36% of the human population and GDP, respectively. Natural land had the lowest carrying capacity for human population and economy. Furthermore, population and economic carrying capacity decreased across the subclasses of land use intensity. Specifically, the median value of human and economic carrying capacity decreased with a decline in the cropping intensity of cropland. Similar trends were also detected for forest, grassland, and water body.
Shown in Figure 6c is the significant variation of NPP among the different land use intensity classes. Semi-artificial land had the highest value of average NPP (235.14 g Cm-2 a-1), which accounted for 49.18% of the total NPP. This is undoubtedly because agricultural practices, such as multiple cropping, irrigation and fertilizer use, have greatly increased the NPP of semi-artificial land by increasing the latter’s yields. In supporting 49.35% of the total NPP, semi-natural land had an average NPP of 201.53 g Cm-2 a-1, which was lower than that from semi-artificial land. Although semi-natural lands such as evergreen broadleaf forest and mixed broadleaf forests had higher NPPs than did semi-artificial land, the sparse forest, shrubs and grassland in semi-natural land typically had lower average NPPs because they experience smaller and fewer human disturbances coupled to their lower vegetation coverage. Natural land had the lowest average NPP, at 28.11 g Cm-2 a-1, largely because of its sparse vegetation coverage and vulnerable physical conditions with low precipitation regimes.
Figure 6 Statistics on population carrying capacity (a), economic carrying capacity (b), NPP (c), and land use efficiency (d) at the different levels of land use intensity. Bars represent the area-weighted means, diamonds are medians; error bars depict inter-quartile range.
The land use efficiency of semi-artificial lands averaged 0.26×10-2yuan·a·g-1C-1. This value was higher than that for semi-natural land. Specific to semi-artificial land, multiple cropped land had a higher land use efficiency than did single-cropped land. Water body had the highest land use efficiency among the semi-natural lands because of its mosaic water body and other productive areas. Natural land had the lowest use efficiency, at less than 0.01×10-2 yuan·a·g-1C-1. In terms of land use intensity subclasses, land use efficiency increased with land use intensity within semi-artificial, semi-natural, and natural lands (Figure 6d).

4 Conclusions and discussion

4.1 Conclusions

Status and changes in land use intensity, as well as their profound consequences on environmental land carrying capacity, have received increasing attention. By integrating remote sensing data with socio-economic data, a two-level hierarchical land use intensity classification system was constructed according to the intensity of human impacts in this study. Then, the land use intensity in China in 2000 at a 1-km spatial resolution was obtained, and spatial patterns of land use intensity at national, regional, and provincial scales were also analyzed. Furthermore, the relationship between land use intensity and land carrying capacity was also explored. The main results from this study include:
(1) Integrating information on land cover, human population distributions, and economic activities in agriculture, forestry, animal husbandry, and fishery, we constructed a two-level comprehensive land use intensity classification system. It could accurately represent the intensity of human modification on the Earth’s land surface (in both space and time) according to the degree of surface disturbance by human activities and reversibility to natural land. The system included 4 classes and 22 nested subclasses. The main classes included artificial land, semi-artificial land, semi-natural land, and natural land. Artificial land was sub-classified based on the population distribution and degree of landscape fragmentation. Semi-artificial land was sub-classified based on information concerning the water conditions and cropping intensity. Semi-natural land was sub-classified considering the human population density and livestock population density. The cumulative area proportions of artificial land, semi-artificial land, semi-natural land and natural land were 0.71%, 19.36%, 58.93%, and 21%, respectively.
(2) Generally, land use in southeastern China, with its denser human population, was more intensive than that in the northwestern part with its sparse population. Huang-Huai-Hai Region had the largest area of artificial land but was inferior to Middle and Lower Reaches of the Yangtze River Region in its land use intensity. Semi-artificial land was widely distributed in Huang-Huai-Hai Region, Middle and Lower Reaches of the Yangtze River Region, and Northeast Region, but most intensively used in Middle and Lower Reaches of the Yangtze River Region, South China Region, and Sichuan Basin. Qinghai-Tibet Region had the largest area of semi-natural land. Natural land with the lowest intensity was concentrated in Gan-Xin Region and Qinghai-Tibet Region.
(3) Human and economic carrying capacity and land use efficiency increased with land use intensity. Artificial land had the highest human and economic carrying capacity and land use efficiency, supporting 24.58% and 35.62% of the total population density and GDP, respectively (using just 0.71% of the total land in China). Semi-artificial land supported 58.24% and 49.61% of human population and GDP using 19.36% of China’s total land area. Semi-artificial land had the highest value of average NPP, accounting for 49.18% of the total NPP. Semi-natural land had lower land use efficiency, but it contributed more NPP supply than did semi-artificial land.

4.2 Discussion

There are several noteworthy differences between the present study and previous research on land use intensity classification systems and mapping. (1) We selected land use intensity metrics that were related to specific land cover types, e.g., grazing intensity of grassland was directly determined by livestock population density (Briske et al., 2015); felling intensity of forest was closely related to human population density (Pahari and Murai, 1999). (2) For cropland cover, we also investigated the cropping intensity, an important metric of cropping frequency (Erb et al., 2014), to better measure cropland use intensity. (3) We presented a high-resolution map quantifying the spatial distribution of land use intensity. Indicators used in measuring land use intensity were derived at a 1-km spatial resolution, except cropping intensity (resampled from 500 m to 1 km to be consistent with other indicators). The 1-km spatial resolution resultant map would effectively characterize the spatial heterogeneity of land use intensity and thus could help reduce uncertainty in earth system simulations. Unlike current domestic research focusing on cropland and urban areas, our study took all land use types into account—this provided systematic and objective information for the impact assessment of land use pattern and intensity on ecosystem resources and environmental carrying capacity. Compared with previous land-change studies, that examined changes in land cover, our research focused on land use intensity, by integrating natural and human drivers of land system change to provide robust and much-needed spatial datasets for a deeper understanding of the underlying drivers and causes of historical changes in land systems and the impact upon them from human activities. Land use intensity is a paramount factor affecting land use efficiency and land carrying capacity. Quantifying the relationship among them is of crucial and prime theoretical importance to better explore, understand, and discover land use change and sustainable land management options.

The authors have declared that no competing interests exist.

References

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Li G D, Fang C L, 2014. Quantitative measure and influencing mechanism of land intensive use in China at the county level.Acta Geographica Sinica, 69(12): 1739-1752. (in Chinese)Land use issue is an important constraining force for economic sustainable development of China. Urban and rural rapid expansion depletes valued land resources under the background of rapid urbanization. An extensive use pattern might cause a serious waste of land resources. The study on influencing mechanism of land intensive use (LIU) in China at the county level is an important tool for effective LIU practice and policy-making. This paper uses OLS model, Spatial Panel Lagged model and Spatial Panel Error model to characterize the influencing mechanisms of five class factors and 17 variables supported by GIS (Geographic Information System) and MATLAB. And a comprehensive data set, including physical geography attributes and socio-economic information with 2286 counties, was developed. Meanwhile, the spatiotemporal pattern of LIU has been discussed by means of GIS. The results show that Spatial Panel Data models are slightly superior to OLS model in terms of significance and confidence level. Regression results of these models indicate that industrialization, urbanization, economic development level, location, transportation and policy have significant impact on LIU of counties. The variables of physical geography are less significant than socio-economic variables. An ignored variable of historical factor, however, became the most significant factor. In the future, the LIU at the county level should enhance favorable factors and reduce disadvantageous ones, which can be acquired by improving the entire level and quality of industrialization and urbanization. We argued that an efficient and complete land market and operating system should be built to reflect marketoriented activities at the first place. Then, according to regional differences, differential LIU regulation policies and measurements should be optimized. Meanwhile, we should pay close attention to the carrying capacity of local resources and environments when conducting LIU practices.

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[26]
Li H M, Aide T M, Ma Y X et al., 2007. Demand for rubber is causing the loss of high diversity rain forest in SW China.Biodiversity and Conservation, 16(6): 1731-1745.

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Liu C W, Li X B, 2006. The changing characteristics of the agricultural land use intensity in China based on the production cost.Journal of Natural Resources, 21(1): 9-15. (in Chinese)Based on the value cost databases of the main crops such as paddy,wheat,maize,cotton,flue-cured tobacco,sugarcane and beet,this paper studied the changing characteristics of the agricultural land use intensity in China during the period 1980-2002.The results showed that:(1)The degree of intensity of the agricultural land use has increased in the last two decades in China,but the degree of intensity declined three times in 1985,1993 and 1998.(2)The increase of the degree of intensity of land use was mainly resulted from the increase of the material cost,but the change of the labour force was the key factor leading to the change of the degree of intensity.(3)There was no essential difference in the intensity changes between different crops;but the characteristics of the degree of intensity of the cultivated land use changes differed from each other between different regions.(4)The increase of the land use intensity in undeveloped regions was greater than in developed regions.The increase of the land use intensity in the developed regions mainly depended on the input of the material cost,and the addition of the labour force cost is not evident.However,in the undeveloped regions the input of the labour force cost equalled the material cost,especially since 1991,the input of the labour force cost has exceeded the input of the material cost.In the period of the decline of the degree of intensity,the decline extent of land use intensity in the developed regions was greater than in the undeveloped regions.

[28]
Liu J Y, Kuang W H, Zhang Z X et al., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s.Journal of Geographical Sciences, 24(2): 195-210.Land-use/land-cover 变化(LUCC ) 有连接到人和自然相互作用。瓷器 Land-Use/cover 数据集(CLUD ) 从 1980 年代末在 5 年的间隔定期被更新到 2010，与基于 Landsat TMETM+ 图象的标准过程。陆地使用动态区域化方法被建议分析主要陆地使用变换。在国家规模的陆地使用变化的空间与时间的特征，差别，和原因然后被检验。主要调查结果如下被总结。越过中国的陆地使用变化(LUC ) 在最后 20 年(19902010 ) 里在空间、时间的特征显示了一个重要变化。农田变化的区域在南方减少了并且在北方，而是仍然是的全部的区域增加了几乎未改变。回收农田从东北被转移到西北。布满建筑物陆地很快膨胀了，主要在东方被散布，并且逐渐地展开到中央、西方的中国。树林首先减少了，然后增加但是荒芜的区域是反面。草地继续减少。在中国的 LUC 的不同空间模式被发现在之间迟了第 20 世纪并且早第 21 世纪。原版 13 个 LUC 地区在一些地区被边界的变化由 15 个单位代替。包括的这些变化(1 ) 的主要空间特征加速的扩大布满建筑物在 Huang-Huai-Hai 区域，东南的沿海的区域，长江的中流区域，和四川盆登陆；(2 ) 从东北中国和东方内部蒙古在北方转移了陆地开垦到绿洲在西北中国的农业区域；(3 ) 从在到稻的东北中国的喂雨的农田的连续转变回答；并且(4 ) 为在内部蒙古，黄土高原，和西南的多山的区域的南部的农业牧剧的交错群落的格林工程的谷物的有效性。在最后二十年，尽管在北方的气候变化在农田影响了变化，政策规定和经济驱动力仍然是越过中国的 LUC 的主要原因。在第 21 世纪的第一十年期间，在陆地使用模式驾驶了变化的人为的因素从单程的陆地开发转移了强调到开发和保存。动态区域化方法被用来在单位的 zoning 边界，地区的内部特征，和生长和减少的空间17

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[29]
Liu J Y, Zhang Z X, Xu X L et al., 2010. Spatial patterns and driving factors of land use change in China during the early 21st century.Journal of Geographical Sciences, 20(4): 483-494.

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[30]
Pahari K, Murai S, 1999. Modelling for prediction of global deforestation based on the growth of human population.The ISPRS Journal of Photogrammetry and Remote Sensing, 54(5/6): 317-324.Deforestation due to ever-increasing activities of the growing human population has been an issue of major concern for the global environment. It has been especially serious in the last several decades in the developing countries. A population-deforestation model has been developed by the authors to relate the population density with the cumulative forest loss, which is defined and computed as the total forest loss until 1990 since prior to human civilisation. NOAA-AVHRR-based land cover map and the FAO forest statistics have been used for 1990 land cover. A simulated land cover map, based on climatic data, is used for computing the natural land cover before the human impacts. With the 1990 land cover map as base and using the projected population growth, predictions are then made for deforestation until 2025 and 2050 in both spatial and statistical forms.

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[31]
Petz K, Alkemade R, Bakkenes M et al., 2014. Mapping and modelling trade-offs and synergies between grazing intensity and ecosystem services in rangelands using global-scale datasets and models.Global Environmental Change, 29: 223-234.Vast areas of rangelands across the world are grazed with increasing intensity, but interactions between livestock production, biodiversity and other ecosystem services are poorly studied. This study explicitly determines trade-offs and synergies between ecosystem services and livestock grazing intensity on rangelands. Grazing intensity and its effects on forage utilization by livestock, carbon sequestration, erosion prevention and biodiversity are quantified and mapped, using global datasets and models. Results show that on average 4% of the biomass produced annually is consumed by livestock. On average, erosion prevention is 10% lower in areas with a high grazing intensity compared to areas with a low grazing intensity, whereas carbon emissions are more than four times higher under high grazing intensity compared to low grazing intensity. Rangelands with the highest grazing intensity are located in the Sahel, Pakistan, West India, Middle East, North Africa and parts of Brazil. These high grazing intensities result in carbon emissions, low biodiversity values, low capacity for erosion prevention and unsustainable forage utilization. Although the applied models simplify the processes of ecosystem service supply, our study provides a global overview of the consequences of grazing for biodiversity and ecosystem services. The expected increasing future demand for livestock products likely increase pressures on rangelands. Global-scale models can help to identify targets and target areas for international policies aiming at sustainable future use of these rangelands.

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[32]
Pittman K, Hansen M C, Becker-Reshef I et al., 2010. Estimating global cropland extent with multi-year MODIS data.Remote Sensing, 2(7): 1844-1863.This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate.

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[33]
Robinson T P, Wint G R W, Conchedda G et al., 2014. Mapping the global distribution of livestock.PLoS One, 9(5): 1-3.Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5 5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.

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[34]
Sala O E, Chapin F S, Armesto J J et al., 2000. Global biodiversity scenarios for the year 2100.Science, 287(5459): 1770-1774.

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[35]
Shriar A J, 2005. Determinants of agricultural intensity index “scores” in a frontier region: An analysis of data from northern Guatemala.Agriculture and Human Values, 22(4): 395-410.Data on farming systems in Petén, Guatemala, were used to develop an agricultural intensity index. The index can be used to assign an intensity “score” to a given farming system based on the array of practices used by the farmer, each practice’s contribution to production intensity, and the scale at which these practices are used. The scores assigned to 118 farmers in three study areas in Petén were analyzed through analysis of variance (ANOVA) to identify the factors that account for the variation in intensity levels, as measured through the index. The analyses reveal that the factors influencing agricultural intensity in Petén vary greatly from one study area to the next. This is due to differences in livelihood opportunities and strategies that, in turn, affect how agriculture fits into the local economy and how and why intensification is pursued. Variation in intensity levels can best be understood by considering the factors at the household and sub-regional scales that influence (a) whether farmers feel a need to intensify, (b) whether they see some benefit in doing so, and (c) whether they have the resources required to intensify production through particular strategies. Close attention must be paid to these factors by conservation and development organizations seeking to influence land use patterns and conserve forest in Petén.

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[36]
Song X Q, Ouyang Z, 2012. Key influences factors of food security guarantee in China during 1999-2007.Acta Geographica Sinica, 67(6): 793-803. (in Chinese)Exploring the key influencing factors of food security guarantee during the typical period is of significance for the development of cultivated land protection and agricultural policy.This paper aims at exploring the factors between 1999 and 2007 which was in the new period of cultivated land protection administration.Methods such as comparison,spatial and econometric analysis are used to analyze the change in cultivated land productivity which was the cause of the disparity between cultivated land area and grain output changes.Results show that farmers'willingness to grow grain which determines cultivated land use intensity is the key factor.The sustained improvement of the willingness in 2003-2007 was mainly resulted from the rise of grain market price.Meanwhile,direct subsidy merely inspired farmers'anticipation for the profit of grain growing at the beginning years of implementing this policy.In addition,suggestions on the development of cultivated land protection are proposed involving improvement of farmers'willingness to grow grain,optimization of inputs in grain growing and improvement of cultivated land protection models.

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[37]
Stone G D, 2001. Theory of the square chicken: Advances in agricultural intensification theory.Asia Pacific Viewpoint, 2001, 42(2/3): 163-180.Scientific understanding of agricultural change has grown considerably since Boserup's seminal 1965 work, but her model's simplicity has provided a foundation for building more complex understandings of farming societies. Much of the development of these more sophisticated understandings has been led by Harold Brookfield. The first section of this paper summarises our current understanding of the salient points of commonality in intensive smallholder systems. The second section looks at findings from studies that relax Boserupian constraints, revealing new kinds of variability in agricultural systems. Both sections stress the need for continued research on the political-economic context of agricultural intensification.

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[38]
Thenkabail P S, Biradar C M, Noojipady P et al., 2009. Global Irrigated Area Map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14): 3679-3733.A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 1002km monthly time‐series for 1997–1999, (b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 102km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 5002km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 102km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 102km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 102km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 1102000 locations, (e) groundtruth data broadly sourced from the degree confluence project (302864 sample locations) and from the GIAM project (102790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 15002m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 46702million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 25202Mha from season one, (b) 17402Mha from season two and (c) 4102Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 39902Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (37002Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).

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[39]
Thenkabail P S, Hanjra M A, Dheeravath V et al., 2011. Global croplands and their water use remote sensing and non-remote sensing perspectives. In: Weng Q H eds. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. Florida: CRC Press, Taylor and Francis Group, 383-419.2009c). The other three studies used a combination of national statistics and geospatial tech-niques (Goldewijk et al. 2009; Portmann, Siebert, and D02ll 2009; Ramankutty et al. 2008; Siebertand D02ll 2008, 2009). 2009.09. 010. Khan, S., and MA Hanjra. 2008.

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[40]
Václavík T, Lautenbach S, Kuemmerle T et al., 2013. Mapping global land system archetypes.Global Environmental Change, 23(6): 1637-1647.Land use is a key driver of global environmental change. Unless major shifts in consumptive behaviours occur, land-based production will have to increase drastically to meet future demands for food and other commodities. One approach to better understand the drivers and impacts of agricultural intensification is the identification of global, archetypical patterns of land systems. Current approaches focus on broad-scale representations of dominant land cover with limited consideration of land-use intensity. In this study, we derived a new global representation of land systems based on more than 30 high-resolution datasets on land-use intensity, environmental conditions and socioeconomic indicators. Using a self-organizing map algorithm, we identified and mapped twelve archetypes of land systems for the year 2005. Our analysis reveals similarities in land systems across the globe but the diverse pattern at sub-national scales implies that there are no ‘one-size-fits-all’ solutions to sustainable land management. Our results help to identify generic patterns of land pressures and environmental threats and provide means to target regionalized strategies to cope with the challenges of global change. Mapping global archetypes of land systems represents a first step towards better understanding the global patterns of human–environment interactions and the environmental and social outcomes of land system dynamics.

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[41]
van Asselen S, Verburg P H, 2012. A land system representation for global assessments and land-use modeling.Global Change Biology, 18(10): 3125-3148.Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.

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[42]
Verburg P H, Erb K H, Mertz O et al., 2013. Land system science: Between global challenges and local realities.Current Opinion in Environmental Sustainability, 5(5): 433-437.This issue of Current Opinion in Environmental Sustainability provides an overview of recent advances in Land System Science while at the same time setting the research agenda for the Land System Science community. Land System Science is not just representing land system changes as either a driver or a consequence of global environmental change. Land systems also offer solutions to global change through adaptation and mitigation and can play a key role in achieving a sustainable future earth. The special issue assembles 14 articles that entail different perspectives on land systems and their dynamics, synthesizing current knowledge, highlighting currently under-researched topics, exploring scientific frontiers and suggesting ways ahead, integrating a plethora of scientific disciplines.

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[43]
Verburg P H, Mertz O, Erb K H et al., 2013. Land system change and food security: towards multi-scale land system solutions. Current Opinion in Environmental Sustainability, 5(5): 494-502.Abstract Land system changes are central to the food security challenge. Land system science can contribute to sustainable solutions by an integrated analysis of land availability and the assessment of the tradeoffs associated with agricultural expansion and land use intensification. A land system perspective requires local studies of production systems to be contextualised in a regional and global context, while global assessments should be confronted with local realities. Understanding of land governance structures will help to support the development of land use policies and tenure systems that assist in designing more sustainable ways of intensification. Novel land systems should be designed that are adapted to the local context and framed within the global socio-ecological system. Such land systems should explicitly account for the role of land governance as a primary driver of land system change and food production.

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[44]
Verburg P H, Van de Steeg J, Veldkamp A et al., 2009. From land cover change to land function dynamics: A major challenge to improve land characterization.Journal of Environmental Management, 90(3): 1327-1335.Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.

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[45]
Wu Y L, Qu F T, 2007. Mechanism of intensive urban land use in china: Theoretical and practical study.Resources Science, 29(6): 106-113. (in Chinese)

[46]
Xiao X M, Hollinger D, Aber J et al., 2004a. Satellite-based modeling of gross primary production in an evergreen needleleaf forest.Remote Sensing Environment, 89(4): 519-534.The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO 2 , between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO 2 eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998–2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO 2 flux data from a CO 2 eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998–2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO 2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

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[47]
Xiao X M, Zhang Q Y, Braswell B et al., 2004b. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data.Remote Sensing of Environment, 91(2): 256-270.Net ecosystem exchange (NEE) of CO 2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO 2 flux measurements at individual CO 2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO 2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.

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[48]
Xie Y, Jiang Q B, 2016. Land arrangements for rural-urban migrant workers in China: Findings from Jiangsu Province.Land Use Policy, 50: 262-267.A massive amount of rural labor in China has migrated to urban areas and transferred to non-agricultural employment. This has resulted in issues regarding how to effectively arrange land that was contracted to them under the household contract responsibility system. Using survey data collected in Jiangsu Province and a multinomial logit model, this article discusses three methods of land arrangement utilized by rural–urban migrant workers (family farming, land transfer and abandonment) and examines the correlates of land arrangement methods. We find that there is a significantly positive relation between family size and the family farming option. The improvements in human capital, higher wages, greater job stability, and a longer commute time between migrants’ cities of employment and their hometown are significantly correlated with land transfer or abandonment. These findings can elucidate China’s land policy in the context of massive rural–urban migration.

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[49]
Xu M Y, 2014. A review of grassland carrying capacity: Perspective and dilemma for research in China on forage-livestock balance.Acta Prataculturae Sinica, 23(5): 321-329. (in Chinese)Ascertaining the correct balance between forage supply and livestock demand is a critical issue for grassland animal husbandry in China.Calculations are typically based on theoretical models that balance energy availability and requirement,and adoption has been hindered by disagreement as to the size and response to various factors of the theoretical coefficients used.In this article we summarize progress in China in livestock feed budgeting research.Adoption of forage-livestock supply and demand calculations based on average net primary productivity and animal feed intake for arid and semi-arid rangeland characterised by uneven precipitation and wide seasonal temperature fluctuation may not deal with the complexity and uncertainty arising from seasonal imbalance between supply and demand within the ecosystem.Despite considerable progress in solving temporal-spatial disequilibrium between grassland productivity and livestock requirement,major challenges remain for"seasonal animal husbandry"and"key pasture".Parameter estimates commonly used as a basis for foragelivestock balance decision-making require standardisation.In particular,there is no consensus as to the application or method of determining parameters such as livestock substitution rate,livestock numbers,and appropriate stocking rate,among others.In future,research into forage production-livestock demand balance should focus on exploring opportunities to reduce seasonal imbalance between forage supply and livestock demand,and on coordination of the ecological,social and economic functions of grassland.

[50]
Xu X L, Shi P J, Yang M C, 2003. The impact of the national land policy on the sustainable arable land use in China since 1949. Journal of Beijing Normal University (Social Science Edition), 2: 115-123. (in Chinese)It's well known that land-use and land-cover change is affected by lots of factors such as GDP, population and policy. In China, the policy plays a vital role on the change of land-use and land-cover. Clear and deep understandings about land-use and land-cover change in China can't be reached without the knowledge of the National land policy. Therefore, this paper reviews the National land policy relevant to land-use and land-cover including the five-year plans, special political affairs, policy and laws roundly from 1949 to 2002 in China. It can be seen that the National land policy affect the land-use and land-cover change by the following ways: firstly, the five-year plans determine the main direction of the land-use and land-cover by making industry and region developing policy and arranging the direction and emphasis of the investment in national capital construction; secondly, the special political affairs always make great affects on the change of land-use and land-cover; thirdly, the rule of laws and notices change one kind of the land-use and land-cover and then change the whole land-use and land-cover ; finally, the laws and bylaws are always trying to maintain the status of the land-use and land-cover.

[51]
Yan H M, Huang H Q, Xiao X M et al., 2008. Spatio-temporal distribution of multiple cropping system s in the Poyang Lake region.Acta Ecologica Sinica, 28(9): 4517-4523. (in Chinese)Agricultural land use and management is one of the most direct means by which humans impact the earth system,with significant negative environmental effects at local to global scales due to altered ecosystem processes,patterns and resource use.Multiple cropping is of particular interest because agricultural intensification has increased pressure on water resources,ecosystems,and biodiversity due to increased water withdrawals for irrigation and higher energy inputs due to mechanization and the production of chemical fertilizer.The Poyang Lake region is one of the most intensified agricultural regions in southern China,and the spatial and temporal pattern of cropping systems is determined not only by climate and water availability.Population pressure,social and economic conditions and policies,flood risks,and individual farmers management decisions also have profound impacts.Therefore,the spatial pattern and temporal process of multiple cropping is quite complex.In this paper,we combined agro-meteorological observation data and MODIS data with a resolution of 500m at 8-day intervals to explore a novel and robust method to examine spatial and temporal dynamics of multiple cropping and crop calendar using a MODIS/EVI time series curve.The results indicate that a cropping intensity algorithm based on this data is potentially applicable for monitoring agricultural intensification.Spatial and temporal variability of multiple cropping may be the result of farmers鈥 dynamic adaptation to local climate,socio-economic conditions,and food security.An explicit spatial and temporal analysis of the complex dynamics of multiple cropping in the context of coupled human and natural systems is necessary for modeling and evaluating the impacts of human activity on global environmental change and food security.

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[52]
Yan H M, Xiao X M, Huang H Q et al., 2014. Multiple cropping intensity in China derived from agro-meteorological observations and MODIS data.Chinese Geographical Science, 24(2): 205-219.Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.

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[53]
Yan H M, Xiao X M, Huang H Q, 2010. Satellite observed crop calendar and its spatio-temporal characteristics in multiple cropping area of Huang-Huai-Hai Plain.Acta Ecologica Sinica, 30(9): 2416-2423. (in Chinese)Multiple cropping is one of the most influenced intensified agricultural land use activity in China because of the increased pressure on water,ecosystems and environment.However,due to the lack of spatial and temporal explicitly data of multiple cropping and crop calendar,there were significant uncertainty in agricultural productivity and Carbon dynamic monitoring,modeling and evaluation on regional to larger scale.In Huang-Huai-Hai Plain,an important agricultural region in China,more than 70% of total cropland land area was planted with winter-wheat and maize double cropping system.It has been proved that identifying multiple cropping and crop calendar and assigning appropriate light use efficiency to C3 and C4 crops could substantially improve our ability to model and evaluate the seasonal dynamics of carbon flux in such winter-wheat and maize rotation system.In this study,we analyze spatial and temporal patterns of crop growth process and crop calendar in the winter-wheat and maize double cropping system using multi-temporal satellite images from the Moderate Resolution Imaging Spectral radiometer(MODIS) and in-situ observation of key crop phenological transition dates,and explore a method to examine and extract crop calendar of each crop season in Huang-Huai-Hai plain from MODIS Enhanced Vegetation Index(EVI) and MODIS Land Surface Water Index(LSWI) time series curves by combining agro-meteorological observation data and MODIS data with a resolution of 500m at 8-day intervals.Multiple cropping distribution,temporal transition characteristics and heterogeneity of the start and the end time of each crop season were examined and analyzed,and the method also was validated by comparing with in-situ observed start date and end date records of winter-wheat and maize.The method and crop calendar products discussed in this paper could be applied in agricultural productivity estimation,biogeochemical cycle modeling and agricultural ecosystem monitoring.

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[54]
Yao C S, Huang L, Lü X et al, 2014. Temporal and spatial change of cultivated land use intensity in China based on emergy theory. Transactions of the Chinese Society of Agricultural Engineering, 30(8): 1-12. (in Chinese)Abstract: Limited cultivated land has become one of the major restrictions for China's social and economic development, and how to use it intensively is the focus of the Chinese government and research scholars. Based on emergy theory and methods, the cultivated land use intensity (I) was composed of production factors intensity (P) and multiples the multiple cropping index (M). On this basis, the paper analyzed the temporal and spatial change law of all the five production factor intensities, which are farm machinery, fertilizer, pesticide, agricultural film and labor, and the multiple cropping index in China from 1990 to 2011. The results showed: Firstly, during the past 22 years, the farm machinery intensity, fertilizer intensity, pesticide intensity, and agricultural film intensity were all in a linear growth trend, and their annual growth rates were 6.59%, 2.89%, 3.88% and 7.42% respectively; while the labor intensity was in a linear decreasing trend, and its decreasing rate was 5.10 percent. In 1996, the possession of industrial supplementary energy intensity, including farm machinery, fertilizer, pesticide, and agricultural film, in the total production factors intensity first exceeded 50 percent, which meant that China had entered the modern agriculture stage in the middle of 1990s. During the study period, multiple cropping index was also in a linear growth, and the annual growth rate is 0.79%; its total increasing rate was 0.1794 in the past 22 years, and was the major driving force of the increase of land use intensity. Secondly, in 1996, the provinces with high labor intensity and low development of modern agriculture were mainly located in the western part of China, and the typical characteristics of these provinces were that they were all rated with a relatively low level of social and economic development; While in the provinces with high development of economic levels and a good industrial foundation, the labor intensity was low and development of modern agriculture was high. From 1996 to 2008, most provinces in the western part of China and some of the coastal provinces in the eastern part of China, labor intensity decreased a lot; while in the provinces with high economic development and the provinces with more land and fewer persons, labor intensity decreased only a little. In the provinces with high economic development in the eastern coastal part of China and some major grain producing areas, industrial supplementary energy intensity increased a lot; In the provinces with high development of modern agriculture, industrial supplementary energy intensity increased only a little. Thirdly, from 1996 to 2008, in the major rice producing areas in southern part of China, the multiple cropping index decreased a lot, which was the major reason that contributed to the decreasing of their land use intensity; In most provinces in the northern part of China, the multiple cropping index increased a lot, which was the major reason that improved their land use intensity.

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[55]
Yu L, Wang J, Clinton N et al., 2013. FROM-GC: 30 m global cropland extent derived through multisource data integration.International Journal of Digital Earth, 6(6): 521-533.We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps (i.e. FROM-GLC, Finer Resolution Observation and Monitoring, Global Land Cover; FROM-GLC-agg) and a 250-m cropland probability map. A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples. A decision tree was then applied to combine two 250-m cropland masks: one existing mask from the literature and the other produced in this study, with the 30-m global land cover map FROM-GLC-agg. For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical (FAOSTAT) database, a final global cropland extent map was composited from the FROM-GLC, FROM-GLC-agg, and two masked cropland layers. From this map FROM-GC (Global Cropland), we estimated the global cropland areas to be 1533.83 million hectares (Mha) in 2010, which is 6.95 Mha (0.45%) less than the area reported by the Food and Agriculture Organization (FAO) of the United Nations for the year 2010. A country-by-country comparison between the map and the FAOSTAT data showed a linear relationship (FROM-GC = 1.05*FAOSTAT -1.2 (Mha) with R-2=0.97). Africa, South America, Southeastern Asia, and Oceania are the regions with large discrepancies with the FAO survey.

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[56]
Zhang F G, Hao J M, Jiang G H et al., 2005. Spatial-temporal variance of urban land use intensity.China Land Science, 19(1): 23-29. (in Chinese)The purpose of the paper is to analyze the spatial-temporal variance of urban land use intensity in order to guide urban land use. Method of comprehensive evaluation, spearman rank relational coefficient and cluster analysis were employed. The results include: (1) During the six years from 1996-2002, the urban land use intensity had been increasing in most of the provinces (Fujian and Hebei, for instance) and had been decreasing in ten provinces (Liaoning and Sichuan, for instance) especially in Anhui; (2) Urban land use intensity was descending sharply from the east, the middle to the west part of China, in which Beijing and Shanghai were the tiptop and Gansu was the lowest; (3) Population change was the unique and most active driving force while the policy, economy and technical factors were the most important external driving forces. The method of comprehensive evaluation can be used to evaluate urban land use intensity, and the outputs fit into actualities and have certain feasibilities.

[57]
Zhang H Y, Fan J W, Shao Q Q, 2015. Land use/land cover change in the grassland restoration program areas in China, 2000-2010.Progress in Geography, 34(7): 840-853. (in Chinese)在3S技术支持下,结合景观格局定量分析方法,基于30m分辨率的土地利用/覆被数据,对中国退牧还草工程区2000-2010年土地利用/覆被时空分布特征进行研究.通过利用土地利用转移矩阵和动态度来判定土地利用变化的速度和区域差异,并在斑块类型和景观水平上分析研究区景观格局特征,探讨土地利用格局变化的生态效应.结果表明:①近10年来,研究区土地利用/覆被类型以草地和其他类用地为主,整体内部结构稳定少动.草地变化面积仅占2000年草地总面积的0.37％;林地、湿地、耕地和人工表面的面积均有所增加;其他类用地面积有所减少.②全区土地综合动态度均小于0.1％,土地利用/覆被变幅较小,除人工表面较活跃外,其他各类型变化相对缓慢,且各省土地利用区域差异较小.③研究区内景观基质未发生改变,区域景观破碎度递减,景观多样性水平上升,景观聚集度和连续性微弱下降,景观整体保持较完整态势.退牧还草工程的实施使土地利用/覆被结构和景观格局均得以优化.

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[58]
Zhu H Y, Sun M H, 2014. Main progress in the research on land use intensification.Acta Geographica Sinica, 69(9): 1346-1357. (in Chinese)Land use intensification is an unimpeachable choice of human beings under the multiple pressures of food security, economic development, and ecological conservation. It is particularly important for the countries with less land resource per capita like China to foster a sustainable intensification in their use of land. The scientific understanding of land-use change, however, is still insuf fi cient to characterise the conditions under which such a sustainable intensification can and will occur. The existing large knowledge gaps should be filled in the future. In this paper, we briefly review main progress in the research on land use intensification. The content is arranged in two sections. The first section focuses on four subtopics:(1) basic characters and their measuring indicators,(2) extremum and potential for input and output,(3) driving factors and limiting factors,(4) ecological effects and sustainable intensification. The second section is on current hotspots: monitoring and mapping land use intensity, land sharing land sparing, policy premise and choice, and urban land "intensification" in China.

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