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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 11: 1611 - 1625

DOI: https://doi.org/10.1007/s11442-018-1532-7

Special Issue: Land system dynamics: Pattern and process

Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000-2050

  • XIA Tian , 1, 2 ,
  • WU Wenbin , 1, * ,
  • ZHOU Qingbo 1 ,
  • TAN Wenxia 2 ,
  • Peter H. VERBURG 3 ,
  • YANG Peng 1 ,
  • YE Liming 4, 5
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  • 1. Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 2. Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province / College of Urban & Environmental Science, Central China Normal University, Wuhan 430079, China
  • 3. Institute for Environmental Studies, VU University Amsterdam, 1081 HV Amsterdam, The Nether-lands
  • 4. CAAS-UGent Joint Laboratory of Global Change and Food Security / Institute of Agricultural Re-sources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 5. Department of Geology (WE13), Ghent University, 9000 Gent, Belgium
*Corresponding author: Wu Wenbin, Professor, E-mail:

Author: Xia Tian (1981-), Associate Professor, specialized in remote sensing monitoring and simulation of the influence of global changes on agriculture. E-mail:

Received date: 2017-03-13

  Accepted date: 2017-09-12

  Online published: 2018-11-20

Supported by

Agricultural Outstanding Talents Research Foundation of Ministry of Agriculture (MOA)

Key Laboratory of Agri-Informatics Foundation of MOA No.2015001

Natural Science Foundation of Hubei Province No.2016CFB558

The Fundamental Research Funds for the Central Universities, No.CCNU15A05058

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications. Many studies have investigated the past changes in land use, but efforts exploring the potential changes in land use and implications under future scenarios are still lacking. Here we simulate the future land use changes and their impacts on ecosystem services in Northeast China (NEC) over the period of 2000-2050 using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model under the scenarios of ecological security (ESS), food security (FSS) and comprehensive development (CDS). The model was validated against remote sensing data in 2005. Overall, the accuracy of the CLUE-S model was evaluated at 82.5%. Obtained results show that future cropland changes mainly occur in the Songnen Plain and the Liaohe Plain, forest and grassland changes are concentrated in the southern Lesser Khingan Mountains and the western Changbai Mountains, while the Sanjiang Plain will witness major changes of the wetlands. Our results also show that even though CDS is defined based on the goals of the regional development plan, the ecological service value (ESV) under CDS is RMB 2656.18 billion in 2050. The ESV of CDS is lower compared with the other scenarios. Thus, CDS is not an optimum scenario for eco-environmental protection, especially for the wetlands, which should be given higher priority for future development. The issue of coordination is also critical in future development. The results can help to assist structural adjustments for agriculture and to guide policy interventions in NEC.

Key words： Northeast China; land use; spatio-temporal change; scenario; ecosystem service

Cite this article

XIA Tian , WU Wenbin , ZHOU Qingbo , TAN Wenxia , Peter H. VERBURG , YANG Peng , YE Liming . Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000-2050[J]. Journal of Geographical Sciences, 2018 , 28(11) : 1611 -1625 . DOI: 10.1007/s11442-018-1532-7

1 Introduction

Land use/land cover change (LUCC) has becoming an important topic in the field of global change research (Lambin et al., 2011). Located at relatively high latitudes (from about 39° to 53°N), Northeast China (NEC) is one of the most important food production regions in China. The NEC is climate-sensitive and has been identified as one of the main areas most susceptible to climate change (Li et al., 2012). It also experienced tremendous socio-economic dynamics over the past several decades. All these together have driven obvious changes in land use, particularly for the class of cropland in NEC. For instance, rice planting areas have expanded quickly from 3% to 13% of China’s total production of rice over the past 30 years (Xia et al., 2016). The NEC is thus among the global hot-spot regions of LUCC studies.
There has been much research to investigate the past LUCC dynamics and environmental consequences in NEC (Chen et al., 2001; Wang et al., 2006; Xia et al., 2014). Indeed, these profound changes in LUCC will continuously move forward in pervasive ways over the next decades. Unfortunately, despite the importance of these changes for human-environment systems, we have little knowledge about the future LUCC (Shearer, 2005; Xu et al., 2015; Zhang et al., 2015; 2016). Although statistical and remote sensing methods are commonly used to explore the spatio-temporal processes of LUCC ( Vliet et al., 2013; Roy et al., 2015; Tian 2015), they are either targeting at specifically small regions or focusing on the past LUCC. Spatially explicit modeling techniques are thus emerging as an alternative to depict the future changes in LUCC at regional scales (Verburg et al., 2009). Because the future has not happened, it offers no facts or testimonies, and provides no means for immediate verification. Uncertainties in social, political and economic development both globally and regionally make it not possible to predict future changes in land use. Instead, it is possible to explore what might happen given certain assumptions about societal developments and environmental changes through the construction of scenarios. Thus, scenario-based modeling provides an appropriate tool to develop plausible visions of future pathways of land use (Shearer, 2005).
During recent years a number of scenario studies have been conducted at a wide range of scales to unravel and assess the possible future land use changes. However, these studies are more concerned about the quantity and location changes in land use under different climate change scenarios (David, 2007; Wang et al., 2015). Few have been done to understand the eco-environmental effects of future land use changes in NEC. This hinders a clear impact assessment of future land use changes, and makes it difficult to work out a trade-off between rational utilization of land resources and better protection of the environment. The integrated impact assessment of future LUCC on ecosystem service is gaining increasing attention in future scenario studies (Liu et al., 2010; Song et al., 2015).
This study thus aims firstly to model the spatio-temporal changes in land use in NEC under future scenarios. More attention is paid to investigate the conversion between cropland and other land use types. Secondly, ecosystem service values are evaluated to quantitatively assess the impacts of different future land use changes. Improved foresight of land use change and its impact can help to better inform policy decisions (Verburg et al., 2006, 2010; Bonilla-Moheno et al., 2012; Letourneau et al., 2012; Stürck et al., 2015).
The study area is located in the northeast of China (115°05°E-135°02°E, 38°40°N- 53°34°N) which consists of the provinces of Liaoning, Jilin, and Heilongjiang (Figure 1). NEC occupies a land mass of 791,800 km2, of which 264,400 km2 is cropland, taking a share of 16.5% of the total cropland area in China. Humid and semi-humid climates prevail in NEC (Chen et al., 2012; Li et al., 2012). The mean annual air temperature ranges from -4.2 to 10.9°C. Most of the region has a base-10°C active accumulated temperature of 1500-3600 degree-days, a frost-free period of 140-170 days, and precipitation of 500-800 mm (60% of the rainfall is concentrated during July-September) (Duan et al., 2016). The main soil types in the northern part are chernozem, meadow soil, and albic soil, while in the southern part it is mainly dark-brown soil. Favorable conditions, such as well-built irrigation facilities, suitable climate and fertile black soil, have made NEC the most important base for food production in China.
Figure 1 Map of Northeast China

2 Methods and data

2.1 Simulation of future land use changes

In this study, the well-known CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model was used to simulate the future changes in land use in NEC. The CLUE-S model uses systems theory to deal with the competition between different land use types (Verburg et al., 2002). The CLUE-S model includes a nonspatial module and a spatial module (Figure 2). The nonspatial module calculates the annual area of demand for land at different target scenarios through an analysis of the drivers of change for natural environment, socio-economy and policies. The demand for different land use types is then allocated to individual grid cells based on the spatial module. In contrast to other models, the CLUE-S model is characterized by its multiscale capability, which can better serve our purpose in combining geophysical and socio-economic parameters to model land use changes under different scenarios (Verburg et al., 2006). In addition, the CLUE-S model had been used to simulated land use changes in many parts of the world (such as Europe, Brazil, Southeast Asia, and China). The model can be used to simulate the spatio-temporal patterns of LUCC for 30-50 years into the future at variable scales ( Verburg and Overmars, 2009; Barreto et al., 2013; Jiang et al., 2016).
Figure 2 CLUE-S model structure
The spatial module is based on spatial distribution probability, land use conversion rules and land use pattern in the year of baseline. The calculation of spatial distribution probability of each land use type is the core of the model simulation. Using binary logistic regression to explore the causal relationship between predetermined variables and changes in land use, the land use spatial distribution probability is calculated using the formula:
$Logit\left( {{P}_{i}} \right)=\ln \left( {{P}_{i}} \right)/\left( 1-{{P}_{i}} \right)={{\beta }_{0}}+{{\beta }_{\text{1}}}{{X}_{1}}_{,i}+{{\beta }_{\text{2}}}{{X}_{2,i}}+...+{{\beta }_{n}}{{X}_{n}}_{,i}$ (1)
where Pi is the probability for a grid cell being in land use type i, and Xi and n are selected driving factors, either physical or socio-economic. Coefficients (β) are estimated through binary logistic regression, using the grid-based land use data as the dependent variable and the selected driving factors as independent variables. In addition, space allocation is achieved based on the land use requirements with total probability (Geoghegan et al., 2001; Serneels et al., 2001). The total probability (TPROPi,u) of grid cell i in land use type u is given by:
$TPRO{{P}_{i,u}}={{P}_{i,u}}+ELA{{S}_{u}}+ITE{{R}_{u}}$ (2)
where Pi,u is the suitability of location i for land use type u (based on the logit model); ELASu is the conversion elasticity for land use type u, which is land use type specific elasticity to change value; and ITERu is an iteration variable specific to land use type u, which is an indicator of the relative competitive strength of the land use type.
The ELAS value indicates the stability of land use conversions, which means the degree of difficulty for one land use type to be converted to other land use types for a certain period of time. For example, cropland can easily be converted to built-up land, while the reverse is difficult. The ELAS parameters range from 0 (easy conversion) to 1 (irreversible change) (Verburg et al., 2002). ELAS values were determined using expert knowledge based on past model behaviors observed (Table 1). Moreover, ITER is another important parameter of the total probability distribution of the model. The distribution area of the land use types is compared with the statistical area of land use (demand data) at the end of each iteration of CLUE-S model runs. If the area of the distribution of land use is less than the statistical area, the model increases the value of ITER; otherwise, it reduces the value of ITER and reallocates the land use area for each land use type.
Table 1 Parameter settings of ELAS
Land use type Cropland Forest Grassland Water body Built-up Wetland Unused land
ELAS 0.6 0.8 0.5 0.9 0.9 0.7 0.4
In addition, a conversion matrix is used to control the transformation between different land use types. Land use conversion rules are used to define whether land use type conversions between various land use types can be achieved. Conversion rules are specified in a conversion matrix, in which a value of 1 means that one land use type can be converted to the other types, and 0 means that one type cannot be converted to the other types. The land use type conversion matrix is set according to the practical situations in NEC.
The model application was designed to run over a period of approximately 50 years with a 5-year time increment, taking the year 2000 as the baseline. The target year of the study is 2050, which has critical importance in national long-term development planning and climate change research. It also facilitates intercomparison between the results obtained here and from other projects. Simulation results of the year 2005 was used for model validation by comparing with remote sensing maps and potential changes in land use in NEC over 2010-2050 under different scenarios were analyzed. The most common method of model validation is by-pixel comparison between the simulation results and the reference data to calculate the Kappa coefficient of agreement (Vliet et al., 2011). However, this is a rather subjective method of validation (Eitelberg et al., 2015; Vliet et al., 2011). In this study, a more objective method for the model goodness of fit by Costanza (1989) was used. It is based on the measuring of the similarity of patterns and the idea that measurement at one resolution is not sufficient to describe complex patterns of land uses. This method yields indices that summarize the way the fit changes as the resolution of measurement changes. An expanding window is used to gradually degrade the resolution of the comparison.

2.2 Assessment of ecosystem service values

The ecosystem service value (ESV) assessment was conducted here to evaluate the impacts of land use changes under different future scenarios. The ecosystem services were divided into supply services, regulating services, cultural services, and support services, following the guidelines of the Millennium Ecosystem Assessment (MA) (Carpenter et al., 2009). Supply services refer to the supply of food products and raw materials. Regulating services include gas release regulation, climate regulation, hydrology regulation, and waste disposal regulation. Cultural services mainly refer to aesthetic value services, while support services improve soil integrity and maintain biodiversity. The ESV is given by the formula:
$ESV=\sum{({{A}_{k}}\times V{{C}_{k}})}$ (3)
where ESV is the estimated ecosystem services value, Ak is the area (hm2), and VCk is the revised ecological value coefficient (RMB/hm2) for land use type k. VCk is calculated as the unit economic value of ecosystem services (Fu et al., 2015). The ecological service value coefficients were adopted in the ESV calculation (Costanza et al., 1997; 2014) (Table 2).
Table 2 Equivalent value per unit area of ecosystem services in NEC
Level 1 Level 2 Cropland Forest Grassland Water body Wetland Unused land
Supply services Food production 1.00 0.33 0.43 0.53 0.36 0.02
Production of materials 0.39 2.98 0.36 0.35 0.24 0.04
Regulating services Gas release regulation 0.72 4.32 1.50 0.51 2.41 0.06
Climate regulation 0.97 4.07 1.56 2.06 13.55 0.13
Hydrological adjustment 0.77 4.09 1.52 18.77 13.44 0.07
Waste treatment 1.39 1.72 1.32 14.85 14.4 0.26
Cultural services Provide aesthetic landscape 0.17 2.08 0.87 4.44 4.69 0.24
Support services Improve soil integrity 1.47 4.02 2.24 0.41 1.99 0.17
Maintain biodiversity 1.02 4.51 1.87 3.43 3.69 0.40

2.3 Scenario setting and land use requirements

The widely accepted definition of scenario was given by the IPCC as “a coherent, internally consistent and plausible description of a possible future state of the world” (Parry et al., 1998). In this study, scenarios were set in accordance with the following considerations in NEC: First, NEC is an important region for grain production in China; second, the eco-environment has received much attention due to the presence of large areas of wetland resources; and third, regional economic development in this traditional industrial base has been a national focus. In total, three different future scenarios were defined, which were described as follows:
(1) Ecological security scenario (ESS): the eco-environment protection in NEC will gain priority. Under this scenario, the areas of forest, grassland, water body, and wetland are increased to improve the eco-environment quality for the region.
(2) Food security scenario (FSS): the increase in food production will be the focus under this scenario. As a result, the areas of cropland increase and the areas of water bodies increase to ensure irrigation for croplands.
(3) Comprehensive development scenario (CDS): there are simultaneous considerations on the requirements for the eco-environment, food security and economic development.
For each scenario, the future demands for land of each land use type were defined in accordance with different government goals (Table 3). The date of land use for each scenario input to the CLUE-S model, and the area and position of each land use type will be controlled to ensure the accuracy of the model simulation.
Table 3 Annual change rate of the area of each land use type under different scenarios (%)
Cropland Forest Grassland Water body Built-up Wetland Unused land
ESS -0.30 0.25 0.30 0.25 0.25 0.30 -0.75
FSS 0.25 -0.05 -1.50 0.25 0.25 -0.85 -2.5
CDS 0.03 0.02 -0.30 0.01 0.25 -0.85 -0.75

2.4 Data

A land use map of NEC in 2000 with seven land use types (cropland, forest, grassland, water body, built-up, wetland, and unused land) was used in this study. This map was visually interpreted and verified based on remote sensing data obtained from the Chinese Academy of Sciences (CAS) (Zhang et al., 2014). Biophysical parameters (i.e., aspect, DEM, slope, mean annual precipitation, distance from rivers, distance from roads, distance from residential area, soil type, ≥0°C accumulated temperature, ≥10°C accumulated temperature, and average annual temperature) and socio-economic parameters (population and per capita GDP) were selected as driving factors for land use change. The soil map was obtained from the Chinese Academy of Agricultural Sciences (Ye et al., 2013), with which 20 soil units were delineated under the FAO legend (Ye et al., 2008). Each soil unit was treated as a driving factor in spatial analysis. All gridded data were processed and converted into ASCII grids with a spatial resolution of 1 km. Statistical data were obtained from the China Statistical Yearbooks. Land use type and area for 2000-2010 were derived from statistical data, as detailed in Table 4. This study analyzes the driving factors of land use by logistic regression in the year 2000. Statistically significant driving factors of land use are used to construct the simulation model (for details see Xia et al., 2016).
Table 4 Type and source of data
Data type Data name Data format Description and source
Land use Land use basic data Grid Land use resources in seven types of basic data type of land use, spatial resolution of 1 km
Biophysical DEM Grid Institute of Geographic Sciences, CAS, spatial resolution of 1 km
Years of average temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Years of average rainfall distribution map Grid The national meteorological data compilation, spatial resolution of 1 km
Years of average≥0°C accumulated temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Years of average of ≥10°C accumulated temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Soil map Grid FAO soil classification
Level 1-3 traffic network distribution map Vector The national fundamental geographic information data
Level 1-3 river water distribution map Vector The national fundamental geographic information data
Town centers Vector The national fundamental geographic information data
Socio-economic Demographic distribution map Grid Institute of Geographic Sciences, CAS, 1 km grid population: people/km2
GDP distribution diagram Grid Institute of Geographic Sciences, CAS, 1 km grid GDP unit: million RMB/km2
Statistical Area of each land type for 2000-2010 Text China Statistical Yearbooks

3 Results

3.1 Land use data validation

As measurement at one resolution is not sufficient to describe complex patterns, the objective method of Costanza (1989) was used to validate the proposed simulation model, which measures the goodness of fit of the model according to a multiple resolution procedure. The fit for each sampling window is estimated as 1 minus the proportion of cells that would have to be changed to make the sampling windows each have the same number of cells in each category, regardless of their spatial arrangement. As space allocation of the CLUE-S model can better control the area of all types of land use, this study needs to validate the accuracy of space by Costanza’s method. The simulation results were compared with the interpreted remote sensing data from 2005 to evaluate model precision with a multiscale, multiresolution analysis method (Figure 3). When the validation is tested using the 1×1 window size, the fit for each sampling accuracy is 0.76. However, when the test window size is expanded (such as 5×5), it is found that the fit value improves (Figure 3). With the continuous expansion of the analysis window, the accuracy of the model improves slightly. The accuracy of the CLUE-S model simulation reaches 0.825. The results indicate that the localized CLUE-S model is better able to simulate the spatial pattern of land use change in NEC.
Figure 3 Multiscale simulation test validation

3.2 Future changes in land use

As shown in Figure 4, modeling results of these three scenarios show diverse land use patterns in 2050 compared with those in 2010. In particular, the different land use conversion types in the region of the Khingan Mountains, the Changbai Mountains, the Songnen Plain, the Sanjiang Plain, and the Ruhr Hu Mountains can be seen. The changes are mainly from cropland to other types, such as forest, grassland, or wetland. The spatial patterns and areas of the conversions involving cropland are given in Figure 5 and Table 5, respectively.
Figure 4 The three scenarios of land use patterns of Northeast China in 2050
Figure 5 The three scenarios (ESS: Ecological security scenario; FSS: Food security scenario; CDS: Comprehensive development scenario) of cropland conversion in Northeast China in 2010-2050
Table 5 Cropland and other land use conversion types in the three scenarios (km2)
Forest into cropland Cropland into forest Grassland into cropland Cropland into grassland Water body into cropland Cropland into water body Built-up into cropland Cropland into built-up Unused land into cropland Cropland into unused land Wetland into cropland Cropland into wetland
ESS 2010-2020 1005 956 452 481 141
2020-2030 9959 668 451 515 369
2030-2040 9147 1352 525 504 458
2040-2050 9044 1359 522 523 545
FSS 2010-2020 1449 4667 14 1280 612
2020-2030 1493 4363 444 688 1001
2030-2040 1626 3621 272 1511 973
2040-2050 1581 3750 161 1509 982
CDS 2010-2020 570 5 519
2020-2030 501 5 346 110
2030-2040 495 230 313
2040-2050 469 32 82 351

Notes: ESS: Ecological security scenario; FSS: Food security scenario; CDS: Comprehensive development scenario

Under the ESS, ecological protection is achieved by expanding the area of forest, grassland, and wetland. By implementing the policy of returning cropland to forest and grassland, the areas of forest, grassland, and wetland are all enlarged under the ESS scenario. In 2010-2020, cropland is converted to forest (1005 km2) and grassland (956 km2) in the western Changbai Mountains, central Lesser Khingan Mountains, and the Ruhr Hu Mountains. The changes in 2020-2030 are similar to those of 2010-2020, and most of the conversions occur spatially next to the areas already changed. Cropland, which is a scarce resource in NEC, continues to lose area. From 2030, the decrease in cropland mainly occurs in the Songnen Plain and the Sanjiang Plain, with most of the cropland transformed into forest (18,191 km2) and grassland (2711 km2). In 2010-2050, the speed of the growth of forest area gradually slows, from 1000 km2/yr to 900 km2/yr. In contrast, the speed of the growth area of grassland gradually increases, from 90 km2/yr to 135 km2/yr. Meanwhile, the conversion of cropland to wetland increases from 14 km2/yr to 55 km2/yr.
Under the FSS, the area of cropland increases to ensure future grain security. In 2010-2030, new croplands are mainly distributed in the RuhrHu Mountains and the north of the Songnen Plain. No significant decrease is allowed for forestland in this strictly protected region under the FSS scenario. In 2010-2050, the speed of conversion from forest to cropland is 150 km2/yr, and conversions mainly occur in the areas that are close to the protected forestland and other scattered areas. Although a large area of grassland is transformed into cropland, the rate decreases in 2010-2050, from 460 km2/yr to 375 km2/yr, which is still relatively high compared with the conversion between other land use types. To maintain the area of cropland, unused land is transformed into cropland at 70-150 km2/yr in the Songnen Plain. Meanwhile, wetland is transformed to cropland at a rate of 61-100 km2/yr in the Sanjiang Plain.
Under the CDS, ecological security, food security objectives, and economic development goals are comprehensively considered in 2010-2050. New cropland, converted from forest, wetland, and unused land, is mainly distributed in the Songnen Plain, the Sanjiang Plain, and along the Songhua and Nenjiang watersheds. The area of paddy field in the Sanjiang Plain increases considerably, with paddy field expanding and covering the majority of the Sanjiang Plain. In total, an area of 774 km2 of wetland is transformed into cropland in 2010-2050. Newly formed forests are mainly distributed in the Greater Khingan Mountains. The areas of wetland in the Sanjiang Plain and unused land in the Songnen Plain continue to decrease. Due to the effect of topography and local policy, newly formed forests are mainly distributed close to existing forest, which are converted back from cropland and grassland over the next 40 years.

3.3 Changes in ecosystems service values

The ecosystem service value (ESV) from land use change in NEC in 2010-2050 under the three scenarios is calculated using Equation (2) (Figure 6). In 2010-2050, ESV increases under the ESS scenario, and decreases under the FSS and CDS scenarios. The ESV of ESS gradually increases from RMB 2,757,052 million (USD 1 = RMB 6.5) to RMB 3,040,264 million, and the ESV of FSS gradually declines from RMB 2,757,052 million to RMB 2,658,122 million, while the ESV of CDS gradually declines from RMB 2,757,052 million to RMB 2,656,180 million. Overall, the ESV of both FSS and CDS show a slight decreasing trend. The decrease of the ESV under FSS is relatively less than that under CDS. However, the ESV under ESS shows an increasing trend as a result of the increased area of forest, grassland, wetland, and water body.
Figure 6 Different scenarios of ESV in 2010-2050
The ESV in NEC is mainly contributed by forest, water body, and wetland for all land use types. As the ecosystem value of forest is relatively high and forest covers about 40% of the area in NEC, the ESV of forest is relatively large, contributing about 65% of the total ESV. Although the water body and wetland only account for 2% of the area in NEC, their contributions to ecosystem services are as high as 18% and 12% of the total ESV, respectively. In contrast, although cropland accounts for about 48% of the area in NEC, it only contributes about 4% of the total ESV.
ESS guarantees ecological safety and the ESV is mainly composed of forest (RMB 1,966,012 million), wetland (RMB 421,927 million), water body (RMB 538,896 million), and grassland (RMB 34,637 million) in 2050. The ESV of cropland reaches RMB 103,110 million in 2050 under FSS. The newly formed croplands are mainly converted from grassland, forest, and wetland, which is also the reason for the gradual decrease of ESV. Compared with FSS, changes in ESV under CDS are not significant. Under CDS, the value of cropland shows a moderate increasing trend. Compared with FSS, a larger part of the ESV under CDS is from the service value of forest and grassland, while the service value of wetland and water body is relatively small.
Regulating services are the main type of ecosystem service in NEC, followed by support services, supply services, and cultural services. A large part of the ESV is generated by regulating services, and is higher than the combined value of the other three services. Under ESS, regulating services, support services, and cultural services play an important part, and their ESV shows an increasing trend. The services in the FSS scenario indicate a decreasing trend. In particular, regulating services decrease the most, at a rate of 1% per decade, followed by supply services with a decline of 0.2%. Under CDS, the changes of these four services are quite diverse. Support services indicate a slow declining trend of 0.2% from 2010, followed by an increasing trend until 2050. Both regulating and cultural services show slow downward trends.
Comparing all three scenarios, the ESV under ESS is the largest, meaning that ESS has the greatest focus on the eco-environment. FSS is characterized by high values of regulation services, and relatively lower values of the supply, support, and cultural services. Under CDS, there is more forest and grassland to improve the ESV. In short, ESS protects forest, grassland, and wetland, and improves the service value of the study area. FSS focuses on the development of cropland, leading to the decrease of ESV in forest, grassland, and wetland. Finally, although CDS is defined according to national long-term development goals, the ESV of CDS is the lowest among the three scenarios.

4 Discussion

This study employed a localized variant of the CLUE-S model to simulate the changes in land uses in NEC under three pre-defined scenarios and analyze the impacts of land use changes on ecosystem services. As shown by the modeling results, the selected factors can adequately account for the land use change pattern in NEC. The spatial distribution of the land use types from the modeling results is consistent with the actual conditions in the region. Given the differences between the three scenarios as defined under the guidelines of national policy, it is possible to make some suggestions for future land use policies in NEC. Hopefully, it will guide a coordinated fulfillment of the ecological, food security and socio-economic development goals in the future.
Although the selected parameters are able to simulate the trend of changes under different scenarios, the accuracy of the simulation results can be improved further by fine-tuning model parameters. The fine-tuned CLUE-S model can effectively simulate the land use change over the next 30 years. Although the model has some limitations, better simulation results have been achieved in many parts of the world through adjustment of model parameters. The simulation results are of great significance to the future land use in NEC. At the same time, the model used 1 km spatial resolution data, however, the simulation accuracy has been controlled for both area and location so as to ensure the accuracy of the final simulation results. As a study of land use change in NEC, the 1 km spatial resolution data can meet the research needs and reflect the changes in land use. Land use demand is set based on national statistics and future development plans and is thus independent of the CLUE-S model. A better characterization of land use demand using an economic model may lead to useful improvement in the accuracy of the land use predictions. Improvement is also foreseen by including a separate policy factor to quantify the effects of government policies on land use change, which are in most of the times prevailing the combined effects of other driving factors, either natural or socio-economic. Therefore, the CLUE-S model can simulate the land use change under different scenarios in the future. Nevertheless, further methodological developments involving human driving factors and statistical modeling are still necessary to further optimize the simulation accuracy.
This analysis takes advantage of contrasting scenarios in the simulation of land use changes. Under the ESS scenario, the areas of forest, grassland, and wetland clearly increase, leading to eco-environmental improvements. On the other hand, the area of cropland decreases greatly, which threatens the national food security in China. Under the FSS scenario, the areas of cropland and water body increase significantly, which provide enough food for future population development. However, due to decrease in the areas of unused land, grassland and wetland, the eco-environment of the NEC will suffer greatly and sustainable development goals will be hard to achieve. Defined in accordance with the overall objectives of sustainable development, the CDS scenario not only protects the forest area and the eco-environment, but also reconciles economic development and food security. Even though the ESS and FSS scenarios are extreme cases which only consider ecological or food security constraints, these two scenarios indicate the direction for balancing ecological, food security, and economic objectives. By comparing the results of ESS and FSS with CDS, further information can be provided for future decision-making. Overall, the CDS scenario is most appropriate to meet the land use targets.
However, LUCC research not only considers the location of newly formed land use types, it also includes the original types of these newly formed land uses into consideration. The goal of land use development in NEC is to balance ecological, food security, and economic development goals. Thus, by studying the transformation of various land use types, we analyzed the ecological value before and after the transformation. This study has not revised all the values involving money with the present value index. It has paid more attention to comparing advantages and disadvantages of each land use scenario with ESV. Under ESS, with the gradual increase in the areas of forest, grassland, and wetland, the ESV increases accordingly and the eco-environment is improved. However, as these environmental friendly land use types are transformed from cropland, the land use intensity decreases. This decrease of cropland leads to food security impacts as the population continues to increase. Thus, ESS is an ideal situation, but one that holds back economic development. Under FSS, to ensure social development, large areas of grassland, forest, and wetland are transformed into cropland. Even though the area of cropland increases significantly, the eco-environment is largely destroyed, and the ESV decreases accordingly. Wetland protection is an important issue in NEC. However, large areas of wetland are transformed into cropland under FSS and CDS. Although NEC has abundant wetland resources, these have been destroyed to different extents in recent years. It would be shortsighted to destroy more wetland for cropland, because the eco-environment would be greatly affected. Compared with ESS and FSS, CDS is set according to the national future land use goals, which comprehensively balance ecological and environmental objectives. Under CDS, land use intensity increases slowly. Surprisingly, the ESV decreases more rapidly under CDS than under FSS; the reason for this is the overuse of unused land and wetland, making the total ESV smaller than under FSS. Even though forest and grassland are protected under CDS, these two land use types only make a small contribution to the total ESV. Under CDS, future resources, which include unused land and wetland, are overdeveloped, leading to a decrease in the ESV. This is an issue that requires careful consideration in the future.

5 Conclusions

Future changes in land use and their impacts over 2000-2050 were simulated under three scenarios in NEC. Under the ESS, the areas of forest, grassland, and wetland are expanded. Cropland is converted to forest and grassland in the western Changbai Mountains, central Lesser Khingan Mountains, RuhrHu Mountains, Songnen Plain and the Sanjiang Plain. The speed of the growth in forest and grassland areas is estimated at about 1000 km2/yr and 100 km2/yr, respectively. Under FSS, the cropland area is predicted to increase in the Songnen Plain and the Liaohe Plain. In the Songnen Plain, most of the new croplands will be converted from unused land. In the Sanjiang Plain, wetland will partially be converted to paddy fields. In other regions, small areas of scattered forest and grassland will be converted into cropland. New forestland will be developed from the northern Greater Khingan Mountains down to the Lesser Khingan Mountains and further southward to the Changbai Mountains. In addition to the current Forest Nature Reserve in the region, the area of forests will steadily increase. The new forestland will be mainly distributed in the southern Lesser Khingan Mountains. Under the CDS, the change of land use primarily occurs in the Songnen Plain and the Sanjiang Plain. Under the ecological environment scenario, forestland is predicted to increase in the Greater Khingan Mountains and the western Changbai Mountains. Our results also show that even though CDS is defined based on the goals of the regional development plan, the ecological service value for CDS is evaluated at RMB 2,656,180 million in 2050. However, the ESVs of ESS and FSS are evaluated at 3,040,264 million and 2,658,122 million, respectively. Our analysis suggests that CDS is not the optimal development scenario. On the contrary, CDS is the worst scenario for protecting the eco-environment. Local governments are recommended to pay more attention to the protection of the eco-environment in general, and the virgin forestlands and wetlands in the NEC in particular, while implementing the government’s land use planning guidelines. It is also advisable to raise the resource use efficiency of various land use types based on scenario results obtained here toward a balanced use of land. More attention should be paid to the protection of the eco-environmental system, so that sustainability is achieved on a solid resource base.

The authors have declared that no competing interests exist.

References

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[23]
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[25]
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[26]
van Vliet J, Bregt A K, Hagen-Zanker A, 2011. Revisiting kappa to account for change in the accuracy assessment of land-use change models.Ecological Modelling, 222(8): 1367-1375.Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K Simulation are compared using the results of a land-use model. It is found that only K Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time.

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[27]
van Vliet J, Hagen-Zanker A, Hurkens J et al., 2013. A fuzzy set approach to assess the predictive accuracy of land use simulations.Ecological Modelling, 261/262: 32-42.The predictive accuracy of land use models is frequently assessed by comparing two data sets: the simulated land use map and the observed land use map at the end of the simulation period. A common statistic for this is Kappa, which expresses the agreement between two categorical maps, corrected for the agreement as can be expected by chance. This chance agreement is based on a stochastic model of random allocation given the distribution of class sizes. Two existing statistics extend Kappa to make it more appropriate for the assessment of land use models: Fuzzy Kappa uses fuzzy set theory to include degrees of similarity, which adds geographical nuance because it distinguishes between small and large disagreement in position and in land use classes. Kappa Simulation, on the other hand, addresses the stochastic model that underlies the expected agreement: when a model starts from an initial land use map and subsequently makes changes to it, a stochastic model of random allocation given the distribution of class sizes has little relevance. The expected accuracy in Kappa Simulation is therefore based on transition probabilities relative to the initial map. This paper presents Fuzzy Kappa Simulation, a statistic that combines the geographical nuance of Fuzzy Kappa with the stochastic model of Kappa Simulation. This new statistic is demonstrated on a case study example and results are compared with other variations of Kappa. The comparison confirms that Fuzzy Kappa Simulation is the only statistic to evaluate models in terms of land use transitions, while also being sensitive to geographical nuance.

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[28]
Verburg P, Berkel D, Doorn A et al., 2010. Trajectories of land use change in Europe: A model-based exploration of rural futures.Landscape Ecology, 25(2): 217-232.Land use change is characterized by a high diversity of change trajectories depending on the local conditions, regional context and external influences. Policy intervention aims to counteract the negative consequences of these changes and provide incentives for positive developments. Region typologies are a common tool to cluster regions with similar characteristics and possibly similar policy needs. This paper provides a typology of land use change in Europe at a high spatial resolution based on a series of different scenarios of land use change for the period 2000鈥2030. A series of simulation models ranging from the global to the landscape level are used to translate scenario conditions in terms of demographic, economic and policy change into changes in European land use pattern. A typology developed based on these simulation results identifies the main trajectories of change across Europe: agricultural abandonment, agricultural expansion and urbanization. The results are combined with common typologies of landscape and rurality. The findings indicate that the typologies based on current landscape and ruralities are poor indicators of the land use dynamics simulated for the regions. It is advocated that typologies based on (simulated) future dynamics of land change are more appropriate to identify regions with potentially similar policy needs.

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[29]
Verburg P H, Overmars K P, 2009. Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the dyna-CLUE model.Landscape Ecology, 24(9): 1167-1181.Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics.

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[30]
Verburg P H, Rounsevell M D A, Veldkamp A, 2006. Scenario-based studies of future land use in Europe.Agriculture, Ecosystems & Environment, 114(1): 1-6.Atmospheric aerosols from natural and anthropogenic processes have both primary and secondary origins, and can influence human health, visibility, and climate. One key process affecting atmospheric concentrations of aerosols is the formation of new particles and their subsequent growth to larger particle sizes. A field study was conducted at the Blodgett Forest Research Station in the Sierra Nevada Mountains of California from May through September of 2002 to examine the effect of biogenic volatile organic compounds on aerosol formation and processing. The study included in-situ measurements of concentration and biosphere-atmosphere flux of VOCs, ozone, aerosol size distribution, aerosol physical and optical properties, and meteorological variables. Fine particle growth events were observed on approximately 30 percent of the 107 days with complete size distribution data. Average particle growth rates measured during these events were 3.8 +/- 1.9 nm hr(-1). Correlations between aerosol properties, trace gas concentrations, and meteorological measurements were analyzed to determine conditions conducive to fine particle growth events. Growth events were typically observed on days with a lesser degree of anthropogenic influence, as indicated by lower concentrations of black carbon, carbon monoxide, and total aerosol volume. Days with growth events also had lower temperatures, increased wind speeds, and larger momentum flux. Measurements of ozone concentrations and ozone flux indicate that gas phase oxidation of biogenic volatile organic compounds occur in the canopy, strongly suggesting that a significant portion of the material responsible for the observed particle growth are oxidation products of naturally emitted very reactive organic compounds.

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[31]
Verburg P H, Soepboer W, Veldkamp A et al., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model.Environmental Management, 30(3): 391-405.Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

DOI PMID

[32]
Wang M, Xiong Z, Yan X, 2015. Modeling the climatic effects of the land use/cover change in eastern China.Physics and Chemistry of the Earth, Parts A/B/C, 87/88: 97-107.This study aims to quantify the contribution of land use/cover change (LUCC) during the last three decades to climate change conditions in eastern China. The effects of farmland expansion in Northeast China, grassland degradation in Northwest China, and deforestation in South China were simulated using the Weather Research and Forecasting (WRF) model in addition to the latest actual land cover datasets. The simulated results show that when forestland is converted to farmland, the air temperature decreased owing to an increase in surface albedo in Northeast China. The climatic effect of grassland degradation on the Loess Plateau was insignificant because of the negligible difference in albedo between grassland and cropland. In South China, deforestation generally led to a decrease in temperature. Furthermore, the temperature decrease caused by the increase in albedo counteracted the warming effects of the evapotranspiration decrease, so the summer temperature change was not significant in South China. Excluding the effects of urbanization in the North China Plain, the LUCC effects across the entire region of East China presented an overall cooling trend. However, the variation in temperature scale and magnitude was less in summer than that in winter. This result is due mainly to the cooling caused by the increase in albedo offset partly by the increase in temperature caused by the decrease in evaporation in summer. Summer precipitation showed a trend of increasing ecreasing ncreasing from southeast to northwest after LUCC, which was induced mainly by the decrease in surface roughness and cyclone circulations appearing northwest of Northeast China, in the middle of the Loess Plateau, and in Yunnan province at 700hPa after forests were converted into farmland. All results will be instructive for understanding the influence of LUCC on regional climate and future land planning in practice.

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[33]
Wang Z, Zhang B, Zhang S et al., 2006. Changes of land use and of ecosystem service values in Sanjiang Plain, Northeast China.Environmental Monitoring and Assessment, 112(1-3): 69-91.

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[34]
Xia T, Wu W, Zhou Q et al., 2014. Spatio-temporal changes in the rice planting area and their relationship to climate change in Northeast China: A model-based analysis.Journal of Integrative Agriculture, 13(7): 1575-1585.Rice is one of the most important grain crops in Northeast China (NEC) and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980–2010 and to analyze their relationship to climate change. To do so, the CLUE-S (conversion of land use and its effects at small region extent) model was first updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods: 1980–1990, 1990–2000 and 2000–2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980–2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This confirmed that climate change, increases in temperature in particular, greatly influenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1°C isoline migration pattern, but with an obvious time-lag effect. These findings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.

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[35]
Xia T, Wu W, Zhou Q et al., 2016. Model-based analysis of spatio-temporal changes in land use in Northeast China.Journal of Geographical Sciences, 26(2): 171-187.Spatially explicit modeling techniques recently emerged as an alternative to monitor land use changes. This study adopted the well-known CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model to analyze the spatio-temporal land use changes in a hot-spot in Northeast China (NEC). In total, 13 driving factors were selected to statistically analyze the spatial relationships between biophysical and socioeconomic factors and individual land use types. These relationships were then used to simulate land use dynamic changes during 1980–2010 at a 1 km spatial resolution, and to capture the overall land use change patterns. The obtained results indicate that increases in cropland area in NEC were mainly distributed in the Sanjiang Plain and the Songnen Plain during 1980–2000, with a small reduction between 2000 and 2010. An opposite pattern was identified for changes in forest areas. Forest decreases were mainly distributed in the Khingan Mountains and the Changbai Mountains between 1980 and 2000, with a slight increase during 2000–2010. The urban areas have expanded to occupy surrounding croplands and grasslands, particularly after the year 2000. More attention is needed on the newly gained croplands, which have largely replaced wetlands in the Sanjiang Plain over the last decade. Land use change patterns identified here should be considered in future policy making so as to strengthen local eco-environmental security.

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[36]
Xu G, Huang X, Zhong T et al., 2015. Assessment on the effect of city arable land protection under the implementation of China’s national general land use plan (2006-2020).Habitat International, 49: 466-473.61We evaluate the implementation effect of China’s National General Land Use Plan.61The paper’s analysis is based on 341 city units.61Panel-data model is applied for quantitative analysis.

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[37]
Ye L, Tang H, Zhu J et al., 2008. Spatial patterns and effects of soil organic carbon on grain productivity assessment in China.Soil Use and Management, 24(1): 80-91.In this paper, we present an assessment of the content and effects of cropland soil organic carbon (SOC) on grain productivity at the national scale in China using a Web-based Land Evaluation System. Homogeneous 5 km 脳 5 km grid data sets of climate, crop, soil and management parameters were created and grain production in 2005 was simulated. Attempts were made to incorporate SOC into the land evaluation procedure and to quantify the potential effects of SOC deficiency on grain productivity. Results were statistically analysed and the modelling approach was validated. National cropland SOC maps were generated. At the national scale, the cropland SOC content averaged 1.20, 0.58, 0.41, 0.31 and 0.26% for the five 20-cm sections consecutively from the surface downwards. At the regional scale it tended to decline slightly from northeast (1.63%) to southwest (1.11%). On average, 64% of grain yield was lost due to SOC deficiency for the humid provinces and 7% for the arid and sub-arid ones. Soil management options are suggested based on the simulation results.

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[38]
Ye L, Xiong W, Li Z et al., 2013. Climate change impact on China food security in 2050.Agronomy for Sustainable Development, 33(2): 363-374.AbstractClimate change is now affecting global agriculture and food production worldwide. Nonetheless the direct link between climate change and food security at the national scale is poorly understood. Here we simulated the effect of climate change on food security in China using the CERES crop models and the IPCC SRES A2 and B2 scenarios including CO2 fertilization effect. Models took into account population size, urbanization rate, cropland area, cropping intensity and technology development. Our results predict that food crop yield will increase +3–1102% under A2 scenario and +402% under B2 scenario during 2030–2050, despite disparities among individual crops. As a consequence China will be able to achieve a production of 572 and 615 MT in 2030, then 635 and 646 MT in 2050 under A2 and B2 scenarios, respectively. In 2030 the food security index (FSI) will drop from +2402% in 2009 to 614.502% and +10.202% under A2 and B2 scenarios, respectively. In 2050, however, the FSI is predicted to increase to +7.102% and +20.002% under A2 and B2 scenarios, respectively, but this increase will be achieved only with the projected decrease of Chinese population. We conclude that 1) the proposed food security index is a simple yet powerful tool for food security analysis; (2) yield growth rate is a much better indicator of food security than yield per se; and (3) climate change only has a moderate positive effect on food security as compared to other factors such as cropland area, population growth, socio-economic pathway and technology development. Relevant policy options and research topics are suggested accordingly.

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[39]
Zhang F, Tiyip T, Feng Z D et al., 2015. Spatio-temporal patterns of land use/cover changes over the past 20 years in the middle reaches of the Tarim River, Xinjiang, China.Land Degradation & Development, 26(3): 284-299.

[40]
Zhang X, Xiong Z, Zhang X et al., 2016. Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: A case study over Northeast China.Climate Dynamics, 46(3/4): 765-778.Rather than simulating the effects of land use and land cover change (LUCC) on the climate using one climate model, as in many previous studies, three regional climate models (Regional Climate Model, version 3; the Weather Research and Forecasting model; and the Regional Integrated Environmental Model System) were used in the present study to simulate changes in temperature due to LUCC. Two experiments (CTL and NE) were designed and run using the three regional climate models. The CTL experiment was used to compare the simulations of the different models and served to illustrate the improvement that could be achieved as a result of employing a multi-model ensemble. The NE experiment was used to evaluate the changes in temperature caused by LUCC in northeast China between 1981 and 2000. The results of the CTL simulations showed that changes in temperature were simulated well by the three regional climate models; however, the simulated temperatures were different, dependent on the model used. The multi-model ensembles [the arithmetic ensemble mean (AEM) and Bayesian model averaging (BMA)] attained better results than any individual model. Of the two ensemble methods, BMA performed better than the AEM. The effects of LUCC on the climate in northeast China were assessed by the differences between the CTL and NE simulations for every RCM and the ensemble simulations. The BMA simulations produced more reasonable results than the other simulations. Based on the results, we can state with some confidence that LUCC in northeast China over the 20-year period studied caused a decrease in temperature, because of an expansion of arable land.

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[41]
Zhang Z, Wang X, Zhao X et al., 2014. A 2010 update of national land use/cover database of China at 1:100000 scale using medium spatial resolution satellite images.Remote Sensing of Environment, 149(149): 142-154.61We update national land use database (NLUD-C) of China in 2010.61Visual interpretation based on professional knowledge was employed for update.61The accuracy for verified first-level type polygons is more than 95.41%.

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