Modelling land use change and environmental impact

Land use change models are tools for understanding and explaining the causes and consequences of land use dynamics. Recently, new models, combining knowledge and tools from biophysical and socio-economic sciences, have become available. This has resulted in spatially explicit models focussed on patterns of change as well as agent-based models focused on the underlying decision processes. These developments improve the use of land use change models in environmental impact studies. This special issue documents these developments: (i) analysing the system properties in a biophysical and socio-economic context at multiple scales; (ii) integrating spatially explicit land use change models in integrated assessment models; (iii) visualising and quantifying the potential effects of land use change in trade-off curves, to support land users and policy makers in their decisions; and (iv) modelling of the actual decision making process with agent-based modelling. A new promising future development is the incorporation of dynamic feedbacks between changing land use and changing environmental conditions and vice versa. Unfortunately such dynamic feedbacks between the socio-economic and biophysical model components are still not or only partially operational in current models and are therefore the most important challenge for land use and environmental modellers.     

济南市耕地变化与社会经济的相关分析

利用济南市1978年以来的统计资料,分析了济南市耕地与社会经济的动态变化过程,建立了耕地变化与社会经济的定量相关模型,预测了济南市的耕地将保持缓慢减少的趋势,建议采取有效的耕地保护措施,利用政策驱动来控制耕地减少,实现耕地总量的动态平衡.     

东北地区典型农区土地利用变化对农业扩张和气候变化的响应研究

大规模的农业扩张和全球气候变化导致东北地区发生剧烈的土地利用/土地覆盖变化。本研究分析了研究区1976—2008年的土地利用变化和生长季各月气温的变化趋势,并结合农业扩张程度,探究LUCC对农业扩张和气候变化的响应,为指导农业发展规划和保障国家粮食安全提供理论依据。结果表明,1976—2008年农田面积逐步增加,生长季气温一直呈上升趋势。1976—2001年生长季气温的上升趋势不稳定,气温变化程度较大;2001—2008年,农业扩张放缓,生长季气温上升趋势显著,且上升趋势一直增加,气温变化比上一时期更稳定;且这两个时期农业扩张和气候变化对土地利用强度的影响在南北和东西方向上均存在明显差异。     

Predicting the distribution of ground beetle species (Coleoptera, Carabidae) in Britain using land cover variables

Predictions of plant and animal species distributions are important for conservation and for the assessment of large-scale ecosystem change. Land cover data are becoming more widely available for use in land management and conservation. We use a logistic regression modelling approach to investigate the utility of these data for modelling. The relationship between the distribution of 137 British ground beetles species and land cover was investigated using data from 1,687 10 km national grid squares. Land cover data were simplified using ordination and the axes used as predictors in logistic regression with presence absence data for individual beetle species as response variables. Significant regression models were generated for all species with first and second axis scores. The amounts of variation explained by models were generally low, but predictions derived from models generally matched the known distributions of the species in Britain. Species with coastal preferences were poorly modelled and predicted to occur throughout lowland Britain whilst a number of species occurring in southern Britain were predicted to occur into Scotland. A validation exercise comparing model predictions with new data from a survey of 59 10 km(2) produced mixed results with the distribution of grassland species being better predicted than riverine species. Jack-knifing was used to assess the robustness of models for four species which differed in their apparent responses to the land cover variables. Methods for improving the predictive power of these models and their potential for use in assessing the impact of global climate change are discussed.     

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.     

Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China

This paper analyzes characteristics, major driving forces and alternative management measures of land-use change in Kunshan, Jiangsu province, China. The study used remote sensing (RS) maps and socio-economic data. Based on RS-derived maps, two change matrices were constructed for detecting land-use change between 1987 and 1994, and between 1994 and 2000 through pixel-to-pixel comparisons. The outcomes indicated that paddy fields, dryland, and forested land moderately decreased by 8.2%, 29% and 2.6% from 1987 to 1994, and by 4.1%, 7.6% and 8% from 1994 to 2000, respectively. In contrast, the following increased greatly from 1987 to 1994: artificial ponds by 48%, urban settlements by 87.6%, rural settlements by 41.1%, and construction land by 511.8%. From 1994 to 2000, these land covers increased by 3.6%, 28.1%, 23.4% and 47.1%, respectively. For the whole area, fragmentation of land cover was very significant. In addition, socio-economic data were used to analyze major driving forces triggering land-use change through bivariate analysis. The results indicated that industrialization, urbanization, population growth, and China's economic reform measures are four major driving forces contributing to land-use change in Kunshan. Finally, we introduced some possible management measures such as urban growth boundary (UGB) and incentive-based policies. We pointed out that, given the rapidity of the observed changes, it is critical that additional studies be undertaken to evaluate these suggested policies, focusing on what their effects might be in this region, and how these might be implemented.     

The Ecological Modelling System of NELUP

Within the NERC/ESRC Land Use Programme ecological change is assumed to be driven principally by agronomic activity. Land use change is defined as being of two types depending on whether there is a complete change of cover or a modification of an existing cover. Some agronomic activities lead to quantitative changes between land cover types and others to more subtle qualitative changes within land cover types. A modelling system based on three distinct models is described for predicting the effects of the two types of change in land cover on the distribution of plants, invertebrates and birds. The potential use of the system in analysing problems of land use change is illustrated using a simple scenario based on changing nitrogenous fertilizer regimes in lowland agriculture.     

Assessing the impact of urbanization on regional net primary productivity in Jiangyin County, China

Urbanization is one of the most important aspects of global change. The process of urbanization has a significant impact on the terrestrial ecosystem carbon cycle. The Yangtze Delta region has one of the highest rates of urbanization in China. In this study, carried out in Jiangyin County as a representative region within the Yangtze Delta, land use and land cover changes were estimated using Landsat TM and ETM+ imagery. With these satellite data and the BEPS process model (Boreal Ecosystem Productivity Simulator), the impacts of urbanization on regional net primary productivity (NPP) and annual net primary production were assessed for 1991 and 2002. Landsat-based land cover maps in 1991 and 2002 showed that urban development encroached large areas of cropland and forest. Expansion of residential areas and reduction of vegetated areas were the major forms of land transformation in Jiangyin County during this period. Mean NPP of the total area decreased from 818 to 699 gCm(-2)yr(-1) during the period of 1991 to 2002. NPP of cropland was only reduced by 2.7% while forest NPP was reduced by 9.3%. Regional annual primary production decreased from 808 GgC in 1991 to 691 GgC in 2002, a reduction of 14.5%. Land cover changes reduced regional NPP directly, and the increasing intensity and frequency of human-induced disturbance in the urbanized areas could be the main reason for the decrease in forest NPP.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Implications of Land Use/Land Cover Change in the Buffer Zone of a National Park in the Tropical Andes

The impacts of land use and land cover (LULC) change in buffer zones surrounding protected ecological reserves have important implications for the management and conservation of these protected areas. This study examines the spatial and temporal patterns of LULC change along the boundary of Rio Abiseo National Park in the Northern Peruvian Andes. Landscape change within four ecological zones was evaluated based on trends expected to occur between 1987 and 2001. Landsat TM and ETM imagery were used to produce LULC classification maps for both years using a hybrid supervised/unsupervised approach. LULC changes were measured using landscape metrics and from-to change maps created by post-classification change detection. Contrary to expectations, tropical upper wet montane forest increased despite being threatened by human-induced fires and cattle grazing of the highland grasslands inside the park. Within the park’s buffer zone, tropical moist forest remnants were fragmented into more numerous and smaller patches between 1987 and 2001; this was in part due to conversion into agricultural land. The methods used in this study provide an effective way to monitor LULC change detection and support the management of protected areas and their surrounding environments.