Hydrological Response to Land Cover Changes and Human Activities in Arid Regions Using a Geographic Information System and Remote Sensing

The hydrological response to land cover changes induced by human activities in arid regions has attracted increased research interest in recent decades. The study reported herein assessed the spatial and quantitative changes in surface runoff resulting from land cover change in the Al-Baha region of Saudi Arabia between 1990 and 2000 using an ArcGIS-surface runoff model and predicted land cover and surface runoff depth in 2030 using Markov chain analysis. Land cover maps for 1990 and 2000 were derived from satellite images using ArcGIS 10.1. The findings reveal a 26% decrease in forest and shrubland area, 28% increase in irrigated cropland, 1.5% increase in sparsely vegetated land and 0.5% increase in bare soil between 1990 and 2000. Overall, land cover changes resulted in a significant decrease in runoff depth values in most of the region. The decrease in surface runoff depth ranged from 25-106 mm/year in a 7020-km² area, whereas the increase in such depth reached only 10 mm/year in a 243-km² area. A maximum increase of 73 mm/year was seen in a limited area. The surface runoff depth decreased to the greatest extent in the central region of the study area due to the huge transition in land cover classes associated with the construction of 25 rainwater harvesting dams. The land cover prediction revealed a greater than twofold increase in irrigated cropland during the 2000-2030 period, whereas forest and shrubland are anticipated to occupy just 225 km² of land area by 2030, a significant decrease from the 747 km² they occupied in 2000. Overall, changes in land cover are predicted to result in an annual increase in irrigated cropland and dramatic decline in forest area in the study area over the next few decades. The increase in surface runoff depth is likely to have significant implications for irrigation activities.     

Uncertainty in life cycle costing for long-range infrastructure. Part I: leveling the playing field to address uncertainties

Purpose

Life cycle costing (LCC) is a state-of-the-art method to analyze investment decisions in infrastructure projects. However, uncertainties inherent in long-term planning question the credibility of LCC results. Previous research has not systematically linked sources and methods to address this uncertainty. Part I of this series develops a framework to collect and categorize different sources of uncertainty and addressing methods. This systematization is a prerequisite to further analyze the suitability of methods and levels the playing field for part II.

Methods

Past reviews have dealt with selected issues of uncertainty in LCC. However, none has systematically collected uncertainties and linked methods to address them. No comprehensive categorization has been published to date. Part I addresses these two research gaps by conducting a systematic literature review. In a rigorous four-step approach, we first scrutinized major databases. Second, we performed a practical and methodological screening to identify in total 115 relevant publications, mostly case studies. Third, we applied content analysis using MAXQDA. Fourth, we illustrated results and concluded upon the research gaps.

Results and discussion

We identified 33 sources of uncertainty and 24 addressing methods. Sources of uncertainties were categorized according to (i) its origin, i.e., parameter, model, and scenario uncertainty and (ii) the nature of uncertainty, i.e., aleatoric or epistemic uncertainty. The methods to address uncertainties were classified into deterministic, probabilistic, possibilistic, and other methods. With regard to sources of uncertainties, lack of data and data quality was analyzed most often. Most uncertainties having been discussed were located in the use stage. With regard to methods, sensitivity analyses were applied most widely, while more complex methods such as Bayesian models were used less frequently. Data availability and the individual expertise of LCC practitioner foremost influence the selection of methods.

Conclusions

This article complements existing research by providing a thorough systematization of uncertainties in LCC. However, an unambiguous categorization of uncertainties is difficult and overlapping occurs. Such a systemizing approach is nevertheless necessary for further analyses and levels the playing field for readers not yet familiar with the topic. Part I concludes the following: First, an investigation about which methods are best suited to address a certain type of uncertainty is still outstanding. Second, an analysis of types of uncertainty that have been insufficiently addressed in previous LCC cases is still missing. Part II will focus on these research gaps.

     

Spatiotemporal land use modelling to assess land availability for energy crops – illustrated for Mozambique

A method and tool have been developed to assess future developments in land availability for bioenergy crops in a spatially explicit way, while taking into account both the developments in other land use functions, such as land for food, livestock and material production, and the uncertainties in the key determinant factors of land use change (LUC). This spatiotemporal LUC model is demonstrated with a case study on the developments in the land availability for bioenergy crops in Mozambique in the timeframe 2005–2030. The developments in the main drivers for agricultural land use, demand for food, animal products and materials were assessed, based on the projected developments in population, diet, GDP and self‐sufficiency ratio. Two scenarios were developed: a business‐as‐usual (BAU) scenario and a progressive scenario. Land allocation was based on land use class‐specific sets of suitability factors. The LUC dynamics were mapped on a 1 km² grid level for each individual year up to 2030. In the BAU scenario, 7.7 Mha and in the progressive scenario 16.4 Mha could become available for bioenergy crop production in 2030. Based on the Monte Carlo analysis, a 95% confidence interval of the amount of land available and the spatially explicit probability of available land was found. The bottom‐up approach, the number of dynamic land uses, the diverse portfolio of LUC drivers and suitability factors, and the possibility to model uncertainty mean that this model is a step forward in modelling land availability for bioenergy potentials.     

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.     

Uncertainty in life cycle costing for long-range infrastructure. Part II: guidance and suitability of applied methods to address uncertainty

Life cycle costing (LCC) is the state-of-the-art method to economically evaluate long-term projects over their life spans. However, uncertainty in long-range planning raises concerns about LCC results. In Part I of this series, we developed a holistic framework of the different types of uncertainty in infrastructure LCCs. We also collected methods to address these uncertainties. The aim of Part II is to evaluate the suitability of methods to cope with uncertainty in LCC. Part I addressed two research gaps. It presented a systematic collection of uncertainties and methods in LCC and, furthermore, provided a holistic categorization of both. However, Part I also raised new issues. First, a combined analysis of sources and methods is still outstanding. Such an investigation would reveal the suitability of different methods to address a certain type of uncertainty. Second, what has not been assessed so far is what types of uncertainty are insufficiently addressed in LCC. This would be a feature to improve accuracy of LCC results within LCC, by suggesting options to better cope with uncertainty. To address these research gaps, we conducted a systematic literature review. Part II analyzed the suitability of methods to address uncertainties. The suitability depends on data availability, type of data (tangible, intangible, random, non-random), screened hotspots, and tested modeling specifications. We identified types of uncertainties and methods that have been insufficiently addressed. The methods include probabilistic modeling such as design of experiment or subset simulation and evolutionary algorithm and Bayesian modeling such as the Bayesian latent Markov decision process. Subsequently, we evaluated learning potential from other life cycle assessment (LCA) and life cycle sustainability assessment (LCSA). This analysis revealed 28 possible applications that have not yet been used in LCC. Lastly, we developed best practices for LCC practitioners. This systematic review complements prior research on uncertainty in LCC for infrastructure, as laid out in Part I. Part II concludes that all relevant methods to address uncertainty are currently applied in LCC. Yet, the level of application is different. Moreover, not all methods are equally suited to address different categories of uncertainty. This review offers guidance on what to do for each source and type of uncertainty. It illustrates how methods can address both based on current practice in LCC, LCA, and LCSA. The findings of Part II encourage a dialog between practitioners of LCC, LCA, and LCSA to advance research and practice in uncertainty analysis.     

基于CA-Markov模型的艾比湖湿地自然保护区土地利用/覆被变化及预测

以艾比湖湿地自然保护区为研究区域,以1998、2006年Landsat TM影像和2014年Landsat OLI影像的土地利用/覆被分类结果为输入数据,采用CA-Markov模型,预测研究区未来的土地利用/覆被格局.在模型建立过程中,通过Markov模型求出转移概率矩阵和转移面积矩阵,确定CA模型转换规则,限制CA模型迭代次数.利用CA-Markov模型模拟预测研究区2014、2022和2030年土地利用/覆被格局,并采用2014年实际土地利用/覆被分类结果验证预测精度,得到2014年各土地利用/覆被类型面积预测误差均≤6.4%,空间位置预测精度达到76.0%.结果表明: 1998—2014年,艾比湖湿地自然保护区林草地、盐碱地、干涸湖床和沙漠增加,其中,盐碱地的增幅最突出,增加了37.4%;水体和其他地类减少,且水体的减少突出,减少了34.8%.2014—2030年,艾比湖湿地自然保护区林草地、盐碱地和沙漠将呈增加趋势,而干涸湖床、水体和其他地类将减少.该研究可以为艾比湖自然保护区的土地利用/覆被动态监测以及可持续发展提供依据.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

What can and can't we say about indirect land‐use change in Brazil using an integrated economic – land‐use change model?

It is commonly recognized that large uncertainties exist in modelled biofuel‐induced indirect land‐use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land‐use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic – land‐use change model. We use the global Computable General Equilibrium model MAGNET, connected to the spatially explicit land‐use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell‐based (5 × 5 km²) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land‐use change, such as greenhouse gas emissions.     

Combined land cover changes and habitat occupancy to understand corridor status of Laljhadi-Mohana wildlife corridor,Nepal

Corridor design is a centripetal conservation tool to facilitate movement between fragmented patches. Increases in anthropogenic activity have caused degradation in forest connectivity, influencing animal movement to a small degree. Laljhadi-Mohana wildlife corridor (LMWC), a corridor between Shuklaphanta National Park (Nepal) and Dudhwa National Park (India) created to be used by Panthera tigris and Elephas maximus in western Nepal, is under pressure of anthropogenic change. Using current knowledge, we analyzed land cover changes (LCC) of LMWC between 2002 and 2012. We used ERDAS IMAGINE 9.2 and Arc GIS 9.2 to process satellite images, and occupancy survey to assess status of corridor. We classified land cover into dense forest, sparse forest, cultivation, water bodies, grassland, expose surfaces, and sand bank as structural attributes of the corridor. Our analysis found dense forest area was reduced by 18.35% in a decade while cultivation and sparse forest increased by 10.15% and 8.89%, respectively. Illegal forest encroachment, resource extraction, grazing pressure, invasive species, and flood were major drivers of forest change. The null occupancy model estimated the highest detection probability of Elephas maximus (0.48 ± 0.08) and the lowest of Axis axis (0.20 ± 0.08). Incorporating site covariates improved occupancy estimates of Sus scrofa (0.82), Axis axis (0.76), Elephas maximus (0.76), Boselaphus tragocamelus (0.66), and Panthera pardus (0.55). Distance to cultivation was the most influential covariate, supported by the expansion of cultivated land in the corridor. LMWC is a functional wildlife corridor despite a decline in forest cover. This decline influenced the number and detection rates of large mammals, instigating crop raiding and conflict. Mitigation measures on LCC drivers, particularly forest encroachment, can improve the functional status of LMWC and raise detection rates of large mammals in future studies.     

The application of the pedigree approach to the distributions foreseen in ecoinvent v3

Purpose

Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.

Methods

A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.

Results and discussion

In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.

Conclusions

Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

     

Impacts of Land Cover Data Selection and Trait Parameterisation on Dynamic Modelling of Species’ Range Expansion

Dynamic models for range expansion provide a promising tool for assessing species’ capacity to respond to climate change by shifting their ranges to new areas. However, these models include a number of uncertainties which may affect how successfully they can be applied to climate change oriented conservation planning. We used RangeShifter, a novel dynamic and individual-based modelling platform, to study two potential sources of such uncertainties: the selection of land cover data and the parameterization of key life-history traits. As an example, we modelled the range expansion dynamics of two butterfly species, one habitat specialist (Maniola jurtina) and one generalist (Issoria lathonia). Our results show that projections of total population size, number of occupied grid cells and the mean maximal latitudinal range shift were all clearly dependent on the choice made between using CORINE land cover data vs. using more detailed grassland data from three alternative national databases. Range expansion was also sensitive to the parameterization of the four considered life-history traits (magnitude and probability of long-distance dispersal events, population growth rate and carrying capacity), with carrying capacity and magnitude of long-distance dispersal showing the strongest effect. Our results highlight the sensitivity of dynamic species population models to the selection of existing land cover data and to uncertainty in the model parameters and indicate that these need to be carefully evaluated before the models are applied to conservation planning.     

Estimating the Effect of Cropland to Prairie Conversion on Peak Storm Run-Off

Much of the original U.S. grassland has undergone conversion to cropland. During the last few years, large first‐ and second‐order watershed scale projects have begun to reconstruct the native tallgrass prairie cover and biodiversity. The effect on watershed hydrological budget is largely unknown, especially concerning storm run‐off. Curve number variability is used to estimate the uncertainty of peak run‐off following the change in cover, given rainfall recurrence and watershed size. The method involves three steps: (1) estimate the time‐of‐concentration for many similar sized watersheds in the region, (2) define the probability distribution for time‐of‐concentration, curve numbers, and watershed area, (3) with these data, generate input variables for a Monte Carlo analysis, which can then be used to predict the mean and confidence interval of peak run‐off. As an example, spatial and hydrological characteristics of first‐ and second‐order watersheds ranging from 2 to 50 km² in the Red River of the North basin provide a log normal probability distribution for time‐of‐concentration. Using the range of watershed area and a β probability distribution for curve number uncertainty, the analysis predicts the change in peak run‐off from an ensemble of watershed realizations that characterize cropland to grassland conversion. Results suggest that given five‐ and 25‐year, 24‐hour rainfall recurrence, average reduction in peak run‐off will range from 50 to 55% and 40 to 45%, respectively, for the basin. A large range of uncertainty at the 80% confidence interval, however, indicates that an accurate prediction requires analysis for specific watersheds.     

Agricultural land-use in China: a comparison of area estimates from ground-based census and satellite-borne remote sensing

We compare estimates of total cropland area, paddy rice area, and irrigated cropland area in China from land cover maps derived from optical remote sensing in 1992–93 (1-km resolution NOAA AVHRR) and county-level agricultural census data for 1990. At national, regional, provincial, and county scales, the total cropland area estimated by remote sensing is 50–100% higher than reported in the agricultural census. For topographically flat North and Central China, there is a high correlation between county-level cropland area estimates by the two approaches. For other regions, the correlation between remote sensing and agricultural census cropland area is much weaker. Throughout China, there is only moderate to weak correlation between remote sensing-based and census-bases estimates of paddy rice area and total irrigated cropland area. It is likely that the census data underestimates and the remote sensing overestimates the actual cropland area. These uncertainties in agricultural land cover estimates will contribute to uncertainty in any large-scale biogeochemical analyses.     

Spatio-temporal modelling of the effect of selected environmental and land-use factors on acid grassland vegetation

特定环境和土地利用因素对酸性草原植被影响的时空建模 酸性草原受到了农业集约化作业(伴随着养分添加)、牲畜密度增加以及土地撂荒等多种因素的威胁。为了认识和量化所选环境和土地利用因子对酸性草地植被观测变化的影响，本研究采用结构方程模型拟合了大尺度时空精度植被覆盖监测数据。通过分层模型结构将测量和采样不确定性的重要来源纳入其中。此外，本研究也将测量和采样的不确定性与过程的不确定性分离，这在生成可能反馈给当地保护管理决策的生态预测时有着重要的意义。研究结果表明，一般而言，大气氮沉降的增加会导致非禾本草本植物的盖度，取而代之的会是更多的以禾草植物为主的酸性草原生境。沙质土壤的酸性相对较强，而土壤类型既会对植被构成直接的影响，也会通过影响土壤pH值的方式对植被产生间接影响。土壤的类型和土壤的pH值都会对酸性草原上的植被造成影响。对于植被覆盖情况在时间上的变化，尽管该模型仅解释了其中相对较小的一部分，但在使用该模型对局部生态状况进行预测并制定具有自适应性的管理计划时，对不确定性的量化仍然是有价值的。     

基于历史情景的FLUS模型邻域权重设置——以闽三角城市群2030年土地利用模拟为例

以闽三角城市群2030年土地利用模拟为例,针对FULS模型邻域权重参数提出一种基于历史情景的设置方法。首先以2015年土地利用数据为基础,结合人工神经网络算法综合12个自然、社会、经济驱动因子计算各土地类型的出现概率和空间分布,然后依据对历史情景的分析,分别用马尔可夫链和分析景观格局指数的方法设定相关参数,最后用自适应惯性竞争元胞自动机模拟闽三角城市群2030年的土地利用情景。分析发现,同时间尺度各土地类型TA （Total Area）的变化量可以较好的反映其扩张强度,由强到弱依次为建设用地、水域及滩涂、其他土地、草地、林地及农田;TA变化量的无量纲值在数据意义和数据结构方面均较好地契合FLUS模型邻域权重的参数要求;结合各土地类型TA变化量和扩张强度间的相互关系来看,到2030年农田受建设用地扩张的影响最为严重,大量土地由农田、林地、草地及其他土地转变为建设用地或水域及滩涂;建设用地持续扩张,闽三角城市群空间一体化格局基本形成,其余各土地类型被进一步分离,同类型斑块更趋于独立发展。综合参数设置过程和模拟结果来看,TA变化量的无量纲值可为FLUS模型的邻域权重参数设置提供一种客观可行的方法。     

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

Aim

This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium‐resolution satellite imagery which was processed consistently across the continent.

Location

The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi‐arid regions. This area covers the Sudanian and Zambezian ecoregions.

Methods

A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts.

Results

Land cover and land‐cover changes were estimated at continental and ecoregion scales and compared with existing pan‐continental, regional and local studies. The overall accuracy of our land‐cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area.

Main conclusions

Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies.     

The relationship between British ground beetles (Coleoptera, Carabidae) and land cover

Aims and methods Ground beetle and satellite‐derived land cover data from 1687 United Kingdom 10 km national grid squares were used to assess the relationship between species pool and cover data in Great Britain using fuzzy classification and constrained ordination. Results Ground beetle species pools classified into nine groups which were related to land cover variables using constrained ordination. There was a strong relationship between upland land cover and three ground beetle groups. Deciduous woodland, coastal and tilled land were associated with three other groups. Three further groups did not appear to be strongly associated with any particular cover, but differed in geographical position. Conclusion The distribution of species pools derived from the British national recording scheme at the 10 km scale was strongly related to satellite‐derived land cover data. There appears to be considerable potential for the use of a synthesis of land cover and ground beetle data in the monitoring of environmental change over a large, countrywide, area.     

Exploring the relationship between land cover and the distribution of water beetle species (Coleoptera) at the regional scale

The advent of remote-sensed satellite land cover data has provided the opportunity to assess the relationship between invertebrate species distributions and individual land cover types. Water beetle species occur in habitats within specific land cover types and the relationship between the distribution of water beetle species and land covers at the regional scale was investigated using records of 154 species from 1018 sites in north-east England. The land covers of tilled land and urban in the lowlands and of shrub heath and heath grassland in the upland areas proved to be most important in explaining the distribution of species. There were both positive and negative associations between some species and other covers such as woodland and the coast. However, a considerable number of species, generally those with a large number of records, showed no strong relationships with any land cover types. The integration of water beetle species recording data and remote-sensed land cover data as a basis for predicting and monitoring both species distribution and environmental change is discussed.     

Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.