A retrospective analysis of land cover change using a polygon shape index

Aim This study tests the hypothesis that the propensity of land cover patches to change is related to their shape and geometric complexity. Location The analysis is based on a 1000‐km² area of the Cairngorms in Scotland, incorporating part of Speyside and the high plateau area within the Grampian Mountains. Methods A combined dataset was created by intersecting 1964 land cover data (derived from archive aerial photography) and 1988 land cover data (from the Land Cover of Scotland dataset). A shape index was calculated for each land cover polygon inside a GIS. Information on land cover change was analysed with reference to land cover class and the polygon shape index using a regression analysis. Results For upland seminatural land cover classes, subject to low levels of management, change is related to polygon shape, such that the more complex patches were found to be more susceptible to change. This relationship breaks down where classes are more intensively managed or have been aggregated into mosaic classes. Conclusions Propensity to change was related to shape index for seminatural land cover classes. This implies that at least some landscape processes, such as anthropogenic disturbance of seminatural land covers, can be linked to ecological theory via measurements of spatial pattern. The study also highlighted some of the cartographic issues involved in estimating changes between land cover classes: there are advantages in replacing the ‘cartographic paradigm’ of comparing two derived datasets (in this case land cover maps) with direct comparison of the digital data — air photographs or satellite imagery. Such a direct approach avoids the compounding of errors introduced by the approximation of each successive air photo as a thematic map.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

土地覆盖变化检测方法比较——以内蒙古草原区为例

随着对地观测技术的不断发展,遥感影像分辨率逐渐提高,促进了基于遥感影像的变化检测从传统像元级的检测向面向对象的检测转变。为了探究面向对象的变化检测方法在土地覆盖变化检测中的有效性和适用性,对面向对象的变化检测方法与常规的变化检测方法进行对比评价。以内蒙古鄂尔多斯和包头地区为试验区,选取2002年及2011年的Landsat TM/ETM+影像为数据源,比较了图像代数运算、图像变换、图像空间结构特征和面向对象的多种变化检测方法,对研究区两期土地覆盖进行了变化检测研究。结果表明:面向对象的变化检测方法在总体精度、kappa系数上都有明显的优越性,总体精度均在87.42%以上,尤其以面向对象的变化矢量分析方法精度最高,达91.56%。此外,主成分差异法也有较好的检测效果,总体精度为87.83%。对总体精度较高的3种方法在不同土地覆盖变化类型中检测效果的研究表明:对于研究区几种主要土地覆盖变化类型,面向对象的变化矢量分析法均有较理想的检测效果,平均精度为85%左右,且始终优于面向对象的光谱向量相似法,以居民地及旱地相关的变化类型最为明显;主成分差异法对不同土地覆盖变化类型检测效果差异很大,对其中4种变化类型的精度甚至达到了93%以上,但对于检测草地与裸地间转化精度很低,甚至只有8.69%;在与工矿用地有关的土地覆盖变化类型中,面向对象的变化矢量分析法的精度明显高于主成分差异法,而在与居民地有关的变化类型中,主成分差异法表现出一定优势。     

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: ?6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: ?47.9%, ?41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.     

晋北地区土地利用覆被格局的演变与模拟

区域土地利用覆被变化及未来发展情景对区域土地管理和可持续发展具有重要意义。以地处农牧交错带、土地利用覆被变化剧烈的晋北地区为研究区,获取其2010、2015年的土地利用覆被(Land use/land cover,LULC)数据,选取高程、人口、经济、气温、降水等9种影响因素作为驱动因子,采用CLUE-S模型拟合研究区2015年的土地覆被格局并判断拟合精度,在此基础上,分别设置了3种社会经济发展情景,模拟这些情景下研究区2020年的土地利用覆被格局演变。结果表明:1)晋北地区土地利用覆被以耕地、林地和草地为主,各类型土地主要呈西北斜向的条带状分布;2)Logistic回归模型可以很好地提取LULC与驱动因子之间的关系,反映不同的驱动因素对不同的土地利用类型分布格局的影响效果及程度;3)CLUE-S模型在晋北地区土地利用覆被格局的拟合上有较好的精度,模拟Kappa系数值达0.89,表明该模型能够很好地模拟晋北地区的土地利用覆被;4)情景模拟结果表明,研究区生态保护情景(c)下的土地利用覆被格局明显优于维持现状情景(a)和经济优先情景(b),建议在未来土地开发利用过程中,应当减缓工矿用地增加速度,严格控制建设用地规模,优化土地利用格局。     

疏勒河中游土地利用与景观格局动态

利用1976年Landsat MSS、1989年Landsat TM、2000年Landsat ETM+和2010年TM遥感影像,运用GIS和景观生态学方法,分析了1976-2010年疏勒河中游玉门市土地利用/覆被和景观格局的变化.结果表明:1976-2010年间,玉门市土地利用类型转移的主要方向是草地和戈壁转化为耕地、耕地转化为建设用地、草地转化为戈壁;土地利用变化经历了“缓慢变化-急剧变化-显著变化”的过程,景观密度持续增大,最大斑块指数先增大后减小,面积加权形状指数增大,形状趋于不规则;斑块间的最邻近距离减小,景观逐渐向具有多种要素的密集格局演变,更加破碎;不同斑块间的分离度减小;景观的多样性和均匀度先减小后增加.农业人口增长和经济发展是研究区土地利用/覆被变化的最直接驱动力,气候和政策因素也是重要的影响因素.     

Analyzing spatial variations in land use/cover distributions: A case study of Nanchang area,China

Quantification of spatial variation is important for analyzing and predicting the environmental and social impacts of land development. This paper presents a density-based framework to analyze spatial variations within land use/cover classes through a case study of the Nanchang area, China. By means of grid sampling, the categorical patches were represented by grid densities, and spatial indicators of class abundance, scale-area curve and neighborhood density were constructed to measure the spatial variables of area, distance and scale. The scale variations within each class were demonstrated by abundance indicators and were divided into three types with different similarity degrees, which were measured by coefficients of congruence. These variations roughly corresponded to the distribution patterns revealed by the scale-area indicators. The scaling behaviors of these patterns exhibited discontinuity and coherence, which were possibly affected by the change rates of some patch characteristics in the classes. The neighborhood density indicators showed that every class was more aggregated at short distances, while multimodal patterns fluctuating in nearly random distributions occurred at considerable distances. The degree of clumping correlated positively with the abundance of each class. The characteristics of distribution sizes, ranges and patch isolation in these classes left some imprints on the variations in aggregation intensity. These findings have implications for data integration, mechanism exploration and methodological framework, which are also needed for management practices.     

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.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

新亚欧大陆桥经济走廊土地覆被变化及驱动力分析

针对年际间的土地覆被变化的空间分异特性及驱动机理解析问题,采用Python和R语言构建了土地覆盖变化的时空动态概率模型和驱动力综合分析模型,实现了21世纪以来"新亚欧大陆桥经济走廊(NECBEC)"土地覆盖时空动态变化特征及驱动机理的定量分析。研究结果表明,在2001—2017年间,NECBEC的土地覆盖变化总体呈现出三增(草地、耕地和建设用地分别增加11457万hm~2、841万hm~2和396万hm~2)和三减(林地、水域和湿地、和未利用地分别减少7409万hm~2、4659万hm~2和626万hm~2)趋势。其中,未利用地和林地主要转换为草地,而草地则主要转为林地和耕地。建设用地年际增加幅度最大,其新增面积中耕地贡献达到50%。另外,自2013年"一带一路"倡议启动以来,NECBEC区域的各种土地覆盖类型之间的相互转换幅度呈现明显增加趋势,而NECBEC沿线国家之间的社会经济发展综合水平集聚性总体上呈减弱趋势,其中综合得分高高聚集区和低低聚集区分别集中在西欧和中亚北部。NECBEC区域的社会经济发展对耕地和建设用地的时空差异性尤为显著。土地覆盖类型在面积变化量和变化速率上,均具有明显的时空分异性。不同的经济发展综合水平对LUCC的类型演替、格局变化和驱动效应不同。     

Integrating geo-biodiversity features in the analysis of landscape patterns

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how specific physical and biological factors, namely geo- and biodiversity features influence landscape patterns. The focus was on microscale relationships and we chose as our focus area a small scale study site covering 3091 ha characterized by vegetation mosaics with multiple patterns. We considered geology, soil and altitude (for geodiversity) and land cover classes (for biodiversity) as superposed layers and we aggregated their elements into a new combined mosaic. Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses. We determined the most important parameters by Principal Component Analysis. The first component was mainly linked to metrics related to size variability, while the second one was related to border complexity. In the reduced space, we delineated three clusters of objects that were characterized by different landscape patterns. We analyzed the underlying geology, soil structure and occurring land cover classes for each cluster. We then performed Redundancy Analysis using geo- and biodiversity features as predictor variables and metrics as response variables. While the land cover acted as explanatory variable for the first principal axis of variation, the geodiversity features (geology and soil) were related to the second one. Specifically, the occurrence of limestone yields more complex borders of patches; some phenomena are visible in situ, such as limestone appearing at the surface as outcrops (lapis) that induce irregular shapes of the patches. Overall, the analyses hinted that, besides the land cover class, the underlying geology plays an important role in defining landscape patterns, and this relationship can be revealed through the use of appropriate statistical tools. On the other hand, the study area is an agro-silvopastoral landscape, where local traditional management practices are also an important driver for the occurrence of specific patterns. Therefore, understanding the links between geo- and biodiversity characteristics and landscape features can contribute to developing appropriate management and planning strategies.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Assessing the influence of historic net and gross land changes on the carbon fluxes of Europe

Legacy effects of land cover/use on carbon fluxes require considering both present and past land cover/use change dynamics. To assess past land use dynamics, model‐based reconstructions of historic land cover/use are needed. Most historic reconstructions consider only the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). Studies about the impact of gross and net land change accounting methods on the carbon balance are still lacking. In this study, we assessed historic changes in carbon in soils for five land cover/use types and of carbon in above‐ground biomass of forests. The assessment focused on Europe for the period 1950 to 2010 with decadal time steps at 1‐km spatial resolution using a bookkeeping approach. To assess the implications of gross land change data, we also used net land changes for comparison. Main contributors to carbon sequestration between 1950 and 2010 were afforestation and cropland abandonment leading to 14.6 PgC sequestered carbon (of which 7.6 PgC was in forest biomass). Sequestration was highest for old‐growth forest areas. A sequestration dip was reached during the 1970s due to changes in forest management practices. Main contributors to carbon emissions were deforestation (1.7 PgC) and stable cropland areas on peaty soils (0.8 PgC). In total, net fluxes summed up to 203 TgC yr^?1 (98 TgC yr^?1 in forest biomass and 105 TgC yr^?1 in soils). For areas that were subject to land changes in both reconstructions (35% of total area), the differences in carbon fluxes were about 68%. Overall for Europe the difference between accounting for either gross or net land changes led to 7% difference (up to 11% per decade) in carbon fluxes with systematically higher fluxes for gross land change data.     

A land cover map of South America

A digital land cover map of South America has been produced using remotely sensed satellite data acquired between 1995 and the year 2000. The mapping scale is defined by the 1 km spatial resolution of the map grid‐cell. In order to realize the product, different sources of satellite data were used, each source providing either a particular parameter of land cover characteristic required by the legend, or mapping a particular land cover class. The map legend is designed both to fit requirements for regional climate modelling and for studies on land cover change. The legend is also compatible with a wider, global, land cover mapping exercise, which seeks to characterize the world's land surface for the year 2000. As a first step, the humid forest domain has been validated using a sample of high‐resolution satellite images. The map demonstrates both the major incursions of agriculture into the remaining forest domains and the extensive areas of agriculture, which now dominate South America's grasslands.     

Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010

Historic land‐cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land‐cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land‐cover/use reconstructions underestimate land‐cover/use changes in Europe for the 1900–2010 period by accounting for net changes only. We empirically analyzed available historic land‐change data, quantified their uncertainty, corrected for spatial‐temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land‐change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr^?1) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land‐change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization.     

中国土地利用格局及其影响因子分析

在遥感技术与 GIS技术的支持下 ,对中国土地利用空间格局及其影响因子进行了分析。首先通过 1∶1 0万中国土地利用数据库经过分层提取 ,生成中国土地利用类型空间分布格局数据库 ,在此基础上对中国土地利用空间格局进行了分析 ;而后在引入景观多样性指数、优势度指数、均匀度指数、破碎度指数的基础上 ,对中国土地利用景观格局进行了定量分析。研究结果表明 :中国土地利用景观多样性指数、优势度指数、均匀度指数、破碎度指数空间分布上具有明显的规律性 :随着人类活动的逐渐增强 ,景观的多样性指数逐渐增加 ,优势度指数逐渐减少 ,破碎度指数逐渐增加 ;而当人类已经彻底改变自然景观 ,多样性指数则逐渐减少 ,优势度指数增加 ,而破碎度指数逐渐减少。同时 ,针对中国土地利用景观格局及其生态背景的特点 ,选择北纬 40°、2 8°和 2 4°三条样带 ,东经 1 0 8°和 1 1 4°两条样带 ,对中国土地利用格局的影响因子进行了分析。     

National inventories of land occupation and transformation flows in the world for land use impact assessment

Purpose

Land use can cause significant impacts on ecosystems and natural resources. To assess these impacts using life cycle assessment (LCA) and ensure adequate decision-making, comprehensive national inventories of land occupation and transformation flows are required. Here, we aim at developing globally differentiated inventories of land use flows that can be used for primary use in life cycle impact assessment or national land planning.

Methods

Using publicly available data and inventory techniques, national inventories for several land use classes were developed. All land use classes were covered with the highest retrievable level of disaggregation within urban, forestry, agriculture and other land use classes, thus differentiating 21 land use classes. For illustrating the application of this newly developed inventory, two different application settings relevant to life cycle impact assessment were considered: the calculation of global normalisation references for 11 land use impact indicators related to soil quality assessment (adopting the methods recommended by the EU Commission) and the determination of generic globally applicable characterisation factors (CFs) resulting from aggregation of country-level CFs for situations for use when land use location is unknown.

Results and discussion

We built national inventories of 21 land occupation and 17 land transformation flows for 225 countries in the world for the reference year 2010. Cross-comparisons with existing inventories of narrower scopes attested its consistency. Detailed analyses of the calculated global normalisation references for the 11 land use impact categories showed different patterns across the land use impact indicators for each country, thus raising attention on key land use impacts specific to each country. Furthermore, the upscaling of country-level CFs to global generic CFs using the land use inventory revealed discrepancies with other alternative approaches using land use data at different resolutions.

Conclusions

In this study, we made a first attempt at developing national inventories of land use flows with sufficient disaggregation level to enable the calculation of normalisation references and differentiated impacts. However, the findings also demonstrated the need to refine the consistency of the inventory, particularly in the combination of land cover and land use data, which should be harmonised in future studies, and to expand it with differentiated coverage of more land use flows relevant to impact assessment.

     

Impact of short-term rainfall fluctuation on interannual land cover change in sub-Saharan Africa

Aim Interannual land cover change plays a significant role in food security, ecosystem processes, and regional and global climate modelling. Measuring the magnitude and location and understanding the driving factors of interannual land cover change are therefore of utmost importance to improve our understanding and prediction of these impacts and to better differentiate between natural and human causes of land cover change. Despite advances in quantifying the magnitude of land cover change, the interpretation of the observed land cover change in terms of climatic, ecological and anthropogenic processes still remains a complex issue. In this paper, we map land cover change across sub‐Saharan Africa and examine the influences of rainfall fluctuations on interannual change. Location The analysis was applied to sub‐Saharan Africa. Methods Ten‐day rainfall estimates (RFE) obtained from National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center (CPC) were used to extract information on inter and intra‐annual rainfall fluctuations. The magnitude of land cover change was quantified based on the multitemporal change vector method measuring year‐to‐year differences in bidirectional reflectance distribution function (BRDF) corrected 16‐day enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectro‐radiometer (MODIS). Statistical models were used to estimate the relationship between short‐term rainfall variability and the magnitude of land cover change. The analysis was stratified first by physiognomic vegetation type and second by chorological data on species distribution to gain insights into spatial variations in response to short‐term rainfall fluctuations. Results The magnitude of land cover change was significantly related to rainfall variability at the 5% level. Stratification considerably strengthened the relationship between the magnitude of change and rainfall variability. Explanatory power of the models ranged from R² = 0.22 for the unstratified model to 0.40–0.96 for the individual models stratified by patterns of species distribution. The total variability explained by the combined models including the influence of rainfall and differences in vegetation response ranged from 22% for the model not stratified by vegetation to 76% when stratified by chorological data. Main conclusions Using this methodology, we were able to measure the contribution of natural variation in precipitation to land cover change. Several ecosystems across sub‐Saharan Africa are highly sensitive to short‐term rainfall variability.     

Elephant response to spatial heterogeneity in a savanna landscape of northern Tanzania

Landscape heterogeneity, namely the variation of a landscape property across space and time, can influence the distribution of a species and its abundance. Quantifying landscape heterogeneity is important for the management of semi‐natural areas through predicting species response to landscape changes, such as habitat fragmentation. In this paper, we tested whether the change in spatial heterogeneity of the vegetation cover due to farming expansion affected the distribution of the African elephant in the Tarangire‐Manyara ecosystem, northern Tanzania. Spatial heterogeneity (based on the normalized difference vegetation index) was characterized at multiple spatial scales using the wavelet transform and the intensity‐dominant scale method. Elephant distribution was estimated from time‐series aerial surveys using a kernel density function. The intensity, which relates to the contrast in vegetation cover, quantified the maximum variation in NDVI across multiple spatial scales, whereas the dominant scale, which represents the scale at which this maximum variation occurs, identified the dominant inter‐patches distance, i.e. the size of dominant landscape features. We related the dominant scale of spatial heterogeneity to the probability of elephant occurrence in order to identify: 1) the scale that maximizes elephant occurrence, and 2) its change between 1988 and 2001. Neither the dominant scale and intensity of spatial heterogeneity, nor the probability of the elephant occurrence changed significantly between 1988 and 2001. The spatial scale maximizing elephant occurrence remained constant at 7000 to 8000 m during each wet season. Compared to the findings of a recent, similar study in Zimbabwe, our results suggest that the change in the dominant scale was relatively small in Tarangire‐Manyara ecosystem and well within the critical threshold for elephant persistence. The method is a useful tool for monitoring ecosystems and their properties.