Landscape pattern and species richness; regional scale analysis from remote sensing

Concern about the future of biodiversity in the wider countryside is stimulating the development of methods for species and ecosystem monitoring over large areas. The objective of this paper is to explore the potential of remotely sensed data for measuring landscape structure as an important determinant of species diversity. Data from the satellite Land Cover Map of Great Britain, a land cover classification of Landsat Thematic Mapper scenes, were used to derive a set of simple measures of landscape structure within 2km x 2km tetrads for three vascular plant families. Results from a model to predict plant diversity from landscape structure alone proved difficult to interpret ecologically and highlighted the need to obtain data on both landscape quality and landscape structure.     

Validation and comparison of 1 km global land cover products in China

Four 1 km global land cover products are currently available to the scientific community: the University of Maryland (UMD) global land cover product, the International Geosphere–Biosphere Programme Data and Information System Cover (IGBP‐DISCover), the MODerate resolution Imaging Spectrometer (MODIS) global land cover product and Global Land Cover 2000 (GLC2000). Because of differences in data sources, temporal scales, classification systems and methodologies, it is important to compare and validate these global maps before using them for a variety of studies at regional to global scales. This study aimed to perform the validation and comparison of the four global land cover datasets, and to examine the suitability and accuracy of different coarse spatial resolution datasets in mapping and monitoring cropland across China. To meet this objective, we compared the four global land cover products with the National Land Cover Dataset 2000 (NLCD‐2000) at three scales to evaluate the accuracy of estimates of aggregated cropland areas in China. This was followed by a spatial comparison to assess the accuracies of the four products in estimating the spatial distribution of cropland across China. A comparative analysis showed that there are varying levels of apparent discrepancies in estimating the cropland of China between these four global land cover datasets, and that both area totals and spatial (dis)agreement between them vary from region to region. Among these, the MODIS dataset has the best fit in depicting China's croplands. The coarse spatial resolution and the per pixel classification approach, as well as landscape heterogeneity, are the main reasons for the large discrepancies between the global land cover datasets tested and the reference data.     

Land cover 2.0

Interest in knowing more about the Earth’s land cover and how it has changed over time motivated the mission and sensor design of early terrestrial remote sensing systems. Rapid developments in computer hardware and software in the last four decades have greatly increased the capacity for satellite data acquisition, downlink, dissemination, and end user science and applications. In 1992, Townshend reviewed the state of land cover mapping using Earth observation data at a pivotal point in time and in the context of years of research and practical experience with Landsat Thematic Mapper (TM), Satellite Pour l’Observation de la Terre (SPOT) High Resolution Visible (HRV) and Advanced Very-High-Resolution Radiometer (AVHRR) data, demonstrating the opportunities and information content possible with increased spatial, spectral, and temporal resolutions. Townshend characterized the state-of-the-art for land cover at that time, identified trends, and shared insights on research directions. Now, on the 25th anniversary of Townshend’s important work, given numerous advances and emerging trends, we revisit the status of land cover mapping with Earth observation data. We posit that a new era of land cover analysis – Land Cover 2.0 – has emerged, enabled by free and open access data, analysis ready data, high performance computing, and rapidly developing data processing and analysis capabilities. Herein we characterize this new era in land cover information, highlighting institutional, computational, as well as theoretical developments that have occurred over the past 25 years, identifying the key issues and opportunities that have emerged. We conclude that Land Cover 2.0 offers efficiencies in information generation that will result in a proliferation of land cover products, reinforcing the need for transparency regarding the input data and algorithms used as well as adoption, implementation, and communication of rigorous accuracy assessment protocols. Further, land cover and land change assessments are no longer independent activities. Knowledge of land change is available to inform and enrich land cover generation.     

The use of digital aerial photography and CORINE‐derived methodology for monitoring recent and historic changes in land cover near UK Natura 2000 sites for the BIOPRESS project

BIOPRESS – Linking pan‐European land cover change to pressures on biodiversity – is a European Community Framework 5 project, which aims to develop a standardised product that will link quantified historical (1950–2000) land cover change to pressures on biodiversity. It exploits archived historic and recent aerial photographs (a data source that has remained consistent over the last 60 years) to assess land cover change around Natura 2000 sites within 30×30 km windows and 15×2 km transects. The CORINE (Coordination of Information on the Environment) land cover mapping methodology has been adapted for use with aerial photographs. Sample sites are mapped to CORINE Land Cover (CLC) classes, and then backdated to assess change. Results from eight UK transects (and associated windows) are presented. Changes in land cover classes are interpreted as pressures: urbanisation, intensification, abandonment, afforestation, deforestation and drainage. Urbanisation was the major pressure in all but two transects (both in the uplands), and intensification was of similar importance in most transects. Afforestation was a significant pressure in two transects. In six out of the eight transects, annual change was greater in the 1990–2000 period than in the 1950–1990 period. The methodology has been demonstrated to provide quantitative results of long‐term land cover change in the UK rural landscape at a spatial scale that is relevant to management decisions. The methods are transferable and applicable to a wide range of landscape studies.     

Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin

An understanding of land use/land cover change at local, regional, and global scales is important in an increasingly human-dominated biosphere. Here, we report on an under-appreciated complexity in the analysis of land cover change important in arid and semi-arid environments. In these environments, some land cover types show a high degree of inter-annual variability in productivity. In this study, we show that ecosystems dominated by non-native cheatgrass (Bromus tectorum) show an inter-annual amplified response to rainfall distinct from native shrub/bunch grass in the Great Basin, US. This response is apparent in time series of Landsat and Advanced Very High Resolution Radiometer (AVHRR) that encompass enough time to include years with high and low rainfall. Based on areas showing a similar amplified response elsewhere in the Great Basin, 20,000 km², or 7% of land cover, are currently dominated by cheatgrass. Inter-annual patterns, like the high variability seen in cheatgrass-dominated areas, should be considered for more accurate land cover classification. Land cover change science should be aware that high inter-annual variability is inherent in annual dominated ecosystems and does not necessarily correspond to active land cover change.     

Integrating Corine Land Cover data and Landsat TM for surface emissivity definition: application to the urban area of Athens,Greece

This study examines the potential of the combined use of the land cover/land use information provided by the Corine Land Cover (CLC) database with Landsat satellite data for the definition and quantitative correlation of emissivity with various land covers and land uses that describe a certain territory. Surface emissivity in the 10.5–12.5 µm wavelength range is derived using Landsat data and the Normalized Difference Vegetation Index Thresholds method (NDVI^THM), whereas mean emissivity values for selected urban/non‐urban land cover types are estimated by integrating the emissivity image with the land cover vector data. The method is applied to the greater Athens area, Greece, in order to estimate the emissivity of various land cover types found within the urban setting. Analysis of variance (ANOVA) indicates statistically significant differences in emissivity associated with different land cover types. Furthermore, statistical results demonstrate that the method is very effective and can provide emissivity values of different land cover types with good accuracy and therefore can quantitatively link emissivity with surface type.     

张掖市土地利用/覆盖变化模拟

土地利用/覆盖变化是全球变化的主要原因,也是与可持续发展密切相关的课题。土地利用/覆盖变化模拟是预测未来土地利用/覆盖变化的重要方法。将国际上先进的CLUE-S模型应用到位于黑河中上游的张掖市,模拟该地区的土地利用/覆盖变化。模拟时段为2001～2020年。模型中将土地利用类型分为:①耕地;②林地;③草地;④水域;⑤城镇用地;⑥未利用地。用回归分析的方法,选择了对该地区土地利用/覆盖变化有重要贡献的7种驱动因子,分别为:与城市的距离、与河流的距离、与道路的距离、人口密度、海拔、坡度、坡向。模拟结果显示:到2020年,林地、草地、水域和城镇用地面积增加,耕地,未利用地面减少。     

The change of land cover and land use and its impact factors in upriver key regions of the Yellow River

Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land‐use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land‐use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land‐Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio‐economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands ‐ Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter‐transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon–Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision‐making tools for local governments as well.     

景观信息流及GIS对脆弱生态异质性制图的分析

分析了依信息学确立的景观信息流在景观异质性制图的意义，及景观信息流的测度、特征和遥感与ＧＩＳ对其获取的方法，探讨了景观信息流转换的表达方式。其次，把景观信息流反映在地学异质信息中，着重分析脆弱生态景观地学的异质性特征、响应以及利用ＧＩＳ建立脆弱生态景观异质性制图的方法，对科尔沁沙地东南部的景观异质性特征作了分析。     

Relating spectral and species diversity through rarefaction curves

Rarefaction represents a powerful analytical approach in ecology for estimating the expected number of species within a given study area from local (α‐diversity) to regional (γ‐diversity) scales. From a landscape perspective, rarefaction curves are directly related to the environmental heterogeneity of the area sampled. The greater the landscape heterogeneity, the greater the expected species diversity. Therefore, remotely sensed images may potentially be used for predicting species diversity through the indirect method of analysing local spectral variation. The aim of this study was to test whether spectral variability can be used as a proxy for species diversity, from local to regional spatial scales. A total of 977 sampling units, each 50 m×50 m, were selected within the Asciano district (Central Italy) following a stratified random sampling. Each sampling unit was manually classified according to the first level of the Corine Land Cover classification legend. Data on plant species composition were collected in 10 m×10 m plots located within 98 random sampling units. The normalized difference vegetation index (NDVI) was calculated from a QuickBird image, and quantized into 8‐bit data (256 digital numbers, DNs) for building spectral rarefaction curves. Only those plots falling within the QuickBird image were used, which had the effect of reducing the thematic legend to two classes: crops and seminatural vegetation. Species and spectral rarefaction curves were then constructed for each land cover class. Rarefaction curves based on species and spectral properties showed similar results, that is a significantly different number of accumulated values given the same sampling effort for the two classes considered. The results of this study suggest that the shape of the spectral rarefaction curves may be an indirect indicator of environmental diversity, and thus may have potential for predicting biodiversity from local to landscape scales.     

Application of multitemporal Landsat data to monitor land cover changes in the Eastern Nile Delta region,Egypt

Due to the progressive increase in development of desert land in Egypt, the demand for efficient and accurate land cover change information is increasing. In this study, we apply the methodology of post‐classification change detection to map and monitor land cover change patterns related to agricultural development and urban expansion in the desert fringes of the Eastern Nile Delta region. Using a hybrid classification approach, we employ multitemporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1984, 1990 and 2003 to produce three land cover/land‐use maps. Post‐classification comparison of these maps was used to obtain ‘from–to’ statistics and change detection maps. The change detection results show that agricultural development increased by 14% through the study period. The average annual rate of land reclamation during 1990–2003 (4511 ha a^?1) was comparable to that during 1984–1990 (4644 ha a^?1), reflecting a systematic national plan for desert reclamation that went into effect. We find that the increase in urbanization (by ca 21 300 ha) during 1990–2003 was predominantly due to encroachment into traditionally cultivated land at the fringes of urban centres. Our results accurately quantify the land cover changes and delineate their spatial patterns, demonstrating the utility of Landsat data in analysing landscape dynamics over time. Such information is critical for making efficient and sustainable policies for resource management.     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.     

土地利用/土地覆盖遥感变化检测方法新进展

土地利用/土地覆盖变化检测是资源环境研究中的一个重要方面,准确、及时的变化信息为人类资源环境的生产和管理提供了决策支持。遥感技术具有实时、快速、覆盖范围广、多光谱、周期性等特点,给土地利用/土地覆盖变化检测提供了理想的数据基础。人类社会的发展对土地利用/土地覆盖变化检测的精度与效率不断提出新的要求,这也使得一些新技术、新方法引入到变化检测中。这些新技术新方法提高了土地利用/土地覆盖变化检测的精度,但在效率方面有待进一步研究。     

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere-Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.     

Land Cover Extraction in the Ejina Oasis by Hyperspectral Remote Sensing

The extraction of land surface coverage is the basis of ecological environment evaluation,vegetation change analysis and regional ecological and hydrological processes.Aerial hyperspectral remote sensing has great advantage in land surface coverage extraction,such as flexible,wide coverage,high spatial resolution and high spectral resolution.Research area has landscape characteristics of vegetation,landscape fragmentation and heterogeneity in Ejina Poplar Forest National Nature Reserve.Comparison and analysis of two methods of dimension reduction based on minimum noise transform and principal component analysis,three supervised classification methods based on maximum likelihood method,support vector machine and object\|oriented classification.Land surface coverage is extracted by NDVI threshold segmentation,minimum noise transform dimensionality reduction method and maximum likelihood classification method according to the characteristics of landscape fragmentation,heterogeneity and high redundancy of hyperspectral data based on the Airborne Hyperspectral Data of Ejina oasis in the lower reaches of Heihe.The land surface coverage results overall accuracy and Kappa coefficient are 87.95% and 0.885 by random sampling based on airborne remote sensing data.The results show that the classification results of high accuracy can provide effective parameters for ecological research.     

Land Surface Temperature Retrieval and Its Spatial Heterogeneity in Summer in Chongqing

Land Surface Temperature(LST)is an important parameter in land surface energy budget.In order to improve the accuracy of LST retrieval by remote sensing methods in summer in urban districts of Chongqing with hot and humid atmosphere condition.an improved methodology was presented with the improved atmospheric transmittance estimated on MODTRAN software using the atmospheric profile data of MERRA in urban districts of Chongqing.LST was retrieved from Landsat 8 TIRS band 10 data using single-window algorithm which apply the improved and unimproved atmospheric transmittance respectively.Then the retrieved LST was compared with the 0 cm soil temperatures observedby 4 meteorological stations.Finally,the spatial heterogeneity of LST was analyzed.The result indicated that：(1)The scheme proposed in this paper can improve LST retrieval in summer in urban districts of Chongqing.The Mean Absolute Error(MAE)decrease from 4.89 K to 1.73 K.(2)The retrieved LST has spatial heterogeneity with different terrain factors.Its lapse rate is about 1.17 K/100 m.It decreases with the increase of slope.Moreover,it has obvious differences with aspect.The flat slope>sunny slope>semi-sunny slope>semi-shady slope>shady slope.There also existed highly significant correlation between the LST and hill shade.The LST increases with the decrease of hill shade.(3)Influenced by land cover,the spatial distribution of LST showed significant differences.The average LST inthe built\|up area is highest,while the wet land is lowest.The difference of average LST in other land cover types is little.     

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day^?1 with a mean of 3.4 mm day^?1, which was validated by Penman–Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBAL's applicability to watersheds with various land cover types and complex terrain were analysed.     

Effects of agent heterogeneity in the presence of a land-market: A systematic test in an agent-based laboratory

Representing agent heterogeneity is one of the main reasons that agent-based models become increasingly popular in simulating the emergence of land-use, land-cover change and socioeconomic phenomena. However, the relationship between heterogeneous economic agents and the resultant landscape patterns and socioeconomic dynamics has not been systematically explored. In this paper, we present a stylized agent-based land market model, Land Use in eXurban Environments (LUXE), to study the effects of multidimensional agents’ heterogeneity on the spatial and socioeconomic patterns of urban land use change under various market representations. We examined two sources of agent heterogeneity: budget heterogeneity, which imposes constraints on the affordability of land, and preference heterogeneity, which determines location choice. The effects of the two dimensions of agents’ heterogeneity are systematically explored across different market representations by three experiments. Agents’ heterogeneity exhibits a complex interplay with various forms of market institutions as indicated by macro-measures (landscape metrics, segregation index, and socioeconomic metrics). In general, budget heterogeneity has pronounced effect on socioeconomic results, while preference heterogeneity is highly pertinent to spatial outcomes. The relationship between agent heterogeneity and macro-measures becomes more complex when more land market mechanisms are represented. In other words, appropriately simulating agent heterogeneity plays an important role in guaranteeing the fidelity of replicating empirical land use change process.     

Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network

Rugged land cover classification accuracies produced by an artificial neural network (ANN) using simulated moderate-resolution remote sensor data exceed overall accuracies produced using the maximum likelihood rule (MLR). Land cover in spatially-complex areas and at broad spatial scales may be difficult to monitor due to ambiguities in spectral reflectance information produced from cloud-related and topographic effects, or from sampling constraints. Such ambiguities may produce inconsistent estimates of changes in vegetation status, surface energy balance, run-off yields, or other land cover characteristics. By use of a 'back-classification' protocol, which uses the same pixels for testing as for training the classifier, tests of ANN versus MLR-based classifiers demonstrated the ANNbased classifier equalled or exceeded classification accuracies produced by the MLR-based classifier in five of six land cover classes evaluated.