Very high resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality

Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that ‘habitat amount’ in the landscape is sufficient to explain patterns of biodiversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch- and landscape levels. As the statistical approaches needed to implement such hierarchical causal models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality.     

Application of landscape ecology and remote sensing for assessment,monitoring and conservation of biodiversity

Landscape ecology, inter alia, addresses the question as to how altered landscape patterns affect the distribution, persistence, and abundance of a species. Landscape ecology plays an important role in integrating the different scales of biodiversity from habitat patch to biome level. Satellite remote sensing technology with multi-sensor capabilities offers multi-scale information on landscape composition and configuration. Advances in geospatial analytical tools and spatial statistics have improved the capability to quantify spatial heterogeneity. Globally, landscape level characterization of biodiversity has become an important discipline of science. Considering the vast extent, heterogeneity, and ecological and economic importance of forest landscapes, significant efforts have been made in India during the past decade to strengthen landscape level biodiversity characterization. The generic frame work of studies comprises preparation of national databases providing information on composition and configuration of different landscapes using multi-scale remote sensing techniques, understanding the landscape patterns using geospatial models to elicit disturbance and diversity patterns and application of this information for bioprospecting and conservation purposes. Studies on hierarchical linkage of multi-scale information to study the processes of change, landscape function, dynamics of habitat fragmentation, invasion, development of network of conservation areas based on the understanding of multi-species responses to landscape mosaics, macro-ecological studies to understand environment and species richness, habitat and species transitions and losses, landscape level solutions to adaptation and mitigation strategies to climate change are a few of the future challenges. The paper presents the current experiences and, analyses in conjunction with international scenario and identifies future challenges of Indian landscape level biodiversity studies.     

Measuring and monitoring linear woody features in agricultural landscapes through earth observation data as an indicator of habitat availability

The loss of natural habitats and the loss of biological diversity is a global problem affecting all ecosystems including agricultural landscapes. Indicators of biodiversity can provide standardized measures that make it easier to compare and communicate changes to an ecosystem. In agricultural landscapes the amount and variety of available habitat is directly correlated with biodiversity levels. Linear woody features (LWF), including hedgerows, windbreaks, shelterbelts as well as woody shrubs along fields, roads and watercourses, play a vital role in supporting biodiversity as well as serving a wide variety of other purposes in the ecosystem. Earth observation can be used to quantify and monitor LWF across the landscape. While individual features can be manually mapped, this research focused on the development of methods using line intersect sampling (LIS) for estimating LWF as an indicator of habitat availability in agricultural landscapes. The methods are accurate, efficient, repeatable and provide robust results. Methods were tested over 9.5 Mha of agricultural landscape in the Canadian Mixedwood Plains ecozone. Approximately 97,000 km of LWF were estimated across this landscape with results useable both at a regional reporting scale, as well as mapped across space for use in wildlife habitat modelling or other landscape management research. The LIS approach developed here could be employed at a variety of scales in particular for large regions and could be adapted for use as a national scale indicator of habitat availability in heavily disturbed agricultural landscape.     

Importance of landscape features and Earth observation derived habitat maps for modelling amphibian distribution in the Alta Murgia National Park

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless, for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscape features (i.e., habitat mosaics) may have an important role for modelling amphibian distribution.There were different aims in this analysis. Firstly, we compared the effectiveness of the information provided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat taxonomy named General Habitat Category (GHC), which is based on the concept of biological forms of dominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing validated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earth observation data according to ecological expert rules involving concepts related to spatial and temporal relationships among LC/LU and habitat classes.Then, we investigated the importance for amphibians of the landscape surrounding ponds within the Italian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are important for the presence/absence of amphibians in this area, and identified which classes are more important for amphibians. The results obtained can provide useful indications to management strategies aiming at the conservation of amphibians within the study area. An information-theoretic approach was adopted to assess whether GHC maps allow to improve the performance of species distribution models. We used the Akaike's Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LU categories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categories can better explain the distribution of frogs, compared to LC/LU classes.     

An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service

An online spatial biodiversity model (SBM) for optimized and automated spatial modelling and analysis of geospatial data is proposed, which is based on web processing service (WPS) and web service orchestration (WSO) in parallel computing environment. The developed model integrates distributed geospatial data in geoscientific processing workflow to compute the algorithms of spatial landscape indices over the web using free and open source software. A case study for Uttarakhand state of India demonstrates the model outputs such as spatial biodiversity disturbance index (SBDI) and spatial biological richness index (SBRI). In order to optimize and automate, an interactive web interface is developed using participatory GIS approaches for implementing fuzzy AHP. In addition, sensitivity analysis and geosimulation experiments are also performed under distributed GIS environment. Results suggest that parallel algorithms in SBM execute faster than sequential algorithms and validation of SBRI with biological diversity shows significant correlation by indicating high R² values.     

A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

融合多尺度影像数据的杞麓湖流域景观格局分析

景观格局是景观斑块秩序和规律的具体体现,分析景观格局特征能够在空间结构上推进流域生态保护红线的划定和管控工作,维持可持续发展的底线。基于不同遥感数据源测算的景观格局可能存在计算结果差异,以及对景观格局描述不一致的问题。本文以杞麓湖流域为研究区,选取最新的Sentinel-2、GF-1和Landsat 8卫星遥感影像,基于斑块尺度、类型尺度和景观尺度的7个景观指数,从景观类型分布和整体格局两个方面探讨了杞麓湖流域景观格局现状,并分析不同数据源对景观格局评价结果的异同。结果表明：①杞麓湖流域的景观格局以林地和耕地为基底,零碎分散着建设用地和其他用地;②不同卫星遥感影像对流域景观格局的描述存在差异,影像分辨率与斑块数目、最大斑块指数、形状指数、分离度指数存在显著的正相关关系,而对于测度整体的香农多样性指数、香农均匀度指数、优势度指数的影响相对较小。     

Assessment of forest fragmentation in the conservation priority Dudhwa landscape,India using FRAGSTATS computed class level metrics

The Dudhwa landscape, a priority conservation area representing Terai ecosystem (woodland-grassland-wetland complex) has witnessed a sea change in past 150 years or so on account of long history of forest management, changes in land use, and rapid economic development. We assessed fragmentation in two constituent protected areas (Dudhwa National Park-DNP and Katerniaghat Wildlife Sanctuary-KAT) of the landscape due to forest management activities (clear cutting, development of rail and road network, and plantations) and compared the magnitude among them using select metrics at the forest class level. We applied FRAGSTATS spatial pattern analysis software (ver.3.3) on different forest classes deciphered by land use/ cover maps generated using IRS P6 LISS IV digital data. Study amply revealed that the forests in DNP are less fragmented and of better habitat quality than forests of KAT. The set of seven metrics (patch density, mean patch size, edge density, mean shape index, mean core area, mean nearest neighbour, and interspersion and juxtaposition index) at the class level quantified in the present study are simple and proved useful for quantifying complex spatial processes and can be used as an effective means of monitoring in Dudhwa landscape.     

Using geoinformatics and geomorphometrics to quantify the geodiversity of Crete,Greece

The geodiversity of Crete is quantified in this study, based on the classification of geomorphometric, geological and climatic factors. A number of geomorphometric variables, extracted from the ASTER Global Digital Elevation Model (ASTER G-DEM) in conjunction with geological and climatic information, are evaluated through various algorithms incorporated into Geographical Information System (GIS) software’s. The derived geoinformatic data sets are then analyzed to produce the geodiversity of Crete. The geodiversity map is used to quantify the geodiversity, by calculating landscape diversity and other spatial pattern indices. Those indices are evaluating the richness, evenness, fragmentation and shape of the landscape patch types. The outcome of this study has highlighted that western Crete is characterized by complex geodiversity with more irregular, elongated and fragmented landscape patterns relative to the eastern part of the island. The geodiversity indices provide insights into the processes shaping landscapes, particularly the “battle” between neotectonic landscape deformation and erosion/deposition. The methodology presented can be useful for decision makers when evaluating a regions geological heritage, planning the management of natural resources, or designating areas for conservation.     

基于RS和GIS的桓仁县乡村聚落景观格局分析

运用RS和GIS技术以及景观分析方法,以辽东山区桓仁县典型的6个乡镇乡村聚落为研究对象,选取乡村聚落斑块数、斑块面积、斑块密度、平均斑块面积、面积加权平均斑块分维数等5个景观指数,从乡村聚落用地、规模、形态、分离度4个方面进行景观空间格局分析.结果表明,桓仁县乡村聚落的景观格局具有用地比重偏少,聚落斑块数量多、平均斑块面积小,聚落规模在0.743～1.292hm2之间,形态分布稀疏零散,分形指数小、斑块稳定,分离度大等特点.说明在这种乡村聚落景观格局作用下,必然导致自然景观缺乏高度连接,聚落周边自然生态环境受到侵扰和破坏.     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.     

Biodiversity Information System for North East India

Abstract

Biodiversity is the variety and variability of flora and fauna in an ecosystem. Articulated into genes, species, and ecosystem, it provides the biological plasticity needed by life on the earth to adapt changes. As we approach towards the forthcoming century, the earth's diversity of life is increasingly at risk through a combination of mostly human induced factors leading to erosion of genetic resources, extinction of species and collapse of ecological systems. Insitu conservation, biotechnology tools for conservation and prospecting, species habitat relationship and following evolutionary process of speciation are some of the challenges. India being one of the mega biodiversity centers of the world is also known for its traditional knowledge of conservation. The varied regions of the country, with unique floristic and faunal richness, their vastness, endemism, heterogeneity and also inaccessibility of large areas have necessitated creation of authentic baseline database on biodiversity. With the advent of Internet based Geographic Information System technology an effort is being made to harness the power of these technologies to facilitate biodiversity conservation.

The information system organizes the data base generated under the project on “Biodiversity Characterization at landscape level using remote sensing and Geographic Information System in North East India” of the Department of Biotechnology and Department of Space, Government of India. The entire database is organized in object oriented relational database using Oracle as Backend and Visual Basic, ASP as front end. The web enabling part comes through uploading the entire spatial and non‐spatial data at a common platform using ArcSDE and ArcIMS The spatial characterization of landscape structures and its linkages with attribute information on the floristic composition, economic valuation, endemism are presented in Biodiversity Information System on a sharable environment. It is a step to evolve with new a mechanism to conserve biological diversity at local, regional and national level.     

Designing a protected area network for conservation planning in <Emphasis Type="Italic">Jhum</Emphasis> landscapes of Garo Hills,Meghalaya

We studied vegetation and land cover characteristics within the existing array of protected areas (PAs) in South Garo Hills of Meghalaya, northeast India and introduce the concept of protected area network (PAN) and methods to determine linkages of forests among existing PAs. We describe and analyse potential elements of a PAN, including PAs, reserved forests, surrounding buffers as zones of influence, and connecting forest corridors, which collectively can provide old-forest habitat for wildlife species linked across a landscape dominated by jhum (shifting cultivation) agriculture. ANOVA and Chisquare analyses of patch characteristics and forest tree diversity suggested the presence of equally species-rich and diverse old forest cover (tropical evergreen, semi-evergreen and deciduous forest types) in portions of unprotected private and community owned land, which could be designated as additions to, and network linkages among, existing PAs. Such additions and linkages would help provide for conservation of elephants and existing native forest biodiversity and would constitute a PAN in the region. Most (80%) of the total forest cover of the region belongs to private or community owned land. Therefore, such additions could be formally recognized under the aegis of the 2003 amendments of the Wildlife (Protection) Act 1972, which include provisions to designate selected forest patches within private lands as Community Reserves.     

公路走廊带景观破碎度的空间分异研究

近年来,我国公路对自然生态环境的影响随着通车里程的增加而日益显著。在公路选线的过程中,全面分析拟建公路走廊带内景观破碎度的空间分异特征,可有效减少公路修建对生态环境的不利影响。本文利用遥感分类技术和GIS(地理信息系统)空间分析方法,以湖南长湘公路走廊带某段土地斑块和景观分类为基础,借助斑块密度指数、最大斑块指数和相似邻近比指数,在斑块类型水平和景观水平两个层面全面分析公路走廊带内自然林地景观、农业景观和城乡建设景观破碎度的空间分异特征。结果表明:长湘公路走廊带内景观破碎度高的地区主要分布在大型斑块边缘、平原地区以及公路沿线;自然林地景观和农业景观的破碎度较低,城乡建设景观的破碎度较高;城乡建设景观的破碎度与公路相关性最强,表现出顺应公路走向的条带状蔓延特征。该研究成果可为公路选线方案的制订和完善提供环境影响方面的参考。     

Object-based class modelling for multi-scale riparian forest habitat mapping

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m² were modelled from 938 forest stand patches (MPS = 6868.20 m²) and 43,742 tree stand patches (MPS = 140.79 m²). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.     

Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats,India

Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = ?5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels.     

Spectral unmixing ofcompact airborne spectrographic imager (CASI) data for quantifying sub-pixel proportions of biophysical parameters in a coastal dune system

In environmentally sensitive and large coastal dune systems, identification and mapping of favourable (sandy), and unfavourable (scrub rich) habitats form the key to coastal conservation and management. In highly mixed floral environments, however, such an identification is difficult with low resolution multispectral imagery. In such cases, spectral unmixing is useful to resolve the “mixed pixel” effects. The Kenfig NNR (National Nature Reserve), South Wales. UK, bestowed with high biodiversity, is facing loss of successionally young slack habitats due to dune stabilisation and vegetation succession. To map such habitats, linear spectral unmixing of airborne MSS (CASI-Compact Airborne Spectrographic Imager) data was performed using the Constrained Least Square (CLS) method, and the sub-pixel proportions of the spectral end members viz. sand, vegetation and shade/moisture were defined. Comparison of the estimated fractions with the growth forms of the dominant vegetation species Salix repens (Creeping willow) with classified digital aerial photographs shows a positive correlation, thus giving us confidence in the mixture modelling technique. Apart from aiding in conservation management, such a fuzzy classification of multi-date imagery helps to delineate sandy and vegetated areas for change detection and landscape/ habitat succession studies.     

The effectiveness of richness,diversity and dominance textural indices when applied on digital ground photographs using a GIS for landscape visual analysis purposes

This study aims to investigate the effectiveness of three main textural indices for the automatic evaluation and classification of landscape scenic quality using digital ground photographs. Based on the textural indices that are commonly used in landscape ecology, the richness, diversity and dominance indices were selected to be examined, and the results from the application of these indices to a sample of ground photographs are described in this paper. The application of the textural indices to the sample of photographs takes place in two different ways, using a geographic information system (GIS) that aims to describe the general and partial heterogeneity of the landscape appearing in each photograph. Furthermore, we investigate the effectiveness of the indices selected as to the classification of the landscape's scenic quality, with the results being compared to other results derived from a research programme of the National Technical University of Athens. Comments and suggestions are presented for further investigation. The main conclusion of the investigation is that the texture indices are sensitized in the landscape's scenic quality, a fact that is positive and encourages the pursuit of further research.     

Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Mapping of habitats with relevance for nature conservation involves the identification of patches of target habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limiting the necessary ground reference to a small sample of target habitats would greatly reduce and therefore support the field mapping effort. We thus aim to answer in this study the question: can semi-automated remote sensing methods help to map such patches without the need of ground references from sites not relevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiency of large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map four habitat types across a patchy landscape of 1000 km² near Munich, Germany. This task was conducted using the low number of 125 ground reference points only along with easily available multi-seasonal RapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectance due to different phenological development across space, continuous transitions between the habitats and the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simple and affordable approach can thus be recommended for a first survey of previously unmapped areas, as a tool for identifying potential gaps in existing habitat inventories and as a first check for changes in the distribution of habitats.     

Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data

The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.