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Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques. 相似文献
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Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts. 相似文献
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Karla Donato Fook Antônio Miguel Vieira Monteiro Gilberto Câmara Marco Antônio Casanova Silvana Amaral 《Transactions in GIS》2009,13(4):379-399
Biodiversity researchers in different institutions deal with predictive models for species distribution. These models are useful for biodiversity conservation policies. Species distribution modelling tools need large datasets from different sources and use many algorithms. To improve biodiversity science, scientists need to share models, data and results, and should be able to reproduce experiments from others. This article presents a geoweb service architecture that supports sharing of modelling results and enables researchers to perform new modelling experiments. We show the feasibility of the proposed architecture by developing a set of prototype services, called Web Biodiversity Collaborative Modelling Services – WBCMS. They provide a set of geospatial web services that support the sharing of species distribution models. The article includes an example of a model instance that explains the WBCMS prototype. We believe that WBCMS shows how to set up a cooperative research network on biodiversity research. 相似文献
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G. Singh A. Velmurugan M. P. Dakhate 《Journal of the Indian Society of Remote Sensing》2009,37(4):573-585
India has a rich repository of flora and fauna, but the rapid decline of wildlife and threat to its habitat has been a serious
cause of concern. Hence, protected areas have been set up to achieve specific conservation objectives to facilitate timely
and reliable information on forest types and its composition, degradation status and their suitability for different species
of flora and fauna. In the present study, evaluation of tiger habitat in Corbett Tiger reserve is carried out using remote
sensing, ground and other ancillary sources and is integrated using GIS using multi-criteria model. The results indicated
that sal, mixed sal, miscellaneous forest, plantation, grassland, agriculture and scrub land are the major land use/land cover
types and majority of the study area is covered under dense forest. Tiger habitat suitability analysis showed that large proportion
of the area (51.4%) was found to be highly suitable followed by moderately suitable area (31%). Further, the correlation drawn
between range-wise suitability area and actual tiger population in Corbett Tiger Reserve CTR indicated a positive correlation
of 0.73. Disturbance to wildlife habitat, vegetation degradation and shrinking passage corridor are the major concern in CTR. 相似文献
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Application of landscape ecology and remote sensing for assessment,monitoring and conservation of biodiversity 总被引：1，自引：0，他引：1
J. S. Singh P. S. Roy M. S. R. Murthy C. S. Jha 《Journal of the Indian Society of Remote Sensing》2010,38(3):365-385
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. 相似文献
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H. Padalia R. R. Bharti Y. P. S. Pundir K. P. Sharma 《Journal of the Indian Society of Remote Sensing》2010,38(3):513-521
Increasing concern for biodiversity conservation at species level resulted in the development of cost effective tools for
getting information at larger scale. Modeling distribution of species using remote sensing and geographic information has
already proved its potentials to get such information with less effort. Pittosporum eriocarpum Royle is an endemic and threatened tree species of Uttarakhand, yet till now its regional distribution is poorly known. This
study using geospatial modelling tools indentified several localities of potential occurrence of this species in the Mussoorie
hills and Doon valley, and also provides information on its habitat specificity. The main objective of the study is to predict
the suitable habitats for endangered plant species in Himalayan region using logistic regression model where availability
of sufficient data on species presenceabsence is a major limitation for larger areas. 相似文献
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Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation. 相似文献
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The Natura 2000 network of protected sites is one of the means to enable biodiversity conservation in Europe. EU member states have to undertake surveillance of habitats and species of community interest protected under the Habitat Directive. Remote sensing techniques have been applied successfully to monitor biodiversity aspects according to Natura 2000, but many challenges remain in assessing dynamics and habitat changes outside protected sites. Grasslands are among the most threatened habitats in Europe. In this paper we tested the integration of expert knowledge into different standard classification approaches to map grassland habitats in Schleswig Holstein, Germany. Knowledge about habitat features is represented as raster information layers, and used in subsequent grassland classifications. Overall classification accuracies were highest for the maximum likelihood and support vector machine approaches using RapidEye time series, but results improved for specific grassland classes when information layers were included in the classification process. 相似文献
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To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India. 相似文献
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干旱区生态系统极易受到气候及土地利用变化的影响,其生物多样性格局及其形成机制是重要的生态学问题。基于新疆地区鸟类及哺乳动物物种多样性数据,结合气候、地形和长时间序列的植被遥感参数产品FAPAR数据等,主要在不同的土地利用类型及海拔带上采用单因子相关分析方法探讨了物种丰富度格局的形成机制。总体来说,不同生境类型中,植被遥感参数因子(DHI、NDVI等)与两种类群物种丰富度分布的相关性强于与气候因子(温度、降水)的相关性。具体而言,植被遥感参数因子中,基于FAPAR的生境指数因子与丰富度的相关性大于基于植被指数的因子(DHI_cumNDVI_cumEVI_cum);气候因子中,在草地生境或者较低的海拔上,年均降水因子对于丰富度分布的解释力强于年均温度因子。这表明在新疆地区,影响鸟类与哺乳类动物物种丰富度分布的主导理论是生境异质性假说与环境稳定性假说,其解释力在多种生境内均强于生产力与环境热量。 相似文献
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The present study adopts an integrative modelling methodology, which combines the strengths of the SLEUTH model and the Conservation Assessment and Prioritization System (CAPS) method. By developing a scenario-based geographic information system simulation environment for Hashtpar City, Iran, the manageability of the landscape under each urban growth scenario is analysed. In addition, the CAPS approach was used for biodiversity conservation suitability mapping. The SLEUTH model was implemented to generate predictive urban layers of the years 2020, 2030, 2040 and 2050 for each scenario (dynamic factors for conservation suitability mapping). Accordingly, conservation suitability surface of the area is updated for each time point and under each urban development storyline. Two-way analysis of variance and Duncan’s new multiple range tests were employed to compare the functionality of the three scenarios. Based on results, the managed urban growth scenario depicted better results for manageability of the landscape and less negative impact on conservation suitability values. 相似文献
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A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications. 相似文献
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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. 相似文献
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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. 相似文献
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针对云南省因土地利用结构变化,自然环境遭到破坏,导致许多物种极度濒危甚至灭绝、生物多样性遭到严重破坏的问题,本文利用最大熵模型（MaxEnt）对云南9种特色优先保护物种进行了环境适宜性分析。首先以资源环境承载力及国土空间开发适宜性为基础,筛选出具有云南特色的9个优先保护物种,然后对各物种的最优生存环境进行了预测。研究结果表明,云南省9种优先保护物种现存分布区总计0.21万km2。经最大熵模型（MaxEnt）预测可知,最优适生分布区可达5.45万km2,是现存分布区的25倍。 相似文献
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Among the most productive ecosystems around the world, wetlands support a wide range of biodiversity such as waterfowl, fish, amphibians, plants and many other species. They also provide ecosystem services that play important roles in relation to nutrient cycling, climate mitigation and adaptation, as well as food security. In this research, we examined and projected the spatiotemporal trends of change in open wetlands by coupling logistic regression, Markov chain methods and a multi-objective land allocation model into a hybrid geosimulation model. To study the changes in open wetlands we used multi-temporal land cover information interpreted from LANDSAT images (1985, 1995, and 2005). We predicted future spatial distributions of open wetlands in the administrative region of Abitibi-Témiscamingue, Quebec, Canada for 2015, 2025, 2035, 2045 and 2055. A comparison and assessment of the model’s outcomes were performed using map-comparison techniques as well as landscape metrics. Change analysis between 1985 and 2005 showed an increase of about 63% in open wetlands, while simulation results indicated that this tendency would persist into 2055 with a continuous augmentation of open wetlands in the region. The spatial distribution of predicted trends in open wetlands could provide support to local biodiversity assessments, management and conservation planning of the open wetlands in Quebec, Canada. 相似文献
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S. Saran R. Joshi S. Sharma H. Padalia V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2010,38(3):535-547
The study explores the use of multiple criteria decision techniques in predicting spatial niche of Brown oak (also known as
Kharsu oak, Quercus semecarpifolia Sm.) formation in midaltitude (2,400–3,500 meter amsl) Kumaun Himalaya. Predictive models using various climatic and topographical
factors influencing Brown oak’s growth and survival were developed to define its current ecological niche. Analytical Hierarchical
Process (AHP) method involving Saaty’s pair-wise comparison was performed to rank the explanatory powers of each compared
variable. Variables were suitably weighted using fuzzy factor standardization scheme to reflect their relative importance
in defining species niche. An optimum indicator was then chosen for deriving a site suitability map of brown oak. This study
establishes the role of aspect in the current distribution of the species along with known influence of altitude. Future niches
of oak has been tracked in the projected climate change scenario of +1°C and +2°C rise in temperature and 20 mm in precipitation.
The results show that on predicted +1°C and +2°C increase in temperature, present habitat of brown oak distribution may be
reduced by 40 per cent and 76 per cent respectively. 相似文献
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Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R2 = 0.74, %RMSE = 23.1%), followed by stand age (R2 = 0.62, %RMSE = 35.6%), and stem density (R2 = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas. 相似文献