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Estimating Class Dynamics for Fuzzy Markov Chain-based Multitemporal Cascade Classification

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第4期10卷 908-912页

作者： Feitosa, Raul Q. Mota, Guilherme L. A. Alves, Andrei O. Costa, Gilson A. O. P. Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22451900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp Syst Engn BR-20550900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Inst Math & Stat BR-20550900 Rio De Janeiro Brazil Univ Estado Rio De Janeiro BR-20550900 Rio De Janeiro Brazil

The key component of a fuzzy Markov chain (FMC)-based multitemporal cascade classifier is the transition possibility matrix (TPM). Such matrix represents the temporal dynamics of the land use/land cover classes in the target site in a given time period. The choice of the TPM estimation approach is a crucial step in the design of FMC-based classifiers, as it strongly influences the final classification accuracy. Moreover, the task of collecting training data may involve considerable effort, since the number of transitions to be represented grows with the square of the number of classes in the application. In spite of their relevance, the TPM estimation has only been addressed superficially in previous publications about FCM-based classification methods. In this letter, we concern some of those aspects and investigate alternative ways of the TPM estimation. Experimental analysis on a multitemporal data set covering a 20-year period sheds light on the conditions under which those alternative estimation approaches may be used, as well as on their impact over the classification performance.

关键词： Fuzzy Markov chains (FMCs) multitemporal cascade classification object-based image analysis

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object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using a Landsat 5 TM Dry-season time series

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JOURNAL OF SPATIAL SCIENCE 2013年第1期58卷 53-77页

作者： Boyden, James Joyce, Karen E. Boggs, Guy Wurm, Penny Charles Darwin Univ Sch Environm & Life Sci Res Inst Environm & Livelihoods Darwin NT 0909 Australia Wheatbelt Nat Resource Management Northam WA 6401 Australia

This paper evaluates the use of multi-temporal Landsat 5 TM for object-based classification of native wetland vegetation and the perennial aquatic weed para grass within Kakadu National Park, Northern Territory, Australia. Using identical training data and segmentation, a nearest-neighbour classification produced from a four-image (dry season) time-series was compared with four 'single-date' classifications produced from the individual images of the same series. A 15-class vegetation map generated from the multi-date classification produced an overall accuracy of 82 percent (kappa = 0.80). This was an average increase in accuracy of 25 percent (kappa = 0.28) compared to single-date classifications. The multi-date image composite also improved segmentation quality and spectral separability of vegetation classes. Reliable maps of wetland vegetation, potentially useful for strategic conservation, can be produced by integrated, object-based, analysis of multi-temporal Landsat.

关键词： object-based image analysis wetland vegetation mapping multi-temporal Landsat weeds

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Identifying permafrost slope disturbance using multi-temporal optical satellite images and change detection techniques

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COLD REGIONS SCIENCE AND TECHNOLOGY 2013年 88卷 37-49页

作者： Rudy, Ashley C. A. Lamoureux, Scott F. Treitz, Paul Collingwood, Adam Queens Univ Dept Geog Kingston ON K7L 3N6 Canada

Active layer detachments (ALDs) are a common form of permafrost slope disturbance that pose a serious risk for infrastructure and can impact environmental and ecological stability in Arctic regions. Effective recognition and detection of slope disturbances are critical for future hazard analysis. Historically, this has primarily been done through manual image interpretation and field mapping, both of which are cost-intensive. Semi-automatic detection techniques have been successfully applied in more temperate regions to identify slope failures, however, little work has been done to map permafrost disturbances. In this paper we present a methodology to detect and map ALDs using multi-temporal IKONOS satellite imagery in combination with vegetation index differencing and object-based image analysis, to semiautomatically identify landscape change associated with ALDs. A normalized difference vegetation index (NDVI) was computed for each of the two dates (2004 and 2010) and then subtracted generating a NDVI difference surface. Using areas where vegetation was removed as a proxy for the presence of ALDs, a multi-resolution segmentation algorithm was used to threshold the NDVI difference map into objects to demarcate regions of similarity (i.e., potential ALDs). To discriminate between disturbed and undisturbed zones a NDVI threshold was applied removing false positives. The thresholded image was then verified with a disturbance inventory collected from the field. These methods were successfully applied to the study area achieving 43% detection accuracy when identifying all ALDs. Morphometric characteristics were used to separate ALDs into two forms, elongate and compact, with accuracies assessed for each. Elongate ALDs, with a detection accuracy of 67%, are typically more destructive, moving substantially more material downslope over longer distances and posing a greater risk for infrastructure. By contrast, compact ALDs are associated with minimal downslope sliding dist

关键词： Permafrost Slope disturbance Active layer detachments Change detection Remote sensing object-based image analysis

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Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling

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ECOLOGICAL MODELLING 2013年 254卷 22-32页

作者： Zohmann, Margit Pennerstorfer, Josef Nopp-Mayr, Ursula Univ Nat Resources & Life Sci Dept Integrat Biol & Biodivers Res Inst Wildlife Biol & Game Management A-1180 Vienna Austria Univ Nat Resources & Life Sci Dept Forest & Soil Sci Inst Forest Entomol Forest Pathol & Forest Protec A-1190 Vienna Austria

The maintenance and restoration of high-quality habitats of wildlife species in alpine ecosystems are key issues in conservation biology. The rock ptarmigan (Lagopus muta helvetica), which prefers open habitats above the treeline, is listed in Annex II of the EU Bird Directive. Large areas identified as potentially important for conservation and restricted financial resources for the implementation of conservation activities necessitate the development of tools supporting habitat monitoring and management. We developed a knowledge-based Habitat Suitability Index (HSI) model for rock ptarmigan and combined it with results of an object-based image analysis of very high resolution (VHR) satellite images (IKONOS) to create a rock ptarmigan habitat suitability map in the eastern Alps of Austria. The mechanistic habitat model contained 9 habitat variables, decisive for summer habitat use of rock ptarmigan (i.e., slope, patchiness, and land cover classes for: rocks, rocks intermixed with vegetation, scree, dwarf shrubs, dwarf pine, alpine/subnival grassland, and forest). We used Definiens Professional 5.0 (c) software for the object-based image analyses, applying multi-resolution segmentation methods. We generated a classification hierarchy comprising the same variables used in the HSI model, each augmented by areas "in shadow", and took into account clouds, water bodies and human infrastructures. We assessed classification accuracies, applying an Error Matrix based on ITA Mask. We reached an overall classification accuracy of 0.75 and a kappa statistic value of 0.70, the latter indicating good to very good agreement. The classification results indicated that the object-oriented image classification approach using VHR data was appropriately used to create an adequate thematic map for further habitat modelling. We calculated the habitat suitability maps using MapModels. Model output was validated with ptarmigan presence-absence data, using signs (droppings) as indicators of

关键词： object-based image analysis image classification IKONOS Habitat suitability modelling Alpine rock ptarmigan Habitat monitoring tool

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Remote sensing image data and automated analysis to describe marine bird distributions and abundances

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ECOLOGICAL INFORMATICS 2013年 14卷 2-8页

作者： Groom, Geoff Stjernholm, Michael Nielsen, Rasmus Due Fleetwood, Andrew Petersen, Ib Krag Aarhus Univ Dept Biosci DK-8410 Ronde Denmark Blom UK Astrolabe Cheddar BS27 3EB Somerset England

Advances in image data capture with airborne digital cameras and in object-based image analysis (OBIA) have provided a basis for new arenas of applied remote sensing, one of which is the direct counting and mapping of animal individuals. The derived data represents significant inputs to population size estimation and study of animal-habitat interactions. One growing application is bird distributions and abundances in relation to EIAs for marine installations such as offshore wind farms and sea crossing transportation infrastructure. The ornithological surveys in relation to the EIA process concerning the Burbo Bank Extension and Walney Extension offshore wind farms that are being undertaken jointly by Aarhus University and BLOM-UK are an example of the former, with monthly surveys of two offshore areas (total area ca. 670 km(2)) in the Irish Sea. image coverage comprises acquisition of 4 cm and 3 cm image data. object based image analysis methods are used to localise marine birds. This paper describes the data processing and analysis procedures used. It presents examples of representations in these image data of five bird species. Examples of non-bird 'background' image patterns that can impact the bird localisation possibilities are presented along with developed countering-strategies. Results of initial assessments of levels of bird under-mapping by the OBIA method are given;the reported overall success level from 18 image frames with respect to under-mapping is over 92%. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Marine birds Environmental impact assessment Offshore wind farms Remote sensing object-based image analysis Aerial survey

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Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random Forests

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2013年第100期23卷 81-94页

作者： Wilschut, L. I. Addink, E. A. Heesterbeek, J. A. P. Dubyanskiy, V. M. Davis, S. A. Laudisoit, A. Begon, M. Burdelov, L. A. Atshabar, B. B. de Jong, S. M. Univ Utrecht Dept Phys Geog NL-3508 TC Utrecht Netherlands Univ Utrecht Fac Vet Med NL-3584 CL Utrecht Netherlands Stavropol Plague Control Res Inst Stavropol 355035 Russia M Aikimbayevs Kazakh Sci Ctr Quarantine & Zoonot Antiplague Inst Alma Ata 050074 Kazakhstan RMIT Univ Sch Math & Geospatial Sci Melbourne Vic 3000 Australia Univ Liverpool Inst Integrat Biol Liverpool L69 3BX Merseyside England Univ Antwerp Dept Biol B-2020 Antwerp Belgium

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape. The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation. In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit. Burrows were successfully classified in all landscape units. In the 'steppe on floodplain' areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the 'floodplain' areas with a more heterogeneous vegetation cover, classification worked least well;there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively. In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data

关键词： object-based image analysis Stratification Landscape epidemiology Vector-borne disease Zoonosis Yersinia pestis Great gerbil Desert environment

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Landscape resource mapping for wildlife research using very high resolution satellite imagery

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METHODS IN ECOLOGY AND EVOLUTION 2013年第10期4卷 982-992页

作者： Recio, Mariano R. Mathieu, Renaud Hall, G. Brent Moore, Antoni B. Seddon, Philip J. Univ Otago Sch Surveying Dunedin New Zealand Univ Otago Dept Zool Dunedin New Zealand CSIR Nat Resource Environm Earth Observat Res Grp Pretoria South Africa

Quantifying wildlife-habitat relationships through resource selection analysis (RSA) has traditionally relied on landscape variables extracted at medium-to-coarse scales and general-purpose digital maps. However, RSA at fine scales, facilitated by accurate positional data obtained using GPS-tags, requires improved measures of habitat resources. The combination of cutting-edge remote sensing technology, such as very high spatial resolution (VHSR) satellite imagery and object-based image analysis (OBIA), can provide landscape maps that are suitable for the extraction of detailed variables at fine scale. We used Quickbird satellite imagery and OBIA to produce a map using the multispectral bands (MULT), and explored the usefulness of the technique for resource identification by combining the panchromatic (highest spatial resolution) and multispectral (spectral information) bands (PAN:MULT) to produce a second map. Each of the mapping methods was used in a heterogeneous braided-river environment in New Zealand to: (1) classify and delimit ground features using an object-based accuracy assessment approach;(2) detect ground features of different sizes;(3) extract independent landscape variables at fine scale (within buffers between 20 and 30m) for separate RSA for introduced hedgehogs (Erinaceus europaeus) and feral cats (Felis catus). Per-pixel accuracy assessment produced overall accuracies of 82% (PAN:MULT) and 79% (MULT). The per-object assessment using shrubs as the testing class yielded further information on classification and delimitation of object accuracy, with accuracies of 80% for shrub patches 30m(2) in MULT and 5m(2) in PAN:MULT. The inclusion of the panchromatic band noticeably improved the identification and delimitation of cover. However, RSA using each of the maps did not yield differences in the best models for cats or hedgehogs. VHSR imagery and OBIA provide a valuable method to identify smaller ground features and thus to produce more detailed landscap

关键词： fine scale object-based image analysis Quickbird resource selection very high spatial resolution wildlife research

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Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

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LIMNOLOGICA 2013年第5期43卷 388-398页

作者： Visser, Fleur Wallis, Caroline Sinnott, Anne M. Univ Worcester Inst Sci & Environm Henwick Grove WR2 6AJ Worcester England

Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations. This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries-Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column. Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from

关键词： Submerged aquatic vegetation Shallow rivers Optical remote sensing object-based image analysis Very high resolution image data

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Integrating field survey and orthophoto information to monitor coastal habitats - A pilot study to develop methods and resolve key issues

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ECOLOGICAL INFORMATICS 2013年 14卷 48-52页

作者： Juel, Anders Ejrnaes, Rasmus Fredshavn, Jesper Groom, Geoff Aarhus Univ Dept Biosci DK-8410 Kaloe Roende Denmark Aarhus Univ Natl Ctr Environm & Energy DK-8600 Silkeborg Denmark

Implementation of mapping and monitoring is necessary to supply sufficient information to guide an effective management of species, habitats and landscapes. Coastal ecosystems can be difficult to monitor effectively in the field due to spatially discontinuous and unpredictable processes such as encroachment, erosion and succession, while coverage of large extents is very expensive. Remote sensing-based monitoring provides an alternative, but satellite image data is often too expensive or too coarse in spatial resolution to detect fine-scale habitat structures. Using Danish coastal habitats as a case, a method is presented for monitoring habitat types and fine-scale structures, based on integration of field-acquired habitat characteristics with the habitat information interpreted from sub-meter RGB/NIR aerial imagery and digital elevation model data. Initial pilot studies show good correspondence between field-observed structure elements and structures delineated through object-based image analysis, while initial classifications results suggest possibilities of discriminating between different types of shrubs, herb communities and non-vegetated structures. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Habitat mapping Monitoring Coastal habitat Habitat structure Orthophoto imagery object-based image analysis

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DEM-based Slope Unit Derivation

DEM-based Slope Unit Derivation

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21st International Conference on Geoinformatics (Geoinformatics)

作者： Xiao, Chenchao Tian, Yuan Li, Ting Gao, Zhifang Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr La Beijing Peoples R China

ISBN: (纸本)9781467362269

This paper focuses on slope unit derivation from DEM by object-based image analysis. A landform classification system consisting of ridge, valley, slope unit, and flat terrain classes is firstly constructed considering the detailed characteristics of slope unit. based on the classification system, a procedure for slope unit derivation from DEM, which takes into consideration the topographic factors including slope gradient, slope aspect and curvature, is designed within multi-scale OBIA framework. The method to determine the optimum segmentation scale by COLV is also introduced. A case study of Shenzhen is carried out to verify the feasibility and accuracy of the designed procedure. The results of the case study shows that when the scale parameter is 20, the derived slope units remarkably denote the detailed landform structure and the bounds of the derived units are largely consistent to the concave and convex terrain bounds.

关键词： slope unit DEM object-based image analysis GIS

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