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作者： Vernon, Zachary Isaac Texas A&M University

Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This

关键词： Bottomland hardwood forests object-based classification rule-based classification segmentation Book Thesis

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Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

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REMOTE SENSING OF ENVIRONMENT 2008年第3期112卷 1051-1061页

作者： Stow, Douglas Hamada, Yuki Coulter, Lloyd Anguelova, ZlatIna San Diego State Univ Dept Geog San Diego CA 92182 USA

An object-based approach to generating shrub cover change maps of potential use for monitoring shrubland habitat reserves was developed and tested. A high fidelity, bi-temporal airborne image data set was generated through frame-based image acquisition, precise image-to-image registration, radiometric normalization, and selection of near-anniversary image acquisition dates with similar precipitation conditions prior to both image acquisitions. Image segmentation and classification processes were applied to the bi-temporal layer stack of very high spatial resolution visible and near infrared (V/NIR) image data, such that shrub change objects were delineated and identified directly. Image segments derived from the bi-temporal V/NIR image data set having I in spatial resolution delineated most shrub change features in a qualitatively realistic manner. A Standard Nearest Neighbor classifier with segment mean and standard deviation measures of Red, NIR, and normalized difference vegetation index (NDVI) image features yielded the shrub change map that agreed more closely with reference data than the classifier based on fuzzy membership functions. The overall accuracy and kappa statistics for the optimal shrub change map were 0.83 and 0.64, respectively, with the predominant error being associated with "over-classification" of no-change objects as some type of shrub change. No statistical difference in accuracies of three- and five-class maps was found, suggesting that changes in true shrubs and sub-shrubs within coastal sage scrub vegetation communities can be differentiated reliably. A net 5% loss of shrub cover was determined for the 1998-2005 period from the shrub change map of the study area. The greatest decrease and net loss of shrub cover occurred within the urban edge zone and within flat-lying areas. Patterns of shrub loss appear to be more related to anthropogenic disturbance than effects of the severe seven-year drought that occurred between image acquisition d

关键词： vegetation change object-based classification linage segmentation airborne multi-spectral camera

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An assessment of Hawaiian dry forest condition with fine resolution remote sensing

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FOREST ECOLOGY AND MANAGEMENT 2008年第7期255卷 2524-2532页

作者： Morales, Rodolfo Martinez Miura, Tomoaki Idol, Travis Univ Hawaii Dept Nat Resources & Environm Management Honolulu HI 96822 USA

Remote sensing offers the potential to spatially map forest cover quickly and reliably for inventory purposes. We developed a new image analysis approach using an integrated methodology of "object-based" image classification techniques and field-based measurements to quantify forest cover in a degraded dry forest ecosystem on the leeward side of the Island of Hawaii. This new approach explicitly recognized the transitional areas between tree crowns and tree shades (tree shadows) as a unique class and fully utilized them for the quantification of canopy cover. object-oriented classification of Ikonos-2 satellite images allowed delineation of tree shades and crowns and the transitional areas between them from objects with similar reflectance and size that were surrounding the trees. These included patches of fountain (Pennisetum setaceum) and kikuyu (Pennisetum clandestinum) grass, lava outcrops and lava-grass mixtures. Crown-shade transitions were clearly differentiated in spite of their wide range of spectral values and reflectance similarities with areas of lava-grass mixture. Segments representing tree shades and dark lava outcrops were also classified into their respective classes even if they were contiguous. The image estimates of canopy cover using the tree shade plus transition classes were linearly related with field estimates of canopy cover (R-2 = 0.86 and slope = 0.976). based on this relationship, dry forest cover throughout the 2627-ha area was estimated at 7.7 +/- 1.9%. An immediate application of this new approach is to select and delineate areas with higher canopy cover in order to concentrate ecological restoration and conservation efforts. Published by Elsevier B.V.

关键词： dry forest Hawaii transitional area object-based classification Ikonos-2

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Assessment of the visual impact of marble quarry expansion (1984-2000) on the landscape of Thasos island, NE Greece

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LANDSCAPE AND URBAN PLANNING 2008年第1期86卷 92-102页

作者： Mouffis, George D. Gitas, Loannis Z. Iliadou, Stavroula Mitri, George H. Aristotle Univ Thessaloniki Fac Forestry & Nat Environm Lab Forest Management & Remote Sensing GR-54124 Thessaloniki Greece

Marble extraction is an economically important and widespread activity in Greece that exists since historical times. The extraction takes place by open-pit quarries in hill slopes. The original landform. is permanently altered and the original vegetation cover is destroyed. The visual impact of the quarries extends over larger areas as noticeable scars of high colour contrast, reducing the aesthetic appeal of the landscape and deteriorating the scenic quality of areas where tourism often is a major constituent of income. The aim of this work was to assess the ecological, landscape and visual impacts of marble quarries on the island of Thasos, NE Greece by employing remote sensing, geographical analysis and landscape metrics. Change detection and object-based classification were used to monitor the landscape dynamics (1984-2000) of marble quarries. A new index was defined to estimate the 'cumulative visibility load' on the landscape. An expansion trend of marble quarries was identified by the creation of new and the enlargement of existing quarries in the NE part of the island on areas previously covered by semi-natural pine forest. The visual impact increased by a factor of 2.52 and although the area of quarries was less than 1% of the landscape in 2000, it affected 13.5% of the island, including areas such as the island's capital and nearby coasts. (c) 2008 Elsevier B.V. All rights reserved.

关键词： landscape change detection remote sensing cumulative visibility load index viewshed object-based classification

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A fuzzy rule base system for object-based feature extraction and classification

A fuzzy rule base system for object-based feature extraction...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XVI

作者： Jin, Xiaoying Paswaters, Scott ITT Visual Informat Solut 4990 Pearl East Circle Boulder CO 80301 USA

ISBN: (纸本)9780819466891

In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "113". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on Stype membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.

关键词： fuzzy logic rule base feature extraction object-based classification very high resolution imagery degree of uzziness membership functions

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Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection

Fusion of RADARSAT fine-beam SAR and QuickBird data for land...

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15th International Conference on Geoinformatics

作者： Ban, Yifang Hu, Hongtao Rangel, Irene Royal Inst Technol Div Geoinformat Dept Urban Planning & Environm SE-10044 Stockholm Sweden

ISBN: (纸本)9780819469120

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

关键词： QuickBird RADARSAT SAR land-cover mapping change detection texture analysis artificial neural network object-based classification data fusion

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Image segmentation for the purpose of object-based classification

Image segmentation for the purpose of object-based classific...

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23rd International Geoscience and Remote Sensing Symposium (IGARSS 2003)

作者： Darwish, A Leukert, K Reinhardt, W Univ Bundeswehr Munich GIS Lab D-85577 Neubiberg Germany

The main aim of this research is to find optimum segmentation parameters for extracting different land cover classes. A relatively new segmentation approach, multiresolution segmentation, is being examined using two d... 详细信息

ISBN: (纸本)0780379292

关键词： multiresolution segmentation object-based classification land-cover classification

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Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region

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JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 2006年第G4期111卷

作者： Arroyo, Lara A. Healey, Sean P. Cohen, Warren B. Cocero, David Manzanera, Jose A. IMIDRA E-28800 Alcala De Henares Spain Univ Nacl Educ Distancia Dept Geog E-28040 Madrid Spain US Forest Serv Pacific NW Res Stn Corvallis OR 97331 USA US Forest Serv Rocky Mt Res Stn Ogden UT 84401 USA Univ Politecn Madrid ETSI Montes E-28040 Madrid Spain

[1] Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high-resolution imagery, fuel-related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object-oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel-based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.

关键词： fuel mapping object-based classification Prometheus system high-resolution data

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Concepts of Image Fusion in Remote Sensing Applications

Concepts of Image Fusion in Remote Sensing Applications

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Vijayaraj, Veeraraghavan Younan, Nicolas H. O'Hara, Charles G. Oak Ridge Natl Lab Oak Ridge TN 37831 USA Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS USA Mississippi State Univ GeoResources Inst Mississippi State MS USA

ISBN: (纸本)9780780395091

Earth observation satellites provide data covering different parts of the electromagnetic spectrum at different spatial, spectral, and temporal resolutions. To utilize these different types of image data effectively, a number of image fusion techniques have been developed. Image fusion is defined as "the set of methods, tools, and means of using data from two or more different images to improve the quality of the information" [1]. The fused image has rich information that will improve the performance of image analysis algorithms. This increase in quality of the information leads to better processing (ex: classification, segmentation) accuracies compared to using the information from one type of data alone. In this paper pixel level and feature level image fusion are applied for the classification of a co-registered QuickBird multispectral and panchromatic images.

关键词： image fusion object-based classification

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Spatial indicators for nature conservation from European to local scale

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ECOLOGICAL INDICATORS 2005年第4期5卷 322-338页

作者： Bock, M Rossner, G Wissen, M Remm, K Langanke, T Lang, S Klug, H Blaschke, T Vrscaj, B DLR German Aerosp Ctr DFD Dept Environm & Geoinformat D-51147 Cologne Germany Univ Tartu Inst Geog EE-51014 Tartu Estonia Salzburg Univ Z GIS Ctr Geoinformat Salzburg Austria Univ Ljubljana Biotech Fac CSES SI-1000 Ljubljana Slovenia

The paper presents an overview of the objectives and exemplary results of the FP 5 project "Spatial Indicators for European Nature Conservation" (SPIN). The SPIN project is focused on the development and testing of advanced classification methods and spatial indicators based on multisensor satellite data and GIS to accomplish monitoring and management tasks in the context of Natura 2000 and nature conservation. A representative selection of eight regional test areas covers a pan-European network and allows comparative investigations to provide accepted recommendations for regional and European nature conservation. The selected results of four case studies are presented and discussed. The range of work covers the production of regional and local habitat maps by object-oriented classification, a case-based reasoning method for change detection as a management support tool for planning and regulating local land use, the selection and application of structural indicators for the monitoring of Natura 2000 habitats and the downscaling and disaggregation of soil information. Results and the further implementation of presented methods are discussed in the conclusions. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： baseline geometry case-based reasoning habitat mapping landscape metrics land cover change object-based classification remote sensing soil map modelling

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