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Land Use classification from Lidar Data and Ortho-Images in a Rural Area

PHOTOGRAMMETRIC RECORD 2012年第140期27卷 401-422页

作者： Bujan, Sandra Gonzalez-Ferreiro, Eduardo Reyes-Bueno, Fabian Barreiro-Fernandez, Laura Crecente, Rafael Miranda, David Univ Santiago de Compostela Santiago De Compostela Spain

Obtaining information on the distribution of rural landscape types is an active research topic within Spanish rural studies. This paper presents a new hierarchical object-based classification method for the automatic detection of various land use classes in a rural area, combining lidar data and aerial images. In view of the upcoming availability of low-density lidar data (0.5 pulses/m2) for most of the territory of Spain, this paper assesses the feasibility and accuracy of the proposed method for various lidar data densities. Such an assessment was conducted using two approaches: firstly, based on the final classification, which produced an overall accuracy over 96% and a kappa index above 0.95 for the combinations of the aerial image and lidar data-sets with four different densities;and secondly, based solely on the areas classified as buildings. In the second approach, the accuracy of the classification for building detection at pixel and object level was assessed. The object-oriented classification of buildings produced an index of correctness of over 99% and an index of completeness of about 95%. The results reveal a high agreement between classification and ground truth data.

关键词： assessment accuracy data fusion detection of buildings lidar data object-based classification ortho-images

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Mapping of Freshwater Lake Wetlands Using object-Relations and Rule-based Inference

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Chinese Geographical Science 2012年第4期22卷 462-471页

作者： RUAN Renzong Susan USTIN State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Nanjing 210098 China Department of Land Air and Water Resources University of California Davis CA 95616 the United States)

Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.

关键词： rule-based inferring object-based classification freshwater lake wetland relation feature Hongze Lake

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Effects of patch characteristics and within patch heterogeneity on the accuracy of urban land cover estimates from visual interpretation

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LANDSCAPE ECOLOGY 2012年第9期27卷 1291-1305页

作者： Zhou, Weiqi Cadenasso, M. L. Chinese Acad Sci Ecoenvironm Sci Res Ctr State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Calif Davis Dept Plant Sci Davis CA 95616 USA

Landscape ecology links landscape pattern to ecological function. Achieving this goal hinges on accurate depiction and quantification of pattern, which is frequently done by visually interpreting remotely sensed imagery. Therefore, understanding both the accuracy of that interpretation and what influences its accuracy is crucial. In addition, imagery is pixel-based but landscape pattern exists, more realistically, as irregularly shaped patches. Patches may contain only one feature type such as trees, but, in some landscapes, patches may contain several different types of features such as trees and buildings. Using a patch-based approach, this paper investigates two types of variables-whole-patch and within-patch-that are hypothesized to influence the accuracy of visually estimating the cover of features within patches. A highly accurate reference map, obtained from object-based classification, was used to evaluate the accuracy of visual estimates of cover within patches. The effects of the variables on the accuracy of these estimates were tested using logistic regressions and multimodel inferential procedures. Though all variables significantly affected the accuracy, the within-patch configuration of features is the most significant factor. In general, errors of cover estimates are more likely to occur when patches are smaller or have more complex shapes, and features within a patch are (1) more diverse;(2) more fragmented;(3) more complex in shape;and (4) physically less connected. These results provide an important first step towards a quantitative, spatially explicit model for predicting error of cover estimates and determining under what circumstances estimation error is most likely to occur.

关键词： Visual interpretation object-based classification HERCULES Accuracy Land cover composition and configuration Pattern analysis Spatial heterogeneity Urban systems

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Forty Years of Change in the Bulrush Marshes of the St. Lawrence Estuary and The Impact of the Greater Snow Goose

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WETLANDS 2012年第6期32卷 1175-1188页

作者： Allard, Matthieu Fournier, Richard A. Grenier, Marcelle Lefebvre, Josee Giroux, Jean-Francois Univ Sherbrooke Dept Appl Geomat Sherbrooke PQ J1K 2R1 Canada Environm Canada Canadian Wildlife Serv Gatineau PQ K1A 0H3 Canada Environm Canada Canadian Wildlife Serv Quebec City PQ G1J 0C3 Canada Univ Quebec Dept Sci Biol Montreal PQ H3C 3P8 Canada

During its spring and fall migrations, the Greater Snow Goose (Chen caerulescens atlanticus) stages in the marshes along the St. Lawrence Estuary in southern Quebec, where it feeds on three-square bulrush (Schoenoplectus americanus) rhizomes. The goose population has grown from 70 000 birds to around one million over the last 40 years, thus increasing pressure on these tidal marshes. To determine the impact of geese on the ecological integrity of the marshes over this period, we used IKONOS satellite imagery and aerial photographs to classify vegetation types. We estimated changes in bulrush cover using the eCognition image analysis software (Trimble). We examined the spectral, textural, and contextual characteristics of the identified classes. The proportion of bulrush cover has declined significantly in the lower marsh since around 1980, and bulrush has been gradually replaced by wild rice (Zizania aquatica var. brevis). We also documented the erosion between the lower and upper marshes along most of the shoreline.

关键词： Remote sensing Bulrush Marsh Greater Snow Goose object-based classification Erosion

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Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期14卷 103-111页

作者： Mohler, Rhett L. Goodin, Douglas G. Kansas State Univ Dept Geog Manhattan KS 66506 USA

Prescribed fire is crucial to the ecology and maintenance of tallgrass prairie, and its application affects a variety of human and natural systems. Consequently, maps showing the location and extent of these fires are critical to managing tallgrass prairies in a manner that balances the needs of all stakeholders. Satellite-based optical remote sensing can provide the necessary input for this mapping, but it requires the development mapping methods that are specific to tallgrass prairie. In this research, we devise and test a suitable mapping method by comparing the efficacy of seven combinations of bands and indices from the MODIS sensor using both pixel and object-based classification methods. Due to the relatively small size of many prescribed fires in tallgrass prairie, scenarios based on the 250 m spatial resolution red and NIR bands outperformed those based on the coarser 500 m spatial resolution bands, and a combination of both red and NIR performed better than each 250 m band individually. object-based classification offered no improvement over pixel-based classification, and performed poorer in some cases. Our results suggest that mapping burned areas in tallgrass prairie should be done at a minimum of 250 m spatial resolution, should used a pixel-based classification technique, and should use a combination of red and NIR. (C) 2011 Published by Elsevier B.V.

关键词： Burned area mapping MODIS object-based classification Tallgrass prairie Grasslands

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object-based classification of ikonos imagery for mapping large-scale vegetation communities in urban areas

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SENSORS 2007年第11期7卷 2860-2880页

作者： Mathieu, Renaud Aryal, Jagannath Chong, Albert K. Univ Otago Sch Surveying Spatial Ecol Res Facil Dunedin New Zealand CSIR Nat Resources & Environm Earth Res Grp ZA-0001 Pretoria South Africa Lincoln Univ Ctr Adv Computat Solut Lincoln New Zealand

Effective assessment of biodiversity in cities requires detailed vegetation maps. To date, most remote sensing of urban vegetation has focused on thematically coarse land cover products. Detailed habitat maps are created by manual interpretation of aerial photographs, but this is time consuming and costly at large scale. To address this issue, we tested the effectiveness of object-based classifications that use automated image segmentation to extract meaningful ground features from imagery. We applied these techniques to very high resolution multispectral Ikonos images to produce vegetation community maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and a multi-scale segmentation algorithm used to produce a hierarchical network of image objects. The upper level included four coarse strata: industrial/commercial (commercial buildings), residential (houses and backyard private gardens), vegetation (vegetation patches larger than 0.8/1ha), and water. We focused on the vegetation stratum that was segmented at more detailed level to extract and classify fifteen classes of vegetation communities. The first classification yielded a moderate overall classification accuracy (64%, k = 0.52), which led us to consider a simplified classification with ten vegetation classes. The overall classification accuracy from the simplified classification was 77% with a k value close to the excellent range (k = 0.74). These results compared favourably with similar studies in other environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spec

关键词： object-based classification remote sensing cities New Zealand biodiversity habitat

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Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs

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APPLIED VEGETATION SCIENCE 2012年第4期15卷 536-547页

作者： Hantson, Wouter Kooistra, Lammert Slim, Pieter A. Alterra Ctr Ecosyst Studies Wageningen Netherlands Wageningen Univ Lab Geo Informat Sci & Remote Sensing NL-6708 PB Wageningen Netherlands

Questions Does remote sensing improve classification of invasive woody species in dunes, useful for shrub management? Does additional height information and an object-based classifier increase woody species classification accuracy? Location The dunes of Vlieland, one of the Wadden Sea Islands, the Netherlands. Methods Extensive monitoring using optical remote sensing and LIDAR deliver large amounts of high-quality data to observe and manage coastal dunes as a defence against the sea in the Netherlands. Using these additional data could increase the accuracy of vegetation mapping and monitoring in coastal areas. In this study, a remote sensing approach has been developed to deliver detailed and standardized maps of (invasive) woody species in the dunes of Vlieland using multispectral aerial photographs and vegetation height derived from LIDAR. Three classification methods were used: maximum likelihood (ML) classification using aerial photographs, ML classification combined with vegetation heights derived from LIDAR (ML+) and object-based (OB) classification. Results The use of vegetation height from the LIDAR data increased the overall classification accuracy from 39% to 50%, but particularly improved classification of the taller woody species. The object-based classification increased the overall accuracy of the ML+ from 50% to 60%. The object-based results are comparable to human visual analysis while offering automated analysis. Conclusions Overall, the object-based classification delivers detailed maps of the woody species that are useful for management and evaluation of alien and invasive species in dune ecosystems.

关键词： Vegetation mapping alien species digital elevation model pixel-based classification object-based classification R osa rugosa P runus serotina H ippophae rhamnoides grey dunes

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Measuring detailed urban vegetation with multisource high-resolution remote sensing imagery for environmental design and planning

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ENVIRONMENT AND PLANNING B-PLANNING & DESIGN 2012年第3期39卷 566-585页

作者： Li, Weiman Radke, John Liu, Desheng Gong, Peng Calif State Polytech Univ Pomona Dept Landscape Architecture Pomona CA 91768 USA Univ Calif Berkeley Dept Landscape Architecture & Environm Planning Berkeley CA 94720 USA Ohio State Univ Dept Geog Columbus OH 43210 USA Ohio State Univ Dept Stat Columbus OH 43210 USA Tsinghua Univ Ctr Earth Syst Sci Beijing 100084 Peoples R China

The availability of high-resolution remote sensing imagery brings both opportunity and challenge to environmental designers and planners in obtaining high-quality landscape information for better design and planning decision making. To meet the challenge, in this paper we introduce a comprehensive approach to measuring urban vegetation data detailed to single patches of trees or shrubs and single patches of lawn or grass with multisource remote sensing imageries. Methodologically, the approach integrates advanced geospatial technologies to achieve the research objective. First, an automatic registration algorithm is applied to align an unorthorectified QuickBird satellite multispectral imagery to a high-resolution United States Geographical Survey orthoimage. Next, an image segmentation process extracts landscape objects from such multisource data for further object-based image classification. Third, the approach takes advantage of the strong power of a group of prioritized spectral, geometric, topological, and thematic image object features to produce satisfactory classification results. The approach was tested in the Oakland Metropolitan Area in California, USA and was assessed with both groundtruthing and imagetruthing data. The paper concludes with a discussion on the potential applications of both the approach and the generated data in environmental design and planning.

关键词： detailed vegetation setting imagery integration object-based classification remote sensing application in environmental design

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Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

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APPLIED GEOMATICS 2012年第4期4卷 245-255页

作者： Hermosilla, T. Diaz-Manso, J. M. Ruiz, L. A. Recio, J. A. Fernandez-Sarria, A. Ferradans-Nogueira, P. Univ Politecn Valencia GeoEnvironm Cartog & Remote Sensing Res Grp Camino Vera S-N E-46022 Valencia Spain Sociedade Desenvolvemento Comarcal Galicia Santiago De Compostela Spain

The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.

关键词： object-based classification Change detection High-resolution imagery Mapping Agriculture

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An object-based method for mapping ephemeral river areas from WorldView-2 satellite data

An object-based method for mapping ephemeral river areas fro...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV part of the 19th International Symposium on Remote Sensing

作者： Figorito, B. Tarantino, E. Balacco, G. Fratino, U. ISSIA CNR Via Amendola 122-D-O I-70126 Bari Italy Politecnico Bari I-70125 Bari Italy

ISBN: (纸本)9780819492715

Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.

关键词： ephemeral river areas object-based classification WorldView-2 imagery

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