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Ecosystem-level dynamics of soil-vegetation features, with implications for conserving a narrowly endemic reptile

LANDSCAPE ECOLOGY 2013年第7期28卷 1371-1385页

作者： Dzialak, Matthew R. Houchen, Dean J. Harju, Seth M. Mudd, James P. Wondzell, John J. Webb, Stephen L. Gould, Nicholas P. Hess, Jennifer E. Winstead, Jeffrey B. Hayden Wing Associates LLC Nat Resource Consultants Laramie WY 82070 USA Samuel Roberts Noble Fdn Inc Dept Comp Sci Ardmore OK 73401 USA

Narrow endemism presents challenges to species occurrence modeling particularly when the distribution of key local habitat features changes across space and time as a function of processes operating at larger scales. One need facing conservation in such settings is a better understanding of the biogeographic dynamics of the larger features that govern occurrence of critical local habitat. The Mescalero-Monahans shinnery sands region of western North America is a dynamic landscape where sand shinnery oak interacts with wind-driven sand to establish dune habitat. This ecosystem supports several narrowly endemic dune-dwelling species including the dunes sagebrush lizard. Using near-anniversary satellite and aerial imagery from 1986, 1998, and 2011, we integrated object-based image classification and statistical analysis to develop and validate a spatially explicit estimate of the sand shinnery oak ecosystem, including dynamics associated with its attrition and emergence, at high resolution throughout an 89,849-km(2) study area encompassing the range of the dunes sagebrush lizard. The spatial estimate of the distribution and extent of the sand shinnery oak soil-vegetation association validated reasonably well (overall accuracy = 0.79;sensitivity = 0.49;specificity = 0.91) and showed that the association declined 10.3 % in extent during the 25-year assessment window. The presence of sand shinnery oak, patch size, and patch isolation were dynamic across space and time;a regression model showed that smaller, isolated patches on the periphery of the system were more likely to be lost over time whereas larger, less isolated, and centrally distributed patches were more likely to persist or expand. This study details broadly applicable methods to accurately delineate landforms throughout large extents, and offers mapping tools specific to issues surrounding Mescalero-Monahans shinnery sands endemics that are readily amenable to testing, refinement, and application in efforts t

关键词： Dune-dwelling species Dunes sagebrush lizard Landscape context Narrow endemics object-based image classification Sand shinnery oak Soil-vegetation dynamics Sustainable landscape management

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Evaluation of Automatic Building Detection Approaches Combining High Resolution images and LiDAR Data

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REMOTE SENSING 2011年第6期3卷 1188-1210页

作者： Hermosilla, Txomin Ruiz, Luis A. Recio, Jorge A. Estornell, Javier Univ Politecn Valencia Geoenvironm Cartog & Remote Sensing Res Grp Valencia 46022 Spain

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

关键词： building detection LiDAR high spatial resolution imagery object-based image classification

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The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

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International Journal of Digital Earth 2009年第3期2卷 219-236页

作者： Y.Gao J.-F.Mas A.Navarrete Centro de Investigaciones en Ecosistemas-Universidad Nacional Autonoma de Mexico(UNAM) Antigua Carretera a Pa tzcuaro No.8701Col.Ex Hacienda de San Jose de la Huerta C.P.58190 MoreliaMichoacanMexico Centro de Investigaciones en Geografıa Ambiental-Universidad Nacional Autonoma de Mexico(UNAM) Antigua Carretera a Pa tzcuaro No.8701Col.Ex Hacienda de San Jose de la Huerta C.P.58190 MoreliaMichoacanMexico

This paper investigates the contribution of multi-temporal enhanced vegetation index(EVI)data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer(MODIS)*** object-oriented classification,similar pixels are firstly grouped together and then classified;the produced result does not suffer the speckled appearance and closer to human *** data are from the MODIS sensor aboard Terra spacecraft.69 EVI data(scenes)were collected during the period of three years(20012003)in a mountainous vegetated *** data sets were used to study the phenology of the land cover *** land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover *** experiments were carried out:one was only with single date MODIS multispectral data,and the other one including also the 69 EVI *** classes were distinguished:temperate forest,tropical dry forest,grassland,irrigated agriculture,rain-fed agriculture,orchards,lava flows and human *** two classifications were evaluated with independent verification data,and the results showed that with multitemporal EVI data,the classification accuracy was improved 5.2%.Evaluated by McNemar’s test,this improved was significant,with significance level p=0.01.

关键词： enhanced vegetation index(EVI) moderate resolution imaging spectral-radiometer(MODIS) phenology object-based image classification

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object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

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REMOTE SENSING OF ENVIRONMENT 2004年第3期92卷 409-413页

作者： Gitas, IZ Mitri, GH Ventura, G Aristotle Univ Thessaloniki Dept Forestry Lab Forest Management & Remote Sensing GR-54124 Thessaloniki Greece Mediterranean Agron Inst Chania Dept Environm Management GR-73100 Iraklion Greece Univ Alcala de Henares Dept Geog Madrid 28801 Spain

Due to the ability of the NOAA-AVHRR sensor to cover a wide area and its high temporal frequency, it is possible to quickly obtain a general overview of the prevailing situation over a large area of terrain and, more specifically, quickly assess the damage caused by a recent large forest fire by mapping the extent of the burned area. The aim of this work was to map a large forest fire that recently took place on the Spanish Mediterranean coast using innovative image classification techniques and low spatial resolution imagery. The methodology involved developing an object-based classification model using spectral as well as contextual object information. The burned area map resulting from the image classification was compared with the fire perimeter provided by the Catalan Environmental Department in terms of spatial overlap and size in order to determine to what extent they were compatible. Results of the comparison indicated a high degree (approximate to 90%) of spatial agreement. The total burned area of the classified image was found to be 6900 ha, compared to a fire perimeter of 6000 ha produced by the Catalan Environmental Department. It was concluded that, although the object-oriented classification approach was capable of affording very promising results when mapping a recent burn on the Spanish Mediterranean coast, the method in question required further assessment to ascertain its ability to map other burned areas in the Mediterranean. (C) 2004 Elsevier Inc. All rights reserved.

关键词： NOAA-AVHRR object-based image classification burned area mapping

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object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Object-based image classification for burned area mapping of...

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Workshop on Fire Prevention and Assessment

关键词： NOAA-AVHRR object-based image classification burned area mapping

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What is the value of a good map?: An example using high spatial resolution imagery to aid riparian restoration

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ECOSYSTEMS 2007年第5期10卷 688-702页

作者： Gergel, Sarah E. Stange, Yulia Coops, Nicholas C. Johansen, Kasper Kirby, Kathryn R. Univ British Columbia Forest Sci Ctr Ctr Appl Conservat Res Vancouver BC V6T 1Z4 Canada Univ British Columbia Forest Sci Ctr Dept Forestry Resource Management Vancouver BC V6T 1Z4 Canada Univ Queensland Sch Geog Planning & Architecture Ctr Remote Sensing & Informat Sci Brisbane Qld 4072 Australia

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes;whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping;however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.

关键词： riparian conservation and restoration object-based image classification high spatial resolution satellite imagery accuracy assessment map accuracy QuickBird confusion matrix cost error matrix coastal streams restoration

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Detecting urban vegetation from different images using an object-based approach in Bartin, Turkey

Detecting urban vegetation from different images using an ob...

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3rd International Conference on Recent Advances in Space Technologies

作者： Tunay, M. Marangoz, M. A. Karakis, S. Atesoglu, A. Zonguldak Karaelmas Univ Dept Forest Engn TR-74100 Bartin Turkey Zonguldak Karaelmas Univ Dept Geodesy & Photogrammetry Engn TR-74100 Bartin Turkey

ISBN: (纸本)9781424410569

Urban vegetation plays an important role for sustainable development policies, environmental conservation and urban planning process of a city. It is necessary to detect the amount of green areas and their distribution to form the ecosystem model of the urban environment. It is quite important to use satellite imagery having different ground sampling distance (GSD) in the economic and accurate detection of urban green areas. Especially object-based image analysis has been frequently used today for object extraction processes. In object-oriented image analysis, not only pixel gray values but also spectral and contextual data that help to distinguish the segments consisting of interrelated pixels on the image are used. For this reason, more positive results are obtained in comparison with pixel-based approaches. In this study, city center of Bartin, in which there is a rich amount of green areas and its vicinity was chosen as the test area. As the satellite image data, LANDSAT 7 ETM+ (28.5m GSD), SPOT 4 Level 2A (20m GSD) and IKONOS (1m GSD) were used. Test area was divided into segments involving lots of different classes on each image. Urban vegetation class was formed by determining the suitable functions for the objects which will be involved in the urban vegetation class. eCognition v4.06 software was used for object-based classification analysis. classification results were transformed into vector data and visual and digital analyses were made using GIS.

关键词： urban vegetation object-based image classification segmentation

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An automated object-based classification approach for updating Corine land cover data

An automated object-based classification approach for updati...

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Conference on Remote Sensing for Environmental Monitoring, GIS Applications and Geology IV

作者： Wehrmann, T Dech, S Glaser, R DLR DFD German Remote Sensing Data Ctr Wessling Germany

ISBN: (纸本)0819455210

In this paper, the framework of an object-based classification approach for land cover and land use classes is presented. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. Remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique - a rather new trend in image classification - with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification.

关键词： object-based image classification automatisation CORINE land cover pattern recognition machine learning human image understanding

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