How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains

Powers, Ryan; Hay, Geoffrey J.; Chen, Gang

doi:10.1016/j.rse.2011.07.009

Cited by 57 publications

(41 citation statements)

References 25 publications

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“…It has achieved some improvements in land-cover completeness and mapping accuracy, compared to per-pixel image analysis [65][66][67]. However, the geoscene-based image analysis (GEOSIBA) proposed in this study aims to delineate and analyze functional zones by concentrating on object aggregations and functional-zone categories.…”

Section: A Comparison Between Geoscene-based Image Analysis and Geobiamentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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Section: A Comparison Between Geoscene-based Image Analysis and Geobiamentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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“…An important component of OBIA classifications is the determination of segmentation scale, which determines the size and similarity of resulting image objects, and parameterization, i.e., the inclusion of texture [20]. Texture is the use of a moving window to quantify measures that represent ideas such as coarseness and roughness [19].…”

Section: Segmentationmentioning

confidence: 99%

“…Over or under segmenting an image can result in lower classification accuracy [23]. In addition, segmentation scale can impact the land cover classes that can be accurately mapped [20]. This study used the mean shift clustering approach to determine segmentation.…”

Section: Segmentationmentioning

confidence: 99%

Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy

et al. 2017

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This study used Quickbird-2 and Worldview-2, high resolution satellite imagery, in a multi-temporal salt marsh mapping and change analysis of Jamaica Bay, New York. An object-based image analysis methodology was employed. The study seeks to understand both natural and anthropogenic changes caused by Hurricane Sandy and salt marsh restoration, respectively. The objectives of this study were to: (1)

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“…Decision tree (DT) analysis uses the dichotomous splitting of data based on thresholds of the most relevant variables in the data. The advantages of using a decision tree classification over standard statistical classifiers are that they can (i) incorporate a variety of data sources (such as the multispectral imagery, digital elevation models and canopy height models used here); (ii) handle both continuous and categorical information; and (iii) the most important variables among those available for the classification are selected [49].…”

Section: Manual Decision Tree Classificationmentioning

confidence: 99%

Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery

Whiteside¹,

Bartolo²

2015

Remote Sensing

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Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is a downstream receiving environment for the Ranger Uranium Mine. Accurate methods for mapping wetland vegetation are required to provide contemporary baselines of annual vegetation dynamics on the floodplain to assist with analysing any potential change during and after minesite rehabilitation. The aim of this study was to develop and test the applicability of geographic object-based image analysis including decision tree classification to classify WorldView-2 imagery and LiDAR-derived ancillary data to map the aquatic vegetation communities of the Magela Creek floodplain. Results of the decision tree classification were compared against a Random Forests classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. The decision tree classification method provided an overall accuracy of 78% which was significantly higher than the overall accuracy of the Random Forests classification (67%). Most of the error in both classifications was associated with confusion between spectrally similar classes dominated by grasses, such as Hymenachne and Pseudoraphis. In addition, the extent of the sedge Eleocharis was under-estimated in both cases. This suggests the method could be useful for mapping wetlands where statistical-based supervised classifications have achieved less than satisfactory results. Based upon the results, the decision tree method will form part of an ongoing operational monitoring program.

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How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains

Cited by 57 publications

References 25 publications

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy

Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery

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