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Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

机译:基于对象的图像分析,可从多光谱图像和专题数据中对河道内的土地覆盖物进行精细区域制图

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Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.View full textDownload full textRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/01431161.2011.637093
机译:为了更好地了解河岸景观以及相关的自然和人为压力对河流生态状况的影响,在区域尺度上准确绘制河岸区域内土地覆盖物多样性是一个重大挑战。由于河岸地区土地覆盖(RALC)的结构(组成和空间组织)通常无法使用中等规模的卫星图像访问,因此需要更精细的空间分辨率图像和特定的制图技术。为此,我们基于特定的多尺度基于对象的图像分析(OBIA)方案开发了分类程序,该方案致力于在不同的地理环境(起伏,气候和地质)中生成精细且可靠的RALC地图。该OBIA方案使用模糊的专家知识分类规则,将非常高分辨率的多光谱图像(卫星或机载)信息与可用的空间专题数据结合在一起。它在Hérault河流域(法国南部)上进行了测试,使用SPOT(SystémeProbatoire d'Observation de la Terre)5 XS影像(10ÂÂ),呈现了对比鲜明的景观和总流长1150 km m像素),航空摄影(0.5 m像素)和一些国家空间主题数据。根据目标土地覆盖物压力类型(六类压力),绘制了RALC图(22类),总精度为89%,kappa指数为83%。实验结果表明,OBIA在多源空间数据中的应用为RALC的制图和监测提供了一种有效的方法,可以在区域或国家范围内实施。我们进一步分析了地图分辨率对河岸空间指标量化的影响,以强调此类数据对于研究河岸尺度上景观对河流生态状况的影响的重要性。查看全文下载全文相关var addthis_config = {ui_cobrand:“泰勒&Francis Online”,services_compact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,更多”,发布号:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/01431161.2011.637093

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