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Expert knowledge for translating land cover/use maps to General Habitat

机译:将土地覆盖/使用图转换为人居的专业知识

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Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed
机译:随着各国制定国际和国家栖息地保护政策和监测系统,在栖息地和景观一级对生物多样性进行监测已在欧洲和其他地区广泛进行。地球观测(EO)数据可通过直接绘制栖息地图或将土地覆盖/利用(LC / LU)地图与上下文空间信息和原位数据相结合,为长期生物多样性监测提供潜在的解决方案。因此,似乎有必要开发一种将LC / LU类转换为栖息地类的自动/半自动翻译框架,但由于域定义的差异,这也具有挑战性。在FP7 BIO_SOS(www.biosos.eu)项目的背景下,作者展示了粮食及农业组织土地覆被分类系统(LCCS)分类法对栖息地类别翻译的可行性。他们还开发了一个框架,可将LCCS类自动转换为最近提出的一般人居类别分类系统,从而能够提供从自然生态系统到全球城市地区的详尽的生境类型分类学。但是,两个映射域之间的术语,植物高度标准和基本原理之间的差异引起了许多一对多和多对多的关系,这表明已发现,这表明需要更多的生态专家知识来解决歧义。本文说明了如何使用类物候,景观中的类拓扑安排,来自多时相甚高空间分辨率(VHR)卫星图像的类谱签名以及植物高度测量来解决此类歧义。关于植物高度,本文还比较了使用从光检测和测距(LIDAR)数据中提取的准确值以及利用EO数据纹理特征(即熵)作为植物高度信息的代理而获得的映射结果,而不使用LIDAR数据可用。讨论了在意大利南部的两个Natura 2000沿海地点的申请

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