Data from: Habitat mapping of coastal wetlands using expert knowledge and Earth Observation (EO) data

1. Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies. Earth Observation (EO) data and techniques are a valuable resource for long-term habitat mapping. Although the use of EO data is well developed for the automatic production of Land Cover (LC) maps this is not the same for habitat maps, which are highly related to biodiversity. 2. In a previous paper, we used the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) environmental attributes (e.g. water quality, lithology, soil surface aspect) for LC-to-habitat class translation. However, these environmental attributes are often not openly available, not updated or are missing. 3. This paper offers an alternative, knowledge-based solution to automatic habitat mapping. When only expert rules and EO data are used, the final overall map accuracy which is obtained by comparing reference ground-truth patches to the ones depicted in the output map, is lower (75.1%) than the accuracy obtained using environmental attributes alone (97.0%). Some ambiguities that still remain in habitat discrimination are resolved by integrating the use of LCCS environmental attributes (if available), and expert rules. 4. In this paper we use Very High Resolution (VHR) satellite data and LIDAR data. LC classes are labelled according to the LCCS taxonomy, which offers a framework to integrate EO data with in situ and ancillary data. Output habitat classes are labelled according to the European Habitats Directive (92/43 EEC Directive) Annex I habitat types and Eunis habitat classification. Two Natura 2000 coastal wetland sites in southern Italy are considered. 5. Synthesis and applications. In this paper we study the exploitation of ecological rules on vegetation pattern, plant phenology and habitat geometric properties for automatic translation of Land Cover (LC) maps to habitat maps in coastal wetlands. The methodology is useful for relatively inaccessible sites (e.g. wetlands) as it does not require in-field campaigns (generally costly) but only the elicitation of ecological expert rules. This can support site (e.g. Natura 2000) managers in long-term automatic habitat mapping. Habitat changes can be automatically detected by comparing map pairs and trends can be quantified. This is particularly useful to satisfy the commitments of the European Habitats Directive (92/43/EEC), which requires Member States to take measures to maintain as, or restore to, favourable conservation status those natural habitat types and species of community interest that are listed in the Annexes to the Directive.

1. 长期生境制图与变化检测对于滨海湿地管理以及评估保护政策的实施效果均至关重要。地球观测（Earth Observation, EO）数据与技术是开展长期生境制图的宝贵资源。尽管利用EO数据自动制作土地覆盖（Land Cover, LC）图的技术已相当成熟，但针对与生物多样性高度相关的生境图的自动制作技术却仍有不足。2. 在先前的研究中，我们借助联合国粮食及农业组织（Food and Agricultural Organization, FAO）土地覆盖分类系统（Land Cover Classification System, LCCS）的环境属性（如水质、岩性、地表坡向）实现了土地覆盖类到生境类的转换。但此类环境属性往往无法公开获取、未及时更新甚至缺失。3. 本文提出了一种基于知识的替代方案，用于自动生境制图。若仅采用专家规则与EO数据进行制图，通过将参考实地真值斑块与输出地图中的对应斑块比对得到的总体制图精度仅为75.1%，低于仅使用环境属性时的97.0%。通过整合LCCS环境属性（若可获取）与专家规则，则可解决生境判别中仍存在的部分歧义问题。4. 本文采用超高分辨率（Very High Resolution, VHR）卫星数据与激光雷达（LIDAR）数据。土地覆盖类按照LCCS分类体系进行标注，该体系提供了将EO数据与实地观测数据及辅助数据相整合的框架。输出的生境类则依据《欧洲生境指令（European Habitats Directive, 92/43 EEC Directive）》附件一所列生境类型以及Eunis生境分类体系进行标注。研究选取了意大利南部两处Natura 2000滨海湿地样点作为研究对象。5. 总结与应用：本文探究了如何利用植被格局、植物物候与生境几何属性相关的生态学规则，实现滨海湿地土地覆盖（LC）图到生境图的自动转换。该方法适用于相对难以抵达的区域（如湿地），因为其无需开展通常成本高昂的野外实地调查，仅需提炼生态学专家规则即可。该方法可辅助Natura 2000样点管理人员开展长期自动生境制图。通过比对不同时期的生境图可自动检测生境变化，并量化其变化趋势。这对于满足《欧洲生境指令（92/43/EEC）》的相关要求尤为实用——该指令要求欧盟成员国采取措施，维持或恢复指令附件中所列的欧盟关注自然生境类型与物种至良好保护状态。