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Online Handbook E-BIND

Bonaire is rich in natural terrestrial ecosystems ranging from dry tropical forest, caves and beaches tosalt lakes and mangroves. The interaction of the natural ecosystems with human activities is reflected in the land cover. Understanding current land cover and how the land is being used is elementary for land management and land use planning. In this study we developed a spatial land cover classification database of Bonaire based on high resolution (2x2 m2) satellite imagery, field observations and supplemented with local knowledge. Bonaire is rijk aan natuurlijke terrestrische ecosystemen, variërend van droge tropische bossen, grotten en stranden tot zoutmeren en mangroves. De interactie van de natuurlijke ecosystemen met de menselijke activiteiten wordt weerspiegeld in de landbedekking. Inzicht in de huidige landbedekking en hoe het land wordt gebruikt is essentieel voor het beheer en de ruimtelijke ordening. In deze studie hebben we een ruimtelijke landbedekkingsclassificatie van Bonaire ontwikkeld op basis van hoge resolutie (2x2 m2) satellietbeelden, veldwaarnemingen en aangevuld met lokale kennis.

The purpose of the work in WP 2 of the MAGIC project is to map, characterize and analyze projections for current and future marginal lands in Europe facing natural constraints and provide a spatially explicit classification that will serve as a basis for developing sustainable best-practice options for industrial crops in Europe. This report provides a description of the approach applied to map marginal lands based on the definition and focus described in D2.1 of MAGIC ‘Definition and classification of marginal lands suitable for industrial crops in Europe’. Different succeeding versions of a spatially explicit database (MAP-DB) of a Marginal Agro-Ecological Zonation (M-AEZ) of Europe will be generated. In this deliverable the methodological approach to mapping the first version of Marginal Agro-Ecological Zonation (M-AEZ) in MAGIC is presented.

This chapter covers the questions of ecosystem definition and the organisation of a monitoring system. It treats where and how ecosystems should be measured and the integration between in situ and RS observations. Ecosystems are characterised by composition, function and structure. The ecosystem level is an essential link in biodiversity surveillance and monitoring between species and populations on the one hand and land use and landscapes on the other. Ecosystem monitoring requires a clear conceptual model that incorporates key factors influencing ecosystem dynamics to base the variables on that have to be monitored as well as data collection methods and statistics. Choices have to be made on the scale at which monitoring should be carried out and eco-regionalisation or ecological stratification are approaches for identification of the units to be sampled. This can be done on expert judgement but nowadays also on stratifications derived from multivariate statistical clustering. Data should also be included from individual research sites over the entire world and from organically grown networks covering many countries. An important added value in the available monitoring technologies is the integration of in situ and RS observations, as various RS technologies are coming into reach of ecosystem research. For global applications this development is essential. We can employ an array of instruments to monitor ecosystem characteristics, from fixed sensors and in situ measurements to drones, planes and satellite sensors. They allow to measure biogeochemical components that determine much of the chemistry of the environment and the geochemical regulation of ecosystems. Important global databases on sensor data are being developed and frequent high resolution RS scenes are becoming available. RS observations can complement field observations as they deliver a synoptic view and the opportunity to provide consistent information in time and space especially for widely distributed habitats. RS has a high potential for developing distribution maps, change detection and habitat quality and composition change at various scales. Hyperspectral sensors have greatly enhanced the possibilities of distinguishing related habitat types at very fine scales. The end-users can use such maps for estimating range and area of habitats, but they could also serve to define and update the sampling frame (the statistical ‘population’) of habitats for which field sample surveys are in place. Present technologies and data availability allow us to measure fragmentation through several metrics that can be calculated from RS data. In situ data have been collected in several countries over a longer term and these are fit for statistical analysis, producing statistics on species composition change, habitat richness and habitat structure. It is now possible to relate protocols for RS and in situ observations based on plant life forms, translate them and provide direct links between in situ and RS data.

Using habitat suitability maps based on vegetation relevés and and amongst others the Copernicus high resolution layer forest, this report describes in detail the method used to produce the probability maps for the EUNIS forest habitat types at level 3. An assessment of these maps has been done for the whole of Slovakia. All the maps are presented in Annex I.

Op locaties waar geen hoogtemetingen beschikbaar zijn, bevat het AHN geen hoogtegegevens. Reden genoeg voor Alterra om een vlakdekkend bestand te maken met alle hoogten boven maaiveld: het bestand Object Hoogte Nederland (OHN). Met OHN is het mogelijk om de vraag te beantwoorden "hoe hoog is deze boom"

De BES eilanden behoren sinds oktober 2010 tot de Nederlandse gemeenten. Het opstellen van natuurbeleidsplannen behoren tot de nieuwe taken. Gedetailleerde hoogtekaarten zijn daarbij van belang. Met name voor Saba en St. Eustatius, met een zeer gevarieerd en complex reliëf, zijn accurate hoogtekaarten van groot belang.

At a global level, protected sites have been established for the primary purpose of conserving biodiversity, with survey and monitoring of habitats undertaken largely within their boundaries. However, because of increasing human populations with greater access to resources, there is a need to now consider monitoring anthropic activities in the surrounding landscapes as pressures and disturbances are impacting on the functioning and biodiversity values of many protected sites. Earth Observation (EO) data acquired across a range of spatial and temporal scales offer new opportunities for monitoring biodiversity over varying time-scales, either through direct or indirect mapping of species or habitats. However, Land Cover (LC) and/or Land Use (LU), rather than habitat maps are generated in many national and international programs and, whilst the translation from one classification to the other is desirable, differences in definitions and criteria have so far limited the establishment of a unified approach. Focusing on both natural and non-natural environments associated with Natura 2000 sites in the Mediterranean, this paper considers the extent to which three common LC/LU taxonomies (CORINE, the Food and Agricultural Organisation (FAO) Land Cover Classification System (FAO-LCCS) and the IGBP) can be translated to habitat taxonomies with minimum use of additional environmental attributes and/or in situ data. A qualitative and quantitative analysis based on the Jaccard’s index established the FAOLCCS as being the most useful taxonomy for harmonizing LC/LU maps with different legends and dealing with the complexity of habitat description and as a framework for translating EO-derived LC/LU to habitat categories. As demonstration, a habitat map of a wetland site is obtained through translation of the LCCS taxonomy.

The present study examines the possibility to identify the different land cover types (natural and artificial) on very high resolution satellite images of the Caribbean islands Bonaire, Saba and St. Eustatius. Linking species habitat requirements with associated land cover types allows for the identification of their potential occurence on the islands.

specific species, the biodiversity values of agricultural land can differ substantially across Europe. From the policy perspective, high nature value agricultural landscapes are of large interest in the framework of agri-environmental measures and rural development objectives. This project aims at developing a new indicator for high nature value farming, in other words indicating areas where European agricultural landscapes have a high biodiversity value, integrating state-of-the-art European data sets. European data sets that have been analysed are e.g. CORINE Land Cover, LUCAS Landscape Elements, UTM Farmland Bird statistics, and FADN land use statistics (e.g. intensity expressed in kg N/ha). The developed methodology aimed to identify different levels of biodiversity values for all agricultural landscapes. The European Landscape Classification LANMAP, provided the landscape units at which the different data sources were integrated and analysed. Identification of the extent as well as the quality of high nature value farming areas is of equal importance. The intention is to contribute to the further development of European assessments and monitoring programmes currently ongoing at the EC and EEA.

Habitat fragmentation is in Europe a serious threat for biodiversity caused by intensive land use practices of our population. Spatial cohesion is a sustainability indicator to determine whether the size and connectivity of ecosystem networks is sufficient for sustainable biodiversity protection. This report presents the model LARCH-SCAN that calculates spatial cohesion of forest ecosystems for specific species groups that function on different spatial scales