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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.

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Earth Observation (EO) images have been extensively used to provide a synoptic view of land cover/use (LC/LU) patterns and land cover/use changes. Land covers are not as clearly relatable to biodiversity in comparison to habitat classifications which can provide more scope for biodiversity monitoring. The main purpose of the paper is to provide an automatic general framework for translating LC maps (in LCCS taxonomy) into habitat maps (in GHC taxonomy) by means of VHR remote sensing data.

Key is the challenge to develop a biodiversity observation system that is transmissible and cost effective. Measuring and reliable reporting of trends and changes in biodiversity requires that data and indicators are collected and analysed in a standard and comparable way. LiDAR is an alternative remote sensing technology that allows to increase the accuracy of biophysical measurements and to extend spatial analysis into the third dimension. The BIO_SOS project shows alternatives to measure habitat diversity as a proxy for biodiversity on the basis of plant life forms. The objective of our study is to assess to what extent LiDAR can be used to map and monitor plant life forms and associated General Habitat Categories (GHCs). The conclusions are that LiDAR provides accurate height measurements on shrubs and trees, even in early spring when no leaves are present. Canopy height models as derived from LiDAR and in combination with very high resolution satellite imagery provides a powerful tool with for the identification of plant life forms and as a direct input for spatial modelling of species distribution. Since LiDAR data are not everywhere available, finding alternatives for height feature extraction from optical imagery for might be worthwhile.

Focusing on the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) and the recently proposed General Habitat Categories (GHCs) classifycation system, this paper illustrates how expert knowledge concerning class spatial arrangement in the scene at hand class, class phenology and class spectral signature in multitemporal EO images can fill the gaps between the two classification systems and provide LC/LU to habitat translation. An application to a Natura 2000 site in Southern Italy which includes a wetland costal area is discussed.

Mücher, C.A. et al., Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea island of Ameland (2019) SDRP Journal of Earth Sciences & Environmental Studies 4(1) RESEARCH HIGHLIGHTS 1. Both RB and RF classification methods performed well in vegetation structure mapping, respectively, 84.1% and 86.4%. 2. RF was preferred over RB, since the former was better able to handle the complexity of the rules needed to distinguish many classes. 3. Exploitation of digital aerial photographs in semi-automatic classification processes remains challenging, due to inaccurate calibration of the reflectance values and the limited number of spectral bands in aerial photographs. 4. High resolution satellite imagery is a good alternative if aerial photographs are not available.