BIOPRESS (‘Linking Pan-European Land Cover Change to Pressures on Biodiversity’), a European Commission funded ‘Global Monitoring for Environment and Security’ project, produced land cover change information (1950—2000) for Europe from aerial photographs and tested the suitability of this for monitoring habitats and biodiversity. The methods and results related to the land cover change work are summarized. Changes in land cover were established through 73 window and 59 transect samples distributed across Europe. Although the sample size was too small and biased to fully represent the spatial variability observed in Europe, the work highlighted the importance of method consistency, the choice of nomenclature and spatial scale. The results suggest different processes are taking place in different parts of Europe: the Boreal and Alpine regions are dominated by forest management; abandonment and intensification are mainly encountered in the Mediterranean; urbanization and drainage are more characteristic of the Continental and Atlantic regions.

Structure from Motion (SfM) photogrammetry applied to photographs captured from Unmanned Aerial Vehicle (UAV) platforms is increasingly being utilised for a wide range of applications including structural characterisation of forests. The aim of this study was to undertake a first evaluation of whether SfM from UAVs has potential as a low cost method for forest monitoring within developing countries in the context of Reducing Emissions from Deforestation and forest Degradation (REDD+). The project evaluated SfM horizontal and vertical accuracy for measuring the height of individual trees. Aerial image data were collected for two test sites; Meshaw (Devon, UK) and Dryden (Scotland, UK) using a Quest QPOD fixed wing UAV and DJI Phantom 2 quadcopter UAV, respectively. Comparisons were made between SfM and airborne LiDAR point clouds and surface models at the Meshaw site, while at Dryden, SfM tree heights were compared to ground measured tree heights. Results obtained showed a strong correlation between SfM and LiDAR digital surface models (R² = 0.89) and canopy height models (R² = 0.75). However, at Dryden, a poor correlation was observed between SfM tree heights and ground measured heights (R² = 0.19). The poor results at Dryden were explained by the fact that the forest plot had a closed canopy structure such that SfM failed to generate enough below-canopy ground points. Finally, an evaluation of UAV surveying methods was also undertaken to determine their usefulness and cost-effectiveness for plot-level forest monitoring. The study concluded that although SfM from UAVs performs poorly in closed canopies, it can still provide a low cost solution in those developing countries where forests have sparse canopy cover (<50%) with individual tree crowns and ground surfaces well-captured by SfM photogrammetry. Since more than half of the forest covered areas of the world have canopy cover <50%, we can conclude that SfM has enormous potential for forest mapping in developing countries.

The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach.

Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.