3D-Pléiades Mapping for IPCC Reporting

Singapore is a Party to the United Nations Framework Convention on Climate Change (UNFCCC) and reports regularly statistics of its greenhouse gas (GHG) emissions and removals from various sectors including for biennial update reporting. The National Parks Board (NParks) of Singapore provides statistics of the Land Use, Land Use Change and Forestry Sector (LULUCF), and has engaged the Austrian Natural Resources Management and International Cooperation Agency (ANRICA) to establish a national GHG Monitoring, Reporting and Verification (MRV) System. The expert institutions involved in the consortium are the Environmental Agency Austria, the Federal Research Centre for Forests, and Joanneum Research. They cover all aspects from remote sensing, biomass and soil inventories to modelling and IPCC reporting. The presentation deals with all the aspects of remote sensing, image analysis and geo-informatics. NParks has decided to implement the collection of precise and large scale activity data on wall to wall basis by means of remote sensing technology. Annual monitoring of land use (LU) and land use change (LUC) is based on different satellite data sources. The historic development, which is needed to analyse trends, is based on annual Spot image mosaics for the years 1989 to 2014 with a ground resolution of 5 to 20m. Recent years are mapped based on very high resolution (VHR) Pléiades data which meets the country’s requirement characterized by heterogeneous spatial LU distribution and the high dynamic of LUC. About a dozen IPCC-compatible land-use categories out of four land-cover classes are distinguished based on rules jointly defined by the consortium. The baseline data collection carried out for the year 2013 served as reference for the mapping of the time series. Ground truth collection was performed by means of VHR data interpretation, field work, and additional data sources provided by NParks. Classification of LUC-categories within each year was carried out based on this ground truth. We chose segmentation of radiometrically calibrated image mosaics and subsequent classification of land cover, followed by rule-based assignment of LU and LUC as a well proven and traceable approach.The LU maps of the years 2015-2017 could be substantially improved by the use of Pléiades data with an enhanced geometric resolution of 0.5 m. Whilst the LU category definitions remain consistent over the entire time series, LU area borders are much more accurate for the current and future LUC assessments. Furthermore, as innovative aspect 3D information derived from stereo image pairs allows for significantly improved distinction between high and low vegetation categories. The challenges of different resolution and accuracy between historic and contemporary data were tackled by inventive techniques supported by redundant mappingo of both Spot and Pleiades data within the years 2014 and 2015. The results are a LU map for each year as well as annual LUC statistics being essential components for the biennial reporting. This is possibly one of the few applications where very high resolution images are used on a wall-to-wall format for purpose of reporting of carbon emissions/removals at a country level.