Conference Agenda

One of the OECD’s core functions is the production of internationally harmonised data, statistics and indicators. Earth observation and other geospatial data offer opportunities to develop new or improved indicators, particularly in the domain of the environment and green growth. Earth observation data is often a unique source of relevant information that is commensurable across countries and at multiple spatial scales, and thus provides opportunities to help fill the many information gaps that OECD countries face at the national and sub-national levels (especially when it comes to monitoring natural resources and environmental sinks) and assessing environmental risks (to humans, built property and economic activity). Importantly, EO data can be combined with socio-demographic and economic data, thereby improving the policy relevance of the indicators. The OECD seeks to build on the rising body of EO and other geospatial data and develop internationally harmonised indicator methodologies to respond to the growing demands for a more granular and more policy-relevant information base and better targeted policy advice.

Applications of Earth observation and other geospatial data have been gaining momentum in OECD's work on the environment and green growth. Examples drawing on on-going work using geospatial data will be presented, such as air pollution exposure, the related economic costs, and distributional aspects. A specific current priority relates to land cover monitoring, aiming to quantify the rate of conversions from natural and semi-natural land cover types, to more anthropogenic land cover associations in a comparable way across all member countries as a proxy indicator of pressures on biodiversity and ecosystems. The associated user requirements for the underlying data will be discussed. Other data needs to support future developments include those arising from demands to better measure the environmental dimension of quality of life, the resilience to environmental risks, and the availability of natural assets.

A formal requirement from the United Nations Framework Convention on Climate Change (UNFCCC) for developing countries to implement the Reducing Emissions from Deforestation and Degradation (REDD+) policy process are national forest monitoring systems (NFMS). African countries committed to this process require national forest and land use class definitions as well as mapping/monitoring systems adjusted to their national circumstances. This paper examines the history of developing these components into operational Earth Observation (EO) based monitoring systems in the past 8 years with the support of the European Space Agency (ESA) projects in the Congo Basin and southern Africa; the user requirements from specific countries will be presented for Cameroon and Malawi. The challenges of transferring the policy and user requirements to technical specifications for EO-products and addressing the variability in forest types and land use change assessment will be noted. The NFMS in these countries started with the baseline year 2010 and aims to provide a continuous monitoring of forest cover and forest cover changes from 2015 onwards using multi-sensoral and -temporal satellite data (Sentinel-1/2, Landsat 8), which are validated with VHR optical data. The advent of the Sentinel-2 data series enhanced dramatically the utilisation of dense time series of multi-temporal satellite imagery to resolve problems caused by phenology changes of forest canopies between the seasons. Furthermore, this data is also needed to monitor forest degradation which can be better detected by assessing forest canopy disturbances with high frequency time series. The data availability improvement also addresses the problem of cloud cover in the tropics. Based on Sentinel 2 data and integration of Landsat 8 imagery the automatic processing chain for the NFMS is comprised of geometric, radiometric and topographic pre-processing steps and an iterative classification procedure that includes a rule based correction system which yields to thematic accuracies above 85%. It was noted that especially for the dry forest biomes these high accuracy levels could not be achieved when using global datasets. Due to the data volume generated with the application of Sentinel for the near real time forest monitoring there is a necessity for cloud processing in the operational systems and this further enables the user community to be directly involved in different aspects of the processing chain. The paper will emphasise the value and merit of user-driven approaches to developing national NFMS and land use monitoring systems in terms of in-country capacity and ownership of the processes.

The need and value of environmental accounting is well recognised internationally. However the development of environmental accounts has exposed some important gaps in the available spatial land cover information. The same issues often also limit the usefulness of land cover classification data in other land and water management applications. This presentation emphasises some of the key innovation needs into the future. Specifically, the requirements for land, water, carbon and environmental condition accounting will be discussed using Australian and global examples. Formal land accounting is currently complicated by the well-known confusion between land cover, land use and land tenure in available spatial data products. Water accounting specifically requires dynamic information on water bodies and irrigated crops, as well as delineation of other hydrological landscape elements such as floodplains, irrigable land and impermeable surfaces. Carbon accounting will require new land cover classification approaches that relate more closely to carbon stocks than to ecological communities. Finally, environmental condition accounting is a conceptually complex challenge but in essence requires new approaches to distinguish man-made from pre-existing land cover types with an indication of the degree of disturbance. In all cases, accounting demands that mapping occurs routinely on a regular (typically annual) basis using a consistent and transparent methodology. New technologies are rapidly changing the way in which land cover information is derived: data processing facilities such as Google Earth Engine empower a large community to develop bespoke land cover products, whereas new sensors (e.g. Sentinel 2) and sensor combinations relax the traditional trade-off between spatial and temporal resolution, supporting new classification approaches that simultaneously consider the spatial, temporal and spectral dimensions.

Existing global inventories of forest carbon stocks are up to debate because regionally strongly divergent. Inventory-based inference generally allows for estimates of carbon stocks at national or sub-national scales with low uncertainty in countries with established national forest inventories. The scarcity of such information across large forest areas, however, advises the use of spaceborne remote sensing imagery for obtaining wall-to-wall, and spatially explicit, carbon stock information. However, spaceborne measurements from optical or radar sensors are only indirectly related to the carbon variable of interest. Spaceborne radar has found limited use in global forest mapping applications so far, despite the availability of global observations from, by now, several missions and the proven sensitivity of, in particular, long wavelength radar backscatter observations to forest variables closely related to forest carbon stocks, e.g., the growing stock volume (GSV) or aboveground biomass (AGB). Large-scale applications of radar data face a number of specific challenges, such as the pronounced sensitivity of the radar measurements to changing environmental imaging conditions and forest structural differences altering the relationship of SAR backscatter observations to the forest biophysical variable of interest.

In this paper, we discuss options for using spaceborne radar data jointly with optical and lidar data, auxiliary datasets from forest inventories, climatological variables and ecosystem classifications, to map forest aboveground biomass globally, while minimizing the reliance on inventory data. A first set of global biomass maps as well as spatially explicit depictions of the associated uncertainties will be presented that have been produced from hyper-temporal stacks of ENVISAT ASAR C-band backscatter data (@1km resolution) and ALOS PALSAR L-band mosaics released by JAXA (@25m resolution). The maps are being produced in the frame of the ESA DUE GlobBiomass project. The spatially explicit datasets of forest aboveground biomass and carbon stocks are the first of its kind, obtained with a single, globally consistent, retrieval approach that allows for local tuning to account for the spatial variability of forest structure. We present current results from our investigations on the reliability of the estimates and compare with existing regional datasets, including inventory data and derived regional statistics and existing national or regional map products. While the systematic global assessment of the accuracy of the biomass estimates has not yet started, a few preliminary indications can be provided. The spatial distribution of aboveground biomass was well captured, with the largest values found in the tropical forests, in temperate forests of the US Pacific Northwest, Chile and South Australia. Carbon stocks of the northern hemisphere are in line with existing observations from on ground surveys. In the tropics, the estimates appear to be in agreement with previous mapping activities except for Southeast Asia where we are currently estimating less biomass. A systematic validation to be preformed in the coming year will help to identify strengths and limitations of carbon stock inventories relying on currently available global SAR datasets and will allow for further optimization of the retrieval approaches regionally.

Up-to-date information on land cover and how it is changing is required by many Sustainable Development Goals and other Multilateral Environmental Agreements. National governments need this information to meet their commitments to these agreements and for their internal regulations and applications. Various assessment bodies and other entities also have important needs. However, because most current approaches to generating land cover products are labor-intensive they have difficulty meeting the varied needs of these users. This results in a variety of important limitations that leave many user needs unmet. These limitations include but are not limited to: fixed number and types of classes; difficulty in generating products for large areas; infrequent and irregular updates; and long latency periods so the product may be out of date by the time it is available. Additionally, because products are generated by a variety of organizations with different mandates they are often inconsistent, making it difficult or impossible to combine or compare products. A better approach is needed.

Fortunately, advances in both science and technology now enable approaches that do not have these limitations. Specifically, improved algorithms that utilize multi-temporal and ancillary information are now practical, and increased data availability and decreased computing costs, among other advances, enable automated, on-demand systems that accept inputs from users to meet their specific needs. Several systems that take advantage of these advances and that support on-demand requests are already being developed. Developing on-demand land cover product generation systems has a variety of significant challenges, particularly for very large or, especially, global areas; reference data for training and validation is probably the most significant challenge at all scales but there are others. These topics were the focus of a workshop held in May, 2016 focused on exploring concepts for a sustainable land cover generation approach that can meet the varied needs of users. The outcome of that workshop and follow-on discussions has led to a suggested, generic architecture for land cover generation; while there are many good variants a “data cube like” approach is a common theme. In this presentation we discuss this new approach, its challenges, and some key steps forward to help it become more widespread so the needs of users can be better met.