|
GCOS Terrestrial ECV T9
Land Cover
(including vegetation type)
Introduction: Land cover and its changes modify the services provided to human society (e.g., the provision of food and fibre, recreational opportunities, etc.), force climate by altering water and energy exchanges with the atmosphere, and change greenhouse gas and aerosol sources and sinks. Land-cover distribution is partly determined by regional climate, so changes in land cover may indicate climate change.
Although land-cover change can be inferred using data from Earth observing satellites, currently available datasets vary in terms of data sources employed and spatial resolution and thematic content, have different types and patterns of thematic accuracy, and use different land-cover classification systems (although improvement has been made in using common standards). It is necessary, and feasible with present-day technology, to provide satellite-based optical systems at 10-30 m resolution with temporal, spectral, and data acquisition characteristics that are consistent with previous systems. Commitments to short-term continuity of this class of observations, such as the Landsat Data Continuity Mission and Sentinel-2, are vital steps, although long-term commitments still need to be secured. The CEOS Land Surface Imaging Constellation has been instigated to promote the effective and comprehensive collection, distribution and application of space-acquired imagery of the land surface.
Datasets characterising global land cover are currently produced at resolutions of between 250 m and 1 km by several space agencies in close cooperation with the research community (especially those research groups participating in the GTOS technical panel Global Observation of Forest and Land Cover Dynamics (GOFC-GOLD)). The lack of compatibility between these products makes it difficult to measure and monitor climate-induced or anthropogenic changes in land cover. A range of approaches has been adopted, e.g., centralized processing using a single method of image classification (e.g., MODLAND, GlobCover)and a distributed approach using a network of experts applying regionally specific methods (e.g., GLC2000). Using a single source of satellite imagery and a uniform classification algorithm has benefits in terms of consistency, but may not yield optimum results for all regions and all land-cover types. Automated land-cover characterisation and land-cover change monitoring thus remains a research priority.
It is necessary that land-cover classification systems and the associated map legends adhere to internationally-agreed standards.96 Such standards should eventually be agreed upon by the UN/ISO Terrestrial Framework (see Action T1). In the near term, however, full benefit should be taken of existing initiatives, e.g., the FAO Land Cover Classification System for legend harmonization and translation, and the legends published by the IGBP and the GTOS GOFC-GOLD. The process of harmonization and translation of existing legends will be strengthened with the new FAO Land Cover Meta Language (LCML). The LCML will be an operational tool to formalize the meaning of any existing land-cover classification/legend according to the latest ISO standards.
As a minimum, new land-cover maps should be produced annually, documenting the spatial distribution of land-cover characteristics with attributes suitable for climate, carbon, and ecosystem models and using a common language for class definitions (e.g., include wetland information describing forest peat lands (boreal), mangroves, sedge grasslands, rush grasslands and seasonally-flooded forests, and area of land under irrigation), at moderate (250 m - 1 km) resolution. Grid-scale information on the percentage of tree, grass, and bare soil cover should ideally also be made available.
In addition to their use in Earth system models, these global products will help identify areas of rapid change, although the development of automated detection of changes in land-cover characteristics remains high on the research agenda. The production of such land-cover datasets will involve space agencies for processing the satellite data used in the database production, the FAO/science panels to ensure legend relevance and standards, and the research community for optimizing image classification approaches. Mechanisms to fund such partnerships are emerging (e.g., the EU Global Monitoring for Environment and Security (GMES) initiative) but are not yet guaranteed on a sustained basis.
Global land-cover databases must also be accompanied by a description of class-by-class thematic/spatial accuracy. The CEOS WGCV, working with GOFC-GOLD and GLCN has published agreed validation protocols, which should be used. The current protocols base accuracy assessment on a sample of high-resolution (1-30 m) satellite imagery, itself validated by in situ observations wherever possible. To better quantify changes in land-cover characteristics, these high-resolution data should also be used for wall-to-wall global mapping at resolutions of 10-30 m. Maps at this resolution are needed at least every 5 years over long time periods (several decades) to assess land-cover change. Global datasets of satellite imagery at 30 m resolution have been assembled for selected years (e.g., 1990, 2000, and 2005), and some regional land-cover maps have been generated from these. The technologies have been developed and tested (e.g., Landsat, Sentinel-2, and the Satellite Pour l’Observation de la Terre High Resolution (SPOT HRV)), and suitable methods for land-cover characterisation on these scales exist. Space agencies should assure that suitable optical sensors with 10-30 m resolution are available for operational monitoring using data acquisition strategies comparable to systems in current operation.
While, at the time of writing, it is not yet clear what methodology would be put in place under the UNFCCC in connection with the proposed implementation of Reducing Emissions from Deforestation and forest Degradation in developing countries (REDD+),97 relevant space agencies under CEOS have agreed to supply, on a regular basis, the high-resolution data necessary for the generation of fine-resolution land-cover maps to support such a methodology. Such a commitment would also provide the basis for the observations needed to meet Actions T27 and T28.
Samples of high-resolution satellite imagery have also been used to estimate change and are proposed, for example, by the FAO Global Land Cover Network and the FAO Forest Resource Assessment. (FRA) Initiatives such as these will provide much needed capacity-building and offer a framework for acquisition of in situ observations to support the satellite image-based monitoring. Such in situ networks will also provide information on how land is being used (as opposed to what is covering it). Land use cannot always be inferred from land cover.
Satellite-derived land (skin) surface temperature is a very dynamic variable responding to both the land surface (albedo, emissivity) and changes in solar irradiance. Since it is hard to reliably relate to other in situ surface temperature measurements, land surface temperature is unsuitable for global, long-term monitoring and not considered an ECV. However, it can inter alia help interpretation of land surface properties including soil moisture, and can therefore be a valuable supporting measurement.
(Source: WMO/IOC Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update) GCOS-138/GOOS-184/GTOS-76/WMO-TD/No. 1523)
Satellite Observations: The spatial information that can be derived from satellite imagery is of value in a wide range of applications, particularly when combined with spectral information from multiple wavebands of a sensor. Satellite Earth observation is of particular value where conventional data collection techniques are difficult, such as in areas of inaccessible terrain, providing cost and time savings in data acquisition – particularly over large areas.
At regional and global scales, low resolution instruments with wide coverage capability and imaging sensors on geostationary satellites are routinely exploited for their ability to provide global data on land cover and vegetation. Land cover change detection is important for understanding global environmental change and has profound implications for ecosystems, biochemical fluxes and climate. Instruments on satellites with wide and frequent coverage provide data useful for spin-off applications. AVHRR on NOAA’s polar orbiting satellite series was originally intended only as a meteorological satellite system, but it has subsequently been used in a multitude of diverse applications, while the Envisat MERIS instrument is being used to generate global land cover imagery at 300 m resolution.
On national and local scales, the spatial resolution requirements for information mean that moderate resolution imaging sensors, such as those on SPOT, Landsat and IRS, and imaging radars (such as those on ERS, Envisat and RADARSAT) are most useful. Such sensors are routinely used as practical sources of information for:
— agriculture monitoring, farming and production forecasting;
— resource exploration and management, e.g. forestry;
— geological surveying for mineral exploration and identification;
— hydrological applications such as flood monitoring;
— civil mapping and planning, involving cartography, infrastructure and urban management;
— coastal zone monitoring, including oil spill detection and monitoring;
— topographic mapping, generation of DEMs.
SAR data are particularly useful in monitoring and mapping floods because they are available even in the presence of thick cloud cover. Instruments on RADARSAT, Envisat, ALOS and TerraSAR-X continue to provide improved capabilities in this field. Such multi-incidence, high resolution SAR systems will also be useful for landslide inventory maps and earthquake prediction. Moreover InSAR techniques can be used to document deformation and topographic changes preceding, and caused by, volcanic eruptions. Volcanic features also have distinctive thermal characteristics which can be detected by thermal imagery, such as that provided by the ASTER radiometer flying on Terra. The IGOS Geo-hazards Theme report provides a comprehensive guide as to the value of satellite Earth observations for such applications. Future SAR instruments will continue to be important for land imagery because of their all-weather, day and night observing capability and high spatial resolution (1–3 metres), as provided by RADARSAT-2 and COSMO-SkyMed.
New instruments, such as AVNIR-2 and PRISM on ALOS, have provided enhanced land observing technology and improved data products. In general, future sensors will benefit from a greater number of sampling channels. NOAA’s VIIRS instrument, for instance, will have multi-channel imaging capabilities and will combine the radiometric accuracy of AVHRR with the high spatial resolution of the OLS flown on DMSP missions.
CEOS has initiated a virtual constellation study team for land surface imaging to provide the coordination framework necessary to secure continuity of moderate resolution imagery used for many land surface applications, including their relation to climate. (Satellite Missions) (Source: CEOS EO Handbook - Earth Observations Plans by Measurement)
References:
Data, Product, Metadata and Information Access
[ECV Matrix Main Page] [About the ECV Matrix] [Reference Documents] [Contact] [Updated June 6, 2010]
|
Non-satellite or in-situ
|
Satellite
|
| |
|
|
