A system integration of ground and remote sensing data that parametrizes Russian territory at a 1km spatial resolution for forests and includes tree species, age, and biomass distribution.

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Russia_map © IIASA

Hybrid Land Cover of Russia (HLCR) is a land cover data set for Russia, developed at IIASA, as part of the European Commission FP6 Project GEOBENE and the GEG-2 project with the Global Environmental Forum of Japan.  Click here for large map.

 

The main idea behind the HLCR project was to systematically integrate all relevant information - in particular, the merging and harmonization of land and forest inventories, ecological monitoring, remote sensing data, and in situ information - and to explore the synergies between them. HLCR is linked to IIASA-based  GeoWiki which aims to clarify and validate data of global land cover.

HLCR was created at a resolution that is high enough to use for full carbon accounting of terrestrial biota and to validate remote sensing products. It thus meets the critical need for updating land-cover information as a basis for accurate modeling of the terrestrial biosphere, for example, resource assessment, biophysical modeling, greenhouse gas studies, and the estimation of possible terrestrial responses and feedbacks to climate change. It will enable, for example, a far better determination of the  contributions of extratropical Northern Hemisphere land areas to the global budget of greenhouse gas fluxes in the biosphere.

Russian-forests © IIASA
Russia is the largest country in the world, with 17.1 million square kilometers. It has the world's largest forest reserves and its lakes contain approximately one-quarter of the world's fresh water

About Hybrid Land Cover of Russia

HLCR is a highly detailed (both spatially and thematically) land cover/use data set covering the whole of Russia, the largest country on Earth. 

To build it, a detailed quantification of land classes was required (for forests – dominant species, age, growing stock, net primary production, etc) as well as additional information regarding the uncertainties in the main biometric and ecological parameters. The data were drawn from a number of suitable data sets, processed in a geographic information system. The main advantage of the methodology used is the ability to link on-ground data and models to remote sensing products.

FAST FACTS

  • By linking on-ground data and models to remote sensing products, HLCR attempts to eliminate inconsistency between different sources of spatial information.
  • HLCR shows that there is more forested land and thus more carbon sinks in northern Eurasia than evident from  previous data. 
  • The data sets for HLCR were developed for the EC FP6 Project, GEOBENE and the GEG-2 project with the Global Environmental Forum, Japan.
  • In additional to regional and continental applications, the possibility of applying this methodology over the globe now exists.

How HLCR works

HLCR has a total of six major land-cover types were identified, namely: forest, agriculture, wetlands, shrubs/grasses, water, and unproductive land. These are further subdivided into the following classes:

  • Forest – each grid links to the updated Russian State Forest Account (SFA) database, which contains areas and growing stock by seven dominant forest species
    distributed by age, site index, and relative stocking), containing 86,613 records
  • Agriculture – five classes, parametrized by 87 administrative units
  • Wetlands – eight classes, parametrized by 83 zones/regions
  • Shrub/grassland – 50 classes, parametrized by 300 zones/regions

In lieu of suitable validation data, a confidence map was produced creating six classes of
confidence in the agreement between the various remote sensing and statistical data sets.

Without accurate baseline measures of crucial data sets such as land use and land cover, there would be little hope of monitoring the effects of global change on vegetation over large regions. 

Challenges

Future challenges include further fine resolution validation of the hybrid land-cover data set for Russia, and its use for assessment of the terrestrial biota full greenhouse gas budget for northern Eurasia.

The algorithm developed for this study is flexible enough to allow for the inclusion of additional existing data sets or newly created data sets in the future (e.g., elevation, lidar biomass, etc).  In addition to regional and continental applications, the possibility of applying this methodology over the globe now exists, with the majority of input data sets used being global.

However, global implementation of the technology used requires substantial efforts: it is impossible to implement based on only a few remote sensing indicators (common practice for many global products) with insufficient use of ground truth data.