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Finnish CORINE 2006 project: determining changes in land cover in Finland between 2000 and 2006

The European Comission introduced the CORINE Programme in 1985 in order to gather information relating to the environment for the European Union. CORINE land cover classification is produced using satellite images and visual interpretation. In Finland, CORINE has been made differently in order to produce more detailed national land cover information at the same time. Finnish CORINE 2000 was based on automated interpretation of satellite images and data integration with existing digital map data. Same process will be repeated with CORINE 2006 as well as possible. The outputs are IMAGE2006 satellite images and mosaics, CORINE 2006 land cover classification and changes 2000-2006. These will be produced in different spatial resolutions: national raster data with spatial resolution of satellite images and European LC and LC changes with MMU of 25 and 5 hectares produced using mainly automated generalization procedures.

Change detection for Finnish CORINE land cover classification

This paper describes the ideas, data and methods to produce Finnish Corine Land Cover 2006 (CLC2006) classification. This version is based on use of existing national GIS data and satellite images and their automated processing, instead of visual interpretation of satellite images. The main idea is that land use information is based on GIS datasets and land cover information interpretation of satellite images. Because Finland participated to CLC2000-project, also changes between years 2000 and 2006 are determined. Finnish approach is good example how national GIS data is used to produce data fulfilling European needs in bottom-up fashion.

European Area Frame Sampling Based on Very High Resolution Images

Initiated in 2007, the Area Frame Sampling Europe subtask of the Seasonal and Annual Change Monitoring Service (SATChMo) Core Service in the geoland2 project delivered its final products in 2012. Three of these are described in this paper: (i) an Area Frame Sampling scheme design that aims at optimizing the statistical accuracy when extrapolating a land cover classification at reasonable cost, (ii) a semi-automatic classification tool that is able to discriminate 10 land cover classes on VHR samples with 0.25ha minimum mapping unit (MMU), and (iii) a highly automatized change detection tool based on Multivariate Alteration Detection (MAD) approach that additionally employs Normalised Difference Vegetation Index (NDVI) and texture characteristics. This later step, as well as giving change/no-change mask provides directions of changes in three main categories, artificialization, revegetation, and devegetation. The algorithms were cast into the form of production chains starting with data acquisition and processing, through the main processing to validation and product dissemination via Spatial Data Infrastructure servers. The whole process was tested on representative set of 114 sites from across the European Union (EU).

Estimating vegetation phenological trends using MODIS NDVI time series

The method to extract phenological information for different land cover types is presented. Phenological features are two different start dates of growing season, date of maximum growth, end of growing season and two growing season lengths. Also, quality indicators are estimated for some phenological features. The method is based on NDVI-time series extracted from MODIS-images. The errors between extracted dates and in-situ measurements are reasonably small. For example, the residuals of the estimation of the start of Flux Growing Season are on only 2 days for broadleaf forest in one Southern Finland hydrological drainage basin. The method has been tested on Northern Boreal forest zone, where there are freezing temperatures and snow during winter.

Revising the land cover and use classification of northern areas for climate modeling

Today, different carbon sources are producing more carbon dioxide than is being absorbed by carbon sinks, contributing towards the instability in the natural balance of carbon dioxide. The goal of the SnowCarbo-project is to improve the model predictions of carbon dioxide by using a variety of Earth Observation, GIS and in situ data in constraining and calibrating the models. The aim of this article is to present different alternatives for land cover data needed in climate and carbon balance modeling, and some preliminary evaluation in the context of climate modeling. The regional climate model REMO developed at Max Planck Institute has been used to simulate the past, present and future climates over wide range of spatial resolutions. These models use Olson ecosystem classification as land cover data, which represents Finnish environment quite badly. Therefore, new versions of land cover data have been constructed based on higher resolution GlobCover and Corine Land Cover classifications as well as classifying different MODIS-products. The results are preliminary, but new versions seem to work better.