Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Europe's ecological backbone: recognising the true value of our mountains

Europe's mountain areas have social, economic and environmental capital of significance for the entire continent. This importance has been recognised since the late 19th century through national legislation; since the 1970s through regional structures for cooperation; and since the 1990s through regional legal instruments for the Alps and Carpathians. The European Union (EU) first recognised the specific characteristics of mountain areas in 1975 through the designation of Less Favoured Areas (LFAs). During the last decade, EU cohesion policy and the Treaty of Lisbon have both focused specifically on mountain

Land cover maps for environmental modeling at multiple scales

As described in the ECOCHANGE proposal, Task01.02.02 “Map production and aggregation”, two major products are generated within this WP. Firstly, land cover maps at high spatial resolutions will be produced for the European Union and for the reference years of 1960, 1990 and 2000. Secondly, thematic and spatial aggregated products will be derived at coarser spatial resolutions in order to synthesize the fragmentation and variability within coarser cells for biodiversity assessment and modelling. The name of the official deliverable is D01.02.01 “Land cover maps for environmental modelling at multiple scales” and includes this report, the digital land cover products and an interactive website to view the data at all thematic and spatial scales

Landelijk Grondgebruiksbestand Nederland (LGN5); vervaardiging, nauwkeurigheid en gebruik

De snelle veranderingen die zich in Nederland voordoen met betrekking tot het gebruik van ruimte zorgen voor een voortdurende behoefte aan actuele informatie over het landgebruik. Het Landelijk Grondgebruiksbestand Nederland (LGN5) is een landsdekkend bestand en geeft het landgebruik weer voor 39 landgebruiksklassen verdeeld over de hoofdklassen agrarisch gebied, bossen, water, stedelijk gebied en natuur. De informatie is opgeslagen in 25*25 meter rastercellen. Het bestand is gebaseerd op satellietbeelden uit de jaren 2003 en 2004. Naast satellietbeelden is er informatie uit o.a. Top10-vector SE, CBS-landbouwstatistiek, luchtfoto¿s, Basis Registratie Percelen (BRP) en LGN4 gebruikt. De algehele nauwkeurigheid van het basisbestand is meer dan 90%, voor het gewassenbestand ligt dit op 80.5%. In de periode 1999/2000 ¿ 2003/2004 hebben zich 0.67% aan landgebruiksveranderingen voorgedaan

CLC2000 land cover database of the Netherlands; monitoring land cover changes between 1986 and 2000

The 1986 CORINE land cover database of the Netherlands was revised and updated on basis of Landsat satellite images and ancillary data. Interpretation of satellite images from 1986 and 2000 resulted in the CLC2000, CLC1986rev and CLCchange databases. A standard European legend and production methodology was applied. Thirty land cover classes were discerned. Most extended land cover types were pastures (231), arable land (211) and complex cultivation patterns (242). Between 1986 and 2000 around 4.76% of land changed its land cover. Most typical change was the conversion of agricultural land into artificial areas. The thematic accuracy of CLC2000 was almost 95% and 88% of the changes were correctly classified as changes

Methodology to define and map the sub-region entities urban, peri-urban and rural areas

KB36-0063-02

Ruimtelijke vergelijking van gemodelleerde biomassa met NDVI; onderzoek ter verbetering van de modellering in de Natuurplanner van het Natuurplanbureau

In dit rapport wordt de ruimtelijke vergelijking van, door SUMO gesimuleerde bladbiomassa, met de via remote sensing verkregen NDVI beschreven. Vergelijking heeft plaats gevonden voor meer dan 90% met vegetatiestructuurtypen gevulde SUMO grids (250m*250m) binnen de EHS. De gevonden relaties voor verschillende tijdstippen, per vegetatiestructuurtype en per functioneel type zijn niet significant of als ze significant zijn wordt slechts een geringe deel van de variantie verklaard. Schaal verschillen tussen de NDVI data en de SUMO data compliceren de ruimtelijke vergelijking. Pseudo-replicatie en de kwaliteit van de SUMO input zijn andere beperkende factoren. Ruimtelijke vergelijking van LGN4 classificatie met de initiële vegetatiestructuurtypen in SUMO geeft op een gedetailleerd niveau grote verschillen

CORINE land cover database of the Netherlands: monitoring land cover changes between 1986 and 2000

This paper describes the methodology for updating the CORINE 1986 database for the Netherlands to CLC2000 and the accuracy of the resulting database. The methodology consisted of computer-assisted visual interpretation of satellite images. Furthermore, topographic maps (analogue and digital), aerial photographs and the national land cover database of the Netherlands (LGN4) were used as additional information in the interpretation and verification processes. During the first step, the geometry and thematic contents of the CORINE 1986 land cover interpretation were revised. Next, the CORINE 1986 was updated on the basis of Landsat 7 ETM images of 1999 and 2000. Land cover changes larger than 5 ha were digitized into the database and labeled to ensure that the land cover changes could be discriminated from other changes in the database. Furthermore, each polygon has an attribute label for the land cover class in 1986 as well as 2000. This procedure ensured consistency between the three databases, because the CORINE 1986 classification, the CORINE 2000 classification as well as the land cover changes could be generated from the same database. The validation was based on a stratified random sample whose true land cover types were taken from aerial photographs. The validation revealed an overall accuracy of nearly 95% for the CLC2000 database (level 3) when taking into account that patches smaller than 25 hectares are not allowed in the CLC database. Omitting this condition reduces the accuracy by 30%. The changes in the CLCchange database have a user and producer's accuracy of 76.1 and 91.1%. The producer's accuracy indicates an overestimation of changes of almost 10%. Comparing the number of changes with the Dutch National database also suggests a slight overestimation of changes