Abstract

An undesirable property of systematic spatial sampling is that there is no known method allowing unbiased estimation of the uncertainty of statistical estimates from these surveys. A number of alternative variance estimation methods have been tested and reported by various authors. Studies comparing these estimators are inconclusive, partly because the studies compare different sets of estimators. In this paper, three estimators recommended in recent studies are compared using a single test dataset with known properties. The first estimator compared in this study (ST4) is based on post-stratification of the data. The second estimator (V08) is using a predetermined correction factor calculated from the spatial autocorrelation. The third estimator (MB) is a model based prediction calculated using values from the semivariogram. MB and ST4 were both found to be fairly accurate, while V08 consistently underestimated the variance in this study. V08 relies on the assumption that the autocorrelation structure in the dataset can be described using a particular exponential function. The most likely explanation of the weak result for V08 is that this assumption is violated by the empirical data used in the experiment. A better correction factor can be calculated, but the safe approach is to use MB or ST4.

Abstract

Detailed descriptions of individual vegetation types shown on vegetation maps can improve the ways in which the composition and spatial structure within the types are understood. The authors therefore examined dwarf shrub heath, a vegetation type covering large areas and found in many parts of the Norwegian mountains. They used data from point samples obtained in a wall-to-wall area frame survey. The point sampling method provided data that gave a good understanding of the composition and structure of the vegetation type, but also revealed a difference between variation within the vegetation type itself (intra-class variation) and variation resulting from the inclusion of other types of vegetation inside the map polygons (landscape variation). Intra-class variation reflected differences in the botanical composition of the vegetation type itself, whereas landscape variation represented differences in the land-cover composition of the broader landscape in which the vegetation type was found. Both types of variation were related to environmental gradients. The authors conclude that integrated point sampling method is an efficient way to achieve increased understanding of the content of a vegetation map and can be implemented as a supporting activity during a survey.

Abstract

There is a growing demand for reliable information about land cover and land resources. The Norwegian area frame survey of land cover and outfield land resources (AR18X18) is a response to this demand. AR18X18 provides unbiased land cover and land resource statistics and constitutes a baseline for studying changes in outfield land resources in Norway and a framework for a national land resource accounting system for the outfields. The area frame survey uses a systematic sampling technique with 0.9 km2 sample plots at 18 km intervals. A complete wall-to-wall land cover map of an entire plot surveyed is obtained in situ by a team of fieldworkers equipped with aerial photographs. The use of sample plots with extended coverage (0.9 km2) ensures that the survey also deals with local variation, thus strengthening the estimates well beyond simple point sampling. The article documents the methodology used in the survey, followed by a discussion of issues raised by the choice of methodology. These issues include the problem of calculating uncertainty and a confidence interval for the estimates, the focus on common rather than rare land cover categories, and the prospect of downscaling the results in order to obtain statistics for subnational regions.

Abstract

The objective of this paper is to examine a method for estimation of land cover statistics for local environments from available area frame surveys of larger, surrounding areas. The method is a simple version of the small-area estimation methodology. The starting point is a national area frame survey of land cover. This survey is post-stratified using a coarse land cover map based on topographic maps and segmentation of satellite images. The approach is to describe the land cover composition of each stratum and subsequently use the results to calculate land cover statistics for a smaller area where the relative distribution of the strata is known. The method was applied to a mountain environment in Gausdal in Eastern Norway and the result was compared to reference data from a complete in situ land cover map of the study area. The overall correlation (Pearson’s rho) between the observed and the estimated land cover figures was r = 0.95. The method does not produce a map of the target area and the estimation error was large for a few of the land cover classes. The overall conclusion is, however, that the method is applicable when the objective is to produce land cover statistics and the interest is the general composition of land cover classes – not the precise estimate of each class. The method will be applied in outfield pasture management in Norway, where it offers a cost-efficient way to screen the management units and identify local areas with a land cover composition suitable for grazing. The limited resources available for in situ land cover mapping can then be allocated efficiently to in-depth studies of the areas with the highest grazing potential. It is also expected that the method can be used to compile land cover statistics for other purposes as well, provided that the motivation is to describe the overall land cover composition and not to provide exact estimates for the individual land cover classes.

Abstract

AR-FJELL is the Norwegian land resource database for the mountain areas. AR-FJELL is not distributed as a separate product from Skog og landskap, but does – together with topographic data (series N50) from the Norwegian Mapping Authority (Statens kartverk) form the basis for the classification of mountain areas in the national land resource maps AR50 and AR250. The five Norwegian AR-FJELL classes are documented through descriptive statistical “profiles” of the actual content of each class. Profiles of the AR-FJELL classes were obtained through a GIS overlay operation between AR-FJELL and the available AR18X18 (Land resource accounting for the Norwegian outfields) survey plots. The distribution of vegetation classes for each AR-FJELL class was compiled from this overlay. The report also consider the distribution of the AR-FJELL classes by elevation asl and the distribution of the vegetation types in the AR18X18 sample. AR18X18 is (2011) only available for parts of Norway. The study should be repeated when a full national coverage is available. This is expected in 2015. The study was carried out with funding from the Norwegian Space Centre.

Abstract

A landscape region can be drawn on a map as a geographic feature with distinct boundaries. Reality is, however, that the change from one landscape to another usually is gradual and that landscapes therefore have uncertain or undetermined boundaries. A thematic map of landscape regions is therefore a too simple model of the landscape. An alternative approach is to consider landscape categories as purely theoretical concepts. With this perspective, a particular geographical location can be more or less affiliated with a number of different landscape categories. Such a conception of landscape does not lead to a traditional thematic map of uniform, non-overlapping regions, but to a landscape model composed of multiple overlapping probability surfaces. This article shows how such a landscape model can be established using binary logistic regression. The method is tested and the result is assessed against an existing landscape map of Norway much used in policy impact analysis in this country. The overall objective is to develop a data driven landscape model that can supplement, elucidate and for some purposes maybe even replace, the qualitative landscape description represented by the traditional landscape map.

Abstract

CORINE Land Cover (CLC) is a seamless European land cover vector database. The Norwegian CLC2000 was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2008 and was produced from existing national land cover datasets wherever available. CLC has a standardized nomenclature with 44 classes. 31 classes are represented in the Norwegian dataset. CLC is a small scale map showing built up areas, agriculture, forest and semi-natural areas, wetlands and water bodies. CLC has a minimum mapping unit of 25 ha. CLC2000 can be used for visualization of the general land cover patterns in Norway at a scale 1:250 000 or smaller. CLC2000 is representing the land cover situation close to year 20001. This report presents the Norwegian CLC2000 project and the methods and automatic generalization processes that were used in the project. CORINE Land Cover is one of four land cover maps (AR5, AR50, AR250 and CLC) published by Skog og landskap. CLC2000 was produced with support from the European Environmental Agency (EEA) who has joint ownership to the product....

Abstract

CORINE Land Cover (CLC) is a seamless European land cover vector database. The Norwegian CLC for the reference year 2006 (CLC2006) was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2009 and was produced according to CLC2006 technical guidelines (EEA 2007). CLC has a common nomenclature with 44 classes that is used throughout Europe. 31 of these classes are found in the Norwegian dataset. A coordinating Technical Team from the European Topic Centre on Land Use and Spatial Information (ETC-LUSI) is coordinating the mapping efforts ensuring that the classification is applied in a similar fashion in each country....

Abstract

All the Norwegian CLC2006 classes are documented through descriptive statistical “profiles” of the actual contents in each class. The CLC2006 profiles are worked through based on an overlay operation between CLC2006 and AR5 (under the timberline) and AR50 (above the timberline). Based on this dataset statistics are generated, that shows the percent distribution of AR5 and AR50 classes in each CLC2006 class. The study was carried out with funding from the Norwegian Space Centre.

Abstract

The change from one landscape to another is gradual. Landscape classes can therefore be considered as theoretical concepts and a particular location or area can be affiliated with a number of different landscape classes. Landscape classification thus becomes a statistical and probabilistic exercise. Such a probabilistic approach to landscape regions can be made operational using a grid model combined with binary logistic regression. This approach was tested on a landscape region in Norway.

Abstract

AR18×18 is an area frame survey of land resources in Norway, methodologically linked to the Lucas survey carried out by Eurostat (Eurostat 2003). The purpose of the survey is to establish an unbiased and accurate land cover and land use statistic providing a description of the state of land resources in Norway. The survey will also provide a baseline for future reports regarding changes in land resources – a national land resource accounting system. AR18×18 is based on Lucas (Land use/cover agricultural survey), a European area frame survey carried out in the EU countries by Eurostat. The sampling units of Lucas are points located on the intersections of an 18 × 18 kilometer grid mesh throughout Europe. Each of these points is the centre of a Primary Statistical Unit (PSU) of 1500 × 600 meters. The Lucas survey is carried out on ten sample points scattered within each PSU. The Norwegian modification of Lucas is to add a land cover survey of the whole PSU following the Norwegian system for vegetation and land cover mapping at intermediate scale (1: 20,000). [...]

Abstract

Change in crown density for Norway spruce (Picea abies) from 1988 to 1993 in three independent forest monitoring projects in southern Norway were compared. An increase in crown density was found in a countywide systematic random sample, whie measurements taken in old-growth forests reported a decline. These contradictory results may be due to: (1) high sensitivity of high-elevation forests to various kinds of environmental impact; (2) differences in stand age and management practice; and (3) different sensitivity to long distance airborne pollutants. The systematic random sample encompassed stans of several age classes from two counties, while the two other studies were restricted to old-growth forest in two smaller are as. A possibe explanation of the differences is thus that the three studies refer to differet popuations as a resut of different sampling strategies.