Statistical detection of multiscale landscape patterns

Detection of discontinuities in landscape patterns is a crucial problem both in ecology and in environmental sciences since they may indicate substantial scale changes in generating and maintaining processes of landscape patches. This paper presents a statistical procedure for detecting distinct scales of pattern for irregular patch mosaics using fractal analysis. The method suggested is based on a piecewise regression model given by fitting different regression lines to different ranges of patches ordered according to patch size (area). Proper shift-points, where discontinuities occur, are then identified by means of an iterative procedure. Further statistical tests are applied in order to verify the statistical significance of the best models selected. Compared to the method proposed by Krummel et al. (1987), the procedure described here is not influenced by subjective choices of initial parameters. The procedure was applied to landscape pattern analysis of irregular patch mosaics (CORINE biotopes) of a watershed within the Map of the Italian Nature Project. Results for three different CORINE patch types are herein presented revealing different scaling properties with special pattern organizations linked to ecological traits of vegetation communities and human disturbance.     

Application of CORINE land-cover mapping to estimate carbon stored in the vegetation of Ireland

The CORINE land cover database for Ireland (in ARC/INFO) is used to estimate the amount of carbon stored (tonnes) by each land-cover (vegetation) type. Carbon store is the area of each CORINE land-cover type multiplied by its carbon density (t C ha⁻¹). Derivations of these carbon densities are described and limitations of data and other empirical evidence discussed. The total vegetation-carbon stores are calculated for Northern Ireland (3·81 Mt), the Republic of Ireland (19·27 Mt) and Ireland (23·08 Mt). Carbon densities are grouped into classes and their distributions across Ireland are mapped. The vegetation-carbon store is taken to include stems, branches, foliage and roots. It does not include litter, microbial biomass and organic carbon in the soil. Forests store 49% of the vegetation carbon on less than 5% of the total CORINE land area, with a further 22% in other semi-natural vegetation. In contrast, pastures account for 56% of the land-cover area, but only 19% of the carbon store. High carbon densities are found in the west and in uplands, reflecting the distribution of forests and semi-natural vegetation, particularly peatland and moors. The inventory of vegetation-carbon stores is an important first step in attempts to monitor changes in carbon sequestration from, and emissions to, the atmosphere by terrestrial vegetation. Greenhouse gas fluxes, including CO₂, and climate warming are global issues which require responses by all countries. Inventories of carbon stores and fluxes therefore need to be comparable between countries so that agreed reductions can be targetted. CORINE land-cover data are available for 19 European Union and adjacent countries and could be used to provide an inventory of carbon stores, and through updating of CORINE, changes in those stores. Commonality in determining the carbon densities of CORINE classes would be required. This study exemplifies how that was achieved in two countries using their national data.     

Land cover types and ecological conditions of the Estonian coast

Detailed analysis of the land cover of the Estonian coastal zone is presented based on Estonian laws on coastal zone management, the CORINE Land Cover (CLC) system, the status of protected areas, and administrative division data of Estonia. By law the coastal zone is defined as a 200-m wide zone landward from the mean sea level line. The length of the Estonian coastline (including the islands) is 3794 km. The 200-m zone of the Estonian coast is very diverse. Out of the 34 CORINE land cover types represented in Estonia 30 are found in the coastal zone. Three dominating land cover types in the coastal zone of Estonia are inland marshes, coniferous forest and semi-natural grassland. Their total share is 47%; the other 27 land cover types represented here cover 53% of the coastal zone. The Estonian coastal zone is generally in a good natural condition. The proportion of artificial surfaces throughout the zone is merely 4.7%, while agricultural landscapes cover only ca. 10%. Land cover data for the coastal zone are also presented by county. Of the 200-m coastal zone 24% is under protection, which is more than twice the value for Estonia as a whole (11%). Legislative protection of the coastal zone is presently satisfactory. The use of the CORINE Land Cover system enables comparisons with other European regions since CLC data have been compiled for most of Europe.     

Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis

This paper presents a statistical method for detecting distinct scales of pattern for mosaics of irregular patches, by means of perimeter–area relationships. Krummel et al. (1987) were the first to develop a method for detecting different scaling domains in a landscape of irregular patches, but this method requires investigator judgment and is not completely satisfying. Grossi et al. (2001) suggested a modification of Krummel's method in order to detect objectively the change points between different scaling domains. Their procedure is based on the selection of the best piecewise linear regression model using a set of statistical tests. Even though the change points were estimated, the null distributions used for testing purposes were those appropriate for known change points. The present paper investigates the effect that estimating the change points has on the underlying distribution theory. The procedure we suggest is based on the selection of the best piecewise linear regression model using a likelihood ratio (LR) test. Each segment of the piecewise linear model corresponds to a fractal domain. Breakpoints between different segments are unknown, so the piecewise linear models are non-linear. In this case, the frequency distribution of the LR statistic cannot be approximated by a chi-squared distribution. Instead, Monte Carlo simulation is used to obtain an empirical null distribution of the LR statistic. The suggested method is applied to three patch types (CORINE biotopes) located in the Val Baganza watershed of Italy.     

Landscape Change and Ecosystem Classification in a Municipal District of a Small City (Isernia,Central Italy)

Landscape changes taking place from 1954 to 1992 in the muncipal district of Isernia city (Central Italy) were described in relation to a system of ecosystem classification. Isernia municipal district was selected for study because recent historic changes in this area represent a typical example of landscape transformation similar to many small cities of Italy and other Mediterranean countries. To assess overall changes, three land cover maps (scale 1:25,000) were derived from panchromatic aerial photographs and field surveys. These were then digitalised in a Geographic Information System. A Land Facet (LF) map was derived by combining a phytoclimatic, a lithostatigrafic and a topographic map, and then digitalised as data layers in the same GIS. Results demonstrated two main landscape transformation trends: forest and semi-natural areas increased (8%), whereas agricultural areas decreased (12%). The urban area was relatively small during the entire analysed period, growing from 1% in 1954, to just 5% in 1992. Forest coverage was significant on reliefs, on hillside ecosystems such as limestone and on clay and marl hills LF. Arable land was particularly significant in flat ecosystems with deeper soils, such as on recent alluvial plain LF. These temporal changes were interpreted as being related to the replacement of traditional farming methods (grazing pastures) with more intensive methods (crop fields), especially on alluvial plains.     

Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balance monitoring or crop models for yield prediction. The different phenological stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the regional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Measurements show that the air above dry surfaces is warmer than above wet areas. We propose a method taking into account the environment of the meteorological stations in order to improve spatial interpolation of air temperature. The aim of this study is to assess the impact of these corrected climatic data in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a land use map summarized in a small number of classes considered as a factor influencing local temperature. This method was applied to a region in south-east France (150×250 km) where daily temperatures were measured on 150 weather stations for two years. Environment classes were extracted from the CORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study. The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature with the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the method works well for air temperature in summer and can lead to significant correction for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0°C (exceptionally 2.5°C for some class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.