Mean Patch Fractal Dimension Research Articles

Diffusion and Coalescence of the Houston Metropolitan Area: Evidence Supporting a New Urban Theory

The authors build on a recent development in urban geographic theory, providing evidence of an oscillatory behavior in spatiotemporal patterns of urban growth. With the aid of remotely sensed data, the spatial extent of urban areas in the Houston (USA) metropolitan region from 1974 to 2002 was analyzed by spatial metrics. Regularities in the spatial urban growth pattern were identified with temporal periods as short as thirty years by means of spatial metric values, including mean nearest-neighbor distance, mean patch area, total number of urban patches, and mean patch fractal dimension. Through changes in these values, a distinct oscillation between phases of diffusion and coalescence in urban growth was revealed. The results suggest that the hypothesized process of diffusion and coalescence may occur over shorter time periods than previously thought, and that the patterns are readily observable in real-world systems.

Dynamic of forest landscape in Heilongjiang Province for one century

With the help of GIS tool of ARC/INFO, ARCVIEW and FRAGSTATS, the map of forest resource distribution of Heilongjiang Province was analyzed in 1896, 1949 and 1981. Using total area, mean patch size, patch density, coefficient of patch size variation, mean patch fractal dimension and mean shape index, we studied the change of forest landscape pattern and the change of each patch types in this region. As a result, the total area of forest landscape and mean patch size decreased sharply, the quantity and density of patches increased, the juxtaposition of patches weakened, the shape of patch tended to become regular, and the border of patch simplified. All these showed that the forest landscape of this area tended to fragment gradually, and the fragment of Korean pine forest is the severest. The diversity of whole forest landscape and the evenness of landscape types distribution reduced gradually. Human impact, instead of climate change and forest community succession, is the most important reason for such dramatic changes.

Effects of spatial aggregation approaches on classified satellite imagery

Spatial aggregations of raster data based on the majority rule have been typically used in landscape ecological studies. It is generally acknowledged that (1) dominant classes increase in abundance while minor classes decrease in abundance or even disappear through aggregation processes; and (2) spatial patterns also change with aggregations. In this paper, we examined an alternative, random rule-based aggregation and its effects on cover type abundance and landscape patterns, in comparison with the majority rule-based aggregation. We aggregated a classified TM imagery (about 1.5 million ha) from 30m (4231 × 3717 pixels) incrementally to 990m resolution (132 pixels × 116 pixels). Cover type proportion, mean patch size ratio, aggregation index (AI), and fractal dimension (FD) were used to assess the effects of aggregation. To compare landscapes under different resolutions, we assumed that the landscapes were least distorted if (1) the cover type proportions and mean patch size ratios among classes were maintained, and (2) all cover types responded in the same way for a given index as aggregation levels increased. For example, distortion is introduced by aggregation if some cover types increase their AI values with increasing aggregation levels while other cover types decrease. Our study indicated that the two spatial aggregation techniques led to different results in cover type proportions and they altered spatial pattern in opposite ways. The majority rule-based aggregations caused distortions of cover type proportions and spatial patterns. Generally, the majority rule-based aggregation filtered out minor classes and produced clumped landscapes. Such landscape representations are relatively easy for interpreting and, therefore, are suitable for land managers to conceptualize spatial patterns of a study region. By contrast, the random rule-based aggregations maintained cover type proportions accurately, but tended to make spatial patterns change toward disaggregation. Overall, the measurements of landscape indices used in this study indicated that

Landscape structure analysis of Kansas at three scales

Recent research in landscape ecology has sought to define the underlying structure of landscape pattern as quantified by landscape pattern metrics. One method used by researchers to address this question involves statistical data reduction techniques. In this study, principal components analysis (PCA) was performed on 27 landscape pattern metrics derived from a Kansas land cover data base at three spatial resolutions: 30 m, 100 m, and 1 km. A hexagonal sampling grid was used to subset the landscape and FRAGSTATS software was used to calculate landscape pattern metrics. The PCA reduced the number of variables from 27 to 5. A five-component PCA solution explained between 81 and 89% of the variation in the data set. The components were interpreted as overall landscape texture, patch shape and size, cropland and grassland class-specific metrics, patch interspersion, and a nearest neighbor attribute. These five dimensions were identified at each resolution level. The first component was stable throughout the resolution levels, whereas the order of importance changed for the latter four components. That most components consistently appeared at each resolution level supports the use of the same subset of pattern metrics for landscape monitoring in the region at different resolutions. The individual metrics emerging as most important were similar to those noted in other research and include the modified Simpson’s diversity index, the area-weighted mean patch fractal dimension, the interspersion and juxtaposition index, and the largest patch index for grasslands.