Area-weighted Mean Patch Research Articles

Spatial pattern analysis for monitoring protected areas

The management of diverse biota within protected areas is affected by both land cover change within a protected area and habitat loss and fragmentation in the surrounding landscape. Satellite images provide a synoptic view of land cover patterns, but the use of such imagery requires careful consideration of sensor type, resolution, extent, and the metrics used to quantify ecologically significant change. We examined these factors for landscape monitoring applications in four small National Parks near Washington, DC: Antietam National Battlefield, Catoctin Mountain Park, Prince William Forest Park and Rock Creek Park. Using 4 m Ikonos, 10 m SPOT, 15 m pan-sharpened Landsat ETM+ and 30 m Landsat ETM+ imagery, the parks and surrounding areas were mapped to National Land Cover system classes. For each park, we examined four methods for defining map extent, including park administrative boundaries, two variable buffer widths, and watershed boundaries, and then analyzed patterns of forest habitat for the maps using a graph theoretic approach (critical dispersal threshold distance) and common landscape metrics (number of patches, percent forest, forest edge density, and forest area-weighted mean patch size). As expected, landscape metrics for maps derived at differing resolutions varied significantly, but map extent often yielded even larger differences. We found that for most applications, coarser scale data (e.g., 30 m Landsat) are adequate for characterizing landscape pattern, although ultimately data from multiple sensors may be appropriate or necessary based on different objectives of landscape monitoring (e.g., mapping single trees vs. forest stands) and the scale at which a resource of interest interacts with the larger landscape (e.g., birds vs. herptiles). Our results provide a strong caution regarding the practical issues associated with combining data sources from multiple satellite sensors for monitoring applications.

Sprawl in Taipei’s peri-urban zone: Responses to spatial planning and implications for adapting global environmental change

Peri-urban areas are often characterized by valuable natural environment and resource bases to provide essential ecosystem functions for urban residents. Observing land cover change over time can reveal the effects and impacts of urbanization in peri-urban areas. This paper investigates the spatial pattern of land conversion in the Taipei–Taoyuan area by interpreting the 1990 and 2006 SPOT images for comparison with the land cover map of 1971. Calculation of the normalized difference vegetation index (NDVI) compares the vegetation greenness on the two satellite images. Eight landscape metrics were used to analyze the spatial and temporal changes of landscape patterns of peri-urbanization in Taipei–Taoyuan area: total urban area, number of urban patches, mean urban patch area, largest patch index, area-weighted mean shape index, area-weighted mean patch fractal dimension index, edge density of urban area, and contagion index. Results indicate that the built-up area in the study region has increased by 130% from 466.32 km 2 in 1971 to 1071.43 km 2 in 2006. A comparison of landscape metrics between urban planned districts and non-urban planned districts indicates that urban sprawl in non-urban planned districts is more severe than in urban planned districts and the landscape in non-urban planned districts is highly fragmented. Existing built-up areas have a greater influence on urban expansion outside urban planned districts than in urban planned districts. It is suggested that a guiding plan is required to integrate planning in both urban and non-urban planned districts to overcome urban sprawl in peri-urban areas.

Monitoring Land Cover Change in Kathmandu City Using Spatial Metrics And Remote Sensing Techniques

This paper explores the urban land cover changes in Kathmandu city area in the past one and half decades. Multi-temporal satellite images and spatial metrics were used to quantify the land cover changes. The study area was setup to 10x10 km covering the Kathmandu metropolitan area. After pre-processing the satellite images, supervised classification with maximum likelihood classifier was applied to create thematic urban land cover maps. Random sampling method was used to create geographic points for each thematic map to assess the accuracy of the maps. Five land use classes: urban builtup area, cultivated land, orchard, water and natural vegetation were identified. For detecting land cover changes quantitatively, a land use land cover conversion matrix was computed between the year 1989- 1999 and 1999-2005. Spatial metrics (patch density, largest patch index, edge density, Euclidian nearest neighbor mean, contagion index and area weighted mean patch fractal dimension index) were computed to evaluate landscape structure of the metropolitan area.0The overall result shows rapid expansion of urban built environment and shrinkage of the agriculture land. The agriculture and orchard lands were mostly transformed into urban uses. The existence of water and vegetation were found very low as compared to other land covers. The urban expansion trend was confined in the peri-urban of the urbanized territory. The overall urban landscape seems to be very complex and fragmented in later years.

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.