A new approach for rescaling land cover data

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.     

Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model

Conceptual frameworks of dryland degradation commonly include ecohydrological feedbacks between landscape spatial organization and resource loss, so that decreasing cover and size of vegetation patches result in higher water and soil losses, which lead to further vegetation loss. However, the impacts of these feedbacks on dryland dynamics in response to external stress have barely been tested. Using a spatially-explicit model, we represented feedbacks between vegetation pattern and landscape resource loss by establishing a negative dependence of plant establishment on the connectivity of runoff-source areas (e.g., bare soils). We assessed the impact of various feedback strengths on the response of dryland ecosystems to changing external conditions. In general, for a given external pressure, these connectivity-mediated feedbacks decrease vegetation cover at equilibrium, which indicates a decrease in ecosystem resistance. Along a gradient of gradual increase of environmental pressure (e.g., aridity), the connectivity-mediated feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state (ecosystem resilience). If environmental conditions improve, these feedbacks increase the pressure release needed to achieve the ecosystem recovery (restoration potential). The impact of these feedbacks on dryland response to external stress is markedly non-linear, which relies on the non-linear negative relationship between bare-soil connectivity and vegetation cover. Modelling studies on dryland vegetation dynamics not accounting for the connectivity-mediated feedbacks studied here may overestimate the resistance, resilience and restoration potential of drylands in response to environmental and human pressures. Our results also suggest that changes in vegetation pattern and associated hydrological connectivity may be more informative early-warning indicators of dryland degradation than changes in vegetation cover.     

Ecosystem-level dynamics of soil-vegetation features, with implications for conserving a narrowly endemic reptile

Narrow endemism presents challenges to species occurrence modeling particularly when the distribution of key local habitat features changes across space and time as a function of processes operating at larger scales. One need facing conservation in such settings is a better understanding of the biogeographic dynamics of the larger features that govern occurrence of critical local habitat. The Mescalero–Monahans shinnery sands region of western North America is a dynamic landscape where sand shinnery oak interacts with wind-driven sand to establish dune habitat. This ecosystem supports several narrowly endemic dune-dwelling species including the dunes sagebrush lizard. Using near-anniversary satellite and aerial imagery from 1986, 1998, and 2011, we integrated object-based image classification and statistical analysis to develop and validate a spatially explicit estimate of the sand shinnery oak ecosystem, including dynamics associated with its attrition and emergence, at high resolution throughout an 89,849-km² study area encompassing the range of the dunes sagebrush lizard. The spatial estimate of the distribution and extent of the sand shinnery oak soil-vegetation association validated reasonably well (overall accuracy = 0.79; sensitivity = 0.49; specificity = 0.91) and showed that the association declined 10.3 % in extent during the 25-year assessment window. The presence of sand shinnery oak, patch size, and patch isolation were dynamic across space and time; a regression model showed that smaller, isolated patches on the periphery of the system were more likely to be lost over time whereas larger, less isolated, and centrally distributed patches were more likely to persist or expand. This study details broadly applicable methods to accurately delineate landforms throughout large extents, and offers mapping tools specific to issues surrounding Mescalero–Monahans shinnery sands endemics that are readily amenable to testing, refinement, and application in efforts to focus sustainable landscape management including conservation of endemic species.     

Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology

Mapping urban vegetation is a prerequisite to accurately understanding landscape patterns and ecological services provided by urban vegetation. However, the uncertainties in fine-scale vegetation biodiversity mapping still exist in capturing vegetation functional types efficiently at fine scale. To facilitate the application of fine-scale vegetation spatial configuration used for urban landscape planning and ecosystem service valuation, we present an approach integrating object-based classification with vegetation phenology for fine-scale vegetation functional type mapping in compact city of Beijing, China. The phenological information derived from two WorldView-2 imagery scenes, acquired on 14 September 2012 and 26 November 2012, was used to aid in the classification of tree functional types and grass. Then we further compared the approach to that of using only one WorldView imagery. We found WorldView-2 imagery can be successfully applied to map functional types of urban vegetation with its high spatial resolution and relatively high spectral resolution. The application of the vegetation phenology into classification greatly improved the overall accuracy of classification from 82.3% to 91.1%. In particular, the accuracies of vegetation types was improved by from 10% to 13.26%. The approach integrating vegetation phenology with high-resolution remote sensed images provides an efficient tool to incorporate multi-temporal data into fine-scale urban classification.     

Changes in landscape patterns in Georgia,USA

The objectives of this study were to determine how landscape patterns in Georgia, USA have changed through time and whether the spatial patterns varied by physiographic region. Historical aerial photography was used to analyze spatial patterns of land use from the 1930's to the 1980's. Land use patterns were quantified by: (1) mean number and size of patches; (2) fractal dimension of patches; (3) amount of edge between land uses; and (4) indices of diversity, dominance, and contagion. Forest cover increased in aerial extent and in mean patch size. The mean size of agricultural patches increased in the coastal plain and decreased in the mountains and piedmont. Edges between land uses decreased through time, indicating less dissection of the landscape. Fractal dimensions also decreased, indicating simpler patch shapes. Indices of diversity and dominance differed through time but not among regions; the contagion index differed among regions but not through time. A geographic trend of decreasing diversity and increasing dominance and contagion was observed from the mountains to the lower coastal plain. Landscape patterns exhibited the greatest changes in the piedmont region. Overall, the Georgia landscape has become less fragmented and more connected during the past 50 years. Changing patterns in the landscape may have implications for many ecological processes and resources.     

Alternative spatial resolutions and estimation of carbon flux over a managed forest landscape in Western Oregon

Spatially-distributed estimates of biologically-driven CO₂ flux are of interest in relation to understanding the global carbon cycle. Global coverage by satellite sensors offers an opportunity to assess terrestrial carbon (C) flux using a variety of approaches and corresponding spatial resolutions. An important consideration in evaluating the approaches concerns the scale of the spatial heterogeneity in land cover over the domain being studied. In the Pacific Northwest region of the United States, forests are highly fragmented with respect to stand age class and hence C flux. In this study, the effects of spatial resolution on estimates of total annual net primary production (NPP) and net ecosystem production (NEP) for a 96 km² area in the central Cascades Mountains of western Oregon were examined. The scaling approach was a simple `measure and multiply' algorithm. At the highest spatial resolution (25 m), a stand age map derived from Landsat Thematic Mapper imagery provided the area for each of six forest age classes. The products of area for each age class and its respective NPP or NEP were summed for the area wide estimates. In order to evaluate potential errors at coarser resolutions, the stand age map was resampled to grain sizes of 100, 250, 500 and 1000 m using a majority filter reclassification. Local variance in near-infrared (NIR) band digital number at successively coarser grain sizes was also examined to characterize the scale of the heterogeneity in the scene. For this managed forest landscape, proportional estimation error in land cover classification at the coarsest resolution varied from –1.0 to +0.6 depending on the initial representation and the spatial distribution of the age class. The overall accuracy of the 1000 m resolution map was 42% with respect to the 25 m map. Analysis of local variance in NIR digital number suggested a patch size on the order of 100–500 m on a side. Total estimated NPP was 12% lower and total estimated NEP was 4% lower at 1000 m compared to 25 m. Carbon flux estimates based on quantifying differences in total biomass stored on the landscape at two points in time might be affected more strongly by a coarse resolution analysis because the differences among classes in biomass are more extreme than the differences in C flux and because the additional steps in the flux algorithm would contribute to error propagation. Scaling exercises involving reclassification of fine scale imagery over a range of grain sizes may be a useful screening tool for stratifying regions of the terrestrial surface relative to optimizing the spatial resolution for C flux estimation purposes.     

Ownership and ecosystem as sources of spatial heterogeneity in a forested landscape,Wisconsin, USA

The interaction between physical environment and land ownership in creating spatial heterogeneity was studied in largely forested landscapes of northern Wisconsin, USA. A stratified random approach was used in which 2500-ha plots representing two ownerships (National Forest and private non-industrial) were located within two regional ecosystems (extremely well-drained outwash sands and moderately well-drained moraines). Sixteen plots were established, four within each combination of ownership and ecosystem, and the land cover on the plots was classified from aerial photographs using a modified form of the Anderson (U.S. Geological Survey) land use and land cover classification system.Upland deciduous forests dominated by northern hardwoods were common on the moraines for both ownerships. On the outwash, the National Forest was dominated by pine plantations, upland deciduous forests, and upland regenerating forests (as defined by <50% canopy coverage). In contrast, a more even distribution among the classes of upland forest existed on private land/outwash. A strong interaction between ecosystem and ownership was evident for most comparisons of landscape structure. On the moraine, the National Forest ownership had a finer grain pattern with more complex patch shapes compared to private land. On the outwash, in contrast, the National Forest had a coarser grain pattern with less complex patch shapes compared to private land. When patch size and shape were compared between ecosystems within an ownership, statistically significant differences in landscape structure existed on public land but not on private land. On public land, different management practices on the moraine and outwash, primarily related to timber harvesting and road building, created very different landscape patterns. Landscape structure on different ecosystems on private land tended to be similar because ownership was fragmented in both ecosystems and because ownership boundaries often corresponded to patch boundaries on private land. A complex relationship exits between ownership, and related differences in land use, and the physical environment that ultimately constrains land use. Studies that do not consider these interactions may misinterpret the importance of either variable in explaining variation in landscape patterns.     

Analysis of fine-scale spatial pattern of a grassland from remotely-sensed imagery and field collected data

An important practical problem in the analysis of spatial pattern in ecological systems is that requires spatially-intensive data, with both fine resolution and large extent. Such information is often difficult to obtain from field-measured variables. Digital imagery can offer a valuable, alternative source of information in the analysis of ecological pattern. In the present paper, we use remotely-sensed imagery to provide a link between field-based information and spatially-explicit modeling of ecological processes. We analyzed one digitized color infrared aerial photograph of a serpentine grassland to develop a detailed digital map of land cover categories (31.24 m × 50.04 m of extent and 135 mm of resolution), and an image of vegetation index (proportional to the amount of green biomass cover in the field). We conducted a variogram analysis of the spatial pattern of both field-measured (microtopography, soil depth) and image-derived (land cover map, vegetation index, gopher disturbance) landscape variables, and used a statistical simulation method to produce random realizations of the image of vegetation index based upon our characterization of its spatial structure. The analysis revealed strong relationships in the spatial distribution of the ecological variables (e.g., gopher mounds and perennial grasses are found primarily on deeper soils) and a non-fractal nested spatial pattern in the distribution of green biomass as measured by the vegetation index. The spatial pattern of the vegetation index was composed of three basic components: an exponential trend from 0 m to 4 m, which is related to local ecological processes, a linear trend at broader scales, which is related to a general change in topography across the study site, and a superimposed periodic structure, which is related to the regular spacing of deeper soils within the study site. Simulations of the image of vegetation index confirmed our interpretation of the variograms. The simulations also illustrated the limits of statistical analysis and interpolations based solely on the semivariogram, because they cannot adequately characterize spatial discontinuities.     

Detecting hot and cold spots in a seagrass landscape using local indicators of spatial association

Eelgrass (Zostera marina) is an important feature of coastal ecosystems in Atlantic Canada, providing a suite of valuable ecosystem services. These services, and its sensitivity to stressors, have prompted efforts to characterize the spatial and temporal dynamics of eelgrass landscapes in order to facilitate management and monitoring of coastal ecosystem health. Current methods for broad-scale mapping of eelgrass rely on aerial remote sensing and may not be appropriate in certain types of landscapes, particularly in turbid waters and areas lacking distinct boundaries. This study takes a novel approach to the quantification and analysis of seagrass landscape structure at multiple spatial scales using acoustic data and local spatial statistics. Data from a single-beam acoustic survey in Richibucto, New Brunswick, Canada were analyzed with geostatistical techniques and the Getis-Ord G _i ^* local spatial statistic in order to detect statistically significant zones of high and low cover in an estuarine seagrass bed. Results showed distinct and significant patterns in seagrass cover at multiple spatial scales within a region of apparently continuous spatial cover. Boundaries between areas of high and low cover were also detected. This study demonstrates how acoustic data and local spatial statistics can be used to quantify landscape pattern and to further the application of landscape techniques in the marine environment.     

Land-use and land-cover dynamics in response to changes in climatic,biological and socio-political forces: the case of southwestern Ethiopia

Few studies of land-use/land-cover change provide an integrated assessment of the driving forces and consequences of that change, particularly in Africa. Our objectives were to determine how driving forces at different scales change over time, how these forces affect the dynamics and patterns of land use/land cover, and how land-use/land-cover change affects ecological properties at the landscape scale. To accomplish these objectives, we first developed a way to identify the causes and consequences of change at a landscape scale by integrating tools from ecology and the social sciences and then applied these methods to a case study in Ghibe Valley, southwestern Ethiopia. Maps of land-use/land-cover change were created from aerial photography and Landsat TM imagery for the period, 1957–1993. A method called `ecological time lines' was developed to elicit landscape-scale explanations for changes from long-term residents. Cropland expanded at twice the speed recently (1987–1993) than two decades ago (1957–1973), but also contracted rapidly between 1973–1987. Rapid land-use/land cover change was caused by the combined effects of drought and migration, changes in settlement and land tenure policy, and changes in the severity of the livestock disease, trypanosomosis, which is transmitted by the tsetse fly. The scale of the causes and consequences of land-use/land-cover change varied from local to sub-national (regional) to international and the links between causes and consequences crossed scales. At the landscape scale, each cause affected the location and pattern of land use/land cover differently. The contraction of cropland increased grass biomass and cover, woody plant cover, the frequency and extent of savanna burning, and the abundance of wildlife. With recent control of the tsetse fly, these ecological changes are being reversed. These complex patterns are discussed in the context of scaling issues and current conceptual models of land-use/land-cover change.     

Statistical models of landscape pattern metrics,with applications to regional scale dynamic forest simulations

Forest managers in Canada need to model landscape pattern or spatial configurationoverlarge (100,000 km²) regions. This presents a scalingproblem, as landscape configuration is measured at a high spatial resolution,but a low spatial resolution is indicated for regional simulation. We present astatistical solution to this scaling problem by showing how a wide range oflandscape pattern metrics can be modelled from low resolution data. Our studyarea comprises about 75,000 km² of boreal mixedwoodforest in northeast Alberta, Canada. Within this area we gridded a sample of 84digital forest cover maps, each about 9500 ha in size, to aresolution of 1 ha and used FRAGSTATS to compute a suite oflandscape pattern metrics for each map. We then used multivariate dimensionreduction techniques and canonical correlation analysis to model therelationship between landscape pattern metrics and simpler stand table metricsthat are easily obtained from non-spatial forest inventories. These analyseswere performed on four habitat types common in boreal mixedwood forests: youngdeciduous, old deciduous, white spruce, and mixedwood types. Using only threelandscape variables obtained directly from stand attribute tables (totalhabitatarea, and the mean and standard deviation of habitat patch size), ourstatistical models explained more than 73% of the joint variation in fivelandscape pattern metrics (representing patch shape, forest interior habitat,and patch isolation). By PCA, these five indices captured much of the totalvariability in the rich set of landscape pattern metrics that FRAGSTATS cangenerate. The predictor variables and strengths of association were highlyconsistent across habitat classes. We illustrate the potential use of suchstatistical relationships by simulating the regional, cumulative effects ofwildfire and forest management on the spatial arrangement of forest patches,using non-spatial stand attribute tables.This revised version was published online in May 2005 with corrections to the Cover Date.     

Functional and structural landscape indicators of intensification,resilience and resistance in agroecosystems in southern Argentina based on remotely sensed data

There is increasing interest in developing criteria to evaluate the environmental implications of intensive agricultural land use. This implies discriminating between nature and man-made effects upon structural and functional attributes of agroecosystems. Adequate indicators of these combined effects should be cost efficient yet compatible with the core of ecological theory on biodiversity, spatial organization and ecosystem stability. We developed resistance-resilience metrics of plant growth to evaluate the intensity of agricultural use in a temperate irrigated basin in southern Argentina. The metrics are based on an analysis of the components of a temporal series of vegetation indices computed at a low resolution from available globally remote sensed reflectance imagery. We related the developed metrics to the properties of the soils and plant canopies observed at field scale and high-resolution imagery of the basin. Soil depth, soil erosion status and land fragmentation account for large fractions of the variance of the distribution of functional groups of the plant canopies and are also correlated with smaller scale attributes of land vegetation cover. Resistance-resilience indicators constitute a cost-efficient and adequate approach to evaluate the degree of intensification of land agricultural use.     

Influence of landscape structure and stand age on species density and biomass of a tropical dry forest across spatial scales

Three central related issues in ecology are to identify spatial variation of ecological processes, to understand the relative influence of environmental and spatial variables, and to investigate the response of environmental variables at different spatial scales. These issues are particularly important for tropical dry forests, which have been comparatively less studied and are more threatened than other terrestrial ecosystems. This study aims to characterize relationships between community structure and landscape configuration and habitat type (stand age) considering different spatial scales for a tropical dry forest in Yucatan. Species density and above ground biomass were calculated from 276 sampling sites, while land cover classes were obtained from multi-spectral classification of a Spot 5 satellite imagery. Species density and biomass were related to stand age, landscape metrics of patch types (area, edge, shape, similarity and contrast) and principal coordinate of neighbor matrices (PCNM) variables using regression analysis. PCNM analysis was performed to interpret results in terms of spatial scales as well as to decompose variation into spatial, stand age and landscape structure components. Stand age was the most important variable for biomass, whereas landscape structure and spatial dependence had a comparable or even stronger influence on species density than stand age. At the very broad scale (8,000–10,500 m), stand age contributed most to biomass and landscape structure to species density. At the broad scale (2,000–8,000 m), stand age was the most important variable predicting both species density and biomass. Our results shed light on which landscape configurations could enhance plant diversity and above ground biomass.     

Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices

Analyzing the effect of scale on landscape pattern indices has been a key research topic in landscape ecology. The lack of comparability of fragmentation indices across spatial resolutions seriously limits their usefulness while multi-scale remotely sensed data are becoming increasingly available. In this paper, we examine the effect of spatial resolution on six common fragmentation indices that are being used within the Third Spanish National Forest Inventory. We analyse categorical data derived from simultaneously gathered Landsat-TM and IRS-WiFS satellite images, as well as TM patterns aggregated to coarser resolutions through majority rules. In general, majority rules tend to produce more fragmented patterns than actual sensor ones. It is suggested that sensor point spread function should be specifically considered to improve comparability among satellite images of varying pixel sizes. Power scaling-laws were found between spatial resolution and several fragmentation indices, with mean prediction errors under 10% for number of patches and mean patch size and under 5% for edge length. All metrics but patch cohesion indicate lower fragmentation at coarser spatial resolutions. In fact, an arbitrarily large value of patch cohesion can be obtained by resampling the pattern to smaller pixel sizes. An explanation and simple solution for correcting this undesired behaviour is provided. Landscape division and largest patch index were found to be the least sensitive indices to spatial resolution effects. This revised version was published online in May 2005 with corrections to the Cover Date. This revised version was published online in July 2006 with corrections to the Cover Date.     

Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions

Context

The open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥?250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m).

Objectives

Based on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed.

Methods

MODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha^?1 year^?1). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed.

Results

We achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R² was?>?0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha^?1 year^?1) and the lowest for forest.

Conclusions

Estimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. It is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape.

     

Research article Canopy dynamics and human caused disturbance on a semi-arid landscape in the Rocky Mountains,USA

Invasion of grasslands by woody plants has been identified as a key indicator of changes in ecosystem structure and function in arid and semi-arid rangelands throughout the world. We investigated changes in the balance between woody and herbaceous components of a semi-arid landscape in western Colorado (USA) using historical aerial photography. Aerial photographs from 1937, 1965–67, and 1994 were sampled at matched locations within overlapping photographs. We modeled change in spatial pattern and heterogeneity across the entire landscape and found a small, net decrease in woody canopy cover; however means disguised normal distributions of change that demonstrated offsetting increases and decreases. We described a region of widespread canopy decline within piñon-juniper forests between 2300 and 2600 m (7500–8500 feet) and a region of predominant increase at lower elevations, between 1800 and 2250 m (5900–7400 feet). It remains unclear whether this shift was driven by climate or by human-caused or natural disturbance. Mean conifer cover decreased within coniferous forests, which counteracted a trend of increased conifer cover in mixed forests, savanna-like woodlands, and the shrub steppe. Disturbance had a significant interaction with cover change in several communities, including forests, savanna and shrublands. Anthropogenic disturbances counteracted successional trends toward canopy closure more than wildfires, but this did not entirely explain observed canopy decline. The natural dynamics in this region also caused diverse changes rather than a simple progression towards increased forest cover. Importantly, temporal change in vegetation varied spatially across the landscape illustrating the importance of landscape level, spatially explicit analyses in characterizing temporal dynamics.     

Fractal characterization of Mytilus edulis L. spatial structure in intertidal landscapes using GIS methods

The blue mussel, Mytilus edulis L., forms dense and variable patch mosaics composed of aggregates of mussel individuals. Knowledge of mussel bed spatial pattern at multiple scales is important for understanding the form and function of intertidal systems where mussels are prominent features. This study extends prior work demonstrating fractal patterns of mussel boundaries in soft-bottom systems at the quadrat-scale by investigating fractal structure using GIS methods at both the quadrat- and bed-scales. The study pursues three goals for mussel beds in eastern Maine: (1) to compare quadrat-scale fractal dimensions obtained using manual methods with those obtained using digital imagery and techniques, (2) to determine if fractal patterns identified at the quadrat-scale are also present at the bed-scale, (3) and to evaluate the effectiveness of aerial photography and image analysis techniques. Photographs of randomly located quadrats (2500 cm² each) were scan digitized and classified into mussel presence/absence classes. Fractal dimensions of mussel/non-mussel boundaries were calculated using the box-counting method and compared with results obtained using analog photographs and methods. Digital aerial photographs at low tide were acquired for beds at two sites and classified using image processing techniques, and bed-scale fractal dimensions were calculated. At the quadrat-scale, fractal dimensions and their relationship with percent cover differed consistently in absolute value from results using manual methods but agreed in demonstrating fractal patterns for all quadrats and a parabolic trend with percent cover very similar to the one revealed manually. At the bed-scale, both sites were shown to be fractal, with higher dimension value for the bed that subjectively appeared more fragmented and highly dissected. Because mussels are important soft-bottom ecosystem engineers, i.e., foundation species that regulate species composition and abundances, the fractal spatial distribution identified in this study suggests that the species affected by them also exhibit fractal patterns. These results indicate the effectiveness of archive imagery and GIS methods for characterizing intertidal systems and point to the feasibility of future image acquisition.     

Bush cover and range condition assessments in relation to landscape and grazing in southern Ethiopia

Progressive growth of bush cover in dry savannahs is responsible for declines in range conditions. In southern Ethiopia, the Booran pastoralists assisted our understanding of spatial patterns of bush cover and range conditions in 54 landscape patch types grouped into six landscape units within an area of 30000 km². The size of landscape patches sampled was 625 m². We assessed the relationships between bush cover, grass cover and bare soil and grazing pressure and soil erosion and changes in range condition. Externally, political conflicts and internally, break down of land use, and official bans on the use of fire promoted bush cover and the decline in range conditions. Bush cover was negatively correlated with grass cover, and positively correlated with bare soil. Grass cover was negatively correlated with bare soil and grazing pressure in most landscape patch types. Grazing pressure was not significantly correlated with bush cover or bare soil, while soil erosion was directly related to bare soil. Soil erosion was absent in 64% of the landscape patch types, and seemingly not a threat to the rangelands. The relationship between bush cover, grass cover, bare soil and soil erosion is complex and related to climate, landscape geology, and patterns of land use. Main threats to range conditions are bush climax, loss of grass cover and unpalatable forbs. Currently, >70% of the landscape patch types are in poor to fair range conditions. Decline in range conditions, unless reversed, will jeopardise the pastoral production system in southern Ethiopia.     

Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation

Resource management strategies have begun to adopt natural landscape disturbance emulation as a means of minimizing risk to ecosystem integrity. Detailed understanding of the disturbance regime and the associated spatial landscape patterns are required to provide a natural baseline for comparison with the results of emulation strategies. Landscape pattern indices provide a useful tool to quantify spatial pattern for developing these strategies and evaluating their success. Despite an abundance of indices and tools to calculate these, practical knowledge of interpretation is rare. Quantifying changes in landscape pattern indices and the meaning of these changes is confounded by index sensitivity to input data characteristics such as spatial extent, spatial resolution, and thematic resolution. Sensitivity has been examined for simulated landscapes but rarely using real data for large areas as real landscapes are more difficult to manipulate systematically than simulated data. While simulated data offer a control, they do not provide an accurate portrayal of reality for practical applications. Our goal was to test the sensitivity of a suite of landscape pattern indices useful for disturbance emulation strategy development and evaluation to spatial extent, spatial resolution, and thematic resolution using current land cover data for a case study of the managed forest of Ontario, Canada. We also examined how sensitivity varies spatially across the study area. We used Landsat TM-based land cover data (> 45.5 million ha), controlling spatial extent (2,500 to 2,560,000 ha), spatial resolution (1 to 16 ha), and thematic resolution (2 to 26 classes). For each index we tested a hypothesis of insensitivity to changes in each input data characteristic using a combination of ANOVA and regression and compared our results with previous studies. Of the 18 indices studied, significant (p< 0.01) effects were found for 17 indices with changes in spatial extent, 13 indices with changes in spatial resolution and 18 indices with changes in thematic resolution. A significant (p < 0.01) linear trend accounted for the majority of the variance for all of the significant relationships identified. Most of the mean index responses were consistent with those interpreted from previous studies of simulated and real landscapes; however, sensitivity varied greatly among indices and over space. We suggest that variation in sensitivity to input data characteristics among indices and over space must be explicitly incorporated in the design of future natural disturbance emulation efforts.This revised version was published online in May 2005 with corrections to the Cover Date.     

Landscape cover type and pattern dynamics in fragmented southern Great Plains grasslands,USA

We documented land cover and landscape pattern changes in an area of northwestern Oklahoma, USA using aerial photography from 1965, 1981, and 1995. This region of the southern Great Plains is fragmented by agricultural activity, and in recent years many remnant native grasslands have experienced extensive invasion by woody juniper (Juniperus virginiana L.). Concurrently, many cropland areas are being planted into perennial forage grasses and converted to intensively managed introduced grasslands as part of the U.S. Conservation Reserve Program (CRP). Our objectives were to document land cover and landscape pattern changes in the region relative to the expansion of juniper and CRP activity. We then examined how local landscape dominance by either anthropogenic or woody vegetation patches affected landscape pattern indices. Land cover changes from 1965 to 1995 included substantial increases in juniper woodlands and mixed woodlands that resulted from juniper encroachment into deciduous woodlands. Introduced grasslands also increased in many areas as a result of CRP implementation. Changes in landscape pattern generally reflected the influx of juniper into many areas. Landscapes dominated by woody vegetation had significantly more patches, smaller patches and patch core areas, more total edge, and higher patch diversity than landscapes dominated by anthropogenic cover types. Results indicate that expanding juniper is exacerbating the fragmentation process initiated by previous human activity, and represents a serious threat to the continued integrity and conservation of remaining southern Great Plains grasslands.This revised version was published online in May 2005 with corrections to the Cover Date.