Context-dependent vegetation dynamics in an African savanna

Understanding the spatio-temporal dynamics of ecological systems is fundamental to their successful management and conservation. Much research and debate has focused on identifying underlying drivers of vegetation change in savannas, yet few have considered the influence of spatial context and heterogeneity. Our goal was to develop deeper understanding of woody vegetation spatio-temporal dynamics through spatially explicit utilization of historical aerial photography and airborne LiDAR (light detection and ranging). We first assessed temporal change in woody vegetation cover through object-based image analysis of an aerial photography record that spanned 59 years from 1942 to 2001. Secondly, we tested the spatial relationships between environmental variables and patterns of woody structure and dynamics at broad (100 ha), medium (10 ha) and fine-scales (1 ha) through canonical correspondence analysis (CCA). Finally, we used LiDAR derived vegetation heights to explore current woody vegetation structure in the context of historical patterns of change. Total percentage woody cover was stable over time, but woody dynamics were highly variable at smaller scales and displayed distinct spatial trends across the landscape. Losses of woody cover on the diverse alluvial substrates were countered by increases of cover on the hillslopes. Analysis of current woody structure in the context of historical change revealed that the increases took place in the form of shrub encroachment and not the replacement of tall trees. We infer that mammalian herbivory contributed substantially to the losses on lowland alluvial soils, whilst shrub encroachment on the upland hillslopes likely stemmed from changes in fire regime and climate. Deeper reflection on spatial variability is needed in the debate around drivers of change in savanna systems, as spatial patterns of change revealed that different drivers underlie vegetation dynamics in different landscape contexts. Spatial heterogeneity needs explicit consideration in the exploration of pattern–process relationships in ecological systems.     

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.     

Laser scanning of fine scale pattern along a hydrological gradient in a peatland ecosystem

Lowland ombrotrophic (rain-fed) peatlands are a declining ecological resource in Europe. Peatlands display characteristic patterns in vegetation and surface topography, linked to ecological function, hydrology, biodiversity and carbon sequestration. Laser scanning provides a means of precisely measuring vegetation pattern in peatlands, and thus holds promise as a tool for monitoring peatland condition. Terrestrial laser scanning (TLS) was used for measurement of vegetation pattern along an eco-hydrological gradient at a UK peatland (Wedholme Flow, Cumbria) at fine grain sizes (<1 cm spatial resolution over 10 m spatial extent). Seven sites were investigated—each showed varying water table and ecological characteristics. TLS data were analysed using semi-variogram analysis which enabled the scale of spatial dependence in vegetation structures to be measured. In addition ecological, hydrological and positional surveys were conducted to elucidate interpretation of spatial patterns. Results show that TLS was able to rapidly measure vegetation patterns associated with eco-hydrological condition classes. Intact sites with hummock-hollow topography showed an isotropic pattern with a grain size or length-scale of 1 m or less (indicated by semi-variogram range). Degraded sites with high shrub cover showed increased sill variance values at larger range distances—typically around 3–4 m. The work presented shows the advantages of TLS methodologies for rapid measurement of 3-D vegetation canopy structure and surface microtopography, at fine spatial scales, in short vegetation. The paper considers how these approaches may be extended to monitoring peatland structure over larger spatial extents from airborne LiDAR systems.     

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.     

Estimates of dryland degradation in Argentina with Fourier signatures from low-altitude monochromatic images with high spatial resolution

Most world drylands are used as graziny lands and undergo degradation of their vegetation cover. The plant cover is typically structured in patchy arrangements, inducing fertility islands critical to maintenance of ecosystem properties. The characteristics of patch structure (size of patches, connectivity-continuity of patch units, etc.) are indicators of the degree of dryland deterioration. We characterized changes in patch structure induced by sheep grazing at a landscape scale using monochromatic low-altitude imagery digitized to a spatial resolution of about 1 m with standard techniques of harmonic analysis applied to develop Fourier signatures. The signatures developed on image line transects were tested with ground samples and mathematical models of plant cover in several dryland fields where spatial deterioration gradients existed. The sensitivity and errors associated to long-wave noise introduced by the geometry of the camera-field-sun spatial arrangement and to high frequency noise introduced by the digitizing process were evaluated by applying suitable filters in the frequency domain. Fourier signatures developed on monochromatic low-altitude imagery proved to be indicative of changes in the patching arrangements of plant cover. We concluded that adequately filtered, high spatial resolution monochromatic images can be used to evaluate the degree of deterioration of dryland landscapes through the computation of selected Fourier signatures in their frequency domain. At comparable cost, aerial photography allows inspecting the landscape at higher spatial resolutions than those attainable with satellite imagery. Also, aerial photos of many areas are available for earlier dates than images from remote sensors, which would allow better inspection of long-term ecosystem changes.This revised version was published online in May 2005 with corrections to the Cover Date.     

Contribution of time-series data cubes to classify urban vegetation types by remote sensing

Mapping urban vegetation types is important for urban planning and assessing environmental justice. Nowadays, despite data cubes projects are providing Analysis Ready Data to facilitate time-series analysis, we did not found studies employing these data for improving urban vegetation mapping. By relying solely on open data and software, this work proposes and evaluates the integration of time-series data cubes in a hybrid image classification method to map the intra-urban space, differentiating Tree cover and Herb-shrub. The urban area of Goiânia, Goiás, Brazil, is the study area. The hybrid method combined object-based classification of a pan-sharpened CBERS-4A WPM image (spatial resolution of 2 m) with the pixel-based classification of Sentinel-2 MSI time-series data cubes (10 m). Both approaches used the Random Forest algorithm. Objects from the CBERS-4A segmentation composed the spatial unit of analysis and the class assignment depended on the Sentinel-2 time-series urban land cover probabilities. Based on both Maps probabilities, Shannon entropy was calculated to attribute the final urban land cover to the objects. Urban land cover probabilities presented similar spatial distribution patterns for both classification approaches. Regarding the thematic maps, the Herb-shrub cover area was 35% higher in Sentinel-2 time-series classification than in GEOBIA classification, but Tree cover was 21% lower. In general, 75% of the study area was equally classified by the initial approaches. However, for 9% of the remaining area, the hybrid classification improved vegetation classes accuracies by 35%, contributing to the vegetation covers identification. Thus, this study contributes to methodological procedures for urban land cover study and demonstrates that hybrid maps based on open data are effective to reduce classification mistakes, allowing more accurate monitoring, planning, and designing of different urban vegetation types. Future research efforts should focus on scale compatibility between data of different spatial resolutions and expand the use of data cubes to integrate time-series information into the GEOBIA classification.     

Spatial vegetation patterns as early signs of desertification: a case study of a desert steppe in Inner Mongolia,China

Proper assessment and early detection of land degradation and desertification is extremely important in arid and semi-arid ecosystems. Recent research has proposed to use the characteristics of spatial vegetation patterns, such as parameters derived from power-law modeling of vegetation patches, for detecting the early signs of desertification. However, contradictory results have been reported regarding the suitability of those proposed indicators. We used an experiment with multiple grazing intensities as an analog of a desertification gradient and evaluated the performance of two predictors of desertification: percent plant cover and a transition from a patch-area distribution characterized by a power law to another portrayed by a truncated power law, in a desert steppe in Inner Mongolia, China. We found that spatial metrics, such as the largest patch index and coefficient of variation of mean patch area had negative linear relationships with grazing intensity, suggesting that vegetation patches became more fragmented and homogeneous under higher grazing pressure. Using a binning-based method to analyze our dataset, we found that the patch-area relationship deviated from a power-law to a truncated power-law model with increasing grazing pressure, while the truncated power law was a better fit than the power law for all plots when binning was not used. These results suggest that the selection of methodology is crucial in using power-law models to detect changes in vegetation patterns. Plant cover was significantly correlated with stocking rate and all spatial metrics evaluated; however, the relationship between cover and vegetation spatial pattern still deserves a thorough examination, especially in other types of ecosystems, before using cover as a universal early sign of desertification. Our results highlight a strong connection between the vegetation spatial pattern and the desertification associated with heavy grazing and suggest that future studies should incorporate information about vegetation spatial pattern in monitoring desertification processes.     

Hierarchical relationships between landscape structure and temperature in a managed forest landscape

Management may influence abiotic environments differently across time and spatial scale, greatly influencing perceptions of fragmentation of the landscape. It is vital to consider a priori the spatial scales that are most relevant to an investigation, and to reflect on the influence that scale may have on conclusions. While the importance of scale in understanding ecological patterns and processes has been widely recognized, few researchers have investigated how the relationships between pattern and process change across spatial and temporal scales. We used wavelet analysis to examine the multiscale structure of surface and soil temperature, measured every 5 m across a 3820 m transect within a national forest in northern Wisconsin. Temperature functioned as an indicator – or end product – of processes associated with energy budget dynamics, such as radiative inputs, evapotranspiration and convective losses across the landscape. We hoped to determine whether functional relationships between landscape structure and temperature could be generalized, by examining patterns and relationships at multiple spatial scales and time periods during the day. The pattern of temperature varied between surface and soil temperature and among daily time periods. Wavelet variances indicated that no single scale dominated the pattern in temperature at any time, though values were highest at finest scales and at midday. Using general linear models, we explained 38% to 60% of the variation in temperature along the transect. Broad categorical variables describing the vegetation patch in which a point was located and the closest vegetation patch of a different type (landscape context) were important in models of both surface and soil temperature across time periods. Variables associated with slope and microtopography were more commonly incorporated into models explaining variation in soil temperature, whereas variables associated with vegetation or ground cover explained more variation in surface temperature. We examined correlations between wavelet transforms of temperature and vegetation (i.e., structural) pattern to determine whether these associations occurred at predictable scales or were consistent across time. Correlations between transforms characteristically had two peaks; one at finer scales of 100 to 150 m and one at broader scales of ^>300 m. These scales differed among times of day and between surface and soil temperatures. Our results indicate that temperature structure is distinct from vegetation structure and is spatially and temporally dynamic. There did not appear to be any single scale at which it was more relevant to study temperature or this pattern-process relationship, although the strongest relationships between vegetation structure and temperature occurred within a predictable range of scales. Forest managers and conservation biologists must recognize the dynamic relationship between temperature and structure across landscapes and incorporate the landscape elements created by temperature-structure interactions into management decisions.     

Landscape characteristics affecting streams in urbanizing regions of the Delaware River Basin (New Jersey,New York,and Pennsylvania,U.S.)

Widespread and increasing urbanization has resulted in the need to assess, monitor, and understand its effects on stream water quality. Identifying relations between stream ecological condition and urban intensity indicators such as impervious surface provides important, but insufficient information to effectively address planning and management needs in such areas. In this study we investigate those specific landscape metrics which are functionally linked to indicators of stream ecological condition, and in particular, identify those characteristics that exacerbate or mitigate changes in ecological condition over and above impervious surface. The approach used addresses challenges associated with redundancy of landscape metrics, and links landscape pattern and composition to an indicator of stream ecological condition across a broad area of the eastern United States. Macroinvertebrate samples were collected during 2000–2001 from forty-two sites in the Delaware River Basin, and landscape data of high spatial and thematic resolution were obtained from photointerpretation of 1999 imagery. An ordination-derived ‘biotic score’ was positively correlated with assemblage tolerance, and with urban-related chemical characteristics such as chloride concentration and an index of potential pesticide toxicity. Impervious surface explained 56% of the variation in biotic score, but the variation explained increased to as high as 83% with the incorporation of a second land use, cover, or configuration metric at catchment or riparian scales. These include land use class-specific cover metrics such as percent of urban land with tree cover, forest fragmentation metrics such as aggregation index, riparian metrics such as percent tree cover, and metrics related to urban aggregation. Study results indicate that these metrics will be important to monitor in urbanizing areas in addition to impervious surface.     

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.     

Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida,USA

The spatial distribution of soil carbon (C) is controlled by ecological processes that evolve and interact over a range of spatial scales across the landscape. The relationships between hydrologic and biotic processes and soil C patterns and spatial behavior are still poorly understood. Our objectives were to (i) identify the appropriate spatial scale to observe soil total C (TC) in a subtropical landscape with pronounced hydrologic and biotic variation, and (ii) investigate the spatial behavior and relationships between TC and ecological landscape variables which aggregate various hydrologic and biotic processes. The study was conducted in Florida, USA, characterized by extreme hydrologic (poorly to excessively drained soils), and vegetation/land use gradients ranging from natural uplands and wetlands to intensively managed forest, agricultural, and urban systems. We used semivariogram and landscape indices to compare the spatial dependence structures of TC and 19 ecological landscape variables, identifying similarities and establishing pattern–process relationships. Soil, hydrologic, and biotic ecological variables mirrored the spatial behavior of TC at fine (few kilometers), and coarse (hundreds of kilometers) spatial scales. Specifically, soil available water capacity resembled the spatial dependence structure of TC at escalating scales, supporting a multi-scale soil hydrology-soil C process–pattern relationship in Florida. Our findings suggest two appropriate scales to observe TC, one at a short range (autocorrelation range of 5.6 km), representing local soil-landscape variation, and another at a longer range (119 km), accounting for regional variation. Moreover, our results provide further guidance to measure ecological variables influencing C dynamics.     

Rangeland biodiversity assessment using fine scale on-ground survey,time series of remotely sensed ground cover and climate data: an Australian savanna case study

Savanna rangelands are undergoing rapid environmental change and the need to monitor and manage landscape health is becoming increasingly an imperative of government agencies and research organizations. Remotely sensed ecological indicators of disturbance offer a potential approach, particularly in the context of issues of scale required to assess and monitor extensive rangeland areas. The objective of this research is to analyse the potential of spatially explicit ecological indicators of disturbance to explain the spatial variability in species diversity and abundance (including introduced flora species) in rangelands. For two mapped rangeland ecosystem types in northern Australia, regression analysis was used to explore the relationships between species diversity and abundance, and remotely sensed ground cover time series statistics, foliage projective cover, and a precipitation deficit index. It was assumed that the ecosystem types used had been mapped to represent uniform vegetation units and consequently predictors of environmental heterogeneity were not used in the regression analysis. It was found that the predictor variables performed well in explaining the variation in species diversity and abundance for the more open, homogenous and less topographically complex basalt ecosystem type and less effectively for the more structurally complex, more wooded and less disturbed metamorphic ecosystem type. The results indicate that, for mapped ecosystem types with low heterogeneity and topographic complexity, ground cover temporal mean and variance are potentially useful indicators of disturbance to species diversity and abundance, provided the local spatial variability in the climate signal is accounted for.     

Community structure of dung beetles in Amazonian savannas: role of fire disturbance,vegetation and landscape structure

Understanding the relative influence of environmental and spatial variables in driving variation in species diversity and composition is an important and growing area of ecological research. We examined how fire, local vegetation structure and landscape configuration interact to influence dung beetle communities in Amazonian savannas, using both hierarchical partitioning and variance partitioning techniques to quantify independent effects. We captured a total of 3,334 dung beetles from 15 species at 22 savanna plots in 2003. The species accumulation curve was close to reaching an asymptote at a regional scale, but curves were variable at the plot level where total abundance ranged from 17 to 410 individuals. Most plots were dominated by just three species that accounted for 87.7% of all individuals sampled. Hierarchical partitioning revealed the strong independent and positive effect of percentage forest cover in the surrounding landscape on total dung beetle abundance and species richness, and richness of uncommon species and the tunneler guild. Forest cover also had a negative effect on community evenness. None of the variables that related to fire affected community metrics. The minimal direct influence of fire was supported by variance partitioning: partialling out the influence of spatial position and vegetation removed all of the individual explanation attributable to fire, whereas 8% of the variance was explained by vegetation and 28% was explained by spatial variables (when partialling out effects of the other two variables). Space-fire and vegetation-fire joint effects explained 14 and 10% of the dung beetle community variability, respectively. These results suggest that much of the variation in dung beetle assemblages in savannas can be attributed to the spatial location of sites, forest cover (which increased the occurrence of uncommon species), and the indirect effects of fires on vegetation (that was also dependent on spatial location). Our study also highlights the utility of partitioning techniques for examining the importance of environment variables such as fire that can be strongly influenced by spatial location.     

Applications of satellite remote sensing to forested ecosystems

Since the launch of the first civilian earth-observing satellite in 1972, satellite remote sensing has provided increasingly sophisticated information on the structure and function of forested ecosystems. Forest classification and mapping, common uses of satellite data, have improved over the years as a result of more discriminating sensors, better classification algorithms, and the use of geographic information systems to incorporate additional spatially referenced data such as topography. Land-use change, including conversion of forests for urban or agricultural development, can now be detected and rates of change calculated by superimposing satellite images taken at different dates. Landscape ecological questions regarding landscape pattern and the variables controlling observed patterns can be addressed using satellite imagery as can forestry and ecological questions regarding spatial variations in physiological characteristics, productivity, successional patterns, forest structure, and forest decline.     

Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation

To successfully use remotely-sensed data in landscape-level management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis, segmenting images into homogeneous regions called objects, has been suggested for increasing accuracy of remotely-sensed products, but little research has gone into determining image object size with regard to scaling of ecosystem properties. We looked at how segmentation of high-resolution Ikonos and medium-resolution Landsat images into successively coarser objects affected multivariate correlations between image data and eight percent-cover measurements of a sagebrush ecosystem. We also looked at changes in correlation as imagery was aggregated into larger square pixels. We found similar canonical correlations between field and image data at the finest scales, but higher for image segmentation than pixel aggregation for both images when scale increased. For image segmentation, correlations between the canonical variables and original field variables were invariant with respect to size of the image objects, suggesting linear scaling of vegetation cover in our study system. We detected a scaling threshold with the Ikonos segmentation and confirmed with a semi-variogram of the sample data. Below the threshold interpretation of the canonical variables was consistent: scale levels differed primarily in the amount of detail portrayed. Above the threshold, meaning of the canonical variables changed. This approach proved useful for evaluating overall utility of images to address an objective, and identified scaling limits for analysis. Selection of appropriate scale for analysis will ultimately depend on the objective being considered.     

Effects of patch characteristics and within patch heterogeneity on the accuracy of urban land cover estimates from visual interpretation

Landscape ecology links landscape pattern to ecological function. Achieving this goal hinges on accurate depiction and quantification of pattern, which is frequently done by visually interpreting remotely sensed imagery. Therefore, understanding both the accuracy of that interpretation and what influences its accuracy is crucial. In addition, imagery is pixel-based but landscape pattern exists, more realistically, as irregularly shaped patches. Patches may contain only one feature type such as trees, but, in some landscapes, patches may contain several different types of features such as trees and buildings. Using a patch-based approach, this paper investigates two types of variables??whole-patch and within-patch??that are hypothesized to influence the accuracy of visually estimating the cover of features within patches. A highly accurate reference map, obtained from object-based classification, was used to evaluate the accuracy of visual estimates of cover within patches. The effects of the variables on the accuracy of these estimates were tested using logistic regressions and multimodel inferential procedures. Though all variables significantly affected the accuracy, the within-patch configuration of features is the most significant factor. In general, errors of cover estimates are more likely to occur when patches are smaller or have more complex shapes, and features within a patch are (1) more diverse; (2) more fragmented; (3) more complex in shape; and (4) physically less connected. These results provide an important first step towards a quantitative, spatially explicit model for predicting error of cover estimates and determining under what circumstances estimation error is most likely to occur.     

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.     

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.     

Physical influences of landscape on a large-extent ecological disturbance: the northeastern North American ice storm of 1998

The 1998 ice storm was a large-extent ecological disturbance that severely affected the eastern Adirondack forests of northern New York. Ice damage produced widespread breakage of limbs and trunks in susceptible trees. Although ice storms are common within northeastern North American forests, the magnitude and extent of the 1998 storm far exceeded damage caused by typical ice storms in the recent past. While plot and stand-scale ecological impacts of ice storms have received attention insofar as tree species vulnerability, stand age susceptibility, and microhabitat alterations, larger-extent damage patterns have not been previously evaluated. The normalized difference vegetation index (NDVI) was employed to assess forest vigor and canopy density in atmospherically corrected Landsat Thematic Mapper (TM) satellite imagery of the Adirondacks. Digital change analysis of the baseline forest condition (1990 NDVI data), and the condition encountered in a post-storm image (1998 NDVI data) was conducted. Forest damage was separated from natural variations in canopy reflectance by employing a generalized linear model that incorporated in situ measurements. A robust empirical variogram analysis revealed that locations of tree damage were significantly correlated for distances up to 300 meters. Intensely-damaged forest exhibited greater spatial dependence, but over a smaller distance. Canopy damage was not greater proximate to stream and forest boundaries, and did not follow our hypothesis of decreasing damage with distance from the boundary. Overall, we show that local topography (elevation and aspect), forest composition (deciduous or coniferous), and the meteorological characteristics of the disturbance event acted together to determine the spatial extent of ice storm damage.This revised version was published online in May 2005 with corrections to the Cover Date.