Assessing topographic patterns in moisture use and stress using a water balance approach

Through its control on soil moisture patterns, topography’s role in influencing forest composition is widely recognized. This study addresses shortcomings in traditional moisture indices by employing a water balance approach, incorporating topographic and edaphic variability to assess fine-scale moisture demand and moisture availability. Using GIS and readily available data, evapotranspiration and moisture stress are modeled at a fine spatial scale at two study areas in the US (Ohio and North Carolina). Model results are compared to field-based soil moisture measurements throughout the growing season. A strong topographic pattern of moisture utilization and demand is uncovered, with highest rates of evapotranspiration found on south-facing slopes, followed by ridges, valleys, and north-facing slopes. South-facing slopes and ridges also experience highest moisture deficit. Overall higher rates of evapotranspiration are observed at the Ohio site, though deficit is slightly lower. Based on a comparison between modeled and measured soil moisture, utilization and recharge trends were captured well in terms of both magnitude and timing. Topographically controlled drainage patterns appear to have little influence on soil moisture patterns during the growing season. In addition to its ability to accurately capture patterns of soil moisture in both high-relief and moderate-relief environments, a water balance approach offers numerous advantages over traditional moisture indices. It assesses moisture availability and utilization in absolute terms, using readily available data and widely used GIS software. Results are directly comparable across sites, and although output is created at a fine-scale, the method is applicable for larger geographic areas. Since it incorporates topography, available water capacity, and climatic variables, the model is able to directly assess the potential response of vegetation to climate change.     

Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index

Forest canopy phenology is an important constraint on annual water and carbon budgets, and responds to regional interannual climate variation. In steep terrain, there are complex spatial variations in phenology due to topographic influences on microclimate, community composition, and available soil moisture. In this study, we investigate spatial patterns of phenology in humid temperate forest as a function of topography. Moderate-resolution imaging spectro-radiometer (MODIS) vegetation indices are used to derive local patterns of topography-mediated vegetation phenology using a simple post-processing analysis and a non-linear model fitting. Elevation has the most explanatory power for all phenological variables with a strong linear relationship with mid-day of greenup period, following temperatures lapse rates. However, all other phenological variables show quadratic associations with elevation, reflecting an interaction between topoclimatic patterns of temperature and water availability. Radiation proxies also have significant explanatory power for all phenological variables. Though hillslope position cannot be adequately resolved at the MODIS spatial resolution (250 m) to discern impacts of local drainage conditions, extended periods of greenup/senescence are found to occur in wet years. These findings are strongly supported by previous field measurements at different topographic positions within the study area. The capability of detecting topography-mediated local phenology offers the potential to detect vegetation responses to climate change in mountainous terrain. In addition, the large, local variability of meteorological and edaphic conditions in steep terrain provides a unique opportunity to develop an understanding of canopy response to the interaction of climate and landscape conditions.     

Rapid sampling of plant species composition for assessing vegetation patterns in rugged terrain

Detailed species composition data are rapidly collected using a high-powered telescope from remote vantage points at two scales: site level and patch level. Patches constitute areas of homogeneous vegetation composition. Multiple samples of species composition are randomly located within the patches. These data are used as site-level data and are also aggregated to provide species composition data at the patch level. The site- and patch-level data are spatially integrated with high resolution (10 m), topographically-derived fields of environmental conditions, such as solar radiation, air temperature, and topographic moisture index in order to evaluate the applicability of the sampling method for modeling relationships between species composition and environmental processes.The methodology provides a balance between sampling efficiency and the accuracy of field data. Application of the method is appropriate for environments where terrain and canopy characteristics permit open visibility of the landscape. We evaluate the nature of data resulting from an implementation of the remote sampling methodology in a steep watershed dominated by closed-canopy chaparral. Analyses indicate that there is minimal bias associated with scaling the data from the site level to the patch level, despite variable patch sizes. Analysis of variance and correlation tests show that the internal floristic and environmental variability of patches is low and stable across the entire sample of patches. Comparison of regression tree models of species cover at the two scales indicates that there is little scale-dependence in the ecological processes that govern patterns of species composition between the site level and patch level. High explanatory power of the regression tree models suggests that the vegetation data are characterized at an appropriate scale to model landscape-level patterns of species composition as driven by topographically-mediated processes. Patch-level sampling reduces the influence of local stochasticity and micro-scale processes. Comparison of models between the two scales can be useful for assessing the processes and associated scales of variability governing spatial patterns of plant species.     

Forest gradient response in Sierran landscapes: the physical template

Vegetation pattern on landscapes is the manifestation of physical gradients, biotic response to these gradients, and disturbances. Here we focus on the physical template as it governs the distribution of mixed-conifer forests in California's Sierra Nevada. We extended a forest simulation model to examine montane environmental gradients, emphasizing factors affecting the water balance in these summer-dry landscapes. The model simulates the soil moisture regime in terms of the interaction of water supply and demand: supply depends on precipitation and water storage, while evapotranspirational demand varies with solar radiation and temperature. The forest cover itself can affect the water balance via canopy interception and evapotranspiration. We simulated Sierran forests as slope facets, defined as gridded stands of homogeneous topographic exposure, and verified simulated gradient response against sample quadrats distributed across Sequoia National Park. We then performed a modified sensitivity analysis of abiotic factors governing the physical gradient. Importantly, the model's sensitivity to temperature, precipitation, and soil depth varies considerably over the physical template, particularly relative to elevation. The physical drivers of the water balance have characteristic spatial scales that differ by orders of magnitude. Across large spatial extents, temperature and precipitation as defined by elevation primarily govern the location of the mixed conifer zone. If the analysis is constrained to elevations within the mixed-conifer zone, local topography comes into play as it influences drainage. Soil depth varies considerably at all measured scales, and is especially dominant at fine (within-stand) scales. Physical site variables can influence soil moisture deficit either by affecting water supply or water demand; these effects have qualitatively different implications for forest response. These results have clear implications about purely inferential approaches to gradient analysis, and bear strongly on our ability to use correlative approaches in assessing the potential responses of montane forests to anthropogenic climatic change.     

Spatio-temporal modelling of broad scale heterogeneity in soil moisture content: a basis for an ecologically meaningful classification of soil landscapes

We describe the classification of landscapes characterised bymineral soil using a model that calculates soil moisture availability on amonthly basis. Scotland is used as a case study area. The model uses potentialsoil moisture deficit, estimated using broad scale (40 × 40 km)climate patterns, in conjunction with meteorological station measurements toobtain finer scale values of climatic soil moisture deficit. Point estimates ofsoil available water are obtained for soil characteristics using appropriatepedotransfer functions, and geostatistical techniques are used to upscale theresults and interpolate to a 1-km grid. Known heterogeneityin soil physical characteristics is used to provide local corrections to thepotential soil moisture deficit, estimated using the climatic variables above.Temporal profiles of monthly water content are modelled for each1-km location and classified into six classes usingunsupervised cluster analysis. The spatial distribution of these classesreflects regional variations in the availability of moisture and energy, onwhich finer-grained topographic patterns are superimposed. In the case study,the broad scale spatial heterogeneity of heathlands and grasslands on mineralsoils in Scotland is shown to be strongly related to the soil moistureclassification. The results can be used in studies investigating the patternsofdistribution of communities at the landscape and regional scale.This revised version was published online in May 2005 with corrections to the Cover Date.     

Climate and interrelated tree regeneration drivers in mixed temperate–boreal forests

Forest compositional shifts in response to climate change are likely to be initially detectable in the understory tree regeneration layer near species range limits. Because many factors in addition to climate, such as seedbed and soil characteristics, overstory composition, and interactions with other understory biota, drive tree regeneration trends, a thorough understanding of the relative importance of all variables as well as their interrelationships is needed. The range limits of several widespread temperate and boreal tree species overlap in the upper Great Lakes region, USA, thus facilitating an observational study over relatively short regional climate gradients. We used redundancy analysis and variation partitioning to quantify the unique, shared, and total explanatory power of four sets of explanatory variables. The results showed that all four variable sets (climate 9.5 %, understory environment 13.7 %, overstory composition 26.3 %, and understory biota 13.8 %) were significantly associated with tree regeneration compositional variation in mixed temperate–boreal forests. Partitioning also revealed high confounded or shared explanatory power, but also that each set contributed significant unique explanatory power not shared with other sets. Spatial patterning in regeneration composition was strongly related to broad scale environmental patterns, while the large majority of unexplained variation did not have a detectable spatial structure, suggesting factors with local scale variability. Future forest shifts across the landscape will depend not only on the rate and direction of climate change but also on how the strengths and interrelationships among other explanatory variables, such as overstory composition and understory biota, shift with a changing climate.     

Small-scale effects of historical land use and topography on post-cultural tree species composition in an Alpine valley in southern Switzerland

Investigations of spatial patterns in forest tree species composition are essential in the understanding of landscape dynamics, especially in areas of land-use change. The specific environmental factors controlling the present patterns, however, vary with the scale of observation. In this study we estimated abundance of adult trees and tree regeneration in a Southern Alpine valley in Ticino, Switzerland. We hypothesized that, at the present scale, spatial pattern of post-cultural tree species does not primarily depend on topographic features but responds instead to small-scale variation in historical land use. We used multivariate regression trees to relate species abundances to environmental variables. Species matrices were comprised of single tree species abundance as well as species groups. Groups were formed according to common ecological species requirements with respect to shade tolerance, soil moisture and soil nutrients. Though species variance could only be partially explained, a clear ranking in the relative importance of environmental variables emerged. Tree basal area of formerly cultivated Castanea sativa (Mill.) was the most important factor accounting for up to 50% of species’ variation. Influence of topographic attributes was minor, restricted to profile curvature, and partly contradictory in response. Our results suggest the importance of biotic factors and soil properties for small-scale variation in tree species composition and need for further investigations in the study area on the ecological requirements of tree species in the early growing stage.     

Relationships of dead wood patterns with biophysical characteristics and ownership according to scale in Coastal Oregon,USA

Dead wood patterns and dynamics vary with biophysical factors, disturbance history, ownership, and management practices; the relative importance of these factors is poorly understood, especially at landscape to regional scales. This study examined current dead wood amounts in the Coastal Province of Oregon, USA, at multiple spatial scales. Objectives were to: (1) describe current regional amounts of several characteristics of dead wood; (2) compare dead wood amounts across ownerships; (3) determine the relative importance, according to spatial scale, of biophysical and ownership characteristics, to regional dead wood abundance. Dead wood plot data were evaluated with respect to explanatory variables at four spatial scales of resolution: plots, subwatersheds, watersheds and subbasins. The relationships of dead wood characteristics with biophysical attributes and ownership were diverse and scale-specific. Region-wide dead wood abundance and types varied among ownerships, with public lands typically having higher amounts of dead wood and more large dead wood than private lands. Regression analysis of total dead wood volume indicated that ownership was important at the subbasin scale. Growing season moisture stress was important at plot, subwatershed, and watershed scales. Topography was important at the two coarser scales. Multivariate analysis of dead wood gradients showed that ownership was important at all scales, topography at the subbasin scale, historical vegetation at watershed and subbasin scales, and current vegetation at plot and subwatershed scales. Management for dead wood and related biodiversity at watershed to landscape scales should consider the distinct dynamics of snags and logs, the importance of historical effects, and the relevance of ownership patterns.     

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.     

Simulating Spatial Nitrogen Dynamics in a Forested Reference Watershed, Hubbard Brook Watershed 6, New Hampshire, USA

We demonstrate that available information on spatial heterogeneity in biotic, topographic, and climatic variables within a forested watershed, Hubbard Brook Experimental Forest (HBEF) Watershed 6, New Hampshire, USA, was sufficient to reproduce the observed elevational pattern in stream NO₃ concentration during the 1982–1992 period. Five gridded maps (N mineralization factor, N uptake factor, precipitation, elevation, and soil depth factor) were created from spatial datasets and successively added to the spatially explicit model SINIC-S as spatially varying input parameters. Adding more spatial information generally improved model predictions, with the exception of the soil depth factor. Ninety percent of the variation in the observed stream NO₃ concentration was explained by the combination of the spatial variation of the N mineralization and N uptake factors. Simulated streamflow NO₃ flux at the outlet point was improved slightly by introducing spatial variability in the model parameters. The model exhibited substantial cell-to-cell variation in soil N dynamics and NO₃ loss within the watershed during the simulation period. The simulation results suggest that the spatial distributions of forest floor organic matter and standing biomass are most responsible for creating the elevational pattern in stream NO₃ concentration within this watershed.     

Influence of land use on water quality in a tropical landscape: a multi-scale analysis

There is a pressing need to understand the consequences of human activities, such as land transformations, on watershed ecosystem services. This is a challenging task because different indicators of water quality and yield are expected to vary in their responsiveness to large versus local-scale heterogeneity in land use and land cover (LUC). Here we rely on water quality data collected between 1977 and 2000 from dozens of gauge stations in Puerto Rico together with precipitation data and land cover maps to (1) quantify impacts of spatial heterogeneity in LUC on several water quality indicators; (2) determine the spatial scale at which this heterogeneity influences water quality; and (3) examine how antecedent precipitation modulates these impacts. Our models explained 30–58% of observed variance in water quality metrics. Temporal variation in antecedent precipitation and changes in LUC between measurements periods rather than spatial variation in LUC accounted for the majority of variation in water quality. Urbanization and pasture development generally degraded water quality while agriculture and secondary forest re-growth had mixed impacts. The spatial scale over which LUC influenced water quality differed across indicators. Turbidity and dissolved oxygen (DO) responded to LUC in large-scale watersheds, in-stream nitrogen concentrations to LUC in riparian buffers of large watersheds, and fecal matter content and in-stream phosphorus concentration to LUC at the sub-watershed scale. Stream discharge modulated impacts of LUC on water quality for most of the metrics. Our findings highlight the importance of considering multiple spatial scales for understanding the impacts of human activities on watershed ecosystem services.     

Modeling Landscape Vegetation Pattern in Response to Historic Land-use: A Hypothesis-driven Approach for the North Carolina Piedmont,USA

Current methods of vegetation analysis often assume species response to environmental gradients is homogeneously monotonic and unimodal. Such an approach can lead to unsatisfactory results, particularly when vegetation pattern is governed by compensatory relationships that yield similar outcomes for various environmental settings. In this paper we investigate the advantages of using classification tree models (CART) to test specific hypotheses of environmental variables effecting dominant vegetation pattern in the Piedmont. This method is free of distributional assumptions and is useful for data structures that contain non-linear relationships and higher-order interactions. We also compare the predictive accuracy of CART models with a parametric generalized linear model (GLM) to determine the relative strength of each predictive approach. For each method, hardwood and pine vegetation is modeled using explanatory topographic and edaphic variables selected based on historic reconstructions of patterns of land use. These include soil quality, potential soil moisture, topographic position, and slope angle. Predictive accuracy was assessed on independent validation data sets. The CART models produced the more accurate predictions, while also emphasizing alternative environmental settings for each vegetation type. For example, relic hardwood stands were found on steep slopes, highly plastic soils, or hydric bottomlands – alternatives not well captured by the homogeneous GLM. Our results illustrate the potential utility of this flexible modeling approach in capturing the heterogeneous patterns typical of many ecological datasets.     

Climatic and topographic controls on patterns of fire in the southern and central Appalachian Mountains,USA

Climate and topography are two important controls on spatial patterns of fire disturbance in forests globally, via their influence on fuel moisture and fuel production. To assess the influences of climate and topography on fire disturbance patterns in a temperate forest region, we analyzed the mapped perimeters of fires that burned during 1930–2003 in two national parks in the eastern United States. These were Great Smoky Mountains National Park (GSMNP) in the southern Appalachian Mountains and Shenandoah National Park (SNP) in the central Appalachian Mountains. We conducted GIS analyses to assess trends in area burned under differing climatic conditions and across topographic gradients (elevation, slope position, and aspect). We developed a Classification and Regression Tree model in order to further explore the interactions between topography, climate, and fire. The results demonstrate that climate is a strong driver of both spatial and temporal patterns of wildfire. Fire was most prevalent in the drier SNP than the wetter GSMNP, and during drought years in both parks. Topography also influenced fire occurrence, with relatively dry south-facing aspects, ridges, and lower elevations burning most frequently. However, the strength of topographic trends varied according to the climatic context. Weaker topographic trends emerged in the drier SNP than GSMNP, and during low-PDSI (dry) years than high-PDSI (wet) years in both parks. The apparent influence of climate on the spatial patterning of fire suggests a more general concept, that disturbance-prone landscapes exhibit weaker fine-scale spatial patterning of disturbance than do less disturbance-prone landscapes.     

Does the scale of our observational window affect our conclusions about correlations between endangered salmon populations and their habitat?

Differences in the strength of species-habitat relationships across scales provide insights into the mechanisms that drive these relationships and guidance for designing in situ monitoring programs, conservation efforts and mechanistic studies. The scale of our observation can also impact the strength of perceived relationships between animals and habitat conditions. We examined the relationship between geographic information system (GIS)-based landscape data and Endangered Species Act-listed anadromous Pacific salmon (Oncorhynchus spp.) populations in three subbasins of the Columbia River basin, USA. We characterized the landscape data and ran our models at three spatial scales: local (stream reach), intermediate (6th field hydrologic units directly in contact with a given reach) and catchment (entire drainage basin). We addressed three questions about the effect of scale on relationships between salmon and GIS representations of landscape conditions: (1) at which scale does each predictor best correlate with salmon redd density, (2) at which scale is overall model fit maximized, and (3) how does a mixed-scale model compare with single scale models (mixed-scale meaning models that contain variables characterized at different spatial scales)? We developed mixed models to identify relationships between redd density and candidate explanatory variables at each of these spatial scales. Predictor variables had the strongest relationships with redd density when they were summarized over the catchment scale. Meanwhile strong models could be developed using landscape variables summarized at only the local scale. Model performance did not improve when we used suites of potential predictors summarized over multiple scales. Relationships between species abundance and land use or intrinsic habitat suitability detected at one scale cannot necessarily be extrapolated to other scales. Therefore, habitat restoration efforts should take place in the context of conditions found in the associated watershed or landscape.     

Agricultural land-use patterns and soil erosion vulnerability of watershed units in Vietnam’s northern highlands

Since the mid eighties, agricultural development and increased population growth in Vietnam’s northern highlands have modified land use patterns and thus, increased the runoff process and soil degradation induced by water erosion. In the last decade, Vietnamese literature has focused on the computation of soil losses over large areas. Most of these spatial and quantitative soil erosion studies do not consider the impact of agricultural land use diversity (spatial heterogeneity), particularly at the watershed scale, and the annual variability of seasonal landscape factors on soil erosion vulnerability and hence, landscape dynamics. We present an integrated approach combining field measurements and observations, GIS and modeling to determine the spatial and temporal dynamics of soil erosion vulnerability according to watershed units and hence, the impact of physical environment components and agricultural land use patterns on landscape evolution. Tables and graphics showing the cropping systems, the periods within a year, and the watershed units that are most vulnerable are presented. The double cultivation cycles for paddy rice fields not only imply two periods of land preparation and establishment that expose the soil surface to raindrop impacts, but also increased soil management practices that decrease the soil’s resistance to detachment. Despite the low levels of soil management practices for the shifting cultivation system, the near absence of soil conservation practices clearly increases their vulnerability. Hence, rainfed cropping systems, mainly soya and cassava, cultivated on sloping lands (hills and mountains) where soil erosion vulnerability is the highest represent the watershed units which are the most prone to soil loss.     

Local,landscape and regional factors structuring benthic macroinvertebrate assemblages in Swedish streams

A total of 694 streams were sampled for benthic macroinvertebrates in the autumn of 1995 as part of the Swedish national stream survey. After removal of sites considered as impacted, data from 428 streams as well as a large number of environmental variables were used to determine the relative importance of local, landscape, and large scale factors in explaining the variability in species composition of benthic stream macroinvertebrates. The environmental variables were divided into seven explanatory variable groups: local physical, local chemical, catchment land use/cover, catchment bedrock geology, Quaternary geology in catchment, regional factors (such as ecoregion) and spatial position. Partial Canonical Correspondence Analysis was used to partition the total explained variance in the species data into these variable groups. The pure (or unique) effects of the seven variable groups accounted for 69.1%, and combinations of variable groups (interaction terms) the remaining 30.9% of the total explained variability. Local scale variables such as in-stream substratum, vegetation in and near the stream (riparian zone), and some chemical variables were most strongly associated with the among-site variability. Local physical (24.4%) and local chemical (20.4%) variables explained the largest part of the among-site variability of community assemblages. These results are of importance when planning conservation and management measurements, implementing large-scale biomonitoring programs, and predicting how human alterations will affect running water ecosystems.This revised version was published online in May 2005 with corrections to the Cover Date.     

Multi-scale factors and long-term responses of Chihuahuan Desert grasses to drought

Factors with variation at broad (e.g., climate) and fine scales (e.g., soil texture) that influence local processes at the plant scale (e.g., competition) have often been used to infer controls on spatial patterns and temporal trends in vegetation. However, these factors can be insufficient to explain spatial and temporal variation in grass cover for arid and semiarid grasslands during an extreme drought that promotes woody plant encroachment. Transport of materials among patches may also be important to this variation. We used long-term cover data (1915–2001) combined with recently collected field data and spatial databases from a site in the northern Chihuahuan Desert to assess temporal trends in cover and the relative importance of factors at three scales (plant, patch, landscape unit) in explaining spatial variation in grass cover. We examined cover of five important grass species from two topographic positions before, during, and after the extreme drought of the 1950s. Our results show that dynamics before, during, and after the drought varied by species rather than by topographic position. Different factors were related to cover of each species in each time period. Factors at the landscape unit scale (rainfall, stocking rate) were related to grass cover in the pre- and post-drought periods whereas only the plant-scale factor of soil texture was significantly related to cover of two upland species during the drought. Patch-scale factors associated with the redistribution of water (microtopography) were important for different species in the pre- and post-drought period. Another patch-scale factor, distance from historic shrub populations, was important to the persistence of the dominant grass in uplands (Bouteloua eriopoda) through time. Our results suggest the importance of local processes during the drought, and transport processes before and after the drought with different relationships for different species. Disentangling the relative importance of factors at different spatial scales to spatial patterns and long-term trends in grass cover can provide new insights into the key processes driving these historic patterns, and can be used to improve forecasts of vegetation change in arid and semiarid areas.     

Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures

We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons. We used three LULC datasets: (1) 0.6 m resolution land cover data; (2) 30 m resolution land cover data (NLCD 2001); and (3) 30 m resolution Normalized Difference Vegetation Index data derived from the same ETM+ images (though from different bands) used for LST calculation. We developed ten models to evaluate effects of spatial and thematic resolution of LULC data on the observed relationships between LST and LULC variables for each season. We found that the directions of the effects of LULC variables on predicting LST were consistent across seasons, but the magnitude of effects, varied by season, providing the strongest predictive capacity during summer and the weakest during winter. Percent of imperviousness was the best predictor on LST with relatively consistent explanatory power across seasons, which alone explained approximately 50 % of the total variation in LST in winter, and up to 77.9 % for summer. Vegetation related variables, particularly tree canopy, were good predictor of LST during summer and fall. Vegetation, particularly tree canopy, can significantly reduce LST. The spatial resolution of LULC data appeared not to substantially affect relationships between LST and LULC variables. In contrast, increasing thematic resolution generally enhanced the explanatory power of LULC on LST, but not to a substantial degree.     

Assessing the utility of topographic variables in predicting structural complexity of tree stands in a reforested urban landscape

The transformation of natural landscapes into impervious built-up surfaces through urbanization is known to significantly interfere with the ecological integrity of urban landscapes and accelerate climate change and associated impacts. Although urban reforestation is widely recognised as an ideal mitigation practice against these impacts, it often has to compete with other lucrative land uses within an urban area. The often limited urban space provided for reforestation therefore necessitates the optimization of the ecological benefits, which demands spatially explicit information. The recent proliferation of tree stands structural complexity (SSC) and topographic data offer great potential for determining the ecological performance of reforested areas across an urban landscape. This study explores the potential of using topographic datasets to predict SSC in a reforested urban landscape and ranks the value of these topographic variables in determining SSC. Tree structural data from a reforested urban area was collected and fed into a tree stand structural complexity index, which was used to indicate ecological performance. Topographic variables (Topographic Wetness Index, slope, Area Solar Radiation and elevation)- were derived from a Digital Elevation Model (DEM) and used to predict SSC using the Partial Least Squares (PLS) regression technique. Results show that SSC varied significantly between the topographic variables. Results also show that the topographic variables could be used to reliably predict SSC. As expected, the Topographic Wetness Index and slope were the most important topographic determinants of SSC while elevation was the least valuable. These results provide valuable spatially explicit information about the ecological performance of the reforested areas within an urban landscape. Specifically, the study demonstrates the value of topographic data as aids to urban reforestation planning.     

Spatial analysis of selected soil attributes across an alpine topographic/snow gradient

The impact of the topographic/snow gradient on soil processes in alpinetundra on Niwot Ridge of the Colorado Front Range (Rocky mts, USA) was assessedusinggeostatistical modeling and a fractal approach. The mean snow depth, whichmeasured between 1984 and 2000, exhibited a smooth spatial continuity acrossthestudy grid area (550 × 400 meter). Soil color variables showed a nestedstructure that was attributed to a confounded effect of various soil-formingfactors on catenary processes. The spatial structure of texture classesexhibited no spatial structure and was explained by data sparsity,cryoturbation, and biological processes that mask the expected long-distancevariations (i.e., 550-m) of the catenary processes. Organic C, pH, bulkdensity,and soil moisture content showed various degrees of spatial continuity, but allindicated that the topographic/snow gradient is not the only dominantsoil-forming factor in this alpine ecosystem. The estimated fractal dimensionDfor the grid landform and the mean snow depth varied between 1.2 and 1.4,indicating that they vary smoothly with long-range variation. The estimatedDofthe soil variables ranged between 1.6 and 1.8, showing a noisy appearance withshort-range variations. These results strongly suggest that most small andmicro-scale variations in the alpine soil environs resulted from the combinedeffect of cryoturbation, biological activity, parent-material and eoliandeposition, whereas the large-scale variations originated as a result of thetopographic/snow gradient.This revised version was published online in May 2005 with corrections to the Cover Date.