Simulated productivity of heterogeneous patches in Southern African savanna landscapes using a canopy productivity model

A daily model of terrestrial productivity is used to simulate the annual productivity of heterogeneous vegetation structure at three savanna/woodland sites along a large moisture gradient in southern Africa. The horizontal distributions of vegetation structural parameters are derived from the three-dimensional canopy structure generated from detailed field observations of the vegetation at each site. Rainfall and daily climatic data are used to drive the model, resulting in a spatially explicit estimate of vegetation productivity in 100 m² patches over an area 810,000 m² (8,100 patches per site). Production is resolved into tree and grass components for each subplot. The model simulates the relative contribution of trees and grasses to net primary productivity (NPP) along the rainfall gradient. These simulated production estimates agree with previously published estimates of productivity in southern African savannas. Water-use efficiency of each site is directly related to the structural composition of the site and the differing water-use efficiencies for tree and grass functional types. To assess the role of spatial scale in governing estimates of vegetation productivity in heterogeneous landscapes, spatial aggregation is performed on the canopy mosaic at the northern-most (wettest) site for 625 m², 2500 m² and 5625 m² resolutions. These simulations result in similar overall patterns of average NPP for both trees and grasses, but drastically reduced distributions of productivity due to reduced structural heterogeneity. In particular, the aggregation of the detailed spatial mosaic to coarser resolutions is seen to eliminate information regarding demographic processes such as regeneration and mortality, and the dependence of grass productivity on over-story density. These results indicate that models of system productivity in savanna/woodland ecosystems must retain high spatial resolution to adequately characterize multi-year structural responses and to accurately represent the contribution of grass biomass to overall ecosystem production.This revised version was published online in May 2005 with corrections to the Cover Date.     

Scale dependence in quantification of land-cover and biomass change over Siberian boreal forest landscapes

We investigated the influence of remote sensing spatial resolution on estimates of characteristic land-cover change (LCC) and LCC-related above-ground biomass change (Δbiomass) in three study sites representative of the East Siberian boreal forest. Data included LCC estimated using an existing Landsat-derived land-cover dataset for 1990 and 2000, and above-ground standing biomass stocks simulated by the FAREAST forest succession model and applied on a pixel basis. At the base 60 m resolution, several landscape pattern metrics were derived to describe the characteristic LCC types. LCC data were progressively degraded to 240, 480, and 960 m. LCC proportions and Δbiomass were derived at each of the coarser resolutions and scale dependences of LCC and Δbiomass were analyzed. Compared to the base 60 m resolution, the Logged LCC type was highly scale dependent and was consistently underestimated at coarser resolutions. The Burned type was under- or over-estimated depending strongly on its patch size. Estimated at the base 60 m resolution, modeled biomass increased in two sites (i.e., 3.0 and 6.4 Mg C ha⁻¹ for the Tomsk and Krasnoyarsk sites, respectively) and declined slightly in one site (i.e., −0.5 Mg C ha⁻¹ for the Irkutsk site) between the two dates. At the degraded resolutions, the estimated Δbiomass increased to 3.3 and 7.0 Mg C ha⁻¹ for the Tomsk and Krasnoyarsk sites, while it declined to −0.8 Mg C ha⁻¹ for the Irkutsk site. Results indicate that LCC and Δbiomass values may be progressively amplified in either direction as resolution is degraded, depending on the mean patch size (MPS) of disturbances, and that the error of LCC and Δbiomass estimates also increases at coarser resolutions.     

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.     

The effects of aggregating sub-grid land surface variation on large-scale estimates of net primary production

The use of large grid cell databases (1/2° to 5°) to drive nonlinear ecosystem process models may create an incompatibility of scales which can often lead to biased outputs. Global simulations of net primary production (NPP) often assume that bias due to averaging of sub-grid variations in climate, topography, soils, and vegetation is minimal, yet the magnitude and behavior of this bias on estimates of NPP are largely unknown. The effects of averaging sub-grid land surface variations on NPP estimates were evaluated by simulating a 1° × 1° land surface area as represented by four successive levels of landscape complexity, ranging from a single computation to 8,456 computations of NPP for the study area. Averaging sub-grid cell landscape variations typical of the northern US Rocky Mountains can result in overestimates of NPP as large as 30 %. Aggregating climate within the 1° cell contributed up to 50 % of the bias to NPP estimates, while aggregating topography, soils, and vegetation was of secondary importance. Careful partitioning of complex landscapes can efficiently reduce the magnitude of this overestimation.     

Effects of stream map resolution on measures of riparian buffer distribution and nutrient retention potential

Riparian ecosystems are interfaces between aquatic and terrestrial environments recognized for their nutrient interception potential in agricultural landscapes. Stream network maps from a broad range of map resolutions have been employed in watershed studies of riparian areas. However, map resolution may affect important attributes of riparian buffers, such as the connectivity between source lands and small stream channels missing in coarse resolution maps. We sought to understand the influence of changing stream map resolution on measures of the river network, near-stream land cover, and riparian metrics. Our objectives were: (1) to evaluate the influence of stream map resolution on measures of the stream network, the character and extent of near-stream zones, and riparian metrics; (2) to compare patterns of variation among different physiographic provinces; and (3) to explore how predictions of nutrient retention potential might be affected by the resolution of a stream map. We found that using fine resolution stream maps significantly increased our estimates of stream order, drainage density, and the proportion of watershed area occurring near a stream. Increasing stream map resolution reduced the mean distance to source areas as well as mean buffer width and increased the frequency of buffer gaps. Measures of percent land cover within 100 m of streams were less sensitive to stream map resolution. Overall, increasing stream map resolution led to reduced estimates of nutrient retention potential in riparian buffers. In some watersheds, switching from a coarse resolution to a fine resolution stream map completely changed our perception of a stream network from well buffered to largely unbuffered. Because previous, broad-scale analyses of riparian buffers used coarse-resolution stream maps, those studies may have overestimated landscape-level buffer prevalence and effectiveness. We present a case study of three watersheds to demonstrate that interactions among stream map resolution and land cover patterns make a dramatic difference in the perceived ability of riparian buffers to ameliorate effects of agricultural activities across whole watersheds. Moreover, stream map resolution affects inferences about whether retention occurs in streams or riparian zones.     

Modeling grain-size dependent bias in estimating forest area: a regional application

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data. The models predicted mean scaling effects of 7.0 and 12.0% (in absolute value with standard deviations of 2.2 and 5.3%) on regional forest cover estimation (ranging from 2.3 and 2.5% to 11.1 and 23.7% at the county level) with standard errors of model estimation 3.1 and 7.1% between 30 m and 1 km, and 30 m and 10 km, respectively, within a 95% confidence interval. Our models improved accuracy of forest cover estimates (in terms of percent) by 63% (at 1-km resolution) and 57% (at 10-km resolution) at the county level relative to those without model adjustment and by 87 and 84% at the regional level in 2001. The model improved 1992 and 2001 regional forest estimation in terms of area for 1-km maps by 15,141 and 7,412 km² (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based regional forest areas as reference.     

The impact of land use/land cover scale on modelling urban ecosystem services

Context

Urbanisation places increasing stress on ecosystem services; however existing methods and data for testing relationships between service delivery and urban landscapes remain imprecise and uncertain. Unknown impacts of scale are among several factors that complicate research. This study models ecosystem services in the urban area comprising the towns of Milton Keynes, Bedford and Luton which together represent a wide range of the urban forms present in the UK.

Objectives

The objectives of this study were to test (1) the sensitivity of ecosystem service model outputs to the spatial resolution of input data, and (2) whether any resultant scale dependency is constant across different ecosystem services and model approaches (e.g. stock- versus flow-based).

Methods

Carbon storage, sediment erosion, and pollination were modelled with the InVEST framework using input data representative of common coarse (25 m) and fine (5 m) spatial resolutions.

Results

Fine scale analysis generated higher estimates of total carbon storage (9.32 vs. 7.17 kg m^?2) and much lower potential sediment erosion estimates (6.4 vs. 18.1 Mg km^?2 year^?1) than analyses conducted at coarser resolutions; however coarse-scale analysis estimated more abundant pollination service provision.

Conclusions

Scale sensitivities depend on the type of service being modelled; stock estimates (e.g. carbon storage) are most sensitive to aggregation across scales, dynamic flow models (e.g. sediment erosion) are most sensitive to spatial resolution, and ecological process models involving both stocks and dynamics (e.g. pollination) are sensitive to both. Care must be taken to select model data appropriate to the scale of inquiry.

     

Quantifying the effect of grazing and shrub-clearing on small scale spatial pattern of vegetation

Disturbances such as grazing, invading species, and clear-cutting, often act at small spatial scales, and means for quantifying their impact on fine scale vegetation patterns are generally lacking. Here we adopt a set of landscape metrics, commonly used for quantifying coarse scale fragmentation, to quantify fine scale fragmentation, namely the fine scale vegetation structure. At this scale, patches often consist of individual plants smaller than 1 m², requiring the grain of the analysis to be much smaller. We used balloon aerial photographs to map fine details of Mediterranean vegetation (pixel size <0.04 m) in experimental plots subjected to grazing and clear-cutting and in undisturbed plots. Landscape metrics are sensitive to scale. Therefore, we aggregated the vegetation map into four coarser scales, up to a resolution of 1 m, and analyzed the effect of scale on the metrics and their ability to distinguish between different disturbances. At the finest scale, six of the seven landscape metrics we evaluated revealed significant differences between treated and undisturbed plots. Four metrics revealed differences between grazed and control plots, and six metrics revealed differences between cleared and control plots. The majority of metrics exhibited scaling relations. Aggregation had mixed effects on the differences between metric values for different disturbances. The control plots were the most sensitive to scale, followed by grazing and clearing. We conclude that landscape metrics are useful for quantifying the very fine scale impact of disturbance on woody vegetation, assuming that the analysis is based on sufficiently high spatial resolution data.     

Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest,California

Context

Spatial scale and pattern play important roles in forest aboveground biomass (AGB) estimation in remote sensing. Changes in the accuracy of satellite images-estimated forest AGBs against spatial scales and pixel distribution patterns has not been evaluated, because it requires ground-truth AGBs of fine resolution over a large extent, and such data are difficult to obtain using traditional ground surveying methods.

Objectives

We intend to quantify the accuracy of AGB estimation from satellite images on changing spatial scales and varying pixel distribution patterns, in a typical mixed coniferous forest in Sierra Nevada mountains, California.

Methods

A forest AGB map of a 143 km² area was created using small-footprint light detection and ranging. Landsat Thematic Mapper images were chosen as typical examples of satellite images, and resampled to successively coarser resolutions. At each spatial scale, pixels forming random, uniform, and clustered spatial patterns were then sampled. The accuracies of the AGB estimation based on Landsat images associated with varying spatial scales and patterns were finally quantified.

Results

The changes in the accuracy of AGB estimation from Landsat images are not monotonic, but increase up to 60–90 m in spatial scale, and then decrease. Random and uniform spatial patterns of pixel distributions yield better accuracy for AGB estimation than clustered spatial patterns. The corrected NDVI (NDVIc) was the best predictor of AGB estimation.

Conclusions

A spatial scale of 60–90 m is recommended for forest AGB estimation at the Sierra Nevada mountains using Landsat images and those with similar spectral resolutions.

     

Selection of scale for Everglades landscape models

This article addresses the problem of determining the optimal “Model Grain” or spatial resolution (scale) for landscape modeling in the Everglades. Selecting an appropriate scale for landscape modeling is a critical task that is necessary before using spatial data for model development. How the landscape is viewed in a simulation model is dependent on the scale (cell size) in which it is created. Given that different processes usually have different rates of fluctuations (frequencies), the question of selection of an appropriate modeling scale is a difficult one and most relevant to developing spatial ecosystem models. The question of choosing the appropriate scale for modeling is addressed using the landscape indices (e.g., cover fraction, diversity index, fractal dimension, and transition probabilities) recently developed for quantifying overall characteristics of spatial patterns. A vegetation map of an Everglades impoundment area developed from SPOT satellite data was used in the analyses. The data from this original 20 × 20 m data set was spatially aggregated to a 40 × 40 m resolution and incremented by 40 meters on up to 1000 × 1000 m (i.e., 40, 80, 120, 160 … 1000) scale. The primary focus was on the loss of information and the variation of spatial indices as a function of broadening “Model Grain” or scale. Cover fraction and diversity indices with broadening scale indicate important features, such as tree islands and brush mixture communities in the landscape, nearly disappear at or beyond the 700 m scale. The fractal analyses indicate that the area perimeter relationship changes quite rapidly after about 100 m scale. These results and others reported in the paper should be useful for setting appropriate objectives and expectations for Everglades landscape models built to varying spatial scales.     

A Method to Efficiently Apply a Biogeochemical Model to a Landscape

Biogeochemical models offer an important means of understanding carbon dynamics, but the computational complexity of many models means that modeling all grid cells on a large landscape is computationally burdensome. Because most biogeochemical models ignore adjacency effects between cells, however, a more efficient approach is possible. Recognizing that spatial variation in model outputs is solely a function of spatial variation in input driver variables such as climate, we developed a method to sample the model outputs in input variable space rather than geographic space, and to then use simple interpolation in input variable space to estimate values for the remainder of the landscape. We tested the method in a 100 km×260 km area of western Oregon, U.S.A. , comparing interpolated maps of net primary production (NPP) and net ecosystem production (NEP) with maps from an exhaustive, wall-to-wall run of the model. The interpolation method can match spatial patterns of model behavior well (correlations>0.8) using samples of only 5 t o 15% of the landscape. Compression of temporal variation in input drivers is a key step in the process, with choice of input variables for compression largely determining the upper bounds on the degree of match between interpolated and original maps. The method is applicable to any model that does not consider adjacency effects, and could free up computational expense for a variety of other computational burdens, including spatial sensitivity analyses, alternative scenario testing, or finer grain-size mapping.     

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.     

Characterizing watershed-delineated landscapes in Pennsylvania using conditional entropy profiles

When the objective is to characterize landscapes with respect to relative degree and type of forest (or other critical habitat) fragmentation, it is difficult to decide which variables to measure and what type of discriminatory analysis to apply. It is also desirable to incorporate multiple measurement scales. In response, a new method has been developed that responds to changes in both the marginal and spatial distributions of land cover in a raster map. Multiscale features of the map are captured in a sequence of successively coarsened resolutions based on the random filter for degrading raster map resolutions. Basically, the entropy of spatial pattern associated with a particular pixel resolution is calculated, conditional on the pattern of the next coarser parent resolution. When the entropy is plotted as a function of changing resolution, we obtain a simple two-dimensional graph called a conditional entropy profile, thus providing a graphical visualization of multi-scale fragmentation patterns.Using eight-category raster maps derived from 30-meter resolution LANDSAT Thematic Mapper images, the conditional entropy profile was obtained for each of 102 watersheds covering the state of Pennsylvania (USA). A suite of more conventional single-resolution landscape measurements was also obtained for each watershed using the FRAGSTATS program. After dividing the watersheds into three major physiographic provinces, cluster analysis was performed within each province using various combinations of the FRAGSTATS variables, land cover proportions and variables describing the conditional entropy profiles. Measurements of both spatial pattern and marginal land cover proportions were necessary to clearly discriminate the watersheds into distinct clusters for most of the state; however, the Piedmont province essentially only required the land cover proportions. In addition to land cover proportions, only the variables describing a conditional entropy profile appeared to be necessary for the Ridge and Valley province, whereas only the FRAGSTATS variables appeared to be necessary for the Appalachian Plateaus province. Meanwhile, the graphical representation of conditional entropy profiles provided a visualization of multi-scale fragmentation that was quite sensitive to changing pattern.     

Regional modeling of grassland biogeochemistry using GIS

We used an ecosystem coupled to a Geographic Information System (GIS) to simulate spatial variability in storage and fluxes of C and N within grassland ecosystems. The GIS contained information on driving variables required to run the model. These were soil texture, monthly precipitation and monthly minimum and maximum temperatures. We overlayed polygon maps of the above variables to produce a driving variable map of our study region. The final map had 768 polygons in 160 unique classes. The ecosystem model was run to a steady state for each class and NPP, soil organic matter (SOM), net N mineralization and trace gas emission were mapped back into the GIS for display. Variation in all of the above propertiees occurred within the region. NPP was primarily controlled by climate and patterns followed spatial variation in precipitation closely. Soil organic matter, in contrast, was controlled largely by soil texture within this climatic range. Error associated with aggregation within the study area showed that spatial averages over the study area could be used to drive simulations of NPP, which is linearly related to rainfall. More spatial detail had to be preserved for accurate simulation of SOM, which is nonlinearly related to texture. Mechanistic regional models form a valuable link between process studies and global models.     

Effects of sensor spatial resolution on landscape structure parameters

We examined the effects of increasing grain size from 20 m to 1100 m on landscape parameters characterizing spatial structure in the northern Wisconsin lake district. We examined whether structural parameters remain relatively constant over this range and whether aggregation algorithms permit extrapolation within this range. Images from three different satellite sensors were employed in this study: (1) the SPOT multispectral high resolution visible (HRV), (2) the Landsat Thematic Mapper (TM), and (3) the NOAA Advanced Very High Resolution Radiometer (AVHRR). Each scene was classified as patches of water in a matrix of land. Spatial structure was quantified using several landscape parameters: percent water, number of lakes (patches), average lake area and perimeter, fractal dimension, and three measures of texture (homogeneity, contrast, and entropy). Results indicate that most measures were sensitive to changes in grain size. As grain size increased from 20 m using HRV image data to 1100 m (AVHRR), the percent water and the number of lakes decreased while the average lake area, perimeter, the fractal dimension, and contrast increased. The other two texture measures were relatively invariant with grain size. Although examination of texture at various angles of adjacency was performed to investigate features which vary systematically with angle, the angle did not have an important effect on the texture parameter values. An aggregation algorithm was used to simulate additional grain sizes. Grain was increased successively by a factor of two from 20 m (the HRV image) to 1280 m. We then calculated landscape parameter values at each grain size. Extrapolated values closely approximated the actual sensor values. Because the grain size has an important effect on most landscape parameters, the choice of satellite sensor must be appropriate for the research question asked. Interpolation between the grain sizes of different satellite sensors is possible with an approach involving aggregation of pixels.     

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.     

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.     

Temporal patterns of ecosystem processes on simulated landscapes in Glacier National Park,Montana, USA

The mechanistic, spatially-explicit fire succession model, Fire-BGC (a Fire BioGeoChemical succession model) was used to investigate long-term trends in landscape pattern under historical and future fire regimes and present and future climate regimes for two 46000 ha landscapes in Glacier National Park, Montana, USA. Fire-BGC has two spatial and temporal resolutions in the simulation architecture where ecological processes that act at a landscape level, such as fire, are simulated annually from information contained in spatial data layers, while stand-level processes such as photosynthesis, transpiration, and decomposition are simulated both daily and annually. Fire is spread across the landscape using the FARSITE fire growth model and subsequent fire effects are simulated at the stand-level. Fire-BGC was used to simulate changes in landscape pattern over 250 years under four scenarios: (1) complete fire exclusion under current climate, (2) historical wildfire occurrence and current climate, (3) complete fire exclusion under a possible future climate, (4) future wildfire occurrence and future climate. Simulated maps of dominant tree species, aboveground standing crop, leaf area index, and net primary productivity (NPP) were contrasted across scenarios using the metrics of patch density, edge density, evenness, contagion, and interspersion. Simulation results indicate that fire influences landscape pattern metrics more that climate alone by creating more diverse, fragmented, and disconnected landscapes. Fires were more frequent, larger, and more intense under a future climate regime. Landscape metrics showed different trends for the process-based NPP map when compared to the cover type map. It may be important to augment landscape analyses with process-based layers as well as structural and compositional layers.