Influence of high-resolution data on the assessment of forest fragmentation

Context

Remote sensing has been a foundation of landscape ecology. The spatial resolution (pixel size) of remotely sensed land cover products has improved since the introduction of landscape ecology in the United States. Because patterns depend on spatial resolution, emerging improvements in the spatial resolution of land cover may lead to new insights about the scaling of landscape patterns.

Objective

We compared forest fragmentation measures derived from very high resolution (1 m²) data with the same measures derived from the commonly used (30 m?×??30 m; 900 m²) Landsat-based data.

Methods

We applied area-density scaling to binary (forest; non-forest) maps for both sources to derive source-specific estimates of dominant (density ≥?60%), interior (≥?90%), and intact (100%) forest.

Results

Switching from low- to high-resolution data produced statistical and geographic shifts in forest spatial patterns. Forest and non-forest features that were “invisible” at low resolution but identifiable at high resolution resulted in higher estimates of dominant and interior forest but lower estimates of intact forest from the high-resolution source. Overall, the high-resolution data detected more forest that was more contagiously distributed even at larger spatial scales.

Conclusion

We anticipate that improvements in the spatial resolution of remotely sensed land cover products will advance landscape ecology through re-interpretations of patterns and scaling, by fostering new landscape pattern measurements, and by testing new spatial pattern-ecological process hypotheses.

     

Post-disturbance recovery of forest cover and tree height differ with management in Central Europe

Context

Recovery from disturbances is a prominent measure of forest ecosystem resilience, with swift recovery indicating resilient systems. The forest ecosystems of Central Europe have recently been affected by unprecedented levels of natural disturbance, yet our understanding of their ability to recover from disturbances is still limited.

Objectives

We here integrated satellite and airborne Lidar data to (i) quantify multi-decadal post-disturbance recovery of two indicators of forest structure, and (ii) compare the recovery trajectories of forest structure among managed and un-managed forests.

Methods

We developed satellite-based models predicting Lidar-derived estimates of tree cover and stand height at 30 m grain across a 3100 km² landscape in the Bohemian Forest Ecosystem (Central Europe). We summarized the percentage of disturbed area that recovered to >?40% tree cover and >?5 m stand height and quantified the variability in both indicators over a 30-year period. The analyses were stratified by three management regimes (managed, protected, strictly protected) and two forest types (beech-dominated, spruce-dominated).

Results

We found that on average 84% of the disturbed area met our recovery threshold 30 years post-disturbance. The rate of recovery was slower in un-managed compared to managed forests. Variability in tree cover was more persistent over time in un-managed forests, while managed forests strongly converged after a few decades post-disturbance.

Conclusion

We conclude that current management facilitates the recovery of forest structure in Central European forest ecosystems. However, our results underline that forests recovered well from disturbances also in the absence of human intervention. Our analysis highlights the high resilience of Central European forest ecosystems to recent disturbances.

     

Historical patterns of fire severity and forest structure and composition in a landscape structured by frequent large fires: Pumice Plateau ecoregion,Oregon, USA

Context

Lack of quantitative observations of extent, frequency, and severity of large historical fires constrains awareness of departure of contemporary conditions from those that demonstrated resistance and resilience to frequent fire and recurring drought.

Objectives

Compare historical and contemporary fire and forest conditions for a dry forest landscape with few barriers to fire spread.

Methods

Quantify differences in (1) historical (1700–1918) and contemporary (1985–2015) fire extent, fire rotation, and stand-replacing fire and (2) historical (1914–1924) and contemporary (2012) forest structure and composition. Data include 85,750-ha tree-ring reconstruction of fire frequency and extent; >?375,000-ha timber inventory following >?78,900-ha fires in 1918; and remotely-sensed maps of contemporary fire effects and forest conditions.

Results

Historically, fires?>?20,000 ha occurred every 9.5 years; fire rotation was 14.9 years; seven fires?>?40,469 ha occurred during extreme drought (PDSI <?? 4.0); and stand-replacing fire occurred primarily in lodgepole (Pinus contorta var. murrayana). In contemporary fires, only 5% of the ecoregion burned in 30 years, and stand-replacing fire occurred primarily in ponderosa (Pinus ponderosa) and mixed-conifer. Historically, density of conifers?>?15 cm dbh exceeded 120 trees/ha on?<?5% of the area compared to 95% currently.

Conclusions

Frequent, large, low-severity fires historically maintained open-canopy ponderosa and mixed-conifer forests in which large fire- and drought-tolerant trees were prevalent. Stand-replacing patches in ponderosa and mixed-conifer were rare, even in fires >?40,469 ha (minimum size of contemporary “megafires”) during extreme drought. In this frequent-fire landscape, mixed-severity fire historically influenced lodgepole and adjacent forests. Lack of large, frequent, low-severity fires degrades contemporary forest ecosystems.

     

Human appropriated net primary productivity as a metric for land use planning: a case study in the US Great Lakes region

Context

Human appropriation of net primary productivity (HANPP) is employed as a measure of human pressures on biodiversity, though largely at global and national scales rather than landscape to regional scales where many conservation decisions take place. Though gaining in familiarity, HANPP is not widely utilized by conservation professionals.

Objectives

This study, encompassing the US side of the Great Lakes basin, examines how regional distributions of HANPP relate to landscape-based biodiversity proxy metrics used by conservation professionals. Our objectives were (1) to quantify the HANPP of managed lands at the county scale; and (2) to assess spatial patterns of HANPP in comparison to landscape diversity and local habitat connectedness to determine if the metric can provide useful information to conservation professionals.

Methods

We aggregated forest and cropland NPP data between 2005 and 2015 and coupled it with previously published potential vegetation maps to quantify the HANPP of each county in the study region. We mapped the outputs at 500 m resolution to analyze spatial relationships between HANPP and landscape metrics of biodiversity potential.

Results

Area-weighted HANPP across our study region averaged 45% of NPP, down to 4.9% in forest-dominated counties. Greater HANPP correlated with reduced landscape diversity (p?<?0.001, r²?=?0.28) and reduced local habitat connectedness (p?<?0.001, r²?=?0.36).

Conclusion

HANPP could be used as an additional tool for conservation professionals during regional-scale land use planning or conservation decision-making, particularly in mixed-use landscapes that both support important biodiversity and have high levels of primary production harvest.

     

Effects of aspect and altitude on carbon cycling processes in a temperate mountain forest catchment

Context

Varying altitudes and aspects within small distances are typically found in mountainous areas. Such a complex topography complicates the accurate quantification of forest C dynamics at larger scales.

Objectives

We determined the effects of altitude and aspect on forest C cycling in a typical, mountainous catchment in the Northern Limestone Alps.

Methods

Forest C pools and fluxes were measured along two altitudinal gradients (650–900 m a.s.l.) at south-west (SW) and north-east (NE) facing slopes. Net ecosystem production (NEP) was estimated using a biometric approach combining field measurements of aboveground biomass and soil CO₂ efflux (SR) with allometric functions, root:shoot ratios and empirical SR modeling.

Results

NEP was higher at the SW facing slope (6.60?±?3.01 t C ha^?1  year^?1), when compared to the NE facing slope (4.36?±?2.61 t C ha^?1 year^?1). SR was higher at the SW facing slope too, balancing out any difference in NEP between aspects (NE: 1.30?±?3.23 t C ha^?1 year^?1, SW: 1.65?±?3.34 t C ha^?1 year^?1). Soil organic C stocks significantly decreased with altitude. Forest NPP and NEP did not show clear altitudinal trends within the catchment.

Conclusions

Under current climate conditions, altitude and aspect adversely affect C sequestering and releasing processes, resulting in a relatively uniform forest NEP in the catchment. Hence, including detailed climatic and soil conditions, which are driven by altitude and aspect, will unlikely improve forest NEP estimates at the scale of the studied catchment. In a future climate, however, shifts in temperature and precipitation may disproportionally affect forest C cycling at the southward slopes through increased water limitation.

     

A combined grazing and fire management may reverse woody shrub encroachment in desert grasslands

Context

Fire and controlled grazing have been widely adopted as management interventions to counteract woody shrub proliferation in many arid and semiarid grassland systems. The actual intensity of grazing and fire, along with the timing of the interventions, however, are difficult to determine in practice.

Objectives

This study aims to establish model simulations to access the long-term landscape changes under different land management scenarios.

Methods

We developed a cellular automata model to evaluate landscape dynamics in response to scenarios of grazing, fire, time of intervention, and initial coverage of grasses and shrubs.

Results

With current grazing intensity and fire suppression, the landscape may shift to a shrub-dominated landscape in 100–150 years. An appropriate combination of grazing and fire management could help maintain over 50% of grass cover and reduce the shrub cover to less than 2%, keeping the landscape highly reversible. Even using 1% grazing intensity and periodic fire once a year, the management tools should be implemented in 60 years, otherwise, they may lose effectiveness and the vegetation transition to grasslands would become impossible.

Conclusions

This study highlighted that the reintroduction of fire not only directly removes shrubs but also reallocates soil water and resources among different microsites, which may accelerate grass recovery and suppress shrub regrowth, potentially reversing the shrub invasion process. The combined grazing and fire management plans should be carried out before a threshold time depending on the chosen management tools.

     

Forest proximity rather than local forest cover affects bee diversity and coffee pollination services

Context

As agricultural demands for land continues to expand, strategies are urgently needed to balance agricultural production with biodiversity conservation and ecosystem service provision in agricultural landscapes.

Objectives

We used a factorial landscape design to assess the relative contributions of forest proximity and local forest cover to bee diversity and the provision of coffee pollination services.

Methods

We quantified bee diversity and fruit set in 24 sun-grown coffee fields in Southeast Region of Brazil that were selected following a factorial sampling design to test the independent effects of local forest cover (in a radius of 400 m) and proximity to forest fragments. To assess the impact of landscape simplification, we also evaluated local coffee cover.

Results

Bee richness and abundance were higher in the proximity of forest fragments, but only bee abundance decreased when the coffee cover dominated the surrounding landscapes. Coffee fruit set was 16% higher overall with bee visitations compared with bee exclusion and increased to 20% when coffee bushes were near forest fragments, and the coffee cover was low. Surprisingly, local forest cover did not affect the bee community or coffee fruit set.

Conclusion

Our results provide clear evidence that the proximity of coffee crops to forest fragments can affect the abundance and richness of bees visiting the coffee flowers and thereby facilitate the provision of pollination services. The positive association between forest proximity and fruit set reinforces the importance of natural vegetation in enhancing bee diversity and, therefore, in the provision of pollination services. The negative effect of coffee cover on fruit set at the local scale suggests that the service demand can surpass the capacity of pollinators to provide it. These effects were independent of the local forest cover, although all studied landscapes had more than 20% remaining forest cover (within a 2 km radius), which is considered the extinction threshold for Atlantic Forest species. Interspersion of forest fragments and coffee plantations in regions with more than 20% of forest cover left could thus be a useful landscape management target for facilitating pollinator flows to coffee crops and thus for increasing coffee yields.

     

Detection of relative differences in phenology of forest species using Landsat and MODIS

Landsat imagery is routinely used to characterize stand-level forest communities, but low temporal resolution makes pixel-wise characterization of phenology difficult. This limitation can be overcome by using multi-year imagery, but organizing Landsat scenes by calendar date ignores phenological gradients across the landscape as well as inter-annual differences in both scene- and pixel-wise phenology. We demonstrate how a spatially generalizable, phenologically-informed approach for re-ordering Landsat pixels can be used to characterize spatial variations in autumn senescence in several forest tree species. Using end-of-season estimates derived from MODIS phenology data, we determined the “days left in season” (DLiS) across Landsat images to produce a synthesized phenological trajectory of the normalized difference infrared index (NDII). We used ground-based species composition data in conjunction with the NDII trajectories to model autumn senescence by species. Absolute phenology differed by one and a half to 3 weeks between northern and southern Wisconsin, USA, but we show that the relative timing of phenology for individual species differs across regions by only 1–3 days when considering senescence with respect to the local end of the season. The progression of species senescence was consistent in lowland stands, starting with green and black ash, followed by silver maple, yellow birch, red maple, and tamarack. The image analyses suggest that senescence progressed more rapidly in southern than northern Wisconsin, starting earlier but taking about ten more days in the north. Our results support the use of MODIS phenological data with multi-year Landsat imagery to detect species with unique phenologies and identify how these vary across the landscape.     

Effect of spatial image support in detecting long-term vegetation change from satellite time-series

Context

Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to detect ecologically meaningful change from satellite time-series.

Objectives

(1) Assess the effects of image resolution in detecting landscape spatial heterogeneity. (2) Compare and evaluate the efficacy of coarse (MODIS) and moderate (Landsat) resolution satellite time-series for detecting ecosystem change.

Methods

Using long-term (~12 year) vegetation monitoring data from grassland and shrubland sites in southern New Mexico, USA, we evaluated the effects of changing image support using MODIS (250-m) and Landsat (30-m) time-series in modeling and detecting significant changes in vegetation using time-series decomposition techniques.

Results

Within our study ecosystem, landscape-scale (>20-m) spatial heterogeneity was low, resulting in a similar ability to detect vegetation changes across both satellite sensors and levels of spatial image support. While both Landsat and MODIS imagery were effective in modeling temporal dynamics in vegetation structure and composition, MODIS was more strongly correlated to biomass due to its cleaner (i.e., fewer artifacts/data gaps) 16-day temporal signal.

Conclusions

The optimization of spatial/temporal scale is critical in ensuring adequate detection of change. While the results presented in this study are likely specific to arid shrub-grassland ecosystems, the approach presented here is generally applicable. Future analysis is needed in other ecosystems to assess how scaling relationships will change under different vegetation communities that range in their degree of landscape heterogeneity.

     

Habitat selection by spotted owls after a megafire reflects their adaptation to historical frequent-fire regimes

Context

Climate and land-use change have led to disturbance regimes in many ecosystems without a historical analog, leading to uncertainty about how species adapted to past conditions will respond to novel post-disturbance landscapes.

Objectives

We examined habitat selection by spotted owls in a post-fire landscape. We tested whether selection or avoidance of severely burned areas could be explained by patch size or configuration, and whether variation in selection among individuals could be explained by differences in habitat availability.

Methods

We applied mixed-effects models to GPS data from 20 spotted owls in the Sierra Nevada, California, USA, with individual owls occupying home ranges spanning a broad range of post-fire conditions after the 2014 King Fire.

Results

Individual spotted owls whose home ranges experienced less severe fire (<?5% of home range severely burned) tended to select severely burned forest, but owls avoided severely burned forest when more of their home range was affected (~ 5–40%). Owls also tended to select severe fire patches that were smaller in size and more complex in shape, and rarely traveled?>?100-m into severe fire patches. Spotted owls avoided areas that had experienced post-fire salvage logging but the interpretation of this effect was nuanced. Owls also avoided areas that were classified as open and/or young forest prior to the fire.

Conclusions

Our results support the hypothesis that spotted owls are adapted to historical fire regimes characterized by small severe fire patches in this region. Shifts in disturbance regimes that produce novel landscape patterns characterized by large, homogeneous patches of high-severity fire may negatively affect this species.

     

Land cover and climate changes drive regionally heterogeneous increases in US insecticide use

Context

Global environmental change is expected to dramatically affect agricultural crop production through a myriad of pathways. One important and thus far poorly understood impact is the effect of land cover and climate change on agricultural insect pests and insecticides.

Objectives

Here we address the following three questions: (1) how do landscape complexity and weather influence present-day insecticide use, (2) how will changing landscape characteristics and changing climate influence future insecticide use, and how do these effects manifest for different climate and land cover projections? and (3) what are the most important drivers of changing insecticide use?

Methods

We use panel models applied to county-level agriculture, land cover, and weather data in the US to understand how landscape composition and configuration, weather, and farm characteristics impact present-day insecticide use. We then leverage forecasted changes in land cover and climate under different future scenarios to predict insecticide use in 2050.

Results

We find different future scenarios—through modifications in both landscape and climate conditions—increase the amount of area treated by ~ 4–20% relative to 2017, with regionally heterogeneous impacts. Of note, we report large farms are more influential than large crop patches and increased winter minimum temperature is more influential than increased summer maximum temperature. However, our results suggest the most important determinants of future insecticide use are crop composition and farm size, variables for which future forecasts are sparse.

Conclusions

Both landscape and climate change are expected to increase future insecticide use. Yet, crop composition and farm size are highly influential, data-poor variables. Better understanding of future crop composition and farm economics is necessary to effectively predict and mitigate increases in pesticide use.

     

Landscape patterns and diversity of meadow plants and flower-visitors in a mountain landscape

Context

Wild flowering plants and their wild insect visitors are of great importance for pollination. Montane meadows are biodiversity hotspots for flowering plants and pollinators, but they are contracting due to tree invasion.

Objectives

This study quantified flowering plants and their flower-visitor species in montane meadows in the western Cascade Range of Oregon. Species diversity in small, isolated meadows was expected to be lower and nested relative to large meadows. Alternatively, landform features may influence richness and spatial turnover.

Methods

Flowering plants and their visitors were sampled in summers of 2011–2017 in twelve montane meadows with varying soil moisture. All flowering plants and all flower-visitors were recorded during five to seven 15 min watches in ten 3?×?3 m plots in each meadow and year.

Results

A total of 178 flowering plant species, 688 flower-visitor species and 137,916 interactions were identified. Richness of flower-visitors was related to meadow patch size, but neither plant nor flower-visitor richness was related to isolation measured as meadow area within 1000 m. Species in small meadows were not nested subsets of those in large meadows. Species replacement accounted for more than 78% of dissimilarity between meadows and was positively related to differences in soil moisture.

Conclusions

Although larger meadows contained more species, landform features have influenced meadow configuration, persistence, and soil moisture, contributing to high plant and insect species diversity. Hence, conservation and restoration of a variety of meadow types may promote landscape diversity of wild plants and pollinators.

     

Observing succession on aspen-dominated landscapes using a remote sensing-ecosystem approach

In the North American upper Great Lakes region, forests dominated by the aspens (Populus grandidentata Michx. – bigtooth aspen, and P. tremuloides Michx. – trembling aspen), which established after late 19th and early 20th century logging, are maturing and succession will create a new forest composition at landscape to regional scales. This study analyzed the capabilities of Landsat ETM+ remote sensing data combined with existing ecological land unit classifications to discriminate and quantify patterns of succession at the landscape scale over the 4200 ha University of Michigan Biological Station (UMBS) in northern Lower Michigan. In a hierarchical approach first multi-temporal Landsat ETM+ was used with a landscape ecosystem classification to map upland forest cover types (overall accuracy 91.7%). Next the aspen cover type was subset and successional pathways were mapped within that type (overall accuracy 89.8%). Results demonstrated that Landsat ETM+ may be useful for these purposes; stratification of upland from wetland types using an ecological land unit classification eliminated confounding issues; multi-temporal methods discriminated evergreen conifer versus deciduous understories. The Landsat ETM+ classifications were then used to quantify succession and its relationship to landform-level ecological land units. Forests on moraine and ice contact landforms are succeeding distinctly to northern hardwoods (95% and 88% respectively); those on outwash and other landforms show greater diversity of successional pathways.     

Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast

Context

Coastal landscapes evolve in response to sea-level rise (SLR) through a variety of geologic processes and ecological feedbacks. When the SLR rate surpasses the rate at which these processes build elevation and drive lateral migration, inundation is likely.

Objectives

To examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States.

Methods

Using an existing probabilistic framework, we quantify the probability of inundation, a measure of vulnerability, under different SLR scenarios on the coastal landscape. Resistant areas—wherein a dynamic response is anticipated—are defined as unlikely (p < 0.33) to inundate. Results are assessed regionally for different land cover types and at 26 sites representing varying levels of land cover diversity.

Results

Modeling results suggest that by the 2050s, 44% of low-lying, habitable land in the region is unlikely to inundate, further declining to 36% by the 2080s. In addition to a decrease in SLR resistance with time, these results show an increasing uncertainty that the coastal landscape will continue to evolve in response to SLR as it has in the past. We also find that resistance to SLR is correlated with land cover composition, wherein sites containing land cover types adaptable to SLR impacts show greater potential to undergo biogeomorphic state shifts rather than inundating with time.

Conclusions

Our findings support other studies that have highlighted the importance of ecological composition and diversity in stabilizing the physical landscape and suggest that flexible planning strategies, such as adaptive management, are particularly well suited for SLR preparation in diverse coastal settings.

     

Grassland-to-crop conversion in agricultural landscapes has lasting impact on the trait diversity of bees

Context

Global pollinator decline has motivated much research to understand the underlying mechanisms. Among the multiple pressures threatening pollinators, habitat loss has been suggested as a key-contributing factor. While habitat destruction is often associated with immediate negative impacts, pollinators can also exhibit delayed responses over time.

Objectives

We used a trait-based approach to investigate how past and current land use at both local and landscape levels impact plant and wild bee communities in grasslands through a functional lens.

Methods

We measured flower and bee morphological traits that mediate plant–bee trophic linkage in 66 grasslands. Using an extensive database of 20 years of land-use records, we tested the legacy effects of the landscape-level conversion of grassland to crop on flower and bee trait diversity.

Results

Land-use history was a strong driver of flower and bee trait diversity in grasslands. Particularly, bee trait diversity was lower in landscapes where much of the land was converted from grassland to crop long ago. Bee trait diversity was also strongly driven by plant trait diversity computed with flower traits. However, this relationship was not observed in landscapes with a long history of grassland-to-crop conversion. The effects of land-use history on bee communities were as strong as those of current land use, such as grassland or mass-flowering crop cover in the landscape.

Conclusions

Habitat loss that occurred long ago in agricultural landscapes alters the relationship between plants and bees over time. The retention of permanent grassland sanctuaries within intensive agricultural landscapes can offset bee decline.

     

CORINE-based landscape indices weakly correlate with plant species richness in a northern European landscape transect

We present the results of one of the few available tests of how CORINE (CLC2000) is likely to perform as a basis for the calculation of landscape indices, for environmental monitoring over large areas. This paper investigates to what extent landscape structural indices based on this widely used European land cover database can be used to predict plant species richness in a 2,000 km² transect in the northeast of Scotland. We investigate both statistical and map resolution issues by comparing the performance of CORINE-based common landscape indices with the same indices derived from a much more detailed geographic data set. In our case study, only shape-related indices show correlation with species richness, but effect size, important for monitoring, is small. The results highlight the area-specific and map specific nature of the performance of landscape indices for protecting plant diversity. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Quantifying green cover change for sustainable urban planning: A case of Kuala Lumpur,Malaysia

Despite the numerous benefits of urban green cover, urban land development has led to its destruction and degradation, including in Malaysia. In this study, time series Landsat satellite imagery were used to monitor green cover changes in Kuala Lumpur (KL), the largest and capital city of Malaysia. An advanced satellite image processing technique that considers the mixed-pixel problem was employed to determine the fraction of green cover in each Landsat pixel. Results show that the total green coverage in Kuala Lumpur decreased by 3% over the first study period, from 6564 ha in 2001–5,891 ha in 2013. However, it increased by 4% in the second, from 6215 ha in 2014–7,310 ha in 2016, and now green cover is 30% of the total land area of KL. These periods were selected to observe the changes in green cover before and after implementation of the “Greening KL” program, which was aimed to plant 100,000 trees in KL by year 2020. Most of the increase in green cover was contributed by trees planted along streets and in recreational parks. Other findings include a loss of ∼9% of green cover in two public parks compared to their total gazetted area, and a loss of green area in other forested parts of KL. Focus group discussions and structured interviews with public, private and non-governmental organizations indicate that green-cover losses can be partly attributed to weak regulations and their poor enforcement. Opportunities to protect and increase green cover in KL are also explored in this study. Such approaches are urgently needed before most of the green areas disappear from the landscape of KL, exacerbating the existing environmental problems in the city.     

A new approach for rescaling land cover data

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

Information theory as a consistent framework for quantification and classification of landscape patterns

Context

Quantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns.

Objective

To demonstrate that the information theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns.

Methods

After presenting information theory in the context of landscapes, information-theoretical metrics were calculated for an exemplar set of landscapes embodying all feasible configurations of land cover patterns. Sequences and 2D parametrization of patterns in this set were performed to demonstrate the feasibility of information theory for the analysis of landscape patterns.

Results

Universal classification of landscape into pattern configuration types was achieved by transforming landscapes into a 2D space of weakly correlated information-theoretical metrics. An ordering of landscapes by any single metric cannot produce a sequence of continuously changing patterns. In real-life patterns, diversity induces complexity—increasingly diverse patterns are increasingly complex.

Conclusions

Information theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification.