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1.
ContextThe open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥?250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m). ObjectivesBased on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed. MethodsMODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha?1 year?1). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed. ResultsWe achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R2 was?>?0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha?1 year?1) and the lowest for forest. ConclusionsEstimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. It is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape. 相似文献
2.
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 km 2 (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based
regional forest areas as reference. 相似文献
3.
Hierarchy theory predicts that a hierarchy of process rates should be reflected in a hierarchy of spatial and temporal scales observable on the landscape. We will show that multiple scales of pattern for total plant cover measured in the field at 1-m resolution are correlated with scales of vegetative pattern obtained from remotely sensed data with resolutions of 25 m 2 and 30 2. Second, using four models based on postulates of hierarchy theory, we will combine the scales of pattern of each individual species within a community to estimate the remotely sensed community scales of pattern. Finally, we will compare the four models using a Bayesian analysis to determine which model best portrays how vegetative patterns of individual species combine to produce remotely observed community patterns. The results of the model comparisons provide an example of how postulates of hierarchy theory can be tested and how individual species patterns can be scaled-up to estimate remotely observed scales of pattern. 相似文献
4.
ContextRecovery 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. ObjectivesWe 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. MethodsWe 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). ResultsWe 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. ConclusionWe 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. 相似文献
5.
ContextHuman 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. ObjectivesThis 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. MethodsWe 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. ResultsArea-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). ConclusionHANPP 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. 相似文献
6.
ContextAs 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. ObjectivesWe 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. MethodsWe 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. ResultsBee 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. ConclusionOur 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. 相似文献
7.
ContextQuantitative 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. ObjectiveTo demonstrate that the information theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns. MethodsAfter 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. ResultsUniversal 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. ConclusionsInformation theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification. 相似文献
8.
ContextForest cover change analyses have revealed net forest gain in many tropical regions. While most analyses have focused solely on forest cover, trees outside forests are vital components of landscape integrity. Quantifying regional-scale patterns of tree cover change, including non-forest trees, could benefit forest and landscape restoration (FLR) efforts.ObjectivesWe analyzed tree cover change in Southwestern Panama to quantify: (1) patterns of change from 1998 to 2014, (2) differences in rates of change between forest and non-forest classes, and (3) the relative importance of social-ecological predictors of tree cover change between classes.MethodsWe digitized tree cover classes, including dispersed trees, live fences, riparian forest, and forest, in very high resolution images from 1998 to 2014. We then applied hurdle models to relate social-ecological predictors to the probability and amount of tree cover gain.ResultsAll tree cover classes increased in extent, but gains were highly variable between classes. Non-forest tree cover accounted for 21% of tree cover gains, while riparian trees constituted 31% of forest cover gains. Drivers of tree cover change varied widely between classes, with opposite impacts of some social-ecological predictors on non-forest and forest cover.ConclusionsWe demonstrate that key drivers of forest cover change, including topography, road distance and historical forest cover, do not explain rates of non-forest tree cover change. Consequently, predictions from medium-resolution forest cover change analyses may not apply to finer-scale patterns of tree cover. We highlight the opportunity for FLR projects to target tree cover classes adapted to local social and ecological conditions. 相似文献
9.
ContextPattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. ObjectivesWe developed an R package motif as a set of open-source tools for pattern-based spatial analysis. MethodsThis package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. ResultsIn this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. ConclusionsThe methods implemented in motif should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The motif package homepage is https://nowosad.github.io/motif. 相似文献
10.
ContextLandscape metrics play an important role in measurement, analysis, and interpretation of spatial patterns of landscapes. There are a variety of different landscape metrics widely used in landscape ecology. However, existing landscape metrics are mostly non-graphic and single-value indices, which may not be sufficient to describe the complex spatial correlation and interclass relationships of various landscapes. As a transition probability diagram over the lag distance, the transiogram, which emerged in recent years, essentially provides a new graphic metric for measuring and visualizing the auto and cross correlations of landscape categories. ObjectivesTo explore the capability of the transiogram for measuring spatial patterns of categorical landscape maps and compare it with existing landscape metrics. MethodsSixteen commonly-used landscape metrics and transiograms (including auto- and cross-transiograms) were estimated and compared for land cover/use classes in four areas with different landscapes. ResultsResults show that (1) these transiograms can provide visual information about the proportions, aggregation levels, interclass adjacencies, and intra-class/interclass correlation ranges of landscape classes; (2) sills and auto-correlation ranges of transiograms are correlated with the values of some landscape metrics; and (3) the peak height ratios of idealized transiograms can effectively represent the juxtaposition strength of neighboring class pairs. ConclusionsThe transiogram can be an effective graphic metric for characterizing the auto-correlation of single classes (through auto-transiograms) and the complex interclass relationships, such as interdependency and juxtaposition, between different landscape classes (through cross-transiograms). 相似文献
11.
Spatially-distributed estimates of biologically-driven CO 2 flux are of interest in relation to understanding the global carbon cycle. Global coverage by satellite sensors offers an opportunity to assess terrestrial carbon (C) flux using a variety of approaches and corresponding spatial resolutions. An important consideration in evaluating the approaches concerns the scale of the spatial heterogeneity in land cover over the domain being studied. In the Pacific Northwest region of the United States, forests are highly fragmented with respect to stand age class and hence C flux. In this study, the effects of spatial resolution on estimates of total annual net primary production (NPP) and net ecosystem production (NEP) for a 96 km 2 area in the central Cascades Mountains of western Oregon were examined. The scaling approach was a simple `measure and multiply' algorithm. At the highest spatial resolution (25 m), a stand age map derived from Landsat Thematic Mapper imagery provided the area for each of six forest age classes. The products of area for each age class and its respective NPP or NEP were summed for the area wide estimates. In order to evaluate potential errors at coarser resolutions, the stand age map was resampled to grain sizes of 100, 250, 500 and 1000 m using a majority filter reclassification. Local variance in near-infrared (NIR) band digital number at successively coarser grain sizes was also examined to characterize the scale of the heterogeneity in the scene. For this managed forest landscape, proportional estimation error in land cover classification at the coarsest resolution varied from –1.0 to +0.6 depending on the initial representation and the spatial distribution of the age class. The overall accuracy of the 1000 m resolution map was 42% with respect to the 25 m map. Analysis of local variance in NIR digital number suggested a patch size on the order of 100–500 m on a side. Total estimated NPP was 12% lower and total estimated NEP was 4% lower at 1000 m compared to 25 m. Carbon flux estimates based on quantifying differences in total biomass stored on the landscape at two points in time might be affected more strongly by a coarse resolution analysis because the differences among classes in biomass are more extreme than the differences in C flux and because the additional steps in the flux algorithm would contribute to error propagation. Scaling exercises involving reclassification of fine scale imagery over a range of grain sizes may be a useful screening tool for stratifying regions of the terrestrial surface relative to optimizing the spatial resolution for C flux estimation purposes. 相似文献
12.
We hypothesized that the spatial configuration and dynamics of periurban forest patches in Barcelona (NE of Spain) played
a minor role in determining plant species richness and assemblage compared to site conditions, and particularly to both direct
(measured at plot level) and potential (inferred from landscape metrics) human-associated site disturbance. The presence of
all understory vascular plants was recorded on 252 plots of 100 m 2 randomly selected within forest patches ranging in size from 0.25 ha to 218 ha. Species were divided into 6 groups, according
to their ecology and conservation status. Site condition was assessed at plot level and included physical attributes, human-induced
disturbance and Quercus spp. tree cover. Landscape structure and dynamics were assessed from patch metrics and patch history. We also calculated
a set of landscape metrics related to potential human accessibility to forests. Results of multiple linear regressions indicated
that the variance explained for non-forest species groups was higher than for forest species richness. Most of the main correlates
corresponded to site disturbance variables related to direct human alteration, or to landscape variables associated to indirect
human effects on forests: Quercus tree cover (a proxy for successional status) was the most important correlate of non-forest species richness, which decreased
when Quercus tree cover increased. Human-induced disturbance was an important correlate of synanthropic and total species richness, which
were higher in recently managed and in highly frequented forests. Potential human accessibility also affected the richness
of most species groups. In contrast, patch size, patch shape and connectivity played a minor role, as did patch history. We
conclude that human influence on species richness in periurban forests takes place on a small scale, whereas large-scale effects
attributable to landscape structure and fragmentation are comparatively less important. Implications of these results for
the conservation of plant species in periurban forests are discussed. 相似文献
13.
ContextThe contribution of forest ecosystem services to human well-being varies over space following the dynamics in forest cover. Use of machine learning models is increasing in projecting forest cover changes and investigating the drivers, yet references are still lacking for selecting machine learning models for spatial projection of forest cover patterns. ObjectivesWe assessed the ability of nonparametric machine learning techniques to project the spatial distribution of forest cover and identify its drivers using a case study of Tasmania, Australia. MethodsWe developed, evaluated, and compared the performance of four nonparametric machine learning models: support vector regression (SVR), artificial neural networks (ANN), random forest (RF), and gradient boosted regression trees (GBRT). ResultsThe results demonstrated that RF far outperformed the other three models in both fitting and projection accuracy, and required less computional costs. GBRT outperformed SVR and ANN in projection accuracy. However, RF exhibited serious overfitting due to the full growth of its decision trees. The influence rankings of explanatory variables on spatial patterns of forest cover were different under the four models. Land tenure type and rainfall were identified among the top four most influential variables by all four models. The ranking produced by the RF model was significantly different with topographic factors associated with land clearing and production costs (elevation and distance to timber facilities) being the two most influential variables. ConclusionsWe encourage practitioners to consider nonparametric machine learning methods, especially RF, when facing problems of complex environmental data modelling. 相似文献
14.
Studies dealing with community similarity are necessary to understand large scale ecological processes causing biodiversity
loss and to improve landscape and regional planning. Here, we study landscape variables influencing patterns of community
similarity in fragmented and continuous forest landscapes in the Atlantic forest of South America, isolating the effects of
forest loss, fragmentation and patterns of land use. Using a grid design, we surveyed birds in 41 square cells of 100 km 2 using the point count method. We used multivariate, regression analyses and lagged predictor autoregressive models to examine
the relative influence of landscape variables on community similarity. Forest cover was the primary variable explaining patterns
of bird community similarity. Similarity showed a sudden decline between 20 and 40% of forest cover. Patterns of land use
had a second order effect; native bird communities were less affected by forest loss in landscapes dominated by tree plantations
(the most suitable habitat for native species) than in landscapes dominated by annual crops or cattle pastures. The effects
of fragmentation were inconclusive. The trade-off between local extinctions and the invasion of extra-regional species using
recently created habitats is probably the mechanism generating the observed patterns of community similarity. Limiting forest
loss to 30–40% of the landscape cover and improving the suitability of human-modified habitats will contribute to maintain
the structure and composition of the native forest bird community in the Atlantic forest. 相似文献
16.
Understanding which environmental conditions are critical for species survival is a critical, ongoing question in ecology. These conditions can range from climate, at the broadest scale, through to elevation and other local landscape conditions, to fine scale landscape patterns of land cover and use. Remote sensing is an ideal technology to monitor and assess changes in these environmental conditions at a variety of spatial and temporal scales, with many studies focusing on the physiological state of vegetation derived from time series of satellite measurements. As vegetation occurs within specific climatic zones, over certain soil, terrain, and land cover types, it can be difficult to decipher the influence of the underlying role of climate, topography, soil, and land cover on the observed vegetation signal. In this article, we specifically addressed this problem by asking the question: what is the relative impact and importance of these different scales of environmental drivers on the temporal and spatial patterns observed on a habitat index derived from remotely sensed data? To find the solution, we utilized a SPOT VEGETATION-normalized difference vegetation index time series of Europe to create a remote-sensing-derived habitat index, which incorporates aspects of productivity, seasonality, and cover. We then compared the observed temporal and spatial variations in the index to a pan-Europe terrestrial classification system, which explicitly incorporates variations in climate, terrain, soil parent material, land cover, and use. Results indicated that the most accurate level of discrimination from the habitat index was at the broadest level of the hierarchy, climate, while the poorest degree of discrimination was associated with elevation. In terms of similarity on the index across time and space, we found that arable and forest cover classes were more similar across elevation and parent materials than across other land cover types within them. Analyzing the remote-sensing index, at multiple scales, provides significant insights into the drivers of satellite-derived greenness indices, as well as highlights the benefit and cautions associated with linking satellite-derived indirect indicators to species distribution modeling and biodiversity. 相似文献
17.
Human modification of forest habitats is a major component of global environmental change. Even areas that remain predominantly forested may be changed considerably by human alteration of historical disturbance regimes. To better understand human influences on the abundance and pattern of forest habitats, we studied forest land cover change from 1936 to 1996 in a 25000 km 2 landscape in the Oregon (USA) Coast Range. We integrated historical forest survey data and maps from 1936 with satellite imagery and GIS data from 1996 to quantify changes in major forest cover types. Change in the total area of closed-canopy forests was relatively minor, decreasing from 68% of the landscape in 1936 to 65% in 1996. In contrast, large-conifer forests decreased from 42% in 1936 to 17% in 1996, whereas small-conifer forests increased from 21% of the landscape in 1936 to 39% in 1996. Linear regression models were used to predict changes in the proportion of large conifer forest as a function of socioeconomic and environmental variables at scales of subbasins (mean size = 1964 km 2, n=13), watersheds (mean size = 302 km 2, n=83), and subwatersheds (mean size = 18 km 2, n=1325). The proportion of land in private ownership was the strongest predictor at all three spatial scales (partial R 2 values 0.57–0.76). The amounts of variation explained by other independent variables were comparatively minor. Results corroborate the hypothesis that differing management regimes on private and public ownerships have led to different pathways of landscape change. Furthermore, these distinctive trajectories are consistent over a broad domain of spatial scales. 相似文献
18.
Accurately measuring the biophysical dimensions of urban trees, such as crown diameter, stem diameter, height, and biomass, is essential for quantifying their collective benefits as an urban forest. However, the cost of directly measuring thousands or millions of individual trees through field surveys can be prohibitive. Supplementing field surveys with remotely sensed data can reduce costs if measurements derived from remotely sensed data are accurate. This study identifies and measures the errors incurred in estimating key tree dimensions from two types of remotely sensed data: high-resolution aerial imagery and LiDAR (Light Detection and Ranging). Using Sacramento, CA, as the study site, we obtained field-measured dimensions of 20 predominant species of street trees, including 30–60 randomly selected trees of each species. For each of the 802 trees crown diameter was estimated from the aerial photo and compared with the field-measured crown diameter. Three curve-fitting equations were tested using field measurements to derive diameter at breast height (DBH) ( r2 = 0.883, RMSE = 10.32 cm) from the crown diameter. The accuracy of tree height extracted from the LiDAR-based surface model was compared with the field-measured height (RMSE = 1.64 m). We found that the DBH and tree height extracted from the remotely sensed data were lower than their respective field-measured values without adjustment. The magnitude of differences in these measures tended to be larger for smaller-stature trees than for larger stature species. Using DBH and tree height calculated from remotely sensed data, aboveground biomass ( r2 = 0.881, RMSE = 799.2 kg) was calculated for individual tree and compared with results from field-measured DBH and height. We present guidelines for identifying potential errors in each step of data processing. These findings inform the development of procedures for monitoring tree growth with remote sensing and for calculating single tree level carbon storage using DBH from crown diameter and tree height in the urban forest. 相似文献
19.
Quantifying landscape dynamics is a central goal of landscape ecology, and numerous metrics have been developed to measure
the influence of human activities on natural landscapes. Composite scores that characterize human modifications to landscapes
have gained widespread use. A parsimonious alternative is to estimate the proportion of a cover type (i.e. natural) within
a spatial neighborhood to characterize both compositional and structural aspects of natural landscapes. Here I extend this
approach into a multi-scale, integrated metric and apply it to national datasets on land cover, housing density, road existence,
and highway traffic volume to measure the dynamics of natural landscapes in the conterminous US. Roughly one-third of the
conterminous US (2.6 million km 2) in 1992 was classified as human-dominated. By 2001 this expanded by 80,800 km 2, and forecasted residential growth by 2030 will potentially lead to an additional loss of up to 92,200 km 2. Wetland cover types were particularly affected. The natural landscapes metric developed here provides a simple, robust measure
of landscape dynamics that has a direct physical interpretation related to proportion of natural habitat affected at a location,
represents landscapes as a gradient of conditions rather predicated on patch/matrix definition, and measures the spatial context
of natural areas. 相似文献
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