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.

Context

Quantifying landscape-scale vegetation disturbances by surface coal mining (SCM) is crucial for assessing and mitigating its negative impacts on the environment. Methods for detecting such disturbances in woody ecosystems exist, but these methods do not work well for deserts and grasslands in arid and semiarid regions because of their sensitive responses to precipitation variations.

Objectives

The objective of this study was to develop a new index to reliably detect the locations and spatial extents of SCM-induced vegetation disturbances in dryland regions in the face of fluctuating precipitation.

Methods

We have developed a vegetation disturbance index (VDI) that combines MODIS EVI data with precipitation data to detect vegetation disturbances by SCM on the Mongolian Plateau during 2000–2015. The VDI is computed by comparing vegetation production per unit precipitation for a given year with a multi-year mean, and by considering distances from coal-mining areas.

Results

Our results show that the VDI was able to adequately distinguish vegetation disturbances by SCM from climate-driven vegetation changes in five selected sites across the Mongolian Plateau.

Conclusions

The VDI provides an effective tool for quantifying the locations, spatial extents, and severity of vegetation disturbances by SCM in arid and semiarid regions.

Context

Habitat complexity in rivers is linked to dynamic fluvial conditions acting at various spatial scales. On regulated rivers in the western United States, tributaries are regions of high energy and disturbance, providing important resource inputs for riparian ecosystems.

Objectives

This study investigated spatial patterns and extents of tributary influence on riparian habitat complexity in the near channel zone along regulated reaches of the Colorado (>?200 km) and Dolores Rivers (~?300 km) in the western United States. Because tributary confluences are regions of increased dynamism, we hypothesized that: (1) geomorphic and land cover complexity would be greatest close to tributary junctions and decrease with distance from tributaries; and (2) patterns in complexity would vary across different sized spatial units.

Methods

Using a combination of remote sensing and spatial analysis, we classified fluvial features and land cover classes to investigate patterns longitudinally at 10-, 25-, and 100-m spatial units in the near channel zone of two regulated rivers.

Results

Using change point analysis and randomization tests, we detected shifts in riparian habitat complexity closer to tributary junctions. Patterns varied across 10-, 25-, and 100-m spatial units in the near channel zone, with significance (p?≤?0.05) recorded for 10- and 25-m spatial units.

Conclusions

Tributary junctions deliver critical resource inputs on regulated systems, providing for increased geomorphic and land cover diversity upstream and downstream of tributaries. We found that patterns of response were non-linear and discontinuous, varying across spatial units and potentially influenced by the degree of mainstem flow regulation.

Context

Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.

Objectives

We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.

Methods

Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.

Results

Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.

Conclusions

Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.

Context

This study examines the spatio-temporal patterns of urban expansion for Yangon and Nay Pyi Taw, the former and new national capitals of Myanmar, and its impact on the regional environment between 2000 and 2013.

Objectives

The objective is to examine different driving forces of urban expansion for Yangon and Nay Pyi Taw, and their environmental consequences during Myanmar’s transitional economy.

Methods

Classified time-series Landsat images are used to evaluate urban expansion processes. Environmental parameters being evaluated in this study include five sets of remotely sensed MODIS land products that are land surface temperature (LST), percent tree cover (PTC), evapotranspiration (ET), terrestrial ecosystem net primary productivity (NPP), and aerosol optical depth (AOD). A time-series trend analysis technique is used to examine the environmental consequences.

Results

The built-up areas in Nay Pyi Taw and Yangon exhibit exponential and polynomial increase, respectively. A 1% increase of built-up area could potentially cause an increase of daytime LST of 0.7 °C, a PTC loss of 2.3%, a decrease in NPP of 34.3 kg/m², and an ET decrease of 42.2 mm for Yangon. Similarly, for Nay Pyi Taw, a 1% increase in built-up area could potentially cause a daytime LST increase of 0.3 °C, a nighttime LST increase of 0.06 °C, a PTC loss of 2.5%, a decrease in NPP of 15.1 kg/m², and a decrease of 19.2 mm ET. No significant change was observed for AOD for either city.

Conclusions

Both cities have experienced extensive urban expansion but with different spatial and temporal characteristics, and their effects on the regional environment are different. Urban expansion of Nay Pyi Taw mainly was government-induced municipal infrastructure development. Yangon’s expansion is mainly caused by population pressure and migration from rural areas. The urban expansion in Yangon was mainly due to reconstruction and renovation, as well as infill development during the study period.

Context

Wildfires play a crucial role in maintaining ecological and societal functions of North American boreal forests. Because of their contagious way of spreading, using statistical methods dealing with spatial autocorrelation has become a major challenge in fire studies analyzing how environmental factors affect their spatial variability.

Objectives

We aimed to demonstrate the performance of a spatially explicit method accounting for spatial autocorrelation in burn rates modelling, and to use this method to determine the relative contribution of climate, physical environment and vegetation to the spatial variability of burn rates between 1972 and 2015.

Methods

Using a 482,000 km² territory located in the coniferous boreal forest of eastern Canada, we built and compared burn rates models with and without accounting for spatial autocorrelation. The relative contribution of climate, physical environment and vegetation to the burn rates variability was identified with variance partitioning.

Results

Accounting for spatial autocorrelation improved the models’ performance by a factor of 1.5. Our method allowed the unadulterated extraction of the contribution of climate, physical environment and vegetation to the spatial variability of burn rates. This contribution was similar for the three groups of factors. The spatial autocorrelation extent was linked to the fire size distribution.

Conclusions

Accounting for spatial autocorrelation can highly improve models and avoids biased results and misinterpretation. Considering climate, physical environment and vegetation altogether is essential, especially when attempting to predict future area burned. In addition to the direct effect of climate, changes in vegetation could have important impacts on future burn rates.

Context

Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains.

Objectives

We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire.

Methods

We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat? imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales.

Results

Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire.

Conclusions

DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Context

Revealing the interaction between landscape pattern and urban land surface temperature (LST) can provide insight into mitigating thermal environmental risks. However, there is no consensus about the key landscape indicators influencing LST.

Objectives

This study sought to identify the key landscape indicators influencing LST considering a large number of landscape pattern variables and multiple scales.

Methods

This study applied ordinary least squares regression and partial least squares regression to explore a combination of landscape metrics and identify the key indicators influencing LST. A total of 49 Landsat images of the main city of Shenzhen, China were examined at 13 spatial scales.

Results

The landscape composition indicators derived from biophysical proportion, a new metric developed in this study, more effectively determined LST variation than those derived from land cover proportion. Area-related landscape configuration indicators independently characterized LST variation, but did not give much more new information beyond that given by land cover proportion. Shape-related landscape configuration indicators were effective in combination with land cover proportion, but their importance was uncertain when temporal and spatial scales varied.

Conclusions

The influence of landscape configuration on LST exists but should not be overestimated. Comparison of numerous variables at multiple spatiotemporal scales can help identify the influence of multiple landscape characteristics on LST variation.

Context

Land-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services.

Objectives

An innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators.

Methods

Founded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers.

Results

The dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators’ response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented.

Conclusions

We outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

Context

Resilience in fire-prone forests is strongly affected by landscape burn-severity patterns, in part by governing propagule availability around stand-replacing patches in which all or most vegetation is killed. However, little is known about drivers of landscape patterns of stand-replacing fire, or whether such patterns are changing during an era of increased wildfire activity.

Objectives

(a) Identify key direct/indirect drivers of landscape patterns of stand-replacing fire (e.g., size, shape of patches), (b) test for temporal trends in these patterns, and (c) anticipate thresholds beyond which landscape patterns of burn severity may change fundamentally.

Methods

We applied structural equation modeling to satellite burn-severity maps of fires in the US Northern Rocky Mountains (1984–2010) to test for direct and indirect (via influence on fire size and proportion stand-replacing) effects of climate/weather, vegetation, and topography on landscape patterns of stand-replacing fire. We also tested for temporal trends in landscape patterns.

Results

Landscape patterns of stand-replacing fire were strongly controlled by fire size and proportion stand-replacing, which were, in turn, controlled by climate/weather and vegetation/topography, respectively. From 1984 to 2010, the proportion of stand-replacing fire within burn perimeters increased from 0.22 to 0.27. Trends for other landscape metrics were not significant, but may respond to further increases proportion stand-replacing fire.

Conclusions

Fires from 1984 to 2010 exhibited tremendous heterogeneity in landscape patterns of stand-replacing fire, likely promoting resilience in burned areas. If trends continue on the current trajectory, however, fires may produce larger and simpler shaped patches of stand-replacing fire with more burned area far from seed sources.

Context

Quantifying variability in landscape-scale surface water connectivity can help improve our understanding of the multiple effects of wetlands on downstream waterways.

Objectives

We examined how wetland merging and the coalescence of wetlands with streams varied both spatially (among ecoregions) and interannually (from drought to deluge) across parts of the Prairie Pothole Region.

Methods

Wetland extent was derived over a time series (1990–2011) using Landsat imagery. Changes in landscape-scale connectivity, generated by the physical coalescence of wetlands with other surface water features, were quantified by fusing static wetland and stream datasets with Landsat-derived wetland extent maps, and related to multiple wetness indices. The usage of Landsat allows for decadal-scale analysis, but limits the types of surface water connections that can be detected.

Results

Wetland extent correlated positively with the merging of wetlands and wetlands with streams. Wetness conditions, as defined by drought indices and runoff, were positively correlated with wetland extent, but less consistently correlated with measures of surface water connectivity. The degree of wetland–wetland merging was found to depend less on total wetland area or density, and more on climate conditions, as well as the threshold for how wetland/upland was defined. In contrast, the merging of wetlands with streams was positively correlated with stream density, and inversely related to wetland density.

Conclusions

Characterizing the degree of surface water connectivity within the Prairie Pothole Region in North America requires consideration of (1) climate-driven variation in wetness conditions and (2) within-region variation in wetland and stream spatial arrangements.

Context

Native vegetation is often used as a proxy for habitat to estimate habitat availability in landscapes. This approach may lead to incorrect estimates of the impacts of habitat loss and fragmentation on species, which have not been thoroughly quantified so far.

Objectives

We quantified to what extent the loss of native vegetation reflect actual habitat loss by native species in landscapes. We tested the hypothesis that habitat availability declines at greater rates than native vegetation and thus is overestimated when it is quantified on the basis of native vegetation.

Methods

Using simulations, we quantified how the loss of native vegetation in artificial and real landscapes affects habitat availability for species with different habitat requirements. We contrasted a generalist species, which uses all native vegetation, with 10 habitat-specialist species classified into three categories (interior, patchy and riparian species).

Results

Habitat availability generally declined at greater rates than native vegetation for all specialist species. This pattern was apparent for different specialist species in a broad range of landscape types. Interior species always lost habitat availability more rapidly than the generalist species. Most riparian species lost habitat availability more rapidly than the generalist species. Responses of patchy species were more complex, depending on their dispersal abilities and landscape structure.

Conclusions

Habitat availability is likely to be overestimated when native vegetation is used as proxy for habitat, because habitat availability will generally decline at greater rates than native vegetation. Therefore, a species-centered approach should be adopted when estimating habitat availability in landscapes.

Context

Biodiversity is modulated by the spatial structure of the landscape. Thus, landscape metrics can be useful indicators of biota integrity and vulnerability, helping in conservation and management decisions.

Objective

We performed the first quantitative analysis of the spatial structure of the Caatinga drylands. We estimated the habitat amount and the fragmentation pattern of this region using a multi-scale perspective.

Methods

Using the Brazilian official database of native remnants, we calculated the number and percentage of remaining fragments per size class and we describe how habitat amount changes along the landscape. By simulating different dispersal capacities, we estimated the functional connectivity among remnants. We also calculated the cumulative core area as a function of different edge effect widths.

Results

Caatinga is subdivided into 47,100 fragments. Although 91% of them are smaller than 500 ha, 720 fragments are larger than 10,000 ha, corresponding to 78% of the remaining vegetation. Potentially, 95% of the vegetation is accessible to species that can cross 1000 m of matrix. With one kilometer of edge effect, the core area is reduced to a quarter of the remaining vegetation. The habitat amount analyzes reinforced the regional differences in the spatial distribution of the remnants.

Conclusions

Caatinga remains well connected for species with moderate and high dispersal capacities. However much of its remaining area is vulnerable to anthropogenic disturbances. Expansion of the protected area network and effective natural resource management to avoid overexploitation of the remnants are key strategies for maintaining the Caatinga biodiversity and its services.

Context

There are few detailed data for short-term (≤?monthly) fluctuations in flowering and nectar availability at relatively large spatial scales. Such information is critical for understanding the governors of variation in flowering and for the management of floral resources assisting the persistence of nectar consumers in landscapes.

Objectives

To obtain monthly measurements of patterns of nectar availability in a 314,400 ha region, and to relate these patterns to potential environmental predictors.

Methods

Flowering was measured at 83 sites in natural vegetation and in eight domestic gardens in subtropical, eastern Australia. A nectar-availability index was developed was based on nectarivore visitation rates and plant-specific flowering patterns. Spatial–temporal patterns were related to environmental variables using boosted regression trees.

Results

The large between-year variation was due mostly to irregular flowering by several eucalypt species. There was a ‘lean season’ in the austral spring (August–September). Coastal vegetation was an important source of nectar for much of the year, including the lean season. Gardens produced prolific nectar throughout the year, peaking in August–October.

Conclusions

Nectar availability was most closely associated with primary productivity over the previous 12 months, average annual solar radiation, topographic wetness, and rainfall over the previous 6 months, although some relationships seemed counter-intuitive. There were large differences in nectar availabilities among broad vegetation types (especially rainforests vs. sclerophyllous forests), which partially accounted for the unintuitive results.

Context

In the interior Northwest, debate over restoring mixed-conifer forests after a century of fire exclusion is hampered by poor understanding of the pattern and causes of spatial variation in historical fire regimes.

Objectives

To identify the roles of topography, landscape structure, and forest type in driving spatial variation in historical fire regimes in mixed-conifer forests of central Oregon.

Methods

We used tree rings to reconstruct multicentury fire and forest histories at 105 plots over 10,393 ha. We classified fire regimes into four types and assessed whether they varied with topography, the location of fuel-limited pumice basins that inhibit fire spread, and an updated classification of forest type.

Results

We identified four fire-regime types and six forest types. Although surface fires were frequent and often extensive, severe fires were rare in all four types. Fire regimes varied with some aspects of topography (elevation), but not others (slope or aspect) and with the distribution of pumice basins. Fire regimes did not strictly co-vary with mixed-conifer forest types.

Conclusions

Our work reveals the persistent influence of landscape structure on spatial variation in historical fire regimes and can help inform discussions about appropriate restoration of fire-excluded forests in the interior Northwest. Where the goal is to restore historical fire regimes at landscape scales, managers may want to consider the influence of topoedaphic and vegetation patch types that could affect fire spread and ignition frequency.

Context

Mapping the presence of trees is an important tool for assessing tree-covered habitats, their changes, and calculating variables, like forest area and fragmentation.

Objective

Despite the popularity of automated pattern recognition to make tree cover maps, their accuracy and precision are rarely tested or compared to more modest methods, like human-based pattern recognition to identify tree cover.

Methods

Here, we test the performance of two computer-generated tree mapping products, the Global Change Forest database and the Carnegie Landsat Analysis System, against ground surveys and a human-made tree cover map created using Google Earth to hand digitize the presence and absence of trees in a diversified agricultural region in Costa Rica (934 km²).

Results

The human-made tree cover map properly classified 100% ground survey sites and explained 81% of the variance in percent of canopy cover values from the field. The Global Change Forest database misclassified 18 of 23 ground survey sites in deforested locations and explained 6% of the variance in percent of canopy cover values from ground surveys. The Carnegie Landsat Analysis System misclassified 9 of 23 ground survey sites in deforested locations and explained 38% of the variance in percent of canopy cover values from the field.

Conclusions

Our results suggest that the Global Change Forest database overestimated tree cover by of 20% and the Carnegie Landsat Analysis System by 1%. We caution landscape ecologists working at fine spatial scales against using computer-generated tree cover, especially in the partially forested lands that increasingly cover the planet.

Context

Habitat destruction is the leading threat to terrestrial biodiversity, isolating remnant habitat in a matrix of modified vegetation.

Objectives

Our goal was to determine how species richness in several broad taxonomic groups from remnant forest was influenced by matrix quality, which we characterized by comparing plant biomass in forest and the surrounding matrix.

Methods

We coupled data on species-area relationships (SARs) in forest remnants from 45 previously published studies with an index of matrix quality calculated using new estimates of plant biomass derived from satellite imagery.

Results

The effect size of SARs was greatest in landscapes with low matrix quality and little forest cover. SARs were generally stronger for volant than for non-volant species. For the terrestrial taxa included in our analysis, matrix quality decreased as the proportion of water, ice, or urbanization in a landscape increased.

Conclusions

We clearly demonstrate that matrix quality plays a major role in determining patterns of species richness in remnant forest. A key implication of our work is that activities that increase matrix quality, such as active and passive habitat restoration, may be important conservation measure for maintaining and restoring biodiversity in modified landscapes.

Context

The Mongolian Plateau, comprising Inner Mongolia, China (IM) and Mongolia (MG) is undergoing consistent warming and accelerated land cover/land use change. Extensive modifications of water-limited regions can alter ecosystem function and processes; hence, it is important to differentiate the impacts of human activities and precipitation dynamics on vegetation productivity.

Objectives

This study distinguished between human-induced and precipitation-driven changes in vegetation cover on the plateau across biome, vegetation type and administrative divisions.

Methods

Non-parametric trend tests were applied to the time series of vegetation indices (VI) derived from MODIS and AVHRR and precipitation from TRMM and MERRA reanalysis data. VI residuals adjusted for rainfall were obtained from the regression between growing season maximum VI and monthly accumulated rainfall (June–August) and were used to detect human-induced trends in vegetation productivity during 1981–2010. The total livestock and population density trends were identified and then used to explain the VI residual trends.

Results

The slope of precipitation-adjusted EVI and EVI2 residuals were negatively correlated to total livestock density (R² = 0.59 and 0.16, p < 0.05) in MG and positively correlated with total population density (R² = 0.31, p < 0.05) in IM. The slope of precipitation-adjusted EVI and EVI2 residuals were also negatively correlated with goat density (R² = 0.59 and 0.19, p < 0.05) and sheep density in MG (R² = 0.59 and 0.13, p < 0.05) but not in IM.

Conclusions

Some administrative subdivisions in IM and MG showed decreasing trends in VI residuals. These trends could be attributed to increasing livestock or population density and changes in livestock herd composition. Other subdivisions showed increasing trends residuals, suggesting that the vegetation cover increase could be attributed to conservation efforts.

Context

Enhancing ground cover vegetation is an important agricultural practice that regulates herbivore and predator insects in agricultural landscapes. However, the effects of ground cover on the spatial distributions of these organisms have scarcely been explored.

Objectives

Our goal was to measure the effects of ground cover on the spatial aggregation and association of insect herbivores and predators, which might contribute to the control of herbivorous pests.

Methods

We conducted our experiments in peach orchards at two sites in eastern China. The two sites have experimental units with ground cover treatments that created a heterogeneous landscape. We conducted a 2-year experiment to investigate the abundance and distribution of herbivores (leafhoppers) and predators (ladybirds), using geostatistics to analyze their spatial aggregation and association.

Results

The abundance of predators increased and that of herbivores decreased in ground cover orchards compared to control orchards without ground cover. The proportion of spatial structure was greater than 0.75 for both herbivores and predators in the control orchards, indicating a lack of spatial aggregation, and less than 0.75 in peach orchards with ground cover, indicating spatial aggregation. The correlation of spatial aggregation between herbivores and predators was significantly positive in the ground cover treatment, indicating association of the two insect guilds. In control orchards, on the other hand, this was not significant.

Conclusions

The presence of ground cover increased predator abundance, spatial aggregation of herbivores and predators as well as their spatial association, suggesting a mechanism for more efficient control of herbivorous pests in peach orchards.

Context

Wildfires are common in localities where there is sufficient productivity to allow the accumulation of biomass combined with seasonality that allows this to dry and transition to a flammable state. An understanding of the conditions under which vegetated landscapes become flammable is valuable for assessing fire risk and determining how fire regimes may alter with climate change.

Objectives

Weather based metrics of dryness are a standard approach for estimating the potential for fires to occur in the near term. However, such approaches do not consider the contribution of vegetation communities. We aim to evaluate differences in weather-based dryness thresholds for fire occurrence between vegetation communities and test whether these are a function of landscape aridity.

Methods

We analysed dryness thresholds (using Drought Factor) for fire occurrence in six vegetation communities using historic fires events that occurred in South-eastern Australia using logistic regression. These thresholds were compared to the landscape aridity for where the communities persist.

Results

We found that dryness thresholds differed between vegetation communities, and this effect could in part be explained by landscape aridity. Dryness thresholds for fire occurrence were lower in vegetation communities that occur in arid environments. These communities were also exposed to dry conditions for a greater proportion of the year.

Conclusions

Our findings suggest that vegetation driven feedbacks may be an important driver of landscape flammability. Increased consideration of vegetation properties in fire danger indices may provide for better estimates of landscape fire risk and allow changes to fire regimes to be anticipated.