Search Results (21)

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems. Full article

The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R² = 0.83). The decrease in R² using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R² by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations. Full article

The purpose of this article was to identify significant differences in the hunting management process in Poland and selected European countries in the context of their impact on the preservation of biodiversity and the implementation of the idea of sustainable development. The goal was achieved through the analysis of hunting management in selected European countries through the prism of the assumptions made by Aldo Leopold in 1933. Based on the analysis carried out, it was found that hunting management in relation to Leopold’s postulates has best been undertaken by France. Moreover, the wild game management process should be actively implemented and based on the still up-to-date, universal postulates of Leopold, which can be treated as a model approach. Full article

The establishment of protected areas for a river (PARs) is an efficient approach for the conservation of its ecosystem and biodiversity. This study selected the free-flowing Qingzhu River, located in the mountains of southwest China and one of 34 global biodiversity hotspots, as a case study. This study applied the ecosystem approach to develop a model for identifying priority conservation areas for a river (PCARs) based on integrity and authenticity. Three model elements were selected, namely streams, forest and human activity, characterized by three indicators: irreplaceability, tree cover and human activity, respectively. The spatial distributions of these indicators were overlaid according to different weights to generate a map (S_CPV) of comprehensive protected value (CPV), which was used to indicate ecosystem integrity and authenticity in the study catchment. Lastly, PCARs were identified by comparing existing protected areas with the calculated S_CPV. The application of the model to the Qingzhu River indicated the area of PCARs to be ~71.88 km², accounting for 15.13% of the total PAR area. Priority reaches for protection were then identified, with many falling within the mainstem of the river in the middle and lower reaches. The total length of priority protected reaches was ~75.97 km, accounting for 49.33% of the total length of the river mainstem within Qingchuan County. This study validated the model at both the theoretical and practical level, confirming that the model is useful for facilitating the precise protection and smart management of rivers. Full article

Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science. Full article

A central tenet of landscape conservation planning is that natural communities can be supported by a connected landscape network that supports many species and habitat types. However, as the planning environment, ecological conditions, and risks and stressors change over time, the areas needed to support landscape connectivity may also shift. We demonstrate an approach designed to assess functional and structural connectivity of an established protected area network that has experienced landscape and planning changes over time. Here we present an approach designed to inform adaptive planning for connectivity with a complementary suite of analytical techniques. Using existing occurrence, movement, and genetic data for six focal species, we create a spatially explicit connectivity assessment based on landscape resistance, paired with a landscape feature geodiversity analysis. Although factors such as cost, conservation goals, and land management strategies must be taken into account, this approach provides a template for leveraging available empirical data and robust analyses to evaluate and adapt planning for protected area networks that can preserve and promote both functional and structural connectivity in dynamic landscapes. Full article

Connectivity and wildlife corridors are often key components to successful conservation and management plans. Connectivity for wildlife is typically modeled in a static environment that reflects a single snapshot in time. However, it has been shown that, when compared with dynamic connectivity models, static models can underestimate connectivity and mask important population processes. Therefore, including dynamism in connectivity models is important if the goal is to predict functional connectivity. We incorporated four levels of dynamism (individual, daily, seasonal, and interannual) into an individual-based movement model for black bears (Ursus americanus) in Massachusetts, USA. We used future development projections to model movement into the year 2050. We summarized habitat connectivity over the 32-year simulation period as the number of simulated movement paths crossing each pixel in our study area. Our results predict black bears will further colonize the expanding part of their range in the state and move beyond this range towards the greater Boston metropolitan area. This information is useful to managers for predicting and addressing human–wildlife conflict and in targeting public education campaigns on bear awareness. Including dynamism in connectivity models can produce more realistic models and, when future projections are incorporated, can ensure the identification of areas that offer long-term functional connectivity for wildlife. Full article

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact. Full article

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R² of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions. Full article

Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre- and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe. Full article

Many dry conifer forests in the southwestern USA and elsewhere historically (prior to the late 1800’s) experienced fairly frequent surface fire at intervals ranging from roughly five to 30 years. Due to more than 100 years of successful fire exclusion, however, many of these forests are now denser and more homogenous, and therefore they have a greater probability of experiencing stand-replacing fire compared to prior centuries. Consequently, there is keen interest in restoring such forests to conditions that are conducive to low-severity fire. Yet, there have been no regional assessments in the southwestern USA that have specifically evaluated those factors that promote low-severity fire. Here, we defined low-severity fire using satellite imagery and evaluated the influence of several variables that potentially drive such fire; these variables characterize live fuel, topography, climate (30-year normals), and inter-annual climate variation. We found that live fuel and climate variation (i.e., year-of-fire climate) were the main factors driving low-severity fire; fuel was ~2.4 times more influential than climate variation. Low-severity fire was more likely in settings with lower levels of fuel and in years that were wetter and cooler than average. Surprisingly, the influence of topography and climatic normals was negligible. Our findings elucidate those conditions conducive to low-severity fire and provide valuable information to land managers tasked with restoring forest structures and processes in the southwestern USA and other regions dominated by dry forest types. Full article

This paper examines the issue of climate change pedagogy and social action in tourism, with particular interest in globally-significant destinations under threat from climate change. Little is understood of the role and responsibility of visitors as key stakeholders in climate change-related action or the potential of such sites to foster environmental learning, as well as social and political action on climate change. Drawing on insights from Aldo Leopold and John Dewey, it is argued here that destinations that are valued intrinsically for their ecological and cultural importance are (or ought to be) sites of enjoyment and pedagogy, facilitating experiential learning, care, responsibility and civic action towards their conservation. An exploratory case study of visitors to the Great Barrier Reef offers corroborative insights for such a “reef ethic” as described in this paper, related to visitor experience, learning and action in this World Heritage Area. The results of this paper support the need for a stronger pedagogic role to be adopted by tourism experience providers and site managers to facilitate climate change literacy and responsible action (hence facilitating global environmental citizenship). Their responsibility and that of reef visitors is discussed further. Full article

The spatial patterns resulting from large fires include refugial habitats that support surviving legacies and promote ecosystem recovery. To better understand the diverse ecological functions of refugia on burn mosaics, we used remotely sensed data to quantify neighborhood patterns of areas relatively unchanged following the 2011 Las Conchas fire. Spatial patterns of refugia measured within 10-ha moving windows varied across a gradient from areas of high density, clustered in space, to sparsely populated neighborhoods that occurred in the background matrix. The scaling of these patterns was related to the underlying structure of topography measured by slope, aspect and potential soil wetness, and spatially varying climate. Using a nonmetric multidimensional scaling analysis of species cover data collected post-Las Conchas, we found that trees and forest associates were present across the refugial gradient, but communities also exhibited a range of species compositions and potential functions. Spatial patterns of refugia quantified for three previous burns (La Mesa 1977, Dome 1996, Cerro Grande 2000) were dynamic between fire events, but most refugia persisted through at least two fires. Efforts to maintain burn heterogeneity and its ecological functions can begin with identifying where refugia are likely to occur, using terrain-based microclimate models, burn severity models and available field data. Full article