Download or read book Using Remotely Sensed Indices of Fire Severity and Vegetation Structure to Identify Patterns of Avian Occurrence Following Changes in Fire Management Policy Within Great Smoky Mountains National Park written by Eli Theofen Rose and published by . This book was released on 2015 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fire Effects Guide written by and published by . This book was released on 1994 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Wildland Fire Danger Estimation and Mapping written by Emilio Chuvieco and published by World Scientific. This book was released on 2003 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents a wide range of techniques for extracting information from satellite remote sensing images in forest fire danger assessment. It covers the main concepts involved in fire danger rating, and analyses the inputs derived from remotely sensed data for mapping fire danger at both the local and global scale. The questions addressed concern the estimation of fuel moisture content, the description of fuel structural properties, the estimation of meteorological danger indices, the analysis of human factors associated with fire ignition, and the integration of different risk factors in a geographic information system for fire danger management.
Download or read book Characterizing the Spatial Patterns and Spatially Explicit Probabilities of Post Fire Vegetation Residual Patches in Boreal Wildfire Scars written by Yikalo Hayelom Araya and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantifying the Relative Importance of Multiple Indices when Predicting Fire Severity in the Western US written by and published by . This book was released on 2016 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: A long history of fire suppression by federal land management agencies has interrupted fire regimes in much of the western United States. Many forest types that historically burned frequently have undergone significant changes in species composition and have heavy accumulations of surface and canopy fuels. Fuel quantity and flammability are important local predictors of fire severity. The climate system operates at both broad and fine spatial and temporal scales to favor conditions that increase fuel loading through biomass accumulation and accelerate drying of fuels; and maintain active fires under favorable concurrent atmospheric conditions. Observed increases in large fire occurrence and area burned in recent decades are explained by warmer, drier, and longer growing season conditions in the West. There has not yet been a large-scale study that examines patterns and controls of high severity fire in the western US. We use a 30 year record of fire severity to identify the controls of high severity fire across the western US, develop statistical probability models for high severity fire occurrence and area burned, and examine the impacts of climate change on high severity fire risk. In examining topography, vegetation and fire-year climate as predictors we found that inclusion of both vegetation and fire-year climate predictors was critical for identifying fires with high fractional fire severity and capturing inter-annual variation in high severity fire occurrence. While a single, west-wide model was able to predict high severity fire occurrence with some accuracy, it was necessary to develop regional models to accurately predict high severity area burned for forests in extreme fire years. A simple generalized Pareto distribution model with maximum temperature the month of fire, annual normalized moisture deficit and location explains forest high severity area burned in a west-wide model, with the exception of years with especially large areas burned with high severity fire: 1988, 2002. With respect to mitigation or management of high severity fire, understanding what drives extreme fire years is critical. For the Northern Rocky Mountains, Sierra Nevada Mountains, and Southwest forests, topography, spring temperature and snowpack condition, and vegetation condition class variables improved our prediction of high severity burned area in extreme fire years. We used the models developed for the Northern Rocky Mountains to examine how fractional area of high severity fire will change with climate. Application of output from global circulation models to large fire occurrence and size models in the Greater Yellowstone Ecosystem indicates that climate conditions by mid-century will result in an increase in the frequency of large fire events and area burned. We applied GCM output to a set of probabilistic models for high severity fire occurrence and burned area for the Greater Yellowstone Ecosystem. We found that fraction of high severity burned area increases to levels by mid-century that are three times greater than a 1961-1990 reference period. These potential changes in high severity area burned and frequency of occurrence may result in changes to species composition in these high elevation forests. If a goal of management is to mitigate extreme fire events in terms of fire severity, we would conclude that knowledge of fire year climate is essential. All of the models we developed predict high severity fire occurrence and area burned with reasonable accuracy in all years when fire year climate and vegetation predictors are included. The inclusion of fire-year climate variables allows these models to forecast inter-annual variability in areas at future risk of high severity fire, beyond what slower-changing fuel conditions alone can accomplish. This allows for more targeted land management, including resource allocation for fuels reduction treatments to decrease the risk of high severity fire. Models like this will be important tools for assessing interactions between changing climate and fuel profiles under a diverse menu of future climate and management scenarios.
Download or read book Remote Sensing of Large Wildfires written by Emilio Chuvieco and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a systematic review of the different applications for remote sensing and geographical information system techniques in research and management of forest fires. The authors have been involved in this field of research for several years. The book also benefits from data generated within the Megafires project, founded under the DG-XII of the European Union. A clear integration of research and experience is provided. New data gathered from fires affecting European countries between 1991 and 1997 are included as well as satellite images and auxiliary cartographic information. Geographic Information System files have been included in the attached CD-ROM depicting land cover, elevation, Koeppen classification climates and NOAA-AVHRR data of all European Mediterranean Europe at 1 sq km resolution. All these files are in Idrisi format and can be easily accessed from any GIS program. An Idrisi viewer has also been included in the CD-ROM.
Download or read book Using NDVI to Assess Departure from Average Greenness and Its Relation to Fire Business written by Robert E. Burgan and published by . This book was released on 1996 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Remote Sensing Procedures to Update Forested Geospatial Datasets After a Landscape Altering Event written by and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The creation of accurate geospatial datasets like vegetation and fire fuel loads is a time consuming effort and these datasets are routinely used by resource managers. Therefore the accuracy of these datasets is vital. Vegetation and fire fuel load datasets often represent a dynamic landscape and landscape altering events such as a wildland fire or a hurricane can drastically change that landscape. The goal of this research is to investigate the use of automated change detection techniques that can not only indicate areas of change but also quantify the magnitude of change that occurred as well. Hurricane Isabel did extensive damage to the forest landscapes of central Virginia in September of 2003, specifically Petersburg National Battlefield. The Rocky Top Fire occurred in July of 2002 in Shenandoah National Park, resulting in a mosaic pattern of burns, covering roughly 1500 acres. The objective of this research was to test the use of remote sensing procedures to update vegetation and fire fuel load spatial datasets. First, using digital orthorectified photomosaics, the automated feature extraction technique Visual Learning System's Feature Analyst, was employed to delineate forest damage following Hurricane Isabel. Second, the satellite based remote sensing technique Normalized Burn Ratio, was utilized to delineate and quantify burn severity on vegetation after the Rocky Top Fire. A third objective was to estimate fire behavior differences between the existing pre-event and the remotely sensed post-event fuel load datasets using the FARSITE model, thereby cataloging the potential need for vegetation and fuel load updates. The results of this research show that, 1) VLS Feature Analyst is an excellent indicator of downed woody debris, 2) the Normalized Burn Ratio is the best technique available for indicating and quantifying the effects of a wildland fire on the landscape, 3) changes in assigned Fuel Models, especially in the Logging Slash group, affect FARSITE outc.
Download or read book The Colorado Front Range written by Thomas T. Veblen and published by . This book was released on 1991 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Photoload Sampling Technique written by Robert E. Keane and published by . This book was released on 2007 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fire managers need better estimates of fuel loading so they can more accurately predict the potential fire behavior and effects of alternative fuel and ecosystem restoration treatments. This report presents a new fuel sampling method, called the photoload sampling technique, to quickly and accurately estimate loadings for six common surface fuel components (1 hr, 10 hr, 100 hr, and 1000 hr downed dead woody, shrub, and herbaceous fuels). This technique involves visually comparing fuel conditions in the field with photoload sequences to estimate fuel loadings. Photoload sequences are a series of downward-looking and close-up oblique photographs depicting a sequence of graduated fuel loadings of synthetic fuelbeds for each of the six fuel components. This report contains a set of photoload sequences that describe the range of fuel component loadings for common forest conditions in the northern Rocky Mountains of Montana, USA to estimate fuel loading in the field. A companion publication (RMRS-RP-61CD) details the methods used to create the photoload sequences and presents a comprehensive evaluation of the technique.
Download or read book Backpacker written by and published by . This book was released on 2007-09 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Backpacker brings the outdoors straight to the reader's doorstep, inspiring and enabling them to go more places and enjoy nature more often. The authority on active adventure, Backpacker is the world's first GPS-enabled magazine, and the only magazine whose editors personally test the hiking trails, camping gear, and survival tips they publish. Backpacker's Editors' Choice Awards, an industry honor recognizing design, feature and product innovation, has become the gold standard against which all other outdoor-industry awards are measured.
Download or read book Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics written by Alfonso Fernández-Manso and published by Mdpi AG. This book was released on 2022-10-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems. Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive, and their correct evaluation and follow-up is posing great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery. This reprint contains studies on new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation recovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We hope this book can help readers become more familiar with this knowledge and foster an increased interest in this field.
Download or read book Remote Sensing and Avian Biodiversity Patterns in the United States written by and published by . This book was released on 2012 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effective conservation planning, a better understanding of avian species richness patterns is needed. In general, habitat structure, climatic stability, and sensed data to characterize these three drivers at a national scale, determine the influence and relative importance of these drivers of avian biodiversity, and produce nationwide, predictive maps of avian species richness for all birds, forest birds, grassland birds, shrubland birds, Neotropical migrants, short-distance migrants, and permanent residents. The quantification of habitat structure from remotely sensed data was a primary objective, including the evaluation of remotely sensed image texture and both horizontal and vertical vegetation structure, such as landscape composition and forest canopy height. These measures explained up to 70 percent of variability in avian species richness across the United States, and vertical and horizontal structure measures were complementary. I then developed models of avian species richness as a function of all three drivers of biodiversity. When modeling avian species richness at the scale of a North American Breeding Bird Survey route, all three factors had some explanatory power, but measures of habitat structure dominated, followed by productivity, then climatic stability. Models for specific avian guilds explained between 21 and 67 percent of the variability in avian species richness. Lastly, in order to generate a product useful to planners and resource managers, I produced a nationwide, 30-m spatial resolution map of predicted avian species richness for each of the seven avian guilds. My dissertation makes several technical, theoretical, and applied contributions to biodiversity conservation. The main technical contribution is the use of remotely sensed image texture over a nationwide extent. Theoretical contributions include the evaluation of the relative importance of vertical and horizontal structure, as well as the relative importance of the three factors driving patterns of avian species richness. The applied contribution is the detailed, nationwide maps of predicted avian species richness, which will assist resource managers in conservation decision-making.
Download or read book IDENTIFYING POTENTIAL PATTERNS OF WILDFIRES IN CALIFORNIA IN RELATION TO SOIL MOISTURE USING REMOTE SENSING written by Adam J. Link and published by . This book was released on 2020 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this study is determining a potential correlation between soil moisture and burn severity as well as examining potential correlations between slope, elevation, wind speed, wind direction and Normalized Difference Vegetation Index (NDVI) value and burn severity within the Mendocino Complex Fire, California, which occurred in 2018. A time-series of the difference Normalized Burn Ratio (dNBR), the difference between pre- and intra-fire NBR values, was calculated via Sentinel-2, soil moisture was mapped using SMAP, and the Digital Elevation Model (DEM) from ASTER was used to derive elevation and slope values. The imagery was obtained from USGS and USDA websites. Images were processed and reprojected to the same spatial resolution (60 m) and projection (UTM Zone 10N, WGS-87). dNBR imagery was subdivided in newly burned areas for each consecutive day for ten days from 29 July 2018 to 31 August 2018. The findings suggested that there was no strong correlation trend consistently found over the proposed period of time between dNBR values and soil moisture content (R ≈ -0.20 to 0.39), slope (R ≈ -0.35 to 0.46), elevation (R ≈ -0.24 to 0.56), wind speed (R ≈ -0.15 to 0.36), and wind direction (R ≈ -0.42 to 0.24). However, a positive correlation between NDVI values and dNBR values was found to be strong and consistent (R ≈ -0.48 to 0.57). This implies that burn severity increased more significantly and frequently with NDVI, a surrogate for vegetation biomass and leaf area index. It can be surmised that soil moisture must reach some higher values before having a possible impact upon burn severity. Considering that the summer of 2018 was one of the warmest and driest summers in the study area's recent history, soil moisture content was relatively low while, simultaneously, vegetation was dry and more prone to burning.
Download or read book Examining Drivers of Post Wildfire Vegetation Dynamics Across Multiple Scales Using Time Series Remote Sensing written by Grant M. Casady and published by . This book was released on 2008 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecosystem response to disturbance is a function of environmental factors interacting at a number ofspatio-temporal scales. This research explored ecosystem response to wildfire as a function of local and broad-scale environmental factors using satellite based time-series remote sensing data. This topic was explored as a series of three independent but related studies. The first study focused on the evaluation of techniques for the analysis of time-series satellite data for describing post-fire vegetation trends at sites in the US, Spain, and Israel. Time-series data effectively described post-fire trends, and reference sites were valuable for differentiating between post-fire effects and other environmental factors. The use of phenological indicators derived from the time-series shows promise as a monitoring tool, but requires further investigation. The next study evaluated the influence of broad-scale climate factors on rates of post-fire vegetation regeneration across the western US. Rates of post-fire regeneration were higher with increased precipitation and higher minimum temperatures. Changes in climate are likely to result in shifts in post-fire vegetation dynamics, leading to important feedbacks into the climate system. The use of time-series data was a valuable tool in measuring trends in post-fire vegetation across a large area and over an extended period. The final study used time-series vegetation data to measure variations in post-fire vegetation response across an extensive 2002 wildfire. Regression tree analysis related post-fire regeneration to local environmental factors such as burn severity, soil properties, vegetation, and topography. Residuals from modeled rates of post-fire regeneration were evaluated in the context of management activities and site characteristics using expert knowledge. Post-fire rates of regeneration were a function of water availability, pre-burn vegetation, and burn severity. Management activities, soil differences, and shifts in vegetation community composition resulted in deviations from the modeled post-fire regeneration rates. The results of these three research studies indicate that remotely sensed time-series vegetation data provide a useful tool for measuring post-fire vegetation dynamics. Both broad-scale and local environmental factors play important roles in defining post-fire vegetation response, and the use of remote sensing and geospatial data sets can be useful in integrating these factors and enhancing management decisions.
Download or read book Integrating Remote Sensing and Field Observations to Investigate the Impacts of Vegetation Dynamics on Emergent Ecological and Hydrological Processes at the Watershed Scale written by Mahsa Khodaee and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale forest dynamics, mostly driven by climate change and disturbance activities, have great implications for forest biodiversity, and ecosystem structures and services. Therefore, monitoring these changes over space and time is critical to improve our understanding of forest responses to climate change and disturbance activities. In this dissertation, remote sensing data were employed in statistical and machine learning (ML) approaches to characterize forest disturbances (infestation and fire) and long-term phenological changes at the eastern United States. Further, vegetation phenological information were integrated with field measurements of meteorological parameters to evaluate hydrologic alterations of forested watersheds. More specifically, three objectives are the focus of this dissertation: (1) developing an accurate approach to characterize the spectral-temporal trajectory of forest disturbances using long-term satellite observations, (2) evaluating the impacts of both vegetation and snowpack seasonal dynamics on emergent watershed-scale hydrologic behavior over vernal and autumnal transition periods, and (3) mapping fire-induced changes in forested landscapes using the combined approach of remote sensing and ML techniques.In the first chapter, using time series analysis of remote sensing imagery, we evaluated the performance of well-known spectral indices in capturing vegetation dynamics following the two major disturbances, fire and hemlock woolly adelgid (HWA; Adelges tsugae Annad) infestation. Our results suggested that the overall performance of Normalized Difference Vegetation Index (NDVI) was the most accurate in detecting disturbances intensity, temporal dynamics, and recovery patterns. Results obtained from our second chapter demonstrated that the lengthened growing season and declined winter snowpack can significantly alter the low-frequency seasonal streamflow distributions by changing the seasonality of evapotranspiration. Our findings also suggested that with continuous declines in winter snowfall, the effect of snowpack dynamics on identifying the seasonal streamflow regimes has weakened, while vegetation phenology has exerted more dominant control. In the third study of this dissertation, we evaluated the performance of four ML classification techniques in estimating fire severity, using spectral indices, topographic parameters, and surface temperature (ST) variables from remote sensing data. We found that Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models had high predictive capability to detect fire severity in regions with mixes of low to medium fire disturbances. Results also indicated that spectral indices, especially Normalized Burn Ratio (NBR) and Tasseled Cap Wetness index (TCW), elevation, aspect and growing season ST had higher relative contributions to fire severity prediction than other variables.
Download or read book The Use of Remote Sensing Indices to Determine Wildland Burn Severity in Semiarid Sagebrush Steppe Rangelands Using Landsat ETM and SPOT 5 written by Jill M. Norton and published by . This book was released on 2006 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study evaluates ten remote sensing indices to detect burned areas and burn severity in a southeastern Idaho study area. While fire-related studies have been performed in forested ecosystems, few have been conducted in sagebrush steppe rangelands. Burn severity, defined as the completeness of aboveground vegetation removal during the burn, is useful in determining the type and location of treatment(s) that land managers can implement to speed recovery and thus in assessing effectiveness and speed of landscape recovery. This study utilizes pre- and post-fire field-based sampling as ground control for image processing of Landsat ETM+ and SPOT 5 multispectral imagery. Single and multi-date indices were validated through accuracy-assessment techniques. Remote sensing indices comparing burned with unburned areas had better overall, user's, and producer's accuracies than indices comparing levels of burn severity. The best burn versus unburned index was the Soil Adjusted Vegetation Index (SAVI; 100% overall accuracy) derived from SPOT imagery, and the best burn severity index was the relative differenced Normalized Burn Ratio (RdNBR; 73% overall accuracy) derived from Landsat imagery. These two indices provided the highest user's and producer's accuracies.
