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Utilization Of Lidar And Multispectral Satellite Imagery To Improve Estimation And Mapping Of Forest Fuel Models And Fuel Loadings In A Mixed Conifer Forest
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Book Synopsis Utilization of Lidar and Multispectral Satellite Imagery to Improve Estimation and Mapping of Forest Fuel Models and Fuel Loadings in a Mixed-conifer Forest by : Riley W. Tschida
Download or read book Utilization of Lidar and Multispectral Satellite Imagery to Improve Estimation and Mapping of Forest Fuel Models and Fuel Loadings in a Mixed-conifer Forest written by Riley W. Tschida and published by . This book was released on 2012 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mapping Surface Fuels Using LIDAR and Multispectral Data Fusion for Fire Behavior Modeling by : Muge Mutlu
Download or read book Mapping Surface Fuels Using LIDAR and Multispectral Data Fusion for Fire Behavior Modeling written by Muge Mutlu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Fires have become intense and more frequent in the United States. Improving the accuracy of mapping fuel models is essential for fuel management decisions and explicit fire behavior prediction for real-time support of suppression tactics and logistics decisions. This study has two main objectives. The first objective is to develop the use of LIght Detection and Ranging (LIDAR) remote sensing to assess fuel models in East Texas accurately and effectively. More specific goals include: (1) developing LIDAR derived products and the methodology to use them for assessing fuel models; (2) investigating the use of several techniques for data fusion of LIDAR and multispectral imagery for assessing fuel models; (3) investigating the gain in fuels mapping accuracy with LIDAR as opposed to QuickBird imagery alone; and, (4) producing spatially explicit digital fuel maps. The second objective is to model fire behavior using FARSITE (Fire Area Simulator) and to investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. Estimates of fuel models were compared with in situ data collected over 62 plots. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne LIDAR data and QuickBird data than with QuickBird imagery alone (76.52%). These two fuel model maps obtained from the first objective were used to see the differences in fire growth with fuel model maps of different accuracies. According to our results, LIDAR derived data provides accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of forests. This study demonstrates the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques.
Book Synopsis Estimation and Mapping of Canopy Fuel Parameters for Mixed-conifer Forests Using Discrete Return Light Detection and Ranging and Multispectral Satellite Data by : Meghan K. Lonneker
Download or read book Estimation and Mapping of Canopy Fuel Parameters for Mixed-conifer Forests Using Discrete Return Light Detection and Ranging and Multispectral Satellite Data written by Meghan K. Lonneker and published by . This book was released on 2008 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Fire and Fuels Extension to the Forest Vegetation Simulator by : Elizabeth D. Reinhardt
Download or read book The Fire and Fuels Extension to the Forest Vegetation Simulator written by Elizabeth D. Reinhardt and published by . This book was released on 2003 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS) simulates fuel dynamics and potential fire behavior over time, in the context of stand development and management. Existing models of fire behavior and fire effects were added to FVS to form this extension. New submodels representing snag and fuel dynamics were created to complete the linkages. This report contains four chapters. Chapter 1 states the purpose and chronicles some applications of the model. Chapter 2 details the model's content, documents links to the supporting science, and provides annotated examples of the outputs. Chapter 3 is a user's guide that presents options and examples of command usage. Chapter 4 describes how the model was customized for use in different regions. Fuel managers and silviculturists charged with managing fire-prone forests can use the FFEFVS and this document to better understand and display the consequences of alternative management actions.
Book Synopsis Wildland Fuel Fundamentals and Applications by : Robert E. Keane
Download or read book Wildland Fuel Fundamentals and Applications written by Robert E. Keane and published by Springer. This book was released on 2014-11-04 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new era in wildland fuel sciences is now evolving in such a way that fire scientists and managers need a comprehensive understanding of fuels ecology and science to fully understand fire effects and behavior on diverse ecosystem and landscape characteristics. This is a reference book on wildland fuel science; a book that describes fuels and their application in land management. There has never been a comprehensive book on wildland fuels; most wildland fuel information was put into wildland fire science and management books as separate chapters and sections. This book is the first to highlight wildland fuels and treat them as a natural resource rather than a fire behavior input. Moreover, there has never been a comprehensive description of fuels and their ecology, measurement, and description under one reference; most wildland fuel information is scattered across diverse and unrelated venues from combustion science to fire ecology to carbon dynamics. The literature and data for wildland fuel science has never been synthesized into one reference; most studies were done for diverse and unique objectives. This book is the first to link the disparate fields of ecology, wildland fire, and carbon to describe fuel science. This just deals with the science and ecology of wildland fuels, not fuels management. However, since expensive fuel treatments are being planned in fire dominated landscapes across the world to minimize fire damage to people, property and ecosystems, it is incredibly important that people understand wildland fuels to develop more effective fuel management activities.
Book Synopsis The Use of LiDAR in Multi-scale Forestry Applications by :
Download or read book The Use of LiDAR in Multi-scale Forestry Applications written by and published by . This book was released on 2017 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest ecosystems are a significant faction of the Earth's landscape, and accurate estimates of forest structures are important for understanding and predicting how forest ecosystems respond to climate change and human activities. Light detection and ranging (LiDAR) technology, an active remote sensing technology, can penetrate the forest canopy and greatly improve the efficiency and accuracy of mapping forest structures, compared to traditional passive optical remote sensing and radar technologies. However, currently, LiDAR has two major weaknesses, the lack of spectral information and the limited spatial coverage. These weaknesses have limited its accuracy in certain forestry applications (e.g., vegetation mapping) and its application in large-scale forest structure mapping. The aim of research described in this dissertation is to develop data fusion algorithms to address these limitations. In this dissertation, the effectiveness of LiDAR in estimating forest structures and therefore monitoring forest dynamics is first compared with aerial imagery by detecting forest fuel treatment activities at the local scale. Then, a vegetation mapping algorithm is developed based on the fusion of LiDAR data and aerial imagery. This algorithm can automatically determine the optimized number of vegetation units in a forest and take both the vegetation species and vegetation structure characteristics into account in classifying the vegetation types. To extend the use of LiDAR in mapping forest structures in areas without LiDAR coverage, a data fusion algorithm is proposed to map fine-resolution tree height from airborne LiDAR, spaceborne LiDAR, optical imagery and radar data in regional scale. Finally, this dissertation further investigates the methodology to integrate spaceborne LiDAR, optical imagery, radar data and climate surfaces for the purpose of mapping national- to global-scale forest aboveground biomass. The proposed data fusion algorithms and the generated regional to global forest structure parameters will have important applications in ecological and hydrologic studies and forest management.
Book Synopsis Using Airborne Laser Altimetry to Characterize Surface Fuels in Western Montana by : Tim E. Wallace
Download or read book Using Airborne Laser Altimetry to Characterize Surface Fuels in Western Montana written by Tim E. Wallace and published by . This book was released on 2010 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantifying surface fuels in forests is problematic for land managers due to the difficulty in measuring fuels of different sizes and spatial variability. Estimating fuel loads is important for identifying departures from historical fire regimes, predicting fire behavior and effects, and prioritizing parcels for fuels reduction. Current field methods of estimation are not always cost-effective nor can they be practical for full coverage at landscape scales. Several studies have examined remote sensing techniques for estimating fuel loads. One of the most promising is Light Detection and Ranging (LiDAR), which thus far has been applied primarily to forest canopies. Metrics derived from LiDAR include canopy base height, canopy bulk density, biomass, crown height, basal area, and tree stem location. This study focuses on the surface fuel bed, defined as the two meter stratum above ground. The relationships between LiDAR-derived surface roughness and fuels were explored in mixed-conifer forest using a relatively sparse LiDAR dataset (~1 point/m2). Surface roughness was imputed as the standard deviation of ground height distribution of laser pulse returns. Field data were derived from the nationally-scoped Fire-Fire Surrogate Study for 432 plots using two opposing azimuth Brown's transects at each sample point. Fuel loading and surface roughness were both highly variable at plot level across the study area. Total biomass could be predicted at a nine ha resolution (R2 = 0.73). Relationships for total biomass in the fuelbed, analyzed at 2.25 ha and 0.07 ha resolutions, showed less correlation (R2 = 0.56 and 0.094, respectively). Individual surface fuel components were analyzed for correlation with surface roughness. A combination of forest floor mass and 1-hour fuels produced the highest correlation (R2 = 0.86). Additionally, LiDAR-derived data were used to derive fire behavior fuel models. Fuel models were classified by decision tree, CART analysis, and unsupervised classification using LiDAR-derived inputs. Results were validated using 101 gridded forest inventory plots. While LiDAR consistently characterized the plots at fine scale, the subjective nature of fuel model designation made statistical validation difficult.
Book Synopsis Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada by : Qin Ma
Download or read book Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada written by Qin Ma and published by . This book was released on 2018 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sierra Nevada forests have provided many economic benefits and ecological services to people in California, and the rest of the world. Dramatic changes are occurring in the forests due to climate warming and long-term fire suppression. Accurate mapping and monitoring are increasingly important to understand and manage the forests. Light Detection and Range (LiDAR), an active remote sensing technique, can penetrate the canopy and provide three-dimensional estimates of forest structures. LiDAR-based forest structural estimation has been demonstrated to be more efficient than field measurements and more accurate than those from passive remote sensing, like satellite imagery. Research in this dissertation aims at mapping and monitoring structural changes in Sierra Nevada forests by taking the advantages of LiDAR. We first evaluated LiDAR and fine resolution imagery-derived canopy cover estimates using different algorithms and data acquisition parameters. We suggested that LiDAR data obtained at 1 point/m2 with a scan angle smaller than 12°were sufficient for accurate canopy cover estimation in the Sierra Nevada mix-conifer forests. Fine resolution imagery is suitable for canopy cover estimation in forests with median density but may over or underestimate canopy cover in extremely coarse or dense forests. Then, a new LiDAR-based strategy was proposed to quantify tree growth and competition at individual tree and forest stand levels. Using this strategy, we illustrated how tree growth in two Sierra Nevada forests responded to tree competition, original tree sizes, forest density, and topography conditions; and identified that the tree volume growth was determined by the original tree sizes and competitions, but tree height and crown area growth were mostly influenced by water and space availability. Then, we calculated the forest biomass disturbance in a Sierra Nevada forest induced by fuel treatments using bi-temporal LiDAR data and field measurements. Using these results as references, we found that Landsat imagery-derived vegetation indices were suitable for quantifying canopy cover changes and biomass disturbances in forests with median density. Large uncertainties existed in applying the vegetation indices to quantify disturbance in extremely dense forests or forests only disturbed in the understory. Last, we assessed vegetation losses caused by the American Fire in 2013 using a new LiDAR point based method. This method was able to quantify fire-induced forest structure changes in basal area and leaf area index with lower uncertainties, compared with traditional LiDAR metrics and satellite imagery-derived vegetation indices. The studies presented in this dissertation can provide guidance for forest management in the Sierra Nevada, and potentially serve as useful tools for forest structural change monitoring in the rest of the world.
Book Synopsis Using LiDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover at Varying Scales by : Alicia Marie Rutledge Griffin
Download or read book Using LiDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover at Varying Scales written by Alicia Marie Rutledge Griffin and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.
Book Synopsis Remote Sensing of Forest Biomass Dynamics Using Landsat-derived Disturbance and Recovery History and Lidar Data by : Dirk Pflugmacher
Download or read book Remote Sensing of Forest Biomass Dynamics Using Landsat-derived Disturbance and Recovery History and Lidar Data written by Dirk Pflugmacher and published by . This book was released on 2011 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change.
Book Synopsis Using Lidar in Wildfire Ecology of the California Sierra-Nevada Forests by : Marek K. Jakubowski
Download or read book Using Lidar in Wildfire Ecology of the California Sierra-Nevada Forests written by Marek K. Jakubowski and published by . This book was released on 2012 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: California's fire suppression policy has dramatically changed Sierra Nevada forests over the last century. Forests are becoming more dense and homogenous, leading to fire regime changes that increase the potential of stand-replacing wildfires over large, continuous areas. To mitigate this problem on public lands, the US Forest Service has proposed to implement strategically placed forest fuel reduction treatments. These treatments have been proved effective in modeled and simulated environments, but their efficacy and impact in real forests is not known. The research described in this dissertation is part of a large multidisciplinary project, known as the Sierra Nevada Adaptive Management Project (SNAMP), that aims to evaluate strategically placed landscape area treatments (SPLATs) in two forests of the Sierra Nevada mountains. Specifically, in this thesis, I investigate the feasibility of using an airborne light detection and ranging (lidar) system to gain accurate information about forest structure to inform wildfire behavior models, forest management, and habitat mapping. First, I investigate the use of lidar data in predicting metrics at the landscape level, specifically to derive surface fuel models and continuous canopy metrics at the plot scale. My results in Chapter 2 indicate that using lidar to predict specific fuel models for FARSITE wildfire behavior model is challenging. However, the prediction of more general fuel models and continuous canopy metrics is feasible and reliable, especially for metrics near the top of the canopy. It is also possible to derive canopy parameters at the individual tree level. In Chapter 3, I compare the ability of two processing methods--object-based image analysis (OBIA) and 3D segmentation of the lidar point cloud--to detect and delineate individual trees. I find that while both methods delineate dominant trees and accurately predict their heights, the lidar-derived polygons more closely resemble the shape of realistic individual tree crowns. Acquiring remotely sensed data at high resolution and over large areas can be expensive, especially in the case of lidar. In Chapter 4, I investigate the ability of lidar data to reliably predict forest canopy metrics at the plot level as the data resolution declines. I show that canopy metrics can be predicted at a reasonable accuracy with data resolutions as low as one pulse per squared meter. These findings will be useful to land managers making cost benefit decisions when acquiring new lidar data. Collectively, the results of this dissertation suggest that remote sensing, and in particular lidar, can reliably and cost-effectively provide forest information across scales--from the individual tree level to the landscape level. These results will be useful for the fire and forest management community in general, as well as being key to the goals of the SNAMP program.
Book Synopsis UAV Photogrammetry and Remote Sensing by : Fernando Carvajal-Ramírez
Download or read book UAV Photogrammetry and Remote Sensing written by Fernando Carvajal-Ramírez and published by MDPI. This book was released on 2021-09-06 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites. The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained. More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products. This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry.
Book Synopsis Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities by : Zhenyu Zhang
Download or read book Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities written by Zhenyu Zhang and published by . This book was released on 2012 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: High resolution spatial data, including airborne LiDAR data and newly available WorldView-2 satellite imagery, offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. Those responsible for monitoring forest changes over time relevant to timber harvesting and native forest conservation see the potential for improved documentation from using such data. However, the transfer of new remote sensing technologies from the research domain into operational forestry applications poses challenges. One of the key challenges is the development of a comprehensive procedure which involves deployment of these new remote sensing data to create forest mapping products that are comparable (or superior) in accuracy to conventional photo-interpreted maps. The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for tree species identification and classification. The research investigations have focused on open forests, and conifer or deciduous forests which are even-aged and of relatively homogenous structures. The suitability of these new remotely sensed data for delineating the structure of complex forest types, particularly for Australian cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This thesis presents ways of processing airborne LiDAR data and high spatial resolution WorldView-2 satellite imagery for characterisation and classification of forest communities in the Strzelecki Ranges, Victoria, Australia. This is a highly disturbed landscape that consists of forestry plantations and large stands of natural forest, including cool temperate rainforest remnants. The k-means clustering algorithm was applied to nonnalised LiDAR points to stratify the vertical forest structure into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. These layer-specific variables were found to be effective in forest classification. Individual trees, including locations and crown sizes, were identified from a LiDAR-derived canopy height model using the TreeVaW algorithm. Augmentation of infonnation extraction from LiDAR data for tree species identification by inclusion of LiDAR intensity data was then tested using statistical analysis techniques. This study demonstrated the contribution of LiDAR-derived intensity variables to the identification of Myrtle Beech (Nothofagus cunninghamii -the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species -notably, Silver Wattle (Acacia dealbata) at the individual tree level. Nonparametric classifiers including support vector machines (SVMs) and decision trees were employed to take full advantage of the rich set of infonnation derived from the LiDAR and WorldView-2 imagery data for further improvement in classification accuracy. It is evident that the SVMs have significant advantages over the traditional classification methods in tenns of classification accuracy. Cool temperate rainforest and adjacent forest species were successfully classified from airborne LiDAR data and WorldView-2 satellite imagery using a decision tree approach to object-based analyses in eCognition software. The improvements in results from the methods developed in this study strongly warrant the operational adoption of airborne LiDAR data and high spatial resolution satellite imagery in the management of Australia's forestry resources.
Book Synopsis Applying Lidar and High-resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic by : Heather E. Greaves
Download or read book Applying Lidar and High-resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic written by Heather E. Greaves and published by . This book was released on 2017 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass---suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.
Book Synopsis LiDAR Principles, Processing and Applications in Forest Ecology by : Qinghua Guo
Download or read book LiDAR Principles, Processing and Applications in Forest Ecology written by Qinghua Guo and published by Academic Press. This book was released on 2023-03-10 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
Book Synopsis Assessing Surface Fuel Hazard in Coastal Conifer Forests Through the Use of LiDAR Remote Sensing by : Christos Koulas
Download or read book Assessing Surface Fuel Hazard in Coastal Conifer Forests Through the Use of LiDAR Remote Sensing written by Christos Koulas and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant tradeoff exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk.
Book Synopsis Sourcebook on Remote Sensing and Biodiversity Indicators by : Holly Strand
Download or read book Sourcebook on Remote Sensing and Biodiversity Indicators written by Holly Strand and published by . This book was released on 2007 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This sourcebook is intended to assist environmental managers and others who work with indicators in pursuing appropriate methods for indicator testing and production, and to offer some guidance to those responsible for the interpretation of indicators and implementation of decisions based on them. Upon reading this document, technical advisers, environmental policy makers, and remote sensing lab directors and project managers should be able to identify specific, relevant uses of remote sensing data for biodiversity monitoring and indicator development related to the CBD." --p. 8.
