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Using Multi Return Lidar Data To Measure Forest And Stand Characteristics In Mixed Coniferous Forests Of North Central Idaho
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Book Synopsis Using Multi-return Lidar Data to Measure Forest and Stand Characteristics in Mixed Coniferous Forests of North Central Idaho by : Tamara A. Conner
Download or read book Using Multi-return Lidar Data to Measure Forest and Stand Characteristics in Mixed Coniferous Forests of North Central Idaho written by Tamara A. Conner and published by . This book was released on 2003 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Master's Theses Directories written by and published by . This book was released on 2004 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Education, arts and social sciences, natural and technical sciences in the United States and Canada".
Book Synopsis Predicting Biophysical Properties of Mixed-conifer Forest Stands in Northern Idaho with Small Footprint LiDAR by : Jennifer L. Rooker
Download or read book Predicting Biophysical Properties of Mixed-conifer Forest Stands in Northern Idaho with Small Footprint LiDAR written by Jennifer L. Rooker and published by . This book was released on 2004 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon by :
Download or read book QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO2) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across H"0,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of>70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems.
Book Synopsis Tree and Terrain Measurements Using Small-footprint Multiple-return Airborne LIDAR Data from Mixed Douglas-fir Forest in the Pacific Northwest by : Neil Thomas Eggleston
Download or read book Tree and Terrain Measurements Using Small-footprint Multiple-return Airborne LIDAR Data from Mixed Douglas-fir Forest in the Pacific Northwest written by Neil Thomas Eggleston and published by . This book was released on 2001 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forest Analytics with R by : Andrew P. Robinson
Download or read book Forest Analytics with R written by Andrew P. Robinson and published by Springer Science & Business Media. This book was released on 2010-11-05 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics.
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 ADVANCING LOW-COST MOBILE REMOTE SENSING TECHNOLOGIES FOR FOREST RESOURCE MANAGEMENT. by : Brennan Holderman
Download or read book ADVANCING LOW-COST MOBILE REMOTE SENSING TECHNOLOGIES FOR FOREST RESOURCE MANAGEMENT. written by Brennan Holderman and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in active sensor technology have made them more readily available than ever before. Light detection and ranging (LiDAR) technology has been used as a tool in forest management for several decades to model tree characteristics at both the stand and plot levels. From aerial mapping campaigns, to static terrestrial scanning, tree structure estimates derived from high density point clouds have proven to be accurate. Historical access to these technologies and services have often been prohibitively expensive, with terrestrial laser scanner costs exceeding six-figures and aerial campaigns costing tens-of-thousands of dollars, daily. This has limited their use in forestry to high-value, or short-rotation, species such as loblolly pine (Pinus taeda) across the American South and Eucalyptus in the tropics. In addition, the conifers across the western U.S. and Europe have been the subject of extensive LiDAR research and deployment. The mixed hardwood deciduous forests characteristic of south-central Pennsylvania present challenges to LiDAR mapping on multiple fronts. The complex upper canopy structure makes isolating individual trees from above difficult. Diverse and variable topography can present significant data capture and processing issues. In addition, present market values of eastern hardwoods limit the stakeholders ability to invest additional resources for advanced inventory techniques and technology. However, as the technology matures, it could allow for more advanced sampling techniques, such as 3P, at similar costs to present plot-based methods.Rapid expansion in the autonomous vehicle sector has created the need for affordable robotic vision sensors. The sensor of choice for many automotive manufacturers are small, affordable LiDAR units. Due to this, large capital investments are no longer required to rapidly collect dense point cloud data. Small, modular LiDAR sensors from companies such as Velodyne LiDAR can now be had for $4,000 USD. The availability of these sensors is only the first step to integrating them into natural resource management solutions and tools. This study has focused on standardizing both the protocols by which a low-cost laser scanner can be deployed in the field, how those data are collected and processed, and an evaluation of the individual stem detection and diameter estimates produced from the dense point clouds. Developing free and open source software solutions, processes and schematics was central to the aim of this study. Chapter 2 provides extensive documentation of system requirements, set-up parameters, embedded computer systems and required software to successfully capture LiDAR data in a variety of configurations. Significant effort was put forth to create a modular LiDAR system that can be deployed both aerially, and terrestrially, with minimal downtime between configurations. To assess the capacity of the system, it was deployed in both static and mobile terrestrial configurations to collect plot-level data in forested stands. Data were captured, processed and compared to in-situ measures taken from 7.5 m plots at three site locations. Estimates of stem diameter, taper, branching structure, and height are central measures to evaluating both biomass and potential market value of individual trees. The traditionally required field work to assess both value and volume are time- and labor-intensive. Two algorithms were evaluated for their ability to detect individual stems and estimate their diameter in the collected point clouds (Chapter 3). The LAS2Rings and TreeLS algorithms use the Hough Transform to identify stem locations and RANSAC fitting to estimate diameter. The algorithms were both able to successfully produce stem locations and diameters. However, due to the small n, statistically significant conclusions could not be drawn when comparing the in-situ and cloud measures. Other limitations were observed throughout the study and potential solutions were addressed to better process and capture data (Chapter 4).The technology presented and developed within this study shows considerable promise. Additional research is required to fully realize its potential as a widely adopted tool for natural resource land managers. This would include refinement of the capture methodology, optimization of the co-registration algorithms, and a substantial increase in in-situ measures for further statistical comparison.
Book Synopsis Visualizing Distributions from Multi-return Lidar Data to Understand Forest Structure by : David Kao
Download or read book Visualizing Distributions from Multi-return Lidar Data to Understand Forest Structure written by David Kao and published by . This book was released on 2004 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatially distributed probability density functions (pdfs) are becoming relevant to the Earth scientists and ecologists because of stochastic models and new sensors that provide numerous realizations or data points per unit area. One source of these data is from multi-return airborne lidar, a type of laser that records multiple returns for each pulse of light sent towards the ground. Data from multi-return lidar is a vital tool in helping us understand the structure of forest canopies over large extents. This paper presents several new visualization tools that allow scientists to rapidly explore, interpret and discover characteristic distributions within the entire spatial field. The major contribution from-this work is a paradigm shift which allows ecologists to think of and analyze their data in terms of the distribution. This provides a way to reveal information on the modality and shape of the distribution previously not possible. The tools allow the scientists to depart from traditional parametric statistical analyses and to associate multimodal distribution characteristics to forest structures. Examples are given using data from High Island, southeast Alaska.
Book Synopsis Improving Forest Inventory and Assessment with LiDAR Data by : Michael J. Falkowski
Download or read book Improving Forest Inventory and Assessment with LiDAR Data written by Michael J. Falkowski and published by . This book was released on 2008 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 4 evaluates the use of LiDAR data for characterizing forest successional stages, across a structurally diverse, mixed-species forest in northern Idaho. A variety of LiDAR-derived metrics were used in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy greater than 95%. The algorithmic model developed ecologically meaningful decision rules based upon LiDAR metrics quantifying mean vegetation height and canopy cover, among others.
Book Synopsis Improving LIDAR-based Tree Species Mapping in Central European Mixed Forests Using Multitemporal Digital Aerial Colour-infrared Photographs by : Yifang Shi
Download or read book Improving LIDAR-based Tree Species Mapping in Central European Mixed Forests Using Multitemporal Digital Aerial Colour-infrared Photographs written by Yifang Shi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Digital colour-infrared (CIR) aerial photographs, which have been collected routinely in many parts of the world, are an invaluable data source for the monitoring and assessment of forest resources. Yet, the potential of these data for automated individual tree species mapping remains largely unexplored. One way to maximize the usefulness of digital CIR aerial photographs for individual tree species mapping is to integrate them with modern and complementary remote sensing technologies such as the light detection and ranging (LiDAR) system and 3D segmentation algorithms. In this study, we examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping for a temperate mixed forest in eastern Germany. Our results showed that the texture features captured by multi-temporal digital CIR orthophotos under different view-illumination conditions were species-specific. As a consequence, combining these texture features with LiDAR metrics significantly improved tree species mapping accuracy (overall accuracy: 77.4%, kappa: 0.68) compared to using LiDAR data alone (overall accuracy: 69.3%, kappa: 0.58). Among various texture features, the average gray level in the near-infrared band was found to contribute most to the classification. Our results suggest that the synergic use of multi-temporal digital aerial photographs and airborne LiDAR data has the potential to accurately classify individual tree species in Central European mixed forests
Book Synopsis Examination of Airborne Discrete-return Lidar in Prediction and Identification of Unique Forest Attributes by : Brian M. Wing
Download or read book Examination of Airborne Discrete-return Lidar in Prediction and Identification of Unique Forest Attributes written by Brian M. Wing and published by . This book was released on 2012 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R2) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications.
Book Synopsis Evaluating the Use of LIDAR Multiple Return Data to Characterize Forest Structure in Croatan National Forest by : Zachary Emerson Arcaro
Download or read book Evaluating the Use of LIDAR Multiple Return Data to Characterize Forest Structure in Croatan National Forest written by Zachary Emerson Arcaro and published by . This book was released on 2008 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: pond pine, longleaf pine, habitat modeling, forest structure, LIDAR.
Book Synopsis Evaluating the Use of LIDAR Multiple Return Data to Characterize Forest Structure in Croatan National Forest by :
Download or read book Evaluating the Use of LIDAR Multiple Return Data to Characterize Forest Structure in Croatan National Forest written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distinguishing Between Live and Dead Standing Tree Biomass on the North Rim of Grand Canyon National Park, USA Using Small-footprint Lidar Data by : YunSuk Kim
Download or read book Distinguishing Between Live and Dead Standing Tree Biomass on the North Rim of Grand Canyon National Park, USA Using Small-footprint Lidar Data written by YunSuk Kim and published by . This book was released on 2009 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and wildfire behavior, and for forest management. Lidar remote sensing has been used successfully to estimate live biomass, but studies focusing on dead biomass are rare. We used lidar data, in conjunction with field measurements from 58 plots to distinguish between and map standing live and dead tree biomass in the mixed coniferous forest of the North Rim of Grand Canyon National Park, USA. Lidar intensity and canopy volume were key variables for estimating live biomass, whereas for dead biomass, lidar intensity alone was critical for accurate estimation. Regression estimates of both live and dead biomass ranged between zero and 600 Mg ha−1, with means of 195.08 Mg ha−1 and 65.73 Mg ha−1, respectively. Cross validation with field data resulted in correlation coefficients for predicted vs. observed of 0.85 for live biomass (RMSE = 50 Mg ha−1 and %RMSE (RMSE as a percent of the mean) = 26). For dead biomass, correlation was 0.79, RMSE was 42 Mg ha−1, and %RMSE was 63. Biomass maps revealed interesting patterns of live and dead standing tree biomass. Live biomass was highest in the ponderosa pine zone, and decreased from south to north through the mixed conifer and spruce-fir forest zones. Dead biomass exhibited a background range of values in these mature forests from zero to 100 Mg ha−1, with lower values in locations having higher live biomass. In areas with high dead biomass values, live biomass was near zero. These areas were associated with recent wildfires, as indicated by fire maps derived from the Monitoring Trends in Burn Severity Project (MTBS). Combining our dead biomass maps with the MTBS maps, we demonstrated the complementary power of these two datasets, revealing that MTBS burn intensity class can be described quantitatively in terms of dead biomass. Assuming a background range of dead biomass up to 100 Mg ha−1, it is possible to estimate and map the contribution to the standing dead tree biomass pool associated with recent wildfire.
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 Light Detection and Ranging Detection of Ladder Fuels in a North Idaho Mixed Conifer Forest by : Chris Powell
Download or read book Light Detection and Ranging Detection of Ladder Fuels in a North Idaho Mixed Conifer Forest written by Chris Powell and published by . This book was released on 2008 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:
