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Tree And Terrain Measurements Using Small Footprint Multiple Return Airborne Lidar Data From Mixed Douglas Fir Forest In The Pacific Northwest
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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:
Author :Mississippi State University. Forest and Wildlife Research Center Publisher : ISBN 13 : Total Pages :264 pages Book Rating :4.:/5 (11 download)
Book Synopsis Annual Report by : Mississippi State University. Forest and Wildlife Research Center
Download or read book Annual Report written by Mississippi State University. Forest and Wildlife Research Center and published by . This book was released on 1998 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Voxel-based Method for Individual Tree Detection Using Airborne Lidar in Eastern U.S. Hardwood Forests by : Jeff Hershey
Download or read book A Voxel-based Method for Individual Tree Detection Using Airborne Lidar in Eastern U.S. Hardwood Forests written by Jeff Hershey and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than a century, forest inventories have been used to support forest management and timber valuation activities. Today's inventories still rely primarily on manual measurements combined with sampling and modeling techniques. In recent years, new opportunities in carbon sequestration and an increasingly sophisticated timber market have prompted a need for more scalable and efficient inventory methods. To meet this demand, the industry has turned to remote sensing--predominantly light detection and ranging (LiDAR), which utilizes lasers to scan and measure features in 3D space. While much progress has been made, data resolution and cost challenges for both airborne and terrestrial LiDAR still exist. Airborne Laser Scanning (ALS) is more efficient for measuring large forest areas but faces challenges with respect to resolution and occlusion, leading to omission of understory trees. Terrestrial Laser Scanning (TLS) performs better in those respects but relies on expensive and typically unwieldy hardware. Area-based LiDAR approaches have been successful for large-scale applications but are not ideal for smaller parcels. As such, a need persists for a LiDAR-based solution that enables efficient generation of large-area forest inventory data yet is scalable to smaller forest plots and a range of forest types. The objective of this study was the development of a scalable individual tree detection method that leveraged airborne LiDAR data and performed well in mixed-species hardwood forests found in the northeastern United States. Existing research in individual tree detection has focused on methods that work well in conifer-dominated forests and homogenous settings such as plantations. These approaches, many of them based on top-down canopy height models, perform less favorably in deciduous stands due to the canopy complexity and crown characteristics inherent to these forest types. The voxel-based method proposed here uses detailed ground-measured tree survey data and leaf-off LiDAR collected in 2019-2020 over the Shavers Creek Watershed in Pennsylvania, United States. The method detected 68% of all reference trees greater than 10cm diameter at breast height (DBH) and 87% of sawtimber-sized trees greater than 28cm DBH, and it performed consistently across 48 subplots in the three-hectare test area. A new tree matching method leveraging linear integer programming was used for training and evaluation of the method. This tool enabled true one-to-one matching of predicted and reference trees and the validation of tree detections. Mean positional accuracy for predicted trees was within one meter of ground-measured reference trees. The results indicate the method has potential to be operationalized for both traditional forest management activities and in meeting the demand for more frequent and scalable inventories spurred by a growing forest carbon sequestration industry.
Book Synopsis Timber Measurement Problems in the Douglas-fir Region of Washington and Oregon by : David Bruce
Download or read book Timber Measurement Problems in the Douglas-fir Region of Washington and Oregon written by David Bruce and published by . This book was released on 1968 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automated Approaches for Extracting Individual Tree Level Forest Information Using High Spatial Resolution Remotely Sensed Data by : Jun Hak Lee
Download or read book Automated Approaches for Extracting Individual Tree Level Forest Information Using High Spatial Resolution Remotely Sensed Data written by Jun Hak Lee and published by . This book was released on 2010 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detailed forest information is increasingly desired not only for forest management purposes but also for maintaining and enhancing sustainable forest ecosystems. Although precise measurements of forests can be gathered by field measurements, they are labor intensive and time consuming especially when obtaining enough measurements over large and heterogeneous forest areas. Therefore we need automated and accurate methods which can supplement field measurements. High spatial resolution remotely sensed data can be applied for this objective because developing technologies keep increasing spatial resolution and make it possible to handle large amounts of remotely sensed digital data by powerful computers at reasonable prices. Although high spatial resolution remotely sensed data holds the potential to be a valuable source of information for forest characteristics, a number of challenges still exist in extracting the desired information from this data. Therefore, it is critical to develop and improve automated methods to extract forest information. In this dissertation, I develop and improve the automated methods of extracting individual tree level forest biophysical parameters using high spatial resolution remotely sensed data. While there are many new remote sensing technologies, such as digital aerial photographs, LiDAR (Light Detection and Ranging), radar, and multispectral (or hyperspectral) data, I mainly focus on small footprint LiDAR and aerial images (by digital frame camera) in this study, because these sensors can provide very high spatial resolution data, which are necessary to extract individual tree level biophysical characteristics. This study consists of three parts, which are basic procedures to exploit high spatial remotely sensed data to extract individual tree level forest biophysical parameters. All three studies are conducted in a mixed-conifer forest at Angelo Coast Range Reserve on the South Fork of the Eel River in Mendocino County, California, USA. First, I develop a robust method to reconstruct Digital Terrain Model (DTM) by classifying raw LiDAR points into ground and non-ground points with the Progressive Terrain Fragmentation (PTF) method. PTF applies iterative steps for searching terrain points by approximating terrain surfaces using the TIN (Triangulated Irregular Network) model constructed from the ground return points. Instead of using absolute slope or offset distance, the proposed method utilizes orthogonal distance to and relative angle between a triangular plane and a node. For that reason, PTF was able to classify raw LiDAR points into ground and non-ground points on a heterogeneous steep forested area with a small number of parameters. The results show the robust performance of the proposed method even under complex terrain conditions. Second, I develop an automated method to detect individual tree tops and delineate individual tree-crown boundaries using airborne LiDAR data. Because of heterogeneous site conditions, I divide the study site into two height classes (high and low trees). For high trees (>= 25 m), I detect tree tops by using a progressive window-size local maximum filter and I conduct an additional verification procedure to reduce false tree top detection by using the shape of canopy profiles between trees. Then, I delineate tree-crown boundaries by marker-controlled watershed segmentation. For low trees (
Book Synopsis Individual Tree Delineation and Species Identification in Deciduous and Mixed Canadian Forests Using High Spatial Resolution Airborne LiDAR and Image Data by : Jili Li
Download or read book Individual Tree Delineation and Species Identification in Deciduous and Mixed Canadian Forests Using High Spatial Resolution Airborne LiDAR and Image Data written by Jili Li and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of individual trees in forests is of great value for the monitoring and sustainable management of forests. For the past decade, remote sensing has been a useful tool for individual tree analysis. However, accuracies of individual tree analysis remain insufficient because of the inadequate spatial resolution of most remote sensing data and unsophisticated methods. The improvement of individual tree analysis becomes feasible because of recent advances in LiDAR (Light Detection And Ranging) and airborne image sensing technologies. However, it is challenging to fully exploit and utilize small-footprint LiDAR data and high spatial resolution imagery for detailed tree analysis. This dissertation presents a number of effective methods on individual tree crown delineation and species classification to improve individual tree analysis with advanced remote sensing data. The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a given forest scene and its determination of the number of trees in a segment based on LiDAR profiles. This framework correctly delineated 74% and 72% of the tree crowns in two plots with mixed-wood and deciduous trees, respectively. The study on individual tree species classification is focused on developing novel LiDAR and image features to characterize tree structures. First of all, coniferous and deciduous trees are classified. Features are extracted from LiDAR data to characterize crown shapes and vertical profiles of individual trees, followed by the C4.5 decision tree classification algorithm. Furthermore, groups of new LiDAR features are developed to characterize the internal structures of a tree. Important features are selected via a genetic algorithm and utilized in the multi-species classification based on linear discriminant analysis. An overall accuracy of 77 .5% is obtained for an investigation on 1, 122 sample trees in natural forests. In addition, statistical features based on gray-level co-occurrence matrix (GLCM) and structural texture-features derived from the local binary pattern (LBP) method are proved to be useful to improve the species classification using high spatial resolution aerial image. The research demonstrates that LiDAR data and high spatial resolution images can be used to effectively characterize tree structures and improve the accuracy and efficiency of individual tree species identification.
Book Synopsis Characterizing Vertical Forest Structure Using Small-footprint Multi-return Airborne LiDAR by : Daniel A. Zimble
Download or read book Characterizing Vertical Forest Structure Using Small-footprint Multi-return Airborne LiDAR written by Daniel A. Zimble and published by . This book was released on 2002 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Using Airborne Lidar to Differentiate Cottonwood Trees in a Riparian Area and Refine Riparian Water Use Estimates by : Alireza Faridhosseini
Download or read book Using Airborne Lidar to Differentiate Cottonwood Trees in a Riparian Area and Refine Riparian Water Use Estimates written by Alireza Faridhosseini and published by . This book was released on 2006 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the Upper San Pedro River Basin, Arizona, USA. The lidar data were acquired in June 2003 and 2004, using Optech's 1233 ALTM (Optech Incorporated, Toronto, Canada). Canopy height, crown diameter, stem diameter at breast height (dbh), canopy cover, and mean intensity of return laser pulses from the canopy surface are estimated for the cottonwood trees from lidar data. The lidar estimates show a good degree of correlation with ground-based measurements. This study also demonstrates that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (p
Book Synopsis 3D Feature Extraction and Geometric Mappings for Improved Parameter Estimation in Forested Terrain Using Airborne LiDAR Data by : Heezin Lee
Download or read book 3D Feature Extraction and Geometric Mappings for Improved Parameter Estimation in Forested Terrain Using Airborne LiDAR Data written by Heezin Lee and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: Scanning laser ranging technology is well suited for measuring point-to-point distances because of its ability to generate small beam divergences. As a result, many of the laser pulses emitted from airborne light detection and ranging (LiDAR) systems are able to reach the ground underneath tree canopies through small (10 cm scale) gaps in the foliage. Using high pulse rate lasers and fast optical scanners, airborne LiDAR systems can provide both high spatial resolution and canopy penetration, and these data have become more widely available in recent years for use in environmental and forestry applications. The small-footprint, discrete-return Airborne Laser Swath Mapping (ALSM) system at the University of Florida (UF) is used to directly measure ground surface elevations and the three-dimensional (3D) distribution of the vegetative material above the soil surface. Field of view geometric mappings are explored to find optical gaps inside forests. First, a method is developed to detect walking trails in natural forests that are obscured from above by the canopy. Several features are derived from the ALSM data and used to constrain the search space and infer the location of trails. Second, a robust and simple procedure for estimating intercepted photosynthetically active radiation (IPAR), which is an important measure of forest timber productivity and of daylight visibility in forested terrain, is presented. Simple scope functions that isolate the relevant LiDAR reflections between observer locations and the sun are defined and shown to give good agreement between the LiDAR-derived estimates and values of IPAR measured in situ. A conical scope function with an angular divergence from the centerline of "7° provided the best agreement with the in situ measurements.
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:
Book Synopsis Canadian Journal of Forest Research. Journal Canadien de la Recherche Forestière by :
Download or read book Canadian Journal of Forest Research. Journal Canadien de la Recherche Forestière written by and published by . This book was released on 2008 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Evaluating Small-footprint Multiple-return Lidar to Identify Individual Tree Characteristics by : Jason M. Stoker
Download or read book Evaluating Small-footprint Multiple-return Lidar to Identify Individual Tree Characteristics written by Jason M. Stoker and published by . This book was released on 2002 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Restoring Forest Resilience in the Sierra Nevada Mixed-conifer Zone, with a Focus on Measuring Spatial Patterns of Trees Using Airborne Lidar by : Sean Medeiros Alexander Jeronimo
Download or read book Restoring Forest Resilience in the Sierra Nevada Mixed-conifer Zone, with a Focus on Measuring Spatial Patterns of Trees Using Airborne Lidar written by Sean Medeiros Alexander Jeronimo and published by . This book was released on 2018 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation I present three studies incorporating lidar data into different aspects of forest restoration. All studies use lidar individual tree detection as source data, in part to enable making measurements of tree spatial patterns in terms of tree clumps and canopy openings. This common focus exists because spatial patterns of trees influence fire and insect behavior, snow retention, tree regeneration, and other key ecosystem functions and services for which humans manage forests. In Chapter 1 I sought to provide this dataset by asking these questions: (1) What is the geographic and environmental distribution of restored active-fire forest patches in the Sierra Nevada mixed-conifer zone? (2) What are the ranges of variation in structure and spatial patterns across restored patches? (3) How do density, tree clumping, and canopy opening patterns vary by topography and climate in restored patches? I analyzed fire history and environmental conditions over 10.8 million ha, including 3.9 million ha in the Sierra Nevada mixed-conifer zone, and found that the 30,379 ha of restored patches were distributed throughout the range but were more abundant on National Park lands (81% of restored areas) than National Forest lands and were positively correlated with lightning strike density. Furthermore, 33% of restored areas were located in western Yosemite National Park and met our criteria for inclusion in this study only after being burned at low and moderate severity in the 2013 Rim Fire. Lidar-measured ranges of variation in reference condition structure were broad, with density ranging from 6-320 trees ha−1 (median 107 trees ha−1), basal area from 2-113 m2 ha−1 (median 21 m2 ha−1), average size of closely associated tree clumps from 1 to 207 trees (median 3.1 trees), and average percent of stand area >6 m from the nearest canopy ranging from 0% to 100% (median 5.1%). These ranges matched past studies reporting density and spatial patterns of contemporary and historical active-fire reference stands in the Sierra Nevada, except this study observed longer tails on distributions due to the spatial completeness of lidar sampling. Reference areas in middle-elevation climate zones had lower density (86 vs. 121 trees ha-1), basal area, (13.7 vs. 31 m2 ha-1), and mean clump size (2.7 vs. 4.0 trees) compared to lower- and higher-elevation classes, while ridgetops had lower density (101 vs. 115 trees ha-1), basal area (19.6 vs. 24.1 m2 ha-1), and mean clump size (3.0 vs. 3.3 trees) but more open space (7.4% vs. 5.1%) than other landforms. In Chapter 2 I developed new methods for integrating lidar data into silvicultural planning at treatment unit and project area scales, with a focus on dry forest restoration treatments. At the stand scale my objective was to delineate the decision space for prescription parameters like density, basal area, and spatial patterns given the soft constraints of reference conditions and the hard constraints of possible transitions given current structure. At the landscape scale my objective was to provide a framework for selecting from available treatment options, stand by stand, to meet different landscape-level goals. I applied the new methods to a case study area in the Lake Tahoe Basin, California and asked in this context: How do structural departures from reference conditions and associated treatment prescriptions vary with topographic position and aspect? I found that ridges and southwest-facing slopes in the study area had a greater degree of departure from the reference envelope and required more density reduction compared to valleys and northeast-facing slopes. In Chapter 3 I used pre- and post-Rim Fire data from the 25.6 ha Yosemite Forest Dynamics Plot (YFPD) to build a model of tree mortality predicted from lidar individual tree detection structural metrics. I calculated metrics at the scale of lidar-detected trees (termed tree-approximate objects, TAOs), at the scale of 0.1 ha plots centered on each TAO, and at the 90×90 m neighborhood scale. I used these to predict TAO mortality at the neighborhood scale and TAO mortality class – immediate or delayed mortality – at the TAO scale. I also tested the inclusion of a set of topoedaphic and burn weather predictors as well as a cross-scale interaction term between the TAO mortality model and the neighborhood-level mortality model. I asked these questions: (1) How does mortality progress 1-4 years post-fire in terms of rates, demographics, and agents? (2) What elements of forest structure and pattern predict immediate and delayed post-fire mortality at scales from TAOs to neighborhoods? (3) How does the prevalence of different mortality agents vary with changes in the important fine-scale predictors of fire mortality? I found that smaller trees were killed in the first year with a 40% mortality rate and the average diameter of killed trees increased each subsequent year while the mortality rate decreased. The topoedaphic and burn weather predictors as well as the cross-scale interaction improved model fit and parsimony, but that the improvement was directional, i.e., including neighborhood-level information improved the TAO-level model but not vice-versa. Important predictors fell into categories of fuel amount, fuel configuration, and burning conditions. Amounts of crown damage for immediately killed trees were higher for TAOs shorter than 51 m and in 0.1 ha areas where mean clump sizes was less than 21 TAOs. The amount of delayed mortality that was directly fire-related was higher when TAO crown base heights were less than 28 m and TAO density in 0.1 ha areas was greater than 170 TAOs ha-1. Crown base heights over 18 m and local TAO density of less than 180 TAOs ha-1 had more beetle kill and less rot. The model performed similarly well on an independent validation dataset of 48 0.25 ha plots spanning the footprint of the Rim Fire within Yosemite as on the YFDP training data, indicating that the model is widely applicable.
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 Ecological Society of America ... Annual Meeting Abstracts by : Ecological Society of America. Meeting
Download or read book Ecological Society of America ... Annual Meeting Abstracts written by Ecological Society of America. Meeting and published by . This book was released on 2002 with total page 900 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Methods for 3-D Forest Characterization and Mapping from Lidar Remote Sensing Data by : Carlos Alberto Silva
Download or read book Advanced Methods for 3-D Forest Characterization and Mapping from Lidar Remote Sensing Data written by Carlos Alberto Silva and published by . This book was released on 2018 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate and spatially explicit measurements of forest attributes are critical for sustainable forest management and for ecological and environmental protection. Airborne Light Detection and Ranging (lidar) systems have become the dominant remote sensing technique for forest inventory, mainly because this technology can quickly provide highly accurate and spatially detailed information about forest attributes across entire landscapes. This dissertation is focused on developing and assessing novel and advanced methods for three dimensional (3-D) forest characterization. Specifically, I map canopy structural attributes of individual trees, as well as forests at the plot and landscape levels in both natural and industrial plantation forests using lidar remote sensing data. Chapter 1 develops a novel framework to automatically detect individual trees and evaluates the efficacy of k-nearest neighbor (k-NN) imputation models for estimating tree attributes in longleaf pine (Pinus palustris Mill.) forests. Although basal area estimation accuracy was poor because of the longleaf pine growth habit, individual tree locations, height and volume were estimated with high accuracy, especially in low-canopy-cover conditions. The root mean square distance (RMSD) for tree-level height, basal area, and volume were 2.96%, 58.62%, and 8.19%, respectively. Chapter 2 presents a methodology for predicting stem total and assortment volumes in industrial loblolly pine (Pinus taeda L.) forest plantations using lidar data as inputs to random forest models. When compared to reference forest inventory data, the accuracy of plot-level forest total and assortment volumes was high; the root mean square error (RMSE) of total, commercial and pulp volume estimates were 7.83%, 7.71% and 8.63%, respectively. Chapter 3 evaluates the impacts of airborne lidar pulse density on estimating aboveground biomass (AGB) stocks and changes in a selectively logged tropical forest. Estimates of AGB change at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg ̇ha−1 when pulse density decreased from 12 to 0.2 pulses ̇m−2. The effects of pulse density were more pronounced in areas of steep slope, but when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and subsequent AGB stocks and change estimates did not exceed 20 Mg ̇ha−1. Chapter 4 presents a comparison of airborne small-footprint (SF) and large-footprint (LF) lidar retrievals of ground elevation, vegetation height and biomass across a successional tropical forest gradient in central Gabon. The comparison of the two sensors shows that LF lidar waveforms are equivalent to simulated waveforms from SF lidar for retrieving ground elevation (RMSE=0.5 m, bias=0.29 m) and maximum forest height (RMSE=2.99 m; bias=0.24 m). Comparison of gridded LF lidar height with ground plots showed that an unbiased estimate of aboveground biomass at 1-ha can be achieved with a sufficient number of large footprints (> 3). Lastly, Appendix A presents an open source R package for airborne lidar visualization and processing for forestry applications.
Book Synopsis Using LiDAR to Estimate the Total Aboveground Live Biomass of Redwood Stands in South Fork Caspar Creek Watershed, Jackson Demonstration State Forest, Mendocino, California by : Hai Hong Vuong
Download or read book Using LiDAR to Estimate the Total Aboveground Live Biomass of Redwood Stands in South Fork Caspar Creek Watershed, Jackson Demonstration State Forest, Mendocino, California written by Hai Hong Vuong and published by . This book was released on 2014 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overall objective of this study is to develop a method for estimating total aboveground live (ABGL) biomass of redwood stands in South Fork Caspar Creek Watershed (SFCCW), Jackson Demonstration State Forest (JDSF), Mendocino, California using airborne LiDAR data. The study focused on two major species: redwood (Sequoia sempervirens or SESE) and Douglas-fir (Pseudotsuga menziesii or PSME). Specifically, the objective includes developing statistical models for tree diameter at breast height (DBH) on LiDAR-derived height for both species. From twenty-three 0.1-ha plots randomly selected within the study area, field measurements (DBH and tree coordinates) were collected for a total of 429 trees of SESE and PSME. Field measurements were taken for all trees having DBH equal to or greater than 25.4cm. In case of LiDAR-derived tree the height, a minimum height of 15m was used for this study. Software programs TreeVaW and FUSION/LDV were used to develop Canopy Height Models (CHM), from which tree heights were extracted. Based on LiDAR-derived height and ground-based DBH, linear regression models were developed. The linear regression models explained 62.65% of the total variation associated with redwood's DBH and 82.58% of Douglas fir's DBH. The predicted DBH was used to estimate the ABGL biomass using Jenkins' formula (Jenkins et al., 2003A). At a single tree level, the average ABGL biomass of 257 SESE trees using predicted DBH was underestimated by about 10.1% compared with that of ABGL biomass using the ground-based DBH. The average ABGL biomass of 172 PSME trees using predicted DBH was underestimated by about 8.0% compared with that of using ground-based DBH. For both species, there was a statistically significant difference in the mean ABGL-biomass between using predicted DBH and ground-based DBH. In case of the twelve randomly-sampled plots, biomass estimates for both species on the rough terrain ( ≥ 15% slope) were significantly lower and more varied than those on the flat terrain (
