Evaluating Forest Volume Estimation at Barksdale Air Force Base Using Lidar and Multispectral Imagery

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ISBN 13 :
Total Pages : 168 pages
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Book Synopsis Evaluating Forest Volume Estimation at Barksdale Air Force Base Using Lidar and Multispectral Imagery by : Richard Edward Brooks

Download or read book Evaluating Forest Volume Estimation at Barksdale Air Force Base Using Lidar and Multispectral Imagery written by Richard Edward Brooks and published by . This book was released on 2009 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating timber volume from a field perspective, with field plots occasionally falling within remote and inaccessible areas, can be a costly and timely endeavor. Remote sensing, with its ability to record information at both the local and regional scale, offers an alternative to traditional field based measurements. Studies have shown that remotely sensed vegetation biomass indices (e.g. NDVI) derived from mid-spatial resolution digital imagery (e.g. Landsat TM, 30-meters), after being corrected for atmospheric effects, topographical differences and shadow, were highly correlated with timber volume. With the recent advent of high spatial resolution digital imagery from the IKONOS and QuickBird satellites providing more textural information about a forest canopy, with spatial resolutions of 4 meters and 2.44 meters for multispectral data respectively, the opportunity to assess forest volume from a distance at a much finer scale has increased. Additionally Lidar (light detection and ranging) is a relatively new remote sensing technology that can accurately estimate the 3-dimensional structure of forest vegetation which is related to forest volume and offers an alternative to ground based measurement of remotely sensed multispectral data.

Evaluation of LiDAR Derived Estimates of Forest Measurement

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ISBN 13 :
Total Pages : 176 pages
Book Rating : 4.:/5 (858 download)

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Book Synopsis Evaluation of LiDAR Derived Estimates of Forest Measurement by : John Chapman

Download or read book Evaluation of LiDAR Derived Estimates of Forest Measurement written by John Chapman and published by . This book was released on 2010 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent advances in Light Detection and Ranging (LiDAR) have allowed for the remote sensing of important forest characteristics to be more reliable and commercially available. The purpose of this study was to provide some insight into the current capability of some commercially available software programs in obtaining bio-physical properties of forests through LiDAR remote sensing. The study assessed the accuracy of LiDAR-derived estimates of forest characteristics including tree crown radius, height, and timber volume against conventional methods of estimation using field-measured samples. The software programs compared in this study are TiFFS (Toolbox for LiDAR Data Filtering and Forest Studies), TreeVaW (Tree Variable Window), and LiDAR Analyst 4.2. Three methods of LiDAR Analyst were compared due to the number of parameter associated with the program. TreeVaW, though not developed as a commercial program, performed the overall best with root mean square error (RMSE) being 12.97 (64.5% of the field mean) for tree count per plot, 5.43 meter (26.5%) for tree height, 1.31 meter (40.7%) for crown radius, 2.71 inch (20.9%) for DBH, and 104.92 cubic foot per acre (65.2%) for timber volume. However, TreeVaW requires the input dataset being a canopy height model in ENVI raster format that has to be processed from raw LiDAR data using other programs beforehand. TiFFS performed with the least accuracy due to its overestimation on tree count. That resulted in a RMSE of 32.36 (161%) for tree count per plot, 5.45 meter (26.5%) for tree height, 1.87 meter (58.2%) for crown radius, 2.74 inch (21.1%) for DBH, and 372.04 cubic foot per acre (231.2%) for timber volume. Even though TiFFS achieved an unsatisfactory accuracy, it had higher correlations between the field-measured and LiDAR-derived data than other programs, with the correlation coefficient (r) at 0.8228 and 0.7076 for mean tree height and timber volume per plot, respectively. If allowing for calibration with training data, TiFFS would be a valuable LiDAR data processing program with its low cost and ease of use. LiDAR Analyst was able to estimate not only crown radius but also DBH. However, its performance is unreliable due to its inability to generate an accurate bare-earth surface in a highly forested area. In turn, this program detected much fewer trees than what were in the field. Even though it allows for user defined parameter input, the outcomes are inconsistent.

Quantifying the Urban Forest Environment Using Dense Discrete Return LiDAR and Aerial Color Imagery for Segmentation and Object-level Biomass Assessment

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ISBN 13 :
Total Pages : 356 pages
Book Rating : 4.:/5 (914 download)

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Book Synopsis Quantifying the Urban Forest Environment Using Dense Discrete Return LiDAR and Aerial Color Imagery for Segmentation and Object-level Biomass Assessment by : Madhurima Bandyopadhyay

Download or read book Quantifying the Urban Forest Environment Using Dense Discrete Return LiDAR and Aerial Color Imagery for Segmentation and Object-level Biomass Assessment written by Madhurima Bandyopadhyay and published by . This book was released on 2015 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management."--Abstract.

Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada

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ISBN 13 :
Total Pages : 284 pages
Book Rating : 4.:/5 (15 download)

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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.

Using LiDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover at Varying Scales

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ISBN 13 :
Total Pages : pages
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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.

Estimation and Modeling of Selected Forest Metrics with Lidar and Landsat

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ISBN 13 :
Total Pages : 145 pages
Book Rating : 4.:/5 (796 download)

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Book Synopsis Estimation and Modeling of Selected Forest Metrics with Lidar and Landsat by : Jacob L. Strunk

Download or read book Estimation and Modeling of Selected Forest Metrics with Lidar and Landsat written by Jacob L. Strunk and published by . This book was released on 2012 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lidar is able to provide height and cover information which can be used to estimate selected forest attributes precisely. However, for users to evaluate whether the additional cost and complication associated with using Lidar merits adoption requires that the protocol to use lidar be thoroughly described and that a basis for selection of design parameters such as number of field plots and lidar pulse density be described. In our first analysis, we examine these issues by looking at the effects of pulse density and sample size on estimation when wall-to-wall lidar is used with a regression estimator. The effects were explored using resampling simulations. We examine both the effects on precision, and on the validity of inference. Pulse density had almost no effect on precision for the range examined, from 3 to .0625 pulses / m2. The effect of sample size on estimator precision was roughly in accordance with the behavior indicated by the variance estimator, except that for small samples the variance estimator had positive bias (the variance estimates were too small), compromising the validity of inference. In future analyses we plan to provide further context for wall-to-wall lidar-assisted estimation. While there is a lot of literature on modeling, there is limited information on how lidar-assisted approaches compare to existing methods, and what variables can or cannot be acquired, or may be acquired with reduced confidence. We expand our investigation of estimation in our second analysis by examining lidar obtained in a sampling mode in combination with Landsat. In this case we make inference about the feasibility of a lidar-assisted estimation strategy by contrasting its variance estimate with variance estimates from a variety of other sampling designs and estimators. Of key interest was how the precision of a two-stage estimator with lidar strips compared with a plot-only estimator from a simple random sampling design. We found that because the long and narrow lidar strips incorporate much of the landscape variability, if the number of lidar strips was increased from 7 to 15 strips, the precision of estimators with lidar can exceed that of estimators applied to plot-only SRS data for a much larger number of plots. Increasing the number of lidar strips is considered to be highly viable since the costs of field plots can be quite expensive in Alaska, often exceeding the cost of a lidar strip. A Landsat-assisted approach used for either an SRS or a two-stage sample was also found to perform well relative to estimators for plot-only SRS data. This proved beneficial when we combined lidar and Landsat-assisted regression estimators for two-stage designs using a composite estimator. The composite estimator yielded much better results than either estimator used alone. We did not assess the effects of changing the number of lidar strips in combination with using a composite estimator, but this is an important analysis we plan to perform in a future study. In our final analysis we leverage the synergy between lidar and Landsat to improve the explanatory power of auxiliary Landsat using a multilevel modeling strategy. We also incorporate a more sophisticated approach to processing Landsat which reflects temporal trends in individual pixels values. Our approach used lidar as an intermediary step to better match the spatial resolution of Landsat and increase the proportion of area overlapped between measurement units for the different sources of data. We developed two separate approaches for two different resolutions of data (30 m and 90 m) using multiple modeling alternatives including OLS and k nearest neighbors (KNN), and found that both resolution and the modeling approach affected estimates of residual variability, although there was no combination of model types which was a clear winner for all responses. The modeling strategies generally fared better for the 90 m approaches, and future analyses will examine a broader range of resolutions. Fortunately the approaches used are fairly flexible and there is nothing prohibiting a 1000 m implementation. In the future we also plan to look at using a more sophisticated Landsat time-series approach. The current approach essentially dampened the noise in the temporal trend for a pixel, but did not make use of information in the trend such as slope or indications of disturbance - which may provide additional explanatory power. In a future study we will also incorporate a multilevel modeling into estimation or mapping strategies and evaluate the contribution of the multilevel modeling strategy relative to alternate approaches.

Estimation of Forest Variables Using Satellite Image Data and Airborne Lidar

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ISBN 13 : 9789157653017
Total Pages : 31 pages
Book Rating : 4.6/5 (53 download)

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Book Synopsis Estimation of Forest Variables Using Satellite Image Data and Airborne Lidar by : Mats Nilsson

Download or read book Estimation of Forest Variables Using Satellite Image Data and Airborne Lidar written by Mats Nilsson and published by . This book was released on 1997 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings of Forest Resource Inventory, Growth Models, Management Planning, and Remote Sensing

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ISBN 13 :
Total Pages : 486 pages
Book Rating : 4.:/5 (319 download)

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Book Synopsis Proceedings of Forest Resource Inventory, Growth Models, Management Planning, and Remote Sensing by : International Union of Forestry Research Organizations

Download or read book Proceedings of Forest Resource Inventory, Growth Models, Management Planning, and Remote Sensing written by International Union of Forestry Research Organizations and published by . This book was released on 1981 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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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ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (833 download)

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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:

Assessing Indicators of Forest Sustainability Using Lidar Remote Sensing

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (68 download)

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Book Synopsis Assessing Indicators of Forest Sustainability Using Lidar Remote Sensing by :

Download or read book Assessing Indicators of Forest Sustainability Using Lidar Remote Sensing written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Province of British Columbia is developing a suite of attributes to assess and monitor forest sustainability. Each attribute is in turn evaluated using a variety of indicators. Recently, digital remote sensing technologies have emerged as both alternative and supplement to traditional monitoring techniques, with light detection and ranging (lidar) in particular showing great promise for estimating a variety of indicators. The goal of this thesis was to review and assess the ability of lidar to estimate selected indicators of forest sustainability. Specifically, digital elevation model (DEM) interpolation (from which indicators are extracted both directly and indirectly) and wildlife tree class distributions were examined. Digital elevation models are a key derivative of lidar data, and their generation is a critical step in the data processing stream. A validation exercise was undertaken to determine which combination of interpolation routine and spatial resolution was the most accurate. Ground returns were randomly subsetted into prediction and validation datasets. Linear, quintic, natural neighbour, spline with tension, regularized spline, inverse distance weighting, and ANUDEM interpolation routines were used to generate surfaces at spatial resolutions of 0.5, 1.0, and 1.5 m. The 0.5 m natural neighbour surface was found to be the most accurate (RMSE=0.17 m). Classification and regression tree analysis indicated that slope and ground return density were the best predictors of interpolation error. The amount and variability of living and dead wood in a forest stand is an important indicator of forest biodiversity. In the second study, the capacity of lidar to estimate the distribution of living and dead trees within forests is investigated. Twenty-two field plots were established in which each stem (DBH>10cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal logistic regression was t.

On the Use of Rapid-scan, Low Point Density Terrestrial Laser Scanning (TLS) for Structural Assessment of Complex Forest Environments

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ISBN 13 :
Total Pages : 93 pages
Book Rating : 4.:/5 (118 download)

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Book Synopsis On the Use of Rapid-scan, Low Point Density Terrestrial Laser Scanning (TLS) for Structural Assessment of Complex Forest Environments by : Ali Rouzbeh Kargar

Download or read book On the Use of Rapid-scan, Low Point Density Terrestrial Laser Scanning (TLS) for Structural Assessment of Complex Forest Environments written by Ali Rouzbeh Kargar and published by . This book was released on 2020 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS."--Abstract.

Advanced Methods for 3-D Forest Characterization and Mapping from Lidar Remote Sensing Data

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ISBN 13 : 9780438392953
Total Pages : 296 pages
Book Rating : 4.3/5 (929 download)

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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.

Remote Sensing of Forest Biomass Dynamics Using Landsat-derived Disturbance and Recovery History and Lidar Data

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Publisher :
ISBN 13 :
Total Pages : 182 pages
Book Rating : 4.:/5 (764 download)

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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.

Evaluating High-resolution Imagery and LiDar for Mapping Structures in the Wildland-urban Interface

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Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Evaluating High-resolution Imagery and LiDar for Mapping Structures in the Wildland-urban Interface by :

Download or read book Evaluating High-resolution Imagery and LiDar for Mapping Structures in the Wildland-urban Interface written by and published by . This book was released on 2008 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project tested remote sensing tools for locating man-made structures in the wildland-urban interface (WUI). Creating fire-suppression plans and responding to wildland fires become much easier when information on the location of critical structures is available. High-resolution digital imagery and lidar data were tested in two study areas. Vegetation ranged from sagebrush and scrub in Oregon to dense forest canopy in Montana. The tools were semi-automated, meaning that users had to interact with the data sets to interpret the structures. The digital imagery used in the study was 1-meter natural-color photography from the National Aerial Imagery Program (NAIP). The multireturn lidar data had 4 returns and a density of 1.7 points per square meter. The tests were completed in the following manner: 1) In the Oregon study area, Feature Analyst, a semi-automated feature-extraction software package developed by Visual Learning Systems (VLS) mapped structures using 1-meter natural-color imagery. The area is sagebrush-dominated urban wildland containing both dense subdivisions and dispersed buildings. 2) In the Montana study area, a lidar feature-extraction software package, VLSs LIDAR Analyst, mapped structures in a dense forest containing dispersed cabins. An independent accuracy assessment was completed for both study sites by comparing the results with manual image interpretation. Although the results for feature extraction using NAIP imagery were promising, roughly 50 percent of the structures were missed (omission error) and an additional 50 percent were wrongly delineated (commission error) in the Oregon study. In the heavily forested area in Montana, LIDAR Analysts analysis of the multireturn lidar data was mediocre because structures were confused with understory forest canopy. Based on these case studies, it appears that feature extraction using high-resolution imagery or multireturn lidar data is only partially effective. We recommend using heads-up image interpretation and manual digitizing for more accurate and timely results.

Deep Maneuver

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Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781727846430
Total Pages : 266 pages
Book Rating : 4.8/5 (464 download)

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Book Synopsis Deep Maneuver by : Jack D Kern Editor

Download or read book Deep Maneuver written by Jack D Kern Editor and published by Createspace Independent Publishing Platform. This book was released on 2018-10-12 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 5, Deep Maneuver: Historical Case Studies of Maneuver in Large-Scale Combat Operations, presents eleven case studies from World War II through Operation Iraqi Freedom focusing on deep maneuver in terms of time, space and purpose. Deep operations require boldness and audacity, and yet carry an element of risk of overextension - especially in light of the independent factors of geography and weather that are ever-present. As a result, the case studies address not only successes, but also failure and shortfalls that result when conducting deep operations. The final two chapters address these considerations for future Deep Maneuver.

Air Force Handbook 1

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ISBN 13 : 9781387952380
Total Pages : 582 pages
Book Rating : 4.9/5 (523 download)

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Book Synopsis Air Force Handbook 1 by : U. S. Air Force

Download or read book Air Force Handbook 1 written by U. S. Air Force and published by . This book was released on 2018-07-17 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook implements AFPD 36-22, Air Force Military Training. Information in this handbook is primarily from Air Force publications and contains a compilation of policies, procedures, and standards that guide Airmen's actions within the Profession of Arms. This handbook applies to the Regular Air Force, Air Force Reserve and Air National Guard. This handbook contains the basic information Airmen need to understand the professionalism required within the Profession of Arms. Attachment 1 contains references and supporting information used in this publication. This handbook is the sole source reference for the development of study guides to support the enlisted promotion system. Enlisted Airmen will use these study guide to prepare for their Promotion Fitness Examination (PFE) or United States Air Force Supervisory Examination (USAFSE).

AU-18 Space Primer

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Publisher : CreateSpace
ISBN 13 : 9781478393559
Total Pages : 354 pages
Book Rating : 4.3/5 (935 download)

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Book Synopsis AU-18 Space Primer by : Air Command Staff College

Download or read book AU-18 Space Primer written by Air Command Staff College and published by CreateSpace. This book was released on 2012-08-01 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: The US National Space Policy released by the president in 2006 states that the US government should "develop space professionals." As an integral part of that endeavor, "AU-18, Space Primer", provides to the joint war fighter an unclassified resource for understanding the capabilities, organizations, and operations of space forces. This primer is a useful tool both for individuals who are not "space aware"-unacquainted with space capabilities, organizations, and operations-and for those who are "space aware," especially individuals associated with the space community, but not familiar with space capabilities, organizations, and operations outside their particular areas of expertise. It is your guide and your invitation to all the excitement and opportunity of space. Last published in 1993, this updated version of the Space Primer has been made possible by combined efforts of the Air Command and Staff College's academic year 2008 "Jointspacemindedness" and "Operational Space" research seminars, as well as select members of the academic year 2009 "Advanced Space" research seminar. Air university Press.