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Optimum Field Plot Allocation For The Calibration Of Forest Inventory Predictions From Airborne Light Detection And Ranging Lidar Data
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Book Synopsis Optimum Field Plot Allocation for the Calibration of Forest Inventory Predictions from Airborne Light Detection and Ranging (LiDAR) Data by : Neil Graham
Download or read book Optimum Field Plot Allocation for the Calibration of Forest Inventory Predictions from Airborne Light Detection and Ranging (LiDAR) Data written by Neil Graham and published by . This book was released on 2015 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In forest management a variety of social, economic and ecological values are promoted through decision making and with the help of forest inventories. Forest inventory attributes are increasingly being predicted through active remote sensing technologies such as airborne laser scanning (ALS). While the ability of ALS technologies to predict forest inventory attributes has been well demonstrated, the optimal method for the allocation of field plots for the calibration of forest inventory predictions from ALS data has yet to be determined. The potential of principal component analysis (PCA) of the LiDAR point-cloud statistics to efficiently guide the allocation of a calibration sample, was investigated, using a conventionally allocated calibration sample as a benchmark."-- from abstract.
Book Synopsis A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using the Area-Based Approach by : Joanne White
Download or read book A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using the Area-Based Approach written by Joanne White and published by . This book was released on 2013-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparison of Low-cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning Across Multiple Forest Types in California by : James E. Lamping
Download or read book Comparison of Low-cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning Across Multiple Forest Types in California written by James E. Lamping and published by . This book was released on 2021 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Science-based forest management requires quantitative information about forest attributes traditionally collected via sampled field plots in a forest inventory program. Remote sensing tools, such as active three-dimensional (3D) Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurement, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this research was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models were comparable to lidar across sites and nadir vs. off-nadir imagery collection, although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest. Surface and canopy height models were shown to have less agreement to lidar, with high canopy density sites captured with off-nadir imagery showing the lowest amounts of agreement. UAS DAP models accurately predicted key forest metrics when compared to field data and were comparable to predictions made by lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions throughout California.
Book Synopsis Development of a Methodology for Predicting Forest Area for Large-area Resource Monitoring by : William H. Cooke
Download or read book Development of a Methodology for Predicting Forest Area for Large-area Resource Monitoring written by William H. Cooke and published by . This book was released on 2001 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a remote-sensing team to develop an image-processing methodology for mapping forest lands over large geographic areds. The team has presented a repeatable methodology, which is based on regression modeling of Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) data. It is a methodology that Forest inventory and Analysis (FIA) survey personnel can implement in any region or area. The term repeatable implies objectivity. Studies in the conterminous United States, Central America and Mexico, and west Texas and Oklahoma have provided valuable insights that address the subjective nature of some of the steps taken in mapping large forest areas. The team has identified seven such steps. They have reduced or eliminated subjectivity in four of the steps and identified two steps in which objectivity can be enhanced.
Book Synopsis Assessing the Effects of Field Plot Size and Stand Structure on Forest Inventory Estimates Derived from Laser Altimetry Data by : Jody Paul Bramel
Download or read book Assessing the Effects of Field Plot Size and Stand Structure on Forest Inventory Estimates Derived from Laser Altimetry Data written by Jody Paul Bramel and published by . This book was released on 2011 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prior research has proven the utility of using lidar and field data in a two-stage procedure to predict forest inventory parameters. However, the effects of varying plot size on the prediction errors are not well understood. We investigated the effects of plot size on prediction errors using lidar data for a western Montana forest using five sizes derived from stem-mapped field data and multiple regression modeling techniques. Models were fitted for maximum and mean heights, stand basal area, stem density, and quadratic mean diameter. A validation routine was performed using an independent dataset and models derived from different plot sizes were assessed using goodness of fit, validation root mean squared error (RMSE), mean error, mean absolute error, and the modeling efficiency statistic. Although trends in model quality varied by inventory parameter, there seemed to be some advantages in using plots greater than 300 m2 in size, as these plots tended to produce models with higher goodness of fit, better modeling efficiency, and lower mean absolute error values. Effects of canopy cover were also examined and showed little effect of varying plot size on low cover plots but potential benefit in using larger plots in high cover areas. The results related to stand structure were contrary to those reported in other studies, but were not surprising based on the complex, multi-story conditions on high cover stands found in the western Montana study area. Finally, the 'scalability' of models was explored by fitting a model across the range of training plot sizes and validating it using the middle (300 m2) size in this study. Larger plots tended to be adaptable as they performed well in predicting forest inventory parameters at different scales.
Book Synopsis Improving Forest Inventory Plot Registration Precision Using Field And Lidar Data by : Adam Erickson
Download or read book Improving Forest Inventory Plot Registration Precision Using Field And Lidar Data written by Adam Erickson and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Precise registration of forest inventory plots is a prerequisite for optimal integration of field measurements with high-resolution remotely sensed data, including ALS and very high resolution (VHR) satellite imagery. Plot positional uncertainty propagates through statistical modeling procedures and, ultimately, reduces the utility of obtained wall-to-wall inventory maps. In most national forest inventory systems, plot registration is obtained using recreational-grade, low-precisionGPS devices. However, even industrial-grade devices yield low-precision coordinates in unfavorable conditions for GPS operation, including steep slopes, high canopy cover, and multilayered vegetation. We introduce a fully automated procedure relying on individual-tree height and position relative to the plot center, as recorded in co-located field and LiDAR data, to improve field data registration accuracy and precision. Results are furnished with an estimate of confidence in the optima obtained. Performance is evaluated using a sample of circular inventory plots stratified across classes of canopy cover and tree height in Oregon, USA.
Book Synopsis Assessment of Airborne Light Detection and Ranging (LiDAR) for Use in Common Forest Engineering Geomatic Applications by : Michael B. Craven
Download or read book Assessment of Airborne Light Detection and Ranging (LiDAR) for Use in Common Forest Engineering Geomatic Applications written by Michael B. Craven and published by . This book was released on 2011 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne Light Detection and Ranging (LiDAR) has become a popular remote sensing technology to create digital terrain models and provide forest inventory information. However, little research has been done to investigate the accuracy of using airborne LiDAR to perform measurement tasks common to Forest Engineering. This thesis contains two manuscripts investigating different measurement scenarios. The first manuscript examines the use of airborne LiDAR to measure existing forest roads in support of a road assessment under four different canopy conditions. It was found that along existing centerlines the LiDAR data had a vertical RMSE of 0.28 m and a horizontal RMSE of 1.21 m. Road grades were estimated to within 1% slope of the value measured in the field and horizontal curve radii were estimated with an average absolute error of 3.17 m. The results suggest that airborne LiDAR is an acceptable method to measure forest road grade, but some caution should be used in measuring horizontal curve radii, particularly on sharp curves. The second manuscript compares profile corridor measurements using airborne LiDAR-derived elevations across different forest canopy types and terrain slopes ranging from 37 to 49%. Both LiDAR-derived DEM and raw LiDAR point elevations were compared to field data. The DEM elevations had an average RMSE error of 0.43 m across all canopy types compared to the field data, while the nearest LiDAR point had an average RMSE of 0.49 m compared to the field data. A skyline payload analysis suggested that profiles based on the DEM outperformed profiles based on nearest point elevations by 5% on average when compared to the field measured profiles. Results suggest that a forest engineer should consider using the DEM value rather than the nearest LiDAR point elevation for terrain elevations at discrete locations, particularly when forest canopy occludes locations of interest.
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 Estimating Forest Structural Characteristics with Airborne Lidar Scanning and a Near-real Time Profiling Laser Systems by : Kaiguang Zhao
Download or read book Estimating Forest Structural Characteristics with Airborne Lidar Scanning and a Near-real Time Profiling Laser Systems written by Kaiguang Zhao and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR (Light Detection and Ranging) directly measures canopy vertical structures, and provides an effective remote sensing solution to accurate and spatiallyexplicit mapping of forest characteristics, such as canopy height and Leaf Area Index. However, many factors, such as large data volume and high costs for data acquisition, precludes the operational and practical use of most currently available LiDARs for frequent and large-scale mapping. At the same time, a growing need is arising for realtime remote sensing platforms, e.g., to provide timely information for urgent applications. This study aims to develop an airborne profiling LiDAR system, featured with on-the-fly data processing, for near real- or real- time forest inventory. The development of such a system involves implementing the on-board data processing and analysis as well as building useful regression-based models to relate LiDAR measurements with forest biophysical parameters. This work established a paradigm for an on-the-fly airborne profiling LiDAR system to inventory regional forest resources in real- or near real- time. The system was developed based on an existing portable airborne laser system (PALS) that has been previously assembled at NASA by Dr. Ross Nelson. Key issues in automating PALS as an on-the-fly system were addressed, including the design of an archetype for the system workflow, the development of efficient and robust algorithms for automatic data processing and analysis, the development of effective regression models to predict forest biophysical parameters from LiDAR measurements, and the implementation of an integrated software package to incorporate all the above development. This work exploited the untouched potential of airborne laser profilers for realtime forest inventory, and therefore, documented an initial step toward developing airborne-laser-based, on-the-fly, real-time, forest inventory systems. Results from this work demonstrated the utility and effectiveness of airborne scanning or profiling laser systems for remotely measuring various forest structural attributes at a range of scales, i.e., from individual tree, plot, stand and up to regional levels. The system not only provides a regional assessment tool, one that can be used to repeatedly, remotely measure hundreds or thousands of square kilometers with little/no analyst interaction or interpretation, but also serves as a paradigm for future efforts in building more advanced airborne laser systems such as real-time laser scanners.
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.
Book Synopsis Annual Design-based Estimation for the Annualized Inventories of Forest Inventory and Analysis by : Hans T. Schreuder
Download or read book Annual Design-based Estimation for the Annualized Inventories of Forest Inventory and Analysis written by Hans T. Schreuder and published by . This book was released on 2000 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Integration of GIS and LiDAR by : Yang Chen
Download or read book Integration of GIS and LiDAR written by Yang Chen and published by . This book was released on 2012 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest management is the management of private or public forest resources to achieve their conservation, social services, and economic values, concerned with the administrative, economic, legal and social aspects. All decision-making, operations-scheduling, and policy-planning require information of high quality. In forest management, this information is acquired by means of forest inventory: the systematic collection of data and information derived from forest measurements. A forest inventory is not only used for estimating the current growing stock, also conducted at several points of time in order to analyse temporal changes and yield forecasting. When conducting a forest inventory several forest parameters need to be taken into account, including individual tree heights, site quality, diameter at breast height, basal area, stocking, and timber volume. The main purpose of forest inventory is to measure these forest characteristics for estimating means and totals of timber products and planning harvest over a defined area (Kangas and Maltamo, 2006). However, it is infeasible to measure all individual trees (whole forest) in a large-scale region; therefore the acquisition of forest attributes is based on sampling. Typically, forest inventory is usually implemented by measuring the sample plots in the field, a proportion of the whole population of trees, to estimate the extent, quantity and condition of the whole forest. Thus, forest inventory in a large-scale plantation based on sampling involves time consuming and labour intensive field data collection. The development of remote sensing techniques makes it possible to conduct large-scale forest surveys with three-dimensional information at various scales from the forest stand level to individual tree level. Particularly, LiDAR (Light Detection and Ranging), an active remote sensing technique, emerges as rapid and efficient tool for forest inventories. It offers the ability to measure forest attributes at the individual tree level. This thesis aims to explore the potential of LiDAR data for automated forest inventory estimates. An integrated GIS tool was developed for constructing a forest inventory system for Pinus radiata plantations in Victoria, Australia. The tool was built as a set of tools running on the desktop GIS software package ArcGIS by integrating spatial analysis, LiDAR data analysis and image segmentation techniques as well as empirical tree models to support forest inventories of Pinus radiata on an individual tree basis. It provides functions for selecting forest plots to extract LiDAR data, building canopy height models (CHM) from the extracted LiDAR data, delineating individual trees on the CHMs by applying the marker controlled watershed segmentation technique, and deriving forest inventory estimates based on the CHMs and identified individual trees through spatial analysis and tree modelling using the empirical models. The integrated GIS tool was applied to a forest inventory of Pinus radiata plantations in Mt. Worth, Victoria, managed by HVP Pty Limited. The inventory results were validated using the field survey data. The tool not only provides a practical means of forest inventory of Pinus radiata plantations in southern Australia, but also a new approach to the development of a fully automated forest inventory system through the integration of advanced GIS and LiDAR technology.
Book Synopsis A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using the Area-based Approach (Version 2.0) by : Joanne White
Download or read book A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using the Area-based Approach (Version 2.0) written by Joanne White and published by . This book was released on 2013 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of an Imputation Method for Geospatial Inventory of Forest Structural Attributes Across Multiple Spatial Scales in the Lake States, U.S.A by : Ram K. Deo
Download or read book Application of an Imputation Method for Geospatial Inventory of Forest Structural Attributes Across Multiple Spatial Scales in the Lake States, U.S.A written by Ram K. Deo and published by . This book was released on 2014 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.
Book Synopsis Comparison of a Fully Mapped Plot Design to Three Alternative Designs for Volume and Area Estimates Using Maine Inventory Data by : Stanford L. Arner
Download or read book Comparison of a Fully Mapped Plot Design to Three Alternative Designs for Volume and Area Estimates Using Maine Inventory Data written by Stanford L. Arner and published by . This book was released on 1998 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling Plot-level Biomass and Volume Using Airborne and Terrestrial Lidar Measurements by : Ryan D. Sheridan
Download or read book Modeling Plot-level Biomass and Volume Using Airborne and Terrestrial Lidar Measurements written by Ryan D. Sheridan and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The United States Forest Service (USFS) Forest Inventory and Analysis (FIA) program provides a diverse selection of data used to assess the status of the nation̕ s forested areas using sample locations dispersed throughout the country. Airborne, and more recently, terrestrial lidar (light detection and ranging) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize three-dimensional vertical forest structure. This study investigates the potential of airborne and terrestrial scanning lidar systems for modeling forest volume and aboveground biomass on FIA subplots in the Malheur National Forest, eastern Oregon. A methodology for the creation of five airborne lidar metric sets (four point cloud-based and one individual tree based) and four terrestrial lidar metric sets (three height-based and one distance-based) is presented. Metrics were compared to estimates of subplot aboveground biomass and gross volume derived from FIA data using national and regional allometric equations respectively. Simple linear regression models from the airborne lidar data accounted for 15 percent of the variability in subplot biomass and 14 percent of the variability in subplot volume, while multiple linear regression models increased these amounts to 29 percent and 25 percent, respectively. When subplot estimates of biophysical parameters were scaled to the plot-level and compared with plot-level lidar metrics, simple linear regression models were able to account for 60 percent of the variability in biomass and 71 percent of the variation in volume. Terrestrial lidar metrics produced moderate results with simple linear regression models accounting for 41 percent of the variability in biomass and 46 percent of the variability in volume, with multiple linear regression models accounting for 71 percent and 84 percent, respectively. Results show that: (1) larger plot sizes help to mitigate errors and produce better models; and (2) a combination of height-based and distance-based terrestrial lidar metrics has the potential to estimate biomass and volume on FIA subplots.
Book Synopsis Integrating Remotely Sensed Data Into Forest Resource Inventories by :
Download or read book Integrating Remotely Sensed Data Into Forest Resource Inventories written by and published by . This book was released on 2014 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past two decades have demonstrated a great potential for airborne Light Detection and Ranging (LiDAR) data to improve the efficiency of forest resource inventories (FRIs). In order to make efficient use of LiDAR data in FRIs, the data need to be related to observations taken in the field. Various modeling techniques are available that enable a data analyst to establish a link between the two data sources. While the choice for a modeling technique may have negligible effects on point estimates, different model techniques may deliver different estimates of precision. This study investigat...
