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

Author: Qin Ma
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ISBN:
Category :
Languages : en
Pages : 284

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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.

Author: Marek K. Jakubowski
Publisher:
ISBN:
Category :
Languages : en
Pages : 97

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California's fire suppression policy has dramatically changed Sierra Nevada forests over the last century. Forests are becoming more dense and homogenous, leading to fire regime changes that increase the potential of stand-replacing wildfires over large, continuous areas. To mitigate this problem on public lands, the US Forest Service has proposed to implement strategically placed forest fuel reduction treatments. These treatments have been proved effective in modeled and simulated environments, but their efficacy and impact in real forests is not known. The research described in this dissertation is part of a large multidisciplinary project, known as the Sierra Nevada Adaptive Management Project (SNAMP), that aims to evaluate strategically placed landscape area treatments (SPLATs) in two forests of the Sierra Nevada mountains. Specifically, in this thesis, I investigate the feasibility of using an airborne light detection and ranging (lidar) system to gain accurate information about forest structure to inform wildfire behavior models, forest management, and habitat mapping. First, I investigate the use of lidar data in predicting metrics at the landscape level, specifically to derive surface fuel models and continuous canopy metrics at the plot scale. My results in Chapter 2 indicate that using lidar to predict specific fuel models for FARSITE wildfire behavior model is challenging. However, the prediction of more general fuel models and continuous canopy metrics is feasible and reliable, especially for metrics near the top of the canopy. It is also possible to derive canopy parameters at the individual tree level. In Chapter 3, I compare the ability of two processing methods--object-based image analysis (OBIA) and 3D segmentation of the lidar point cloud--to detect and delineate individual trees. I find that while both methods delineate dominant trees and accurately predict their heights, the lidar-derived polygons more closely resemble the shape of realistic individual tree crowns. Acquiring remotely sensed data at high resolution and over large areas can be expensive, especially in the case of lidar. In Chapter 4, I investigate the ability of lidar data to reliably predict forest canopy metrics at the plot level as the data resolution declines. I show that canopy metrics can be predicted at a reasonable accuracy with data resolutions as low as one pulse per squared meter. These findings will be useful to land managers making cost benefit decisions when acquiring new lidar data. Collectively, the results of this dissertation suggest that remote sensing, and in particular lidar, can reliably and cost-effectively provide forest information across scales--from the individual tree level to the landscape level. These results will be useful for the fire and forest management community in general, as well as being key to the goals of the SNAMP program.

Author: Sean Medeiros Alexander Jeronimo
Publisher:
ISBN:
Category :
Languages : en
Pages : 308

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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.

Author: Kenneth S. Blonski
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ISBN:
Category : Forest litter
Languages : en
Pages : 222

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Author: Kurt Martin Menning
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Category : Fire ecology
Languages : en
Pages : 476

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During a century of fire suppression forest may have become more homogeneous. In many western forests fire is being reintroduced yet the effects are not well understood. The best method of reintroducing fire to forested ecosystems has been debated. To assess the effects of disturbance on structure we must quantify heterogeneity in the forest before and after disturbance. I explored means to assess heterogeneity and measured it in the mixed conifer forest and in the litter base that vectors fire. I present a new statistical metric for describing the distribution of forest structure. The metric can be used to assess forest structural heterogeneity at different spatial scales. Ground and surface fuels are essential for vectoring a contagious disturbance like fire. Fire models often assume uniform fuel characteristics for a forest community. I explored how litter bulk density could vary by dominant tree species. I modeled how variability in litter bulk density could affect fire behavior and found differences in fireline intensity and rate of spread among different dominant tree species.--Adapted from abstract.

Author: Madhurima Bandyopadhyay
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ISBN:
Category : Optical radar
Languages : en
Pages : 356

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"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.

Author:
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Category :
Languages : en
Pages :

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Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO2) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across H"0,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of>70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems.

Author: Valerie Rapp
Publisher:
ISBN:
Category : Forests and forestry
Languages : en
Pages : 12

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Author: Jim Bouldin
Publisher:
ISBN:
Category :
Languages : en
Pages : 460

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Author: Alexandra N. Kazakova
Publisher:
ISBN:
Category : Forest canopies
Languages : en
Pages : 61

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Stand level spatial distribution is a fundamental part of forest structure that influences many ecological processes and ecosystem functions. Vertical and horizontal spatial structure provides key information for forest management. Although horizontal stand complexity can be measured through stem mapping and spatial analysis, vertical complexity within the stand remains a mostly visual and highly subjective process. Tools and techniques in remote sensing, specifically LiDAR, provide three dimensional datasets that can help get at three dimensional forest stand structure. Although aerial LiDAR (ALS) is the most widespread form of remote sensing for measuring forest structure, it has a high omission rate in dense and structurally complex forests. In this study we used terrestrial LiDAR (TLS) to obtain high resolution three dimensional point clouds of plots from stands that vary by density and composition in the second-growth Pacific Northwest forest ecosystem. We used point cloud slicing techniques and object-based image analysis (OBIA) to produce canopy profiles at multiple points of vertical gradient. At each height point we produced segments that represented canopies or parts of canopies for each tree within the dataset. The resulting canopy segments were further analyzed using landscape metrics to quantify vertical canopy complexity within a single stand. Based on the developed method, we have successfully created a tool that utilizes three dimensional spatial information to accurately quantify the vertical structure of forest stands. Results show significant differences in the number and the total area of the canopy segments and gap fraction between each vertical slice within and between individual forest management plots. We found a significant relationship between the stand density and composition and the vertical canopy complexity. The methods described in this research make it possible to create horizontal stand profiles at any point along the vertical gradient of forest stands with high frequency, therefore providing ecologists with measures of horizontal and vertical stand structure. Key Words: Terrestrial laser scanning, canopy structure, landscape metrics, aerial laser scanning, lidar, calibration, Pacific Northwest