Estimating Forest Structure From Lidar And High Spatial Resolution Imagery For The Prediction Of Succession And Species Composition

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Estimating Forest Structure from LiDAR and High Spatial Resolution Imagery for the Prediction of Succession and Species Composition

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ISBN 13 :
Total Pages : 366 pages
Book Rating : 4.:/5 (918 download)

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Book Synopsis Estimating Forest Structure from LiDAR and High Spatial Resolution Imagery for the Prediction of Succession and Species Composition by :

Download or read book Estimating Forest Structure from LiDAR and High Spatial Resolution Imagery for the Prediction of Succession and Species Composition written by and published by . This book was released on 2015 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Light detection and ranging (LiDAR) and high spatial resolution data combined with advanced statistical techniques have, within the last decade, contributed significantly to advances in the development of enhanced Forest Resource Inventories (FRI). The goal of this research was to explore how these data sources and statistical techniques could be utilized for predicting successional stages and species composition within a complex temperate forest ecosystem in Ontario. This research also explored the possibility of generating tree lists from these same data sources that would forecast equivalent future forest conditions compared to in situ collected FRI data. For the characterization of vertical structure within forest stands a new LiDAR metric was developed, i.e., the Vertical Complexity Index (VCI). Logistic regressions were then applied to predict successional stages while boosted regression trees were adopted for the quantification of relative abundance of upper canopy species using LiDAR and high spatial resolution data. k-Nearest Neighbor imputation was used for generating individual juvenile tree information from LiDAR whereas adult tree information was generated from: i) an individual tree crown (ITC) classification; and ii) from predicted stem density and species' relative abundance. Successional stages were well predicted using LiDAR variables (i.e., VCI, Lorey's height and standard deviation of height) with a classification accuracy (Khat) of 86%. Average prediction accuracy was 0.71 when LiDAR variables related to biotic and disturbance processes were included. Correlations between in situ and imputed juvenile tree information were moderate, ranging from 0.50 to 0.69. Stem density model fit (adj. R2) was 0.51 for conifer and 0.74 for hardwood stands. As for generating adult tree lists, ITC significantly underestimated stem density while both approaches underestimated species composition. This research clearly demonstrated that LIDAR variables that capture structural forest attributes can successfully be used to characterize structurally distinct successional stages and upper canopy species' abundances in landscapes with limited topographical variation. Juvenile trees were more difficult to characterize with LiDAR variables as was the ability to generate tree lists. However, this research provides insights how to advance the characterization of juvenile trees and develop tree lists from LiDAR and high spatial resolution data.

Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities

Download Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities PDF Online Free

Author : Zhenyu Zhang
Publisher :
ISBN 13 :
Total Pages : 896 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities by : Zhenyu Zhang

Download or read book Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities written by Zhenyu Zhang and published by . This book was released on 2012 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: High resolution spatial data, including airborne LiDAR data and newly available WorldView-2 satellite imagery, offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. Those responsible for monitoring forest changes over time relevant to timber harvesting and native forest conservation see the potential for improved documentation from using such data. However, the transfer of new remote sensing technologies from the research domain into operational forestry applications poses challenges. One of the key challenges is the development of a comprehensive procedure which involves deployment of these new remote sensing data to create forest mapping products that are comparable (or superior) in accuracy to conventional photo-interpreted maps. The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for tree species identification and classification. The research investigations have focused on open forests, and conifer or deciduous forests which are even-aged and of relatively homogenous structures. The suitability of these new remotely sensed data for delineating the structure of complex forest types, particularly for Australian cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This thesis presents ways of processing airborne LiDAR data and high spatial resolution WorldView-2 satellite imagery for characterisation and classification of forest communities in the Strzelecki Ranges, Victoria, Australia. This is a highly disturbed landscape that consists of forestry plantations and large stands of natural forest, including cool temperate rainforest remnants. The k-means clustering algorithm was applied to nonnalised LiDAR points to stratify the vertical forest structure into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. These layer-specific variables were found to be effective in forest classification. Individual trees, including locations and crown sizes, were identified from a LiDAR-derived canopy height model using the TreeVaW algorithm. Augmentation of infonnation extraction from LiDAR data for tree species identification by inclusion of LiDAR intensity data was then tested using statistical analysis techniques. This study demonstrated the contribution of LiDAR-derived intensity variables to the identification of Myrtle Beech (Nothofagus cunninghamii -the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species -notably, Silver Wattle (Acacia dealbata) at the individual tree level. Nonparametric classifiers including support vector machines (SVMs) and decision trees were employed to take full advantage of the rich set of infonnation derived from the LiDAR and WorldView-2 imagery data for further improvement in classification accuracy. It is evident that the SVMs have significant advantages over the traditional classification methods in tenns of classification accuracy. Cool temperate rainforest and adjacent forest species were successfully classified from airborne LiDAR data and WorldView-2 satellite imagery using a decision tree approach to object-based analyses in eCognition software. The improvements in results from the methods developed in this study strongly warrant the operational adoption of airborne LiDAR data and high spatial resolution satellite imagery in the management of Australia's forestry resources.

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

Author : Richard Edward Brooks
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.:/5 (859 download)

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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.

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

Author : Qin Ma
Publisher :
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.

The Use of LiDAR in Multi-scale Forestry Applications

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ISBN 13 :
Total Pages : 284 pages
Book Rating : 4.:/5 (993 download)

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Book Synopsis The Use of LiDAR in Multi-scale Forestry Applications by :

Download or read book The Use of LiDAR in Multi-scale Forestry Applications written by and published by . This book was released on 2017 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest ecosystems are a significant faction of the Earth's landscape, and accurate estimates of forest structures are important for understanding and predicting how forest ecosystems respond to climate change and human activities. Light detection and ranging (LiDAR) technology, an active remote sensing technology, can penetrate the forest canopy and greatly improve the efficiency and accuracy of mapping forest structures, compared to traditional passive optical remote sensing and radar technologies. However, currently, LiDAR has two major weaknesses, the lack of spectral information and the limited spatial coverage. These weaknesses have limited its accuracy in certain forestry applications (e.g., vegetation mapping) and its application in large-scale forest structure mapping. The aim of research described in this dissertation is to develop data fusion algorithms to address these limitations. In this dissertation, the effectiveness of LiDAR in estimating forest structures and therefore monitoring forest dynamics is first compared with aerial imagery by detecting forest fuel treatment activities at the local scale. Then, a vegetation mapping algorithm is developed based on the fusion of LiDAR data and aerial imagery. This algorithm can automatically determine the optimized number of vegetation units in a forest and take both the vegetation species and vegetation structure characteristics into account in classifying the vegetation types. To extend the use of LiDAR in mapping forest structures in areas without LiDAR coverage, a data fusion algorithm is proposed to map fine-resolution tree height from airborne LiDAR, spaceborne LiDAR, optical imagery and radar data in regional scale. Finally, this dissertation further investigates the methodology to integrate spaceborne LiDAR, optical imagery, radar data and climate surfaces for the purpose of mapping national- to global-scale forest aboveground biomass. The proposed data fusion algorithms and the generated regional to global forest structure parameters will have important applications in ecological and hydrologic studies and forest management.

LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest

Author : Graham Wesley Pope
Publisher :
ISBN 13 :
Total Pages : 184 pages
Book Rating : 4.:/5 (815 download)

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Book Synopsis LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest by : Graham Wesley Pope

Download or read book LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest written by Graham Wesley Pope and published by . This book was released on 2012 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies. In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community.

Mapping Forest Structure, Species Gradients and Growth in an Urban Area Using Lidar and Hyperspectral Imagery

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Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (934 download)

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Book Synopsis Mapping Forest Structure, Species Gradients and Growth in an Urban Area Using Lidar and Hyperspectral Imagery by :

Download or read book Mapping Forest Structure, Species Gradients and Growth in an Urban Area Using Lidar and Hyperspectral Imagery written by and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Urban forests play an important role in the urban ecosystem by providing a range of ecosystem services. Characterization of forest structure, species variation and growth in urban forests is critical for understanding the status, function and process of urban ecosystems, and helping maximize the benefits of urban ecosystems through management. The development of methods and applications to quantify urban forests using remote sensing data has lagged the study of natural forests due to the heterogeneity and complexity of urban ecosystems. In this dissertation, I quantify and map forest structure, species gradients and forest growth in an urban area using discrete-return lidar, airborne imaging spectroscopy and thermal infrared data. Specific objectives are: (1) to demonstrate the utility of leaf-off lidar originally collected for topographic mapping to characterize and map forest structure and associated uncertainties, including aboveground biomass, basal area, diameter, height and crown size; (2) to map species gradients using forest structural variables estimated from lidar and foliar functional traits, vegetation indices derived from AVIRIS hyperspectral imagery in conjunction with field-measured species data; and (3) to identify factors related to relative growth rates in aboveground biomass in the urban forests, and assess forest growth patterns across areas with varying degree of human interactions. The findings from this dissertation are: (1) leaf-off lidar originally acquired for topographic mapping provides a robust, potentially low-cost approach to quantify spatial patterns of forest structure and carbon stock in urban areas; (2) foliar functional traits and vegetation indices from hyperspectral data capture gradients of species distributions in the heterogeneous urban landscape; (3) species gradients, stand structure, foliar functional traits and temperature are strongly related to forest growth in the urban forests; and (4) high uncertainties in our ability to map forest structure, species gradient and growth rate occur in residential neighborhoods and along forest edges. Maps generated from this dissertation provide estimates of broad-scale spatial variations in forest structure, species distributions and growth to the city forest managers. The associated maps of uncertainty help managers understand the limitations of the maps and identify locations where the maps are more reliable and where more data are needed.

Estimating Forest Canopy Attributes Via Airborne, High-resolution, Multispectral Imagery in Midwest Forest Types

Author : Demetrios Gatziolis
Publisher :
ISBN 13 :
Total Pages : 568 pages
Book Rating : 4.3/5 (129 download)

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Book Synopsis Estimating Forest Canopy Attributes Via Airborne, High-resolution, Multispectral Imagery in Midwest Forest Types by : Demetrios Gatziolis

Download or read book Estimating Forest Canopy Attributes Via Airborne, High-resolution, Multispectral Imagery in Midwest Forest Types written by Demetrios Gatziolis and published by . This book was released on 2003 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Old-Growth Forests

Author : Christian Wirth
Publisher : Springer Science & Business Media
ISBN 13 : 3540927069
Total Pages : 518 pages
Book Rating : 4.5/5 (49 download)

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Book Synopsis Old-Growth Forests by : Christian Wirth

Download or read book Old-Growth Forests written by Christian Wirth and published by Springer Science & Business Media. This book was released on 2009-07-07 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many terms often used to describe old-growth forests imply that these forests are less vigorous, less productive and less stable than younger forests. But research in the last two decades has yielded results that challenge the view of old-growth forests being in decline. Given the importance of forests in battling climate change and the fact that old-growth forests are shrinking at a rate of 0.5% per year, these new results have come not a moment too soon. This book is the first ever to focus on the ecosystem functioning of old-growth forests. It is an exhaustive compendium of information that contains original work conducted by the authors. In addition, it is truly global in scope as it studies boreal forests in Canada, temperate old-growth forests in Europe and the Americas, and global tropical forests. Written in part to affect future policy, this eminently readable book is as useful for the scientist and student as it is for the politician and politically-interested layman.

Forestry Applications of Airborne Laser Scanning

Author : Matti Maltamo
Publisher : Springer Science & Business Media
ISBN 13 : 9401786631
Total Pages : 460 pages
Book Rating : 4.4/5 (17 download)

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Book Synopsis Forestry Applications of Airborne Laser Scanning by : Matti Maltamo

Download or read book Forestry Applications of Airborne Laser Scanning written by Matti Maltamo and published by Springer Science & Business Media. This book was released on 2014-04-08 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This book provides a comprehensive, state-of-the-art review of the research and application of ALS in a broad range of forest-related disciplines, especially forest inventory and forest ecology. However, this book is more than just a collection of individual contributions – it consists of a well-composed blend of chapters dealing with fundamental methodological issues and contributions reviewing and illustrating the use of ALS within various domains of application. The reviews provide a comprehensive and unique overview of recent research and applications that researchers, students and practitioners in forest remote sensing and forest ecosystem assessment should consider as a useful reference text.

Using Leaf-off LiDAR in Modeling Forest Canopy Structure and Assessing the Effect of Spatial Resolution in Landscape Analyses

Author :
Publisher :
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.:/5 (931 download)

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Book Synopsis Using Leaf-off LiDAR in Modeling Forest Canopy Structure and Assessing the Effect of Spatial Resolution in Landscape Analyses by :

Download or read book Using Leaf-off LiDAR in Modeling Forest Canopy Structure and Assessing the Effect of Spatial Resolution in Landscape Analyses written by and published by . This book was released on 2014 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Biodiversity in Dead Wood

Author : Jogeir N. Stokland
Publisher : Cambridge University Press
ISBN 13 : 0521888735
Total Pages : 525 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Biodiversity in Dead Wood by : Jogeir N. Stokland

Download or read book Biodiversity in Dead Wood written by Jogeir N. Stokland and published by Cambridge University Press. This book was released on 2012-04-26 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of wood-inhabiting fungi, insects and vertebrates, discussing habitat requirements along with strategies for maintaining biodiversity.

Whitebark Pine Communities

Author : Diana F. Tomback
Publisher : Island Press
ISBN 13 : 9781597263207
Total Pages : 462 pages
Book Rating : 4.2/5 (632 download)

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Book Synopsis Whitebark Pine Communities by : Diana F. Tomback

Download or read book Whitebark Pine Communities written by Diana F. Tomback and published by Island Press. This book was released on 2001 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whitebark pine is a dominant feature of western high-mountain regions, offering an important source of food and high-quality habitat for species ranging from Clark's nutcracker to the grizzly bear. But in the northwestern United States and southwestern Canada, much of the whitebark pine is disappearing. Why is a high-mountain species found in places rarely disturbed by humans in trouble? And what can be done about it.Whitebark Pine Communities addresses those questions, explaining how a combination of altered fire regimes and fungal infestation is leading to a rapid decline of this once abundant -- and ecologically vital -- species. Leading experts in the field explain what is known about whitebark pine communities and their ecological value, examine its precarious situation, and present the state of knowledge concerning restoration alternatives. The book. presents an overview of the ecology and status of whitebark pine communities offers a basic understanding of whitebark pine taxonomy, distribution, and ecology, including environmental tolerances, community disturbance processes, regeneration processes, species interactions, and genetic population structure identifies the threats to whitebark pine communities explains the need for management intervention surveys the extent of impact and losses to dateMore importantly, the book clearly shows that the knowledge and management tools are available to restore whitebark pine communities both locally and on a significant scale regionally, and it provides specific information about what actions can and must be taken.Whitebark Pine Communities offers a detailed portrait of the ecology of whitebark pine communities and the current threats to them. It brings together leading experts to provide in-depth information on research needs, management approaches, and restoration activities, and will be essential reading for ecologists, land managers, and anyone concerned with the health of forest ecosystems in the western United States.

Spatial Pattern Analysis of Forest Structure and Species Composition in a Northern Minnesota Forest

Author : Katherine Dodds LeJeune
Publisher :
ISBN 13 :
Total Pages : 178 pages
Book Rating : 4.:/5 (241 download)

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Book Synopsis Spatial Pattern Analysis of Forest Structure and Species Composition in a Northern Minnesota Forest by : Katherine Dodds LeJeune

Download or read book Spatial Pattern Analysis of Forest Structure and Species Composition in a Northern Minnesota Forest written by Katherine Dodds LeJeune and published by . This book was released on 1991 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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

Author : Dirk Pflugmacher
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.

Lidar Remote Sensing Of Forest Canopy Structure

Author : Christopher Felix Hansen
Publisher :
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Lidar Remote Sensing Of Forest Canopy Structure by : Christopher Felix Hansen

Download or read book Lidar Remote Sensing Of Forest Canopy Structure written by Christopher Felix Hansen and published by . This book was released on 2015 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest horizontal and vertical structure that is important to forest productivity and wildlife habitat. Indeed, LiDAR data have been shown to provide accurate estimates to forest structural parameters and measures of higher trophic levels (e.g., avian abundance and diversity). However, links between forest structure and tree function have not been evaluated using LiDAR. This study was designed and scaled to assess the relationship of LiDAR to multiple aspects of forest structure and higher trophic levels (arthropod and bird populations), which included the ground-based collection of percent crown and understory closure, as well as arthropod and avian abundance and diversity data. Additional plot-based measures were added to assess the relationship of LiDAR to forest health and productivity. High-resolution discrete-return LiDAR data (flown summer of 2009) were acquired for the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA. LiDAR data were classified into four canopy structural categories: 1) high crown and high understory closure, 2) high crown and low understory closure, 3) low crown and high understory closure, and 4) low crown and low understory closure. Nearby plots from each of the four LiDAR categories were grouped into "blocks" to assess the spatial consistency of data. Ground-based measures of forest canopy structure, site, stand and individual tree measures were collected on nine 50 m-plots from each LiDAR category (36 plots total), during summer of 2012. Analysis of variance was used to assess the relationships between LiDAR and a suite of tree function measures. Our results show the novel ability of LiDAR to assess forest health and productivity at the stand and individual tree level. We found significant correspondence between LiDAR categories and our ground-based measures of tree function, including xylem increment growth, foliar nutrition, crown health, and stand mortality. Furthermore, we found consistent reductions in xylem increment growth, decreases in foliar nutrition and crown health, and increases in stand mortality related to high understory closure. This suggests that LiDAR measures can reflect competitive interactions, not just among overstory trees for light, but also interactions between overstory trees and understory vegetation for resources other than light (e.g., nutrients). High-resolution LiDAR data show promise in the assessment of forest health and productivity related to tree function.

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

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

Author : Madhurima Bandyopadhyay
Publisher :
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.