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Quantifying Forest Aboveground Carbon Pools And Fluxes Using Multi Temporal Lidar A Report On Field Monitoring Remote Sensing Mmv Gis Integration And Modeling Results For Forestry Field Validation Test To Quantify Aboveground Tree Biomass And Carbon
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Book Synopsis QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon by :
Download or read book QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Field Measurements for Forest Carbon Monitoring by : Coeli M Hoover
Download or read book Field Measurements for Forest Carbon Monitoring written by Coeli M Hoover and published by Springer Science & Business Media. This book was released on 2008-10-20 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the summer of 2003, a workshop was held in Portsmouth, NH, to discuss land measurement techniques for the North American Carbon Program. Over 40 sci- tists representing government agencies, academia and nonprofit research organi- tions located in Canada, the US and Mexico participated. During the course of the workshop a number of topics were discussed, with an emphasis on the following: • The need for an intermediate tier of carbon measurements. This level of study would be more extensive than state-level inventories of the US Forest Service Forest Inventory and Analysis Program, but less detailed than intensive ecos- tem studies sites such as those in Long Term Ecological Research network. This tier would ideally provide a basis to link and scale remote sensing measurements and inventory data, and supply data required to parameterize existing models (see Wofsy and Harriss 2002, Denning et al. 2005). • The design criteria that such a network of sites should meet. The network and s- pling design should be standardized, but flexible enough to be applied across North America. The design also needs to be efficient enough to be implemented without the need for large field crews, yet robust enough to provide useful information. Finally, the spatial scale must permit easy linkage to remotely sensed data. • The key variables that should be measured at each site, and the frequency of measurement.
Book Synopsis Quantifying forest above ground carbon content using LIDAR remote sensing by : G L (Genevieve) Patenaude
Download or read book Quantifying forest above ground carbon content using LIDAR remote sensing written by G L (Genevieve) Patenaude and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Vertical Structure and Aboveground Biomass of Tropical Forests from Lidar Remote Sensing by : Fabio Guimarães Gonçalves
Download or read book Vertical Structure and Aboveground Biomass of Tropical Forests from Lidar Remote Sensing written by Fabio Guimarães Gonçalves and published by . This book was released on 2015 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for obtaining accurate, spatially explicit estimates of biomass density in tropical forests are required to reduce uncertainties in the global carbon cycle, and to support international climate agreements and emerging carbon markets. Three-dimensional (3-D) remote sensing techniques sensitive to the vertical structure of vegetation provide a unique opportunity for mapping and monitoring forest carbon stocks across large areas in the tropics. However, approaches to forest biomass estimation from remotely sensed structure have yet to be fully developed to deliver the required biomass accuracy. In this research, we use airborne laser scanning (ALS), space-based lidar observations (ICESat/GLAS), and detailed in situ measurements made at the Tapajós National Forest, Brazil, to advance methods of biomass estimation in tropical regions from remotely sensed structure. The overall objectives were to (1) test and refine methods for the extraction of structural information from lidar data; (2) determine the accuracy of lidar estimates of structure in relation to detailed field measurements of vertical structure; and (3) develop and validate structure-based models for optimal prediction of aboveground biomass. Because remote sensing approaches to biomass estimation begin with field estimates of biomass, field plot data collected at Tapajós were also used to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for calibration of remote sensing data. This included an evaluation of the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. Results show that a new approach to biomass estimation based on Fourier transforms of lidar profiles significantly improves predictions of aboveground biomass ranging from 2 to 538 Mg ha−1 in primary and secondary forests. Data from two different regions in the Amazon were used to demonstrate and test this method in a range of conditions. The improvement in biomass estimation performance was consistent across sites and the approach was integrated in a multi-stage scaling strategy to biomass estimation to produce a wall-to-wall map of biomass across a large area in the Amazon.
Book Synopsis The Use of Remote Sensing in the Modeling of Forest Productivity by : H.L. Gholz
Download or read book The Use of Remote Sensing in the Modeling of Forest Productivity written by H.L. Gholz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forests comprise the greatest storage of carbon on land, provide fuel for millions, are the habitat for most terrestrial biodiversity, and are critical to the economies of many countries. Yet changes in the extent and dynamics of forests are inherently difficult to detect and quantify. Remote sensing technologies may facilitate the measurement of some key forest properties which, when combined with other information contained in various computer models, may allow for the quantification of critical forest functions. This book explores how remote sensing and computer modeling can be combined to estimate changes in the carbon storage, or productivity, of forests - from the level of the leaf to the level of the globe. Land managers, researchers, policy makers and students will all find stimulating discussions among an international set of experts at the cutting edge of the interface between science, technology and management.
Book Synopsis Multi-temporal Terrestrial Lidar for Estimating Individual Tree Dimensions and Biomass Change by : Shruthi Srinivasan
Download or read book Multi-temporal Terrestrial Lidar for Estimating Individual Tree Dimensions and Biomass Change written by Shruthi Srinivasan and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) provides accurate understory information rapidly through non-destructive methods. This study developed algorithms to extract individual tree height, diameter at breast height (DBH), and crown width in plots at Ecosystem Science and Management (ESSM) research area and Huntsville, Texas. Further, the influence of scan settings and processing choices on the accuracy of deriving tree measurements was also investigated. The study also developed models to estimate aboveground biomass (AGB) and investigate different conceptual approaches to study tree level growth in forest structural parameters and AGB using multi-temporal TLS datasets. DBH was retrieved by cylinder fitting at different height bins. Individual trees were extracted from the TLS point cloud to determine tree heights and crown widths. The R-squared value ranged from 0.91 to 0.97 when field measured DBH was validated against TLS derived DBH using different methods. An accuracy of 92% was obtained for predicting tree heights. The R-squared value was 0.84 and RMSE was 1.08 m when TLS derived crown widths were validated using field measured crown widths. Examples of underestimations of field measured forest structural parameters due to tree shadowing have also been discussed in this study. Correction factors should be applied or multiple high resolution scans should be conducted to reduce the errors in estimation of forest structural parameters. TLS geometric and statistical parameters were derived for individual trees and used as explanatory variables to estimate AGB. An extensive literature review reveals that this is the first study to model the change in AGB using different innovative and conceptual approaches with multi-temporal TLS data. Tree level AGB growth was studied over a period of three years using three different approaches. Results showed that TLS derived geometric parameters were better correlated to field measured AGB. Promising results for AGB change were obtained using the direct modeling approach; hence forest growth could be studied independent of any field measurements when biomass models are available. However, the models could be improved by incorporating more trees with a wide range of DBH and tree heights. The results from this study will benefit foresters, planners, and other remote sensing studies from airborne and spaceborne platforms, for map upscaling, data fusion, or calibration purposes. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151740
Book Synopsis Remote Sensing of Above Ground Biomass by : Lalit Kumar
Download or read book Remote Sensing of Above Ground Biomass written by Lalit Kumar and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local-regional-global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.
Book Synopsis Estimating Forest Tree Carbon Using Remote Sensing Data and Techniques by : Adeel Ahmad
Download or read book Estimating Forest Tree Carbon Using Remote Sensing Data and Techniques written by Adeel Ahmad and published by . This book was released on 2017-08-11 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2013 in the subject Geography / Earth Science - Physical Geography, Geomorphology, Environmental Studies, grade: 3.87, University of the Punjab (Institute of Geology), course: Geomatics, language: English, abstract: Alarming rates of carbon increase in the atmosphere threatens our planet for sustainability. For avoiding carbon release and to sequester the carbon emissions from the forests and other sources, nations of the world are striving hard to design methodologies for monitoring carbon emission and sequester rates in the forests and other carbon pools. This book presents a research based on a very modern approach to estimate carbon in the moist temperate Himalayan forest of Ayubia National Park (ANP), Pakistan. Latest Geospatial techniques including Remote Sensing and Geographical Information System (GIS) were incorporated to estimate carbon in the forest of ANP using the up-to-date statistical approaches. Objectives of this research included landcover mapping of the study area and estimating and mapping the carbon in the forest as well as biomass for the same area. High spatial resolution Satellite Pour I'Observation de la Terre-5 (SPOT-5) satellite imagery is used to map the landcover of the study area. Different bands from Landsat-5 Thematic Mapper (TM) and SPOT-5 satellite images have been incorporated with inclusion of topographic factors to estimate the carbon stock. The results showed that three landcover types of ANP forest including mix forest, conifer forest and shadow conifer forest have 104,781.62, 445,290.61 and 301,972.37 tons of total aboveground carbon (AGC) respectively as calculated from field inventory data with an average of 252.26 tC/Ha as compared to the previously calculated average of AGC of 222.99 tC/Ha at ANP. This research concluded that multiple and non-linear regression models can estimate carbon more accurately as compared to linear regression models. Linear regression correlations were poor while t
Book Synopsis Monitoring Forest Carbon Sequestration with Remote Sensing by : Huaqiang Du
Download or read book Monitoring Forest Carbon Sequestration with Remote Sensing written by Huaqiang Du and published by Mdpi AG. This book was released on 2023-04-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The forest, as the main body of the terrestrial ecosystem, has a huge carbon sink function and plays an important role in coping with global climate change. This book on "Monitoring forest carbon sequestration with remote sensing" mainly focuses on new remote sensing theories, methods, and technologies for monitoring carbon sinks in forest ecosystems (including urban forest ecosystems) and calls for papers that present original research on the following broad topics: Application of new remote sensing techniques to estimate forest aboveground biomass carbon storage and soil carbon storage. Coupling remote sensing and ecosystem models to simulate the carbon cycle of a forest ecosystem. Application of new sensors or algorithms to retrieve vegetation parameters closely related to forest carbon sink functions, such as leaf area index, tree height, chlorophyll, maximum rate of rubisco carboxylase activity, sun-induced chlorophyll fluorescence, forest age, etc. Integration of multi-temporal or multi-sensor data to detect dynamic changes in and disturbances of forest resources.
Book Synopsis Automated Aboveground Carbon Estimation of Forests with Remote Sensing by : Piper Gordon
Download or read book Automated Aboveground Carbon Estimation of Forests with Remote Sensing written by Piper Gordon and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Canada's forests are believed to contain 86 gigatons of carbon, stored above and below ground. These forests are large in area, making them difficult to monitor using conventional means. Understanding the carbon cycle and the role of forests as carbon sinks is crucial in the investigation and mitigation of climate change to address national obligations. One economical solution for monitoring the carbon content of Canada's forests is the development of an automated computer system which uses multisource remotely sensed data to estimate the aboveground carbon of trees. The process involves data fusion of remotely sensed hyperspectral data for tree species information and lidar (light detection and ranging) and radar (radio detection and ranging) for tree height. The size and dimensionality of the data necessitate the efficient use of computing resources for analysis. The outcome is a useful carbon measuring system. The three research questions are: (1) How do we map with remote sensing aboveground carbon in the forests? (2) How do we determine the accuracies of these aboveground carbon maps? (3) How can an automated system be designed for creating aboveground carbon maps?
Book Synopsis Remote Sensing of Forest Environments by : Michael A. Wulder
Download or read book Remote Sensing of Forest Environments written by Michael A. Wulder and published by Springer Science & Business Media. This book was released on 2003-04-30 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Table of contents
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.
Book Synopsis Advances in Remote Sensing for Global Forest Monitoring by : Erkki Tomppo
Download or read book Advances in Remote Sensing for Global Forest Monitoring written by Erkki Tomppo and published by MDPI. This book was released on 2021-09-01 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.
Book Synopsis Remote Sensing for Sustainable Forest Management by : Steven E. Franklin
Download or read book Remote Sensing for Sustainable Forest Management written by Steven E. Franklin and published by CRC Press. This book was released on 2001-06-13 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: As remote sensing data and methods have become increasingly complex and varied - and increasingly reliable - so have their uses in forest management. New algorithms have been developed in virtually every aspect of image analysis, from classification to enhancements to estimating parameters. Remote Sensing for Sustainable Forest Management reviews t
Book Synopsis Understanding Forest Disturbance and Spatial Pattern by : Michael A. Wulder
Download or read book Understanding Forest Disturbance and Spatial Pattern written by Michael A. Wulder and published by CRC Press. This book was released on 2006-07-27 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing and GIS are increasingly used as tools for monitoring and managing forests. Remotely sensed and GIS data are now the data sources of choice for capturing, documenting, and understanding forest disturbance and landscape pattern. Sitting astride the fields of ecology, forestry, and remote sensing/GIS, Understanding Forest Disturbanc
Book Synopsis Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters by : Francisco Javier García-Haro
Download or read book Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters written by Francisco Javier García-Haro and published by MDPI. This book was released on 2019-09-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.
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.
