Above-ground Biomass Estimation Using High Spatial Resolution Multispectral Remote Sensing in an Oak-saw Palmetto Scrub Community PDF Download
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Author: Theresa Katherine Burcsu
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
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Book Description
Author: Theresa Katherine Burcsu
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
Get Book Here
Book Description
Author: Lalit Kumar
Publisher: MDPI
ISBN: 3039212095
Category : Science
Languages : en
Pages : 264
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Book Description
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.
Author: Ana Cristina Gonçalves
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above-ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above-ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5)ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.
Author: Adélia M. O.
Publisher:
ISBN:
Category : Technology
Languages : en
Pages :
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Book Description
Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above-ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above-ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.
Author: Lola Fatoyinbo
Publisher: BoD – Books on Demand
ISBN: 953510313X
Category : Technology & Engineering
Languages : en
Pages : 338
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Book Description
The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass. Three separate remote sensing technologies are available today to measure ecosystem biomass: passive optical, radar, and lidar. There are many measurement methodologies that range from the application driven to the most technologically cutting-edge. The goal of this book is to address the newest developments in biomass measurements, sensor development, field measurements and modeling. The chapters in this book are separated into five main sections.
Author: Lalit Kumar
Publisher:
ISBN: 9783039212101
Category : Electronic books
Languages : en
Pages : 1
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Book Description
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.
Author: José Aranha
Publisher: MDPI
ISBN: 3036505687
Category : Science
Languages : en
Pages : 276
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Book Description
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
Author: Pen Chow Lau
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 79
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Book Description
Author: José Mauricio Galeana
Publisher:
ISBN:
Category : Science
Languages : en
Pages :
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Book Description
Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation.
Author: Avery Miller
Publisher:
ISBN:
Category : Imaging systems
Languages : en
Pages : 0
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Book Description
"Coastal salt marshes sequester large quantities of “blue carbon” in plant biomass and sediments, and provide numerous other valuable ecosystem functions and services. However, these ecosystems are increasingly threatened by external stressors, including rising sea levels and a changing climate, which have resulted in large losses of tidal marsh habitat. Measuring plant biomass is critical for understanding how carbon storage may be affected as stressors continue to cause marsh losses, and for improving conservation and management efforts. A number of studies have quantified aboveground biomass (AGB) in salt marshes using remote sensing techniques, and with the development of high resolution sensors there is excellent potential to improve estimates over large scales. However, few studies have evaluated how variability in spatial resolution and viewing angle across platforms impacts AGB estimates, despite the large range of potential imaging systems available. Using 3 cm and 6 cm resolution nadir hyperspectral drone imagery, and 0.5-3 cm oblique imagery collected from a ground-based camera at three viewing angles from two different-aged barrier island salt marshes in Virginia, USA, I evaluated the accuracy of regression models predicting S. alterniflora AGB from vegetation indices across resolution and viewing angle. The overall best performing linear regression models were obtained using the 3 cm nadir drone imagery. However, the best 6 cm regression models demonstrated only minor losses in accuracy relative to 3 cm. AGB estimates from obliquely angled imagery were less accurate than either nadir resolution. The most accurate oblique models were obtained at the highest viewing angle, with performance decreasing as the viewing angle became shallower. These results suggest that not all platforms perform similarly within salt marsh ecosystems, and that both spatial resolution and viewing angle must be considered in choice of imaging systems."--Abstract.
