Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration ...Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R<sup>2</sup> = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R<sup>2</sup> = 0.74) with field calculated biomass compared to HH (R<sup>2</sup> = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R<sup>2</sup> = 0.859) compared to optical (R<sup>2</sup> = 0.788) and SAR (R<sup>2</sup> = 0.742).展开更多
Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of ...Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.展开更多
Tropical rainforests are crucial in maintaining about 70% of the world’s plant and animal biodiversity and are also the highest terrestrial carbon reservoir. This study aimed to determine the tree species composition...Tropical rainforests are crucial in maintaining about 70% of the world’s plant and animal biodiversity and are also the highest terrestrial carbon reservoir. This study aimed to determine the tree species composition, structure and carbon stocks of the Deng Deng National Park which is a semi-deciduous tropical forest (plots 1 and 2 and the transition zone to the savannah (plot 3). Plots demarcation and enumeration followed standard protocols for permanent monitoring plots. The inventory of tree species ≥ 2 cm revealed a total of 5523 individuals of 64 species in 53 genera belonging to 26 families with plot 2 having the highest (2135 individuals/ha) and plot 3 the least (1291 individuals/ha). Tabernaemontana crassa was the most important tree species in the tropical forest and Lecythis idatimon in the savannah. Basal area was highest in the tropical forest and least in the savannah. The diameter distribution of trees in all forest types displayed a reverse J-pattern. Aboveground biomass was highest in the tropical forest (530.2 ± 66.4 t·C/ha) and least in the savannah (184.3 ± 20.1 t·C/ha). The carbon stock of the above ground biomass was twice as much as that of the below ground biomass, soil organic matter and litter. The total carbon stock estimated in all pools was 278.75 t·C/ha. The study site was poor in plant diversity, biomass and carbon stock, indicating a disturbed site with the absence of large trees and undergoing natural regeneration. This underlines an urgent need for efficient restoration management practices.展开更多
This paper presents a survey of image processing techniques proposed in the literature forextracting key cereal crop growth metrics from high spatial resolution, typically proximalimages. The descriptive crop growth m...This paper presents a survey of image processing techniques proposed in the literature forextracting key cereal crop growth metrics from high spatial resolution, typically proximalimages. The descriptive crop growth metrics considered are: crop canopy cover, aboveground biomass, leaf area index (including green area index), chlorophyll content, andgrowth stage. The paper includes an overview of relevant fundamental image processingtechniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for groundtruth measurement are described. Image processing methods for metric estimation aredescribed in detail. The performance of the methods is reviewed and compared. The surveyreveals limitations in image processing techniques for cereal crop monitoring such as lackof robustness to lighting conditions, camera position, and self-obstruction. Directions forfuture research to improve performance are identified.展开更多
文摘Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R<sup>2</sup> = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R<sup>2</sup> = 0.74) with field calculated biomass compared to HH (R<sup>2</sup> = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R<sup>2</sup> = 0.859) compared to optical (R<sup>2</sup> = 0.788) and SAR (R<sup>2</sup> = 0.742).
文摘Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.
文摘Tropical rainforests are crucial in maintaining about 70% of the world’s plant and animal biodiversity and are also the highest terrestrial carbon reservoir. This study aimed to determine the tree species composition, structure and carbon stocks of the Deng Deng National Park which is a semi-deciduous tropical forest (plots 1 and 2 and the transition zone to the savannah (plot 3). Plots demarcation and enumeration followed standard protocols for permanent monitoring plots. The inventory of tree species ≥ 2 cm revealed a total of 5523 individuals of 64 species in 53 genera belonging to 26 families with plot 2 having the highest (2135 individuals/ha) and plot 3 the least (1291 individuals/ha). Tabernaemontana crassa was the most important tree species in the tropical forest and Lecythis idatimon in the savannah. Basal area was highest in the tropical forest and least in the savannah. The diameter distribution of trees in all forest types displayed a reverse J-pattern. Aboveground biomass was highest in the tropical forest (530.2 ± 66.4 t·C/ha) and least in the savannah (184.3 ± 20.1 t·C/ha). The carbon stock of the above ground biomass was twice as much as that of the below ground biomass, soil organic matter and litter. The total carbon stock estimated in all pools was 278.75 t·C/ha. The study site was poor in plant diversity, biomass and carbon stock, indicating a disturbed site with the absence of large trees and undergoing natural regeneration. This underlines an urgent need for efficient restoration management practices.
基金This survey forms part of the CONSUS program which is funded under the Science Foundation Ireland Strategic Partnerships Program(16/SPP/3296)and is co-funded by Origin Enterprises Plc.
文摘This paper presents a survey of image processing techniques proposed in the literature forextracting key cereal crop growth metrics from high spatial resolution, typically proximalimages. The descriptive crop growth metrics considered are: crop canopy cover, aboveground biomass, leaf area index (including green area index), chlorophyll content, andgrowth stage. The paper includes an overview of relevant fundamental image processingtechniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for groundtruth measurement are described. Image processing methods for metric estimation aredescribed in detail. The performance of the methods is reviewed and compared. The surveyreveals limitations in image processing techniques for cereal crop monitoring such as lackof robustness to lighting conditions, camera position, and self-obstruction. Directions forfuture research to improve performance are identified.
