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above ground biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Journal of Geographic Information System 2016年第4期8卷 506-516页

作者： Kilaparthi Kiran Kumar Masahiko Nagai Apichon Witayangkurn Kunnaree Kritiyutanant Shinichi Nakamura School of Engineering and Technology Asian Institute of Technology Bangkok Thailand

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 (R2 = 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 (R2 = 0.74) with field calculated biomass compared to HH (R2 = 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 (R2 = 0.859) compared to optical (R2 = 0.788) and SAR (R2 = 0.742)

关键词： above ground biomass Spectral Indices Backscattering LANDSAT 8 ALOS-2

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A survey of high resolution image processing techniques for cereal crop growth monitoring

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Information Processing in Agriculture 2022年第2期9卷 300-315页

作者： Sanaz Rasti Chris J.Bleakley N.M.Holden Rebecca Whetton David Langton Gregory O’Hare School of Computer Science University College DublinDublinIreland School of Agriculture and Food Science University College DublinDublinIreland School of Biosystems and Food Engineering University College DublinDublinIreland Origin Enterprises plc DublinIreland

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.

关键词： Crop canopy cover above ground biomass Leaf area index Chlorophyll content Growth stage Cereal image processing

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Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

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Journal of Forestry Research 2018年第1期29卷 151-161页

作者： Chaofan Wu Hongxiang Tao Manyu Zhai Yi Lin Ke Wang Jinsong Deng Aihua Shen Muye Gan Jun Li Hong Yang College of Environmental and Resource Sciences Zhejiang University Hangzhou 310058 China Zhejiang Forestry Academy Hangzhou 310023 China School of Civil Engineering and Environmental Sciences and School of Meteorology University of Oklahoma Norman OK 73019 USA State Key Laboratory of Hydro Science and Engineering Department of Hydraulic Engineering Tsinghua University Beijing 100084 China

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest（RF） and support vector machine were adopted to estimate above ground biomass（AGB） using widely used Landsat imagery in the region,especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.

关键词： above ground biomass Ecological forest Forest management Landsat Random forest

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A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

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International Journal of Digital Earth 2019年第9期12卷 995-1012页

作者： Zelong Yang Wenwen Li Qi Chen Sheng Wu Shanjun Liu Jianya Gong The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of Geographical Sciences and Urban Planning Arizona State UniversityTempeAZUSA Department of Geography and Environment University of Hawaii at MānoaHonoluluHIUSA School of Computer and Information Science Southwest UniversityChongqingPeople’s Republic of China School of Resources and Civil Engineering Northeastern UniversityShenyang

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.

关键词： above ground biomass cloud computing Google Earth Engine visualization

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Stand Diversity and Carbon Stock of a Tropical Forest in the Deng Deng National Park, Cameroon

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Open Journal of Ecology 2023年第7期13卷 461-496页

作者： Seraphine E. Mokake Babila K. Weyi Neculina Anyinkeng Lyonga M. Ngoh Obenarreyneke E. Berkeley Egbe E. Andrew Department of Plant Biology Faculty of Science University of Douala Douala Cameroon Department of Plant Science Faculty of Science University of Buea Buea Cameroon Tropical Plant Exploratory Group Mundemba Cameroon Faculty of Agriculture and Veterinary Medicine University of Buea Buea Cameroon

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.

关键词： Diversity above ground biomass Below ground biomass Carbon Stock Deng Deng National Park

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Quantification by allometric equations of carbon sequestered by Tectona grandis in different agroforestry systems

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Journal of Forestry Research 2013年第4期24卷 699-702页

作者： Avinash Jain S.A.Ansari Tropical Forest Research Institute

Non destructive methods for quantification of carbon seques- tration in tropical trees are inadequately developed. We described a stan- dardized method for estimating carbon stock in teak （Tectona grandis Linn. F.）. We developed linear allometric equations using girth at breast height （GBH）, height and age to quantify above ground biomass （AGB）. We used AGB to estimate carbon stock for teak trees of different age groups （1.5, 3.5, 7.5, 13.5, 18.5 and 23.5 years）. The regression equation with GBH, y = 3.174x - 21.27, r2=0.898 （p 〈0.01）, was found precise and convenient due to the difficulty in determination of height and age in dense natural forests of teak. The equation was evaluated in teak agroforestry systems that included Triticum aestivum （wheat）, Cicer arietinum （gram）, Withania somnifera （ashwagandha）,

关键词： Tectona grandis agroforestry system allometric equation carbon sequestration above ground biomass

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