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Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

机译:来自泰国素叻他尼省的光学和SAR遥感数据结合的地上生物量评估

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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).
机译:今天,由于温室气体的排放和森林砍伐,大气中的碳含量主要增加。森林在通过光合作用的螯合过程从大气中吸收二氧化碳方面起着关键作用,并以木材生物量的形式存储,该生物量占全球碳的近70%-80%。环境中不同形式的生物质包括农产品,木材,可再生能源和固体废物。因此,估计环境中的生物质含量至关重要。在过去,生物量是通过森林清查技术估算的,这会消耗大量时间和成本。传统的目录林技术无法获得生物量的空间分布,因此引入遥感技术在生物量评估中的应用来解决该问题。分类地图的总体准确度表明,Surat Thani的土地特征在地图上显示的精度为91.13%,而地面上的土地特征也有所不同。光学(LANDSAT-8)和合成孔径雷达(ALOS-2)遥感数据都用于地面生物量(AGB)评估。存储在树的树枝和茎中的生物质称为地上生物质。 20个30 m×30 m的地面样本图,用于通过异速方程计算生物量。光学遥感根据回归分析(R2 = 0.813),根据土壤调整植被指数(SAVI)和植被比指数(RVI)的光谱指数计算生物量。合成孔径雷达(SAR)是一种新兴技术,它使用高频波长进行生物量估算。与通过线性回归分析生成生物量模型所使用的HH(R2 = 0.43)相比,ALOS-2的HV反向散射与现场计算的生物量显示出良好的关系(R2 = 0.74)。与光学(R2 = 0.788)和SAR(R2 = 0.742)相比,光谱指数(SAVI,RVI)和HV(ALOS-2)SAR反向散射的结合使人工林生物量精度提高到(R2 = 0.859)。

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