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首页> 外文会议>Asian conference on remote sensing;ACRS >MONITORING FOREST ABOVE-GROUND BIOMASS OF GUJARAT STATE USING MULTI-TEMPORAL SYNTHETIC APERTURE RADAR DATA
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MONITORING FOREST ABOVE-GROUND BIOMASS OF GUJARAT STATE USING MULTI-TEMPORAL SYNTHETIC APERTURE RADAR DATA

机译：利用多时间合成孔径雷达数据监测古杰拉特州地上生物量

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Synthetic Aperture Radar (SAR) data has shown great potential in retrieval of forest above-ground biomass (AGB) due to the capability of SAR to provide more dynamic range for vegetation growth variables as compared to optical data. Estimations of forest AGB of Gujarat state was carried out for multiple years using C-band Radar Imaging Satellite-1 (RISAT-1) and L-band Advanced Land Observing Satellite Phased Arrayed L-band Synthetic Aperture Radar (ALOS-PALSAR 1/2) data. In the present study, topographically corrected Medium Resolution ScanSAR (MRS) data of Indian RISAT-1 acquired during 2015-16 and global SAR mosaic products in HH/HV polarizations produced from Japanese ALOS-PALSAR 1/2 data for the years 2007-10 and 2015-16 were used to retrieve temporal forest AGB of Gujarat through semi-empirical model based on multi-linear regression coefficients of HH and HV polarization backscatter with field measured forest biomass. Gujarat has four major forest types namely, (1) tropical moist deciduous forest, (2) littoral and swamp forest, (3) tropical dry deciduous forest and (4) northern tropical thorn forest. Different model coefficients were derived for these forest types based on extensive ground measured forest parameters and the biomass maps of Gujarat were generated. High correlations were observed between γ°HV and γ°HH/HV with field measured biomass over different forest vegetation types with biomass densities ranging from 20-120 t/ha. The study has also presented the advantages and limitations of C and L-band SAR data for estimation of forest AGB with varying biomass densities and has demonstrated how selection of suitable observation period of SAR data enhances retrieval of AGB of deciduous forests.
机译：合成孔径雷达（SAR）数据已显示出在森林地上生物量（AGB）检索中的巨大潜力，因为与光学数据相比，SAR具有为植被生长变量提供更多动态范围的能力。使用C波段雷达成像卫星1（RISAT-1）和L波段高级陆地观测卫星相控阵L波段合成孔径雷达（ALOS-PALSAR 1/2）对古吉拉特邦森林AGB进行了多年估算） 数据。在本研究中，采用地形校正的2015-16年印度RISAT-1采集的中分辨率ScanSAR（MRS）数据以及从2007-10年日本ALOS-PALSAR 1/2数据产生的HH / HV极化的全球SAR镶嵌产品分别使用HH​​和HV极化反向散射的多线性回归系数和实地测得的森林生物量，通过半经验模型和2015-16和2015-16来通过半经验模型检索古吉拉特邦的临时森林AGB。古吉拉特邦有四种主要的森林类型，即（1）热带湿润落叶林，（2）沿海和沼泽林，（3）热带干燥落叶林和（4）北部热带刺林。根据广泛的地面测得的森林参数得出这些森林类型的不同模型系数，并生成古吉拉特邦的生物量图。观察到γ°HV和γ°HH / HV与不同森林植被类型上实地测得的生物量之间的高度相关性，生物量密度为20-120 t / ha。这项研究还提出了C和L波段SAR数据在估算生物量密度不同的森林AGB方面的优势和局限性，并证明了选择合适的SAR数据观测期如何增强落叶林AGB的提取。

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