Mapping biomass of a northern forest using multifrequency SAR data

Ranson, K.J.; Sun, Guoqing

doi:10.1109/36.295053

Cited by 205 publications

(119 citation statements)

References 9 publications

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“…Higher wavelengths and cross polarization (HV&VH) of SAR data show good results in biomass estimation. Application of multi frequency SAR data [14] increases the saturation effect and accuracy of biomass estimation. Dual polarization-L band (HH & HV) is the most commonly used for tropical forest biomass estimation [15].…”

Section: Introductionmentioning

confidence: 99%

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

Kumar¹,

Nagai²,

Witayangkurn³

et al. 2016

JGIS

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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. K. K. Kumar et al.507

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Section: Introductionmentioning

confidence: 99%

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

Kumar¹,

Nagai²,

Witayangkurn³

et al. 2016

JGIS

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“…The sensitivity of radar backscatter to above-ground biomass density is both wavelength and polarization dependent. This relates to the depth of penetration of different wavelengths, typically concentrating on the dominance of volume (canopy) scattering in X-band (3 cm) and C-band (5 cm) in contrast to branch scattering at 23 cm (Lband) and trunk-ground interactions at P-band (50 cm) (Ranson and Sun, 1994). Whereas short wavelength X-and C-band backscatter is sensitive mainly to canopy architecture (e.g.…”

Section: Synthetic Aperture Radar For Estimating Biomass and Carbon Cmentioning

confidence: 99%

The role of remote sensing in the development of SMART indicators for ecosystem services assessment

2016

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“…8). We applied an empirical linear correction, derived from a transect of primary forest, to account a ramp in s°with range (Leckie 1990, Ranson andSun 1994). The range ramp across this transect varied from 0.4 to 1.5 dB (see table 1).…”

Section: Jers-1 Sar Data Pre-processingmentioning

confidence: 99%

Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and radiometric considerations for operational monitoring

Salas¹,

Ducey²,

Rignot³

et al. 2002

International Journal of Remote Sensing

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Abstract. While the role of Synthetic Aperture Radar (SAR) in operational tropical forest monitoring has yet to be de ned, it is nevertheless a critical technology for improving our understanding of deforestation and secondary vegetation in the tropics. In order to understand the role of this technology in operational monitoring a systematic evaluation, relative to other existing technologies of its performance is required. In this paper we evaluate the spatial, temporal, and noise constraints of JERS SAR data for mapping and monitoring biomass of secondary vegetation in Rondonia, Brazil. Our results indicate that the variability in stand estimates of biomass is high and that the source of the majority of the variability is not from speckle and the intrinsic texture of the secondary vegetation but likely due to diVerences in environmental conditions resulting in diVerential background scattering properties. Multitemporal analysis signi cantly improves biomass estimates to the point where it is possible to map changes in biomass. Slight reductions in the variability in estimates of normalized radar cross-section greatly improve biomass estimation.

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Mapping biomass of a northern forest using multifrequency SAR data

Cited by 205 publications

References 9 publications

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

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

The role of remote sensing in the development of SMART indicators for ecosystem services assessment

Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and radiometric considerations for operational monitoring

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