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).

/pdf/above-ground-biomass-assessment-from-combined-optical-and-4w7grjyv1v.pdf

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

Aspects of forest biomass mapping using SAR (Synthetic Aperture Radar) data were studied in study sites in northern Sweden, Germany, and south-eastern Finland. Terrain topography – via the area of a resolution cell – accounted for 61 percent of the total variation in a Seasat (L-band) SAR scene in a hilly and mountainous study site. A methodology – based on least squares adjustment of tie point and ground control point observations in a multi-temporal SAR mosaic dataset – produced a tie point RMSE (Root Mean Square Error) of 56 m and a GCP RMSE of 240 m in the African mosaic of the GRFM (Global Rain Forest Mapping) project. The mosaic consisted of 3624 JERS SAR scenes. A calibration revision methodology – also based on least squares adjustment and points in overlap areas between scenes – removed a calibration artifact of about 1 dB. A systematic search of the highest correlation between forest stem volume and backscattering amplitude was conducted over all combinations of transmit and receive polarisations in three AIRSAR scenes in a German study site. In the P-band, a high and narrow peak around HV-polarisation was found, where the correlation coefficient was 0.75, 0.59, and 0.71 in scenes acquired in August 1989, June 1991, and July 1991, respectively. In other polarisations of P-band, the correlation coefficient was lower. In L-band, the polarisation response was more flat and correlations lower, between 0.54 and 0.70 for stands with a stem volume 100 m/ha or less. Three summer-time JERS SAR scenes produced very similar regression models between forest stem volume and backscattering amplitude in a study site in south-eastern Finland. A model was proposed for wide area biomass mapping when biomass accuracy requirements are not high. A multi-date regression model employing three summer scenes and three winter scenes produced a multiple correlation coefficient of 0.85 and a stem volume estimation RMSE of 41.3m/ha. JERS SAR scenes that were acquired in cold winter conditions produced very low correlations between stem volume and backscattering amplitude.

/pdf/techniques-for-wide-area-mapping-of-forest-biomass-using-39tc5v32jf.pdf

Techniques for wide-area mapping of forest biomass using radar data

K&C Science Report – Phase 1, Detection of deforestation in Swedish forest

Synthetic Aperture Radar (SAR) is known to have a response that is directly related to the amount of living material that it interacts with. It is this property that our research seeks to exploit in order to better understand carbon dynamics in the Amazon. The vegetation density causes the radar response to saturate such that vegetation that is more dense than some threshold is indistinguishable from each other. However, the areas of regrowth are likely to have a low enough biomass during the first 10 years of regrowth to be accurately assessed using radar. Our efforts involve obtaining appropriate pairs of radar images at L and C bands from different sites and for both seasons. These data are then orthorectified to allow accurate calibration and incidence angle correction. The seasonality of the data is used to deal with the moisture sensitivity of the data, and the different frequency data is used to help classify the data into several classes for use in class-specific biomass estimates. We have chosen 2 sites in Brazil for our study. we use the JERS-1 (L-band) and RADARSAT (C-band) data to create a 2-channel composite. These data are then classified into the following classes: flat area (water, bare soil), short vegetation, regrowth, and trees. We report on the accuracy of both our classification and biomass estimation efforts.

Regrowth biomass estimation in the amazon using JERS-1/RADARSAT SAR composites

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Regrowth biomass estimation in the amazon using JERS-1/RADARSAT SAR composites