Skip to content
Research Article Open access CC BY 4.0

Assessment of Forest Above Ground Biomass with SAR Data Using Linear Modeling in Joida Taluk, Uttara Kannada, India

A. G. Koppad, Gowri B Gowda, Rachana, Anup Das

International Journal of Environment and Climate Change · pp. 241–259 · Published 12 Dec 2024

10.9734/ijecc/2024/v14i124622

Abstract

The study was conducted in Joida taluk of Uttara Kannada district to assess the forest aboveground biomass using L band data. In this study, an attempt was made to estimate the aboveground biomass using SAR backscatter. The study area covered dense, moderately dense, and sparse forests. Nearly 0.01 percent of the forest area was sampled through 30 sampling plots. The point center quadrate (PCQ) method was used to select the tree and collected the tree growth parameters viz., tree height, diameter at breast height (DBH), and diameter at the tree base. The sampling plots were randomly selected in all types of forest and the location details such as the latitude, longitude, and altitude were collected from GPS. The tree crown density was measured with a densitometer. Each sample plot of forest aboveground biomass (AGB) was estimated using specific gravity and field-measured forest parameters. The fully polarimetric quad-pol (HH, HV, VV &VH) space borne SAR data of the Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2) of Advanced Land Observing Satellite-2 (ALOS-2) is used in this study for the natural forest of Joida Taluk of Uttara Kannada, Karnataka, India. The Orthorectification was performed on the radiometrically calibrated SAR data to measure the normalized radar cross-section of all the field-measured forest plot locations. SAR backscatter-based Multiple Linear Regression (MLR) model was implemented to retrieve forest aboveground biomass of the study area. The cross-polarization (HV) had shown a good correlation with forest above ground biomass. Forest Stem Volume varies from 16.11 m3/ha to1235.79m3/ha (12.88 t/ha to 1000.98 t/ha). The higher values are from the plots of dense forests having higher DBH and height. The sampled area included dense forest, moderately dense forest, and sparse forest and hence the variation in biomass has occurred. The Multiple Linear Regression (MLR) analysis was performed to estimate the aboveground biomass of the natural forest areas of the Joida taluk. This study used four combinations (HH &HV, VV &HH, HV & VH, VV&VH) of polarimetric channels in the forest aboveground biomass retrieval. Among them, the combination of HH and HV polarization shows a good correlation with field and predicted biomass. The predicted biomass from HH & HV polarisation varies from 79t/ha to 267t/ha, VV & VH shows the biomass from 62t/ha to 236t/ha, VV& HH showed from 62t/ha to 205 t/ha and HV&VH indicated from 79t/ha to 268t/ha. Among them the HH and HV polarization backscatter can be used to retrieve the aboveground biomass of forests though linear modeling. The RMSE and R2values that were obtained from the MLR for the polarimetric combinations HH & HV and HH & VV were 78 t/ha and 0.86, and 81 t/ha and 0.85 respectively. Forest AGB retrieval from HH & HV polarization-based MLR model has shown the best results. The results indicated that the backscatter from different polarisation could be used for developing the MLR model for estimating above ground biomass.

Forest above ground biomass SAR polarization backscatter multiple linear regression (MLR)

References (33)

  1. 1 The potential and challenge of remote sensing‐based biomass estimation [DOI]
  2. 2 Dependence of radar backscatter on coniferous forest biomass [DOI]
  3. 3 Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use [DOI]
  4. 4 Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images [DOI]
  5. 5 Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China [DOI]
  6. 6 Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks [DOI]
  7. 7 Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data [DOI]
  8. 8 Improved Biomass Calibration and Validation With Terrestrial LiDAR: Implications for Future LiDAR and SAR Missions [DOI]
  9. 9 Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest [DOI]
  10. 10 Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass [DOI]
  11. 11 Land cover classification of spaceborne multifrequency SAR and optical multispectral data using machine learning [DOI]
  12. 12 Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data [DOI]
  13. 13 Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data [DOI]
  14. 14 Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam [DOI]
  15. 15 Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia [DOI]
  16. 16 Polarimetric scattering model for estimation of above ground biomass of multilayer vegetation using ALOS-PALSAR quad-pol data [DOI]
  17. 17 PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation [DOI]
  18. 18 Effect of vegetation structure on above ground biomass in tropical deciduous forests of Central India [DOI]
  19. 19 Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval [DOI]
  20. 20 Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass [DOI]
  21. 21 Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon [DOI]
  22. 22 PolInSAR Coherence and Entropy‐Based Hybrid Decomposition Model [DOI]
  23. 23 Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data [DOI]
  24. 24 UAVSAR Tomography for Vertical Profile Generation of Tropical Forest of Mondah National Park, Gabon [DOI]
  25. 25 Polarimetric Calibration and Spatio‐Temporal Polarimetric Distortion Analysis of UAVSAR PolSAR Data [DOI]
  26. 26 Spaceborne PolInSAR and ground-based TLS data modeling for characterization of forest structural and biophysical parameters [DOI]
  27. 27 Performance of PolSAR backscatter and PolInSAR coherence for scattering characterization of forest vegetation using single pass X-band spaceborne synthetic aperture radar data [DOI]
  28. 28 Bistatic PolInSAR Inversion Modelling for Plant Height Retrieval in a Tropical Forest [DOI]
  29. 29 Spaceborne PolInSAR tomography for vertical profile retrieval of forest vegetation [DOI]
  30. 30 A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data [DOI]
  31. 31 Multifrequency Spaceborne Synthetic Aperture Radar Data for Backscatter-Based Characterization of Land Use and Land Cover [DOI]
  32. 32 Spaceborne SAR Tomography for Vertical Profile Retrieval of Forest Vegetation [DOI]
  33. 33 Integrating ALOS-PALSAR and ground based observations for forest biomass estimation for REDD+ in Cambodia [DOI]

Cited by 0

No indexed citations yet.

Related research

Article metrics

Real usage data collected on this platform.

22

Page views

0

PDF downloads

9

Outbound clicks

0

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.