Indicating saturation limits of multi-sensor satellite data in estimating aboveground biomass of a mangrove forest
Indicating saturation limits of multi-sensor satellite data in estimating aboveground biomass of a mangrove forest
Carbon sequestration in aboveground biomass remains understudied because of the difficulties in conducting field observations and saturation of remote sensing datasets. This study aimed to address the challenges associated with estimating forest AGB using remote sensing data from a dense mangrove ecosystem that has reached saturation limits. A mangrove ecosystem can reach saturation limits when the vegetation density and AGB are so high that remote sensing instruments, such as radar and lidar, cannot accurately measure further increases in biomass because of the sensors' limited penetration and resolution capabilities. This study evaluated the potential and limitations of using dual-polarised microwave data from Sentinel-1A and PALSAR-2, as well as spectral reflectance data from Sentinel-2, to estimate the AGB of the Bhitarkanika Wildlife Sanctuary (BWS), which is the second largest mangrove site in India. Using stratified random sampling, 314 elementary sampling units of 20 m × 20 m (0.04 ha) were used to record the diameter at breast height, tree height, and stand density for estimating AGB. Different band combinations of multisensor datasets were utilised to identify the best predictor variables for estimating the AGB and their corresponding saturation limits. Sentinel-1A demonstrated saturation at 123 Mg/ha (R
2 = 0.17) for AGB using VV polarisation, followed by 93 Mg/ha (R
2 = 0.55), 91 Mg/ha (R
2 = 0.26), and 96 Mg/ha (R
2 = 0.17) for the three red-edge bands at wavelengths of 705, 749, and 783 nm, respectively, of Sentinel-2 data. Red-edge bands are sensitive to chlorophyll and vegetation structure, and therefore, S2REP and REIP attained the maximum saturation limit at an AGB of 80 Mg/ha (R
2 = 0.26, 0.3, respectively). The highest correlation (R
2 = 0.9) and the maximum AGB of 326.06 Mg/ha was captured by the variable HH × HV of the L-band of the PALSAR-2 due to its penetration and interaction capacity with biomass components and less susceptible to interference from surface conditions and atmospheric effects. This study confirmed the advantages of longer-wavelength L-band data over C-band and multispectral optical bands for AGB estimation and identified the best predictor variables. This approach highlights the efficacy of different predictor variables for AGB estimation and the complementary strengths of multisensor datasets for navigating saturation limits. This framework could offer a practical guide for variable selection based on forest density and canopy structure from upcoming SAR missions, including NISAR.
NISAR, PALSAR-2, Predictor variables, Sentinel-1 & 2, Vegetation Indices
2483-2500
Jagadish, Buddolla
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Behera, Mukunda dev
c518f934-4dea-40bd-a947-a561686ee674
Prakash, A. Jaya
b7342c8b-dc92-4107-8205-1cdcd5fa1503
Paramanik, Somnath
d71db5f8-af87-4409-9b93-471e65ce7f65
Ghosh, Sujit M.
a43fa7bd-10af-4508-b8b1-3ff85dc317ae
Patnaik, C.
3a9db3b4-d9f9-4dab-acc5-e637ae848f17
Das, A.
2a0d6cea-309b-4053-a62e-234807f89306
10 August 2024
Jagadish, Buddolla
b305f398-f49d-441b-8a53-167060bc4bce
Behera, Mukunda dev
c518f934-4dea-40bd-a947-a561686ee674
Prakash, A. Jaya
b7342c8b-dc92-4107-8205-1cdcd5fa1503
Paramanik, Somnath
d71db5f8-af87-4409-9b93-471e65ce7f65
Ghosh, Sujit M.
a43fa7bd-10af-4508-b8b1-3ff85dc317ae
Patnaik, C.
3a9db3b4-d9f9-4dab-acc5-e637ae848f17
Das, A.
2a0d6cea-309b-4053-a62e-234807f89306
Jagadish, Buddolla, Behera, Mukunda dev, Prakash, A. Jaya, Paramanik, Somnath, Ghosh, Sujit M., Patnaik, C. and Das, A.
(2024)
Indicating saturation limits of multi-sensor satellite data in estimating aboveground biomass of a mangrove forest.
Journal of the Indian Society of Remote Sensing, 52 (11), .
(doi:10.1007/s12524-024-01968-1).
Abstract
Carbon sequestration in aboveground biomass remains understudied because of the difficulties in conducting field observations and saturation of remote sensing datasets. This study aimed to address the challenges associated with estimating forest AGB using remote sensing data from a dense mangrove ecosystem that has reached saturation limits. A mangrove ecosystem can reach saturation limits when the vegetation density and AGB are so high that remote sensing instruments, such as radar and lidar, cannot accurately measure further increases in biomass because of the sensors' limited penetration and resolution capabilities. This study evaluated the potential and limitations of using dual-polarised microwave data from Sentinel-1A and PALSAR-2, as well as spectral reflectance data from Sentinel-2, to estimate the AGB of the Bhitarkanika Wildlife Sanctuary (BWS), which is the second largest mangrove site in India. Using stratified random sampling, 314 elementary sampling units of 20 m × 20 m (0.04 ha) were used to record the diameter at breast height, tree height, and stand density for estimating AGB. Different band combinations of multisensor datasets were utilised to identify the best predictor variables for estimating the AGB and their corresponding saturation limits. Sentinel-1A demonstrated saturation at 123 Mg/ha (R
2 = 0.17) for AGB using VV polarisation, followed by 93 Mg/ha (R
2 = 0.55), 91 Mg/ha (R
2 = 0.26), and 96 Mg/ha (R
2 = 0.17) for the three red-edge bands at wavelengths of 705, 749, and 783 nm, respectively, of Sentinel-2 data. Red-edge bands are sensitive to chlorophyll and vegetation structure, and therefore, S2REP and REIP attained the maximum saturation limit at an AGB of 80 Mg/ha (R
2 = 0.26, 0.3, respectively). The highest correlation (R
2 = 0.9) and the maximum AGB of 326.06 Mg/ha was captured by the variable HH × HV of the L-band of the PALSAR-2 due to its penetration and interaction capacity with biomass components and less susceptible to interference from surface conditions and atmospheric effects. This study confirmed the advantages of longer-wavelength L-band data over C-band and multispectral optical bands for AGB estimation and identified the best predictor variables. This approach highlights the efficacy of different predictor variables for AGB estimation and the complementary strengths of multisensor datasets for navigating saturation limits. This framework could offer a practical guide for variable selection based on forest density and canopy structure from upcoming SAR missions, including NISAR.
Text
ISRS-D-24-00361_R1
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Accepted/In Press date: 27 July 2024
Published date: 10 August 2024
Keywords:
NISAR, PALSAR-2, Predictor variables, Sentinel-1 & 2, Vegetation Indices
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Local EPrints ID: 495789
URI: http://eprints.soton.ac.uk/id/eprint/495789
ISSN: 0255-660X
PURE UUID: 70474a46-dc66-4cd5-9ab7-249994b8e16e
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Date deposited: 22 Nov 2024 17:44
Last modified: 29 Nov 2024 15:33
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Contributors
Author:
Buddolla Jagadish
Author:
Mukunda dev Behera
Author:
A. Jaya Prakash
Author:
Somnath Paramanik
Author:
Sujit M. Ghosh
Author:
C. Patnaik
Author:
A. Das
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