Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods
Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, s...
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MDPI AG
2024-10-01
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| author | Maurizio Santoro Oliver Cartus Oleg Antropov Jukka Miettinen |
| author_facet | Maurizio Santoro Oliver Cartus Oleg Antropov Jukka Miettinen |
| author_sort | Maurizio Santoro |
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| description | Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. |
| format | Article |
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| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
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| spelling | doaj-art-7bd5e902c11b4f95be24d5ce1a42b8442024-11-08T14:40:44ZengMDPI AGRemote Sensing2072-42922024-10-011621407910.3390/rs16214079Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting MethodsMaurizio Santoro0Oliver Cartus1Oleg Antropov2Jukka Miettinen3Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, SwitzerlandGamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, SwitzerlandVTT Technical Research Centre of Finland, P.O. Box 1000, 02044 Espoo, FinlandVTT Technical Research Centre of Finland, P.O. Box 1000, 02044 Espoo, FinlandSatellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.https://www.mdpi.com/2072-4292/16/21/4079forestSAR backscattergrowing stock volumeSentinel-1ALOS-2 PALSAR-2ICESat-2 |
| spellingShingle | Maurizio Santoro Oliver Cartus Oleg Antropov Jukka Miettinen Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods Remote Sensing forest SAR backscatter growing stock volume Sentinel-1 ALOS-2 PALSAR-2 ICESat-2 |
| title | Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods |
| title_full | Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods |
| title_fullStr | Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods |
| title_full_unstemmed | Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods |
| title_short | Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods |
| title_sort | estimation of forest growing stock volume with synthetic aperture radar a comparison of model fitting methods |
| topic | forest SAR backscatter growing stock volume Sentinel-1 ALOS-2 PALSAR-2 ICESat-2 |
| url | https://www.mdpi.com/2072-4292/16/21/4079 |
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