Relating Forest Parameters To Sar Data

Toan, Thuy Le; Beaudoin, A.; Riom, J.; Guyon, Dominique

doi:10.1109/igarss.1991.579979

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Sentinel-1 imagery data accessible via GEE consists of radiometric calibrated, orthorectified SAR C-band backscatter images available in four polarization modes: (1) single co-polarization: vertical transmitting, vertical receiving (VV), (2) single co-polarization: horizontal transmitting, horizontal receiving (HH), (3) dual-band cross-polarization: vertical transmitting, vertical and horizontal receiving (VV and VH), and (4) dual-band crosspolarization: horizontal transmitting, horizontal and vertical receiving (HH and HV). As argued by [19], HH and HV polarizations are less useful for landslide detection, thus only VV and VH polarization were considered due to their sensitivity to forest biomass structure [71].…”

Section: Data Preparationmentioning

confidence: 99%

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Peters,

Liu,

Keppel

et al. 2024

Remote Sensing

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Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

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Section: Data Preparationmentioning

confidence: 99%

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Peters,

Liu,

Keppel

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Sentinel-1 imagery data accessible via GEE consists of radiometric calibrated, ortho-rectified SAR C-band backscatter images available in four polarization modes: 1) Single co-polarization: vertical transmitting, vertical receiving (VV), 2) Single co-polarization: horizontal transmitting, horizontal receiving (HH), 3) dual-band cross-polarization: vertical transmitting, vertical and horizontal receiving (VV and VH), and 4) dual-band cross-polarization: horizontal transmitting, horizontal and vertical receiving (HH and HV). As argued by [23], HH and HV polarizations are less useful for landslide detection, thus only VV and VH polarization were considered due to their sensitivity to forest biomass structure [75].…”

Section: Data Preparationmentioning

confidence: 99%

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

Peters,

Liu,

Keppel

et al. 2024

Preprint

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Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

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“…With the launch of PALSAR-2 in 2014, it becomes a unique and highly useful sensor with high-resolution, wide swath width and image quality until now (EORC). At L-band, the penetration depth exceeds the crown layer and mechanism involving the lower part of the canopy presented a strong relationship with biomass from the major contribution of branches and trunk (Toan, Beaudoin, Riom, & Guyon, 1992). Much successful application of PALSAR-1/-2 data for forest biomass mapping was recognized based on backscatter intensities (Suzuki, Kim, and Ishii 2013;Mermoz 2014) or different techniques such as polarimetry and interferometry (Chowdhury, Thiel, Schmullius, & Stelmaszczuk-Górska, 2013;Neumann, Saatchi, Ulander, & Fransson, 2012;Thiel & Schmullius, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass of Bamboo and Mixed Bamboo Forest in Thua Thien-Hue Province, Viet Nam Using Palsar-2 and Landsat Oli Data

Tuong¹,

Tani²,

Wang³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

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Relating Forest Parameters To Sar Data

Cited by 10 publications

References 6 publications

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

Estimating Aboveground Biomass of Bamboo and Mixed Bamboo Forest in Thua Thien-Hue Province, Viet Nam Using Palsar-2 and Landsat Oli Data

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