Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

Brigot, Guillaume; Simard, Marc; Colin-Koeniguer, Élise; Boulch, Alexandre

doi:10.3390/rs11040381

Cited by 23 publications

(16 citation statements)

References 34 publications

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“…Linear regression between the lidar-based and PolInSAR-model-based vegetation height for the temperate and tropical forest showed a 0.89 coefficient of determination with 2.3 m RMSE and a 0.98 coefficient of determination with 2.1 m RMSE [81]. Several other studies were also conducted in which highly accurate results were reported by the researchers [68,[82][83][84][85].…”

Section: Discussionmentioning

confidence: 82%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

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Section: Discussionmentioning

confidence: 82%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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“…A SAR operating at wavelengths ranging from a few centimeters to a few meters is more sensitive to larger canopy components [2]. The main scatterers in a canopy are the elements having dimensions of the order of the wavelength as shown in Figure 2.…”

Section: Frequency Bands For Monitoring Forest Regions Using Polsar Smentioning

confidence: 99%

“…Besides the economic and commercial importance of the forests that provide timber and wood, forest canopy layer has a great role in providing vital ecosystem services [1,2]. Accordingly, the most common application in vegetation observation has become the estimation of the forest vertical structure with emphasis on accurate estimation of the canopy layer thickness.…”

Section: Introductionmentioning

confidence: 99%

“…Also, PolInSAR techniques have been applied to retrieve the vegetation height based on the RVoG model [6,7]. A method depending on the L-band PolInSAR data is presented in [2] to predict the canopy height and canopy cover through neuronal network learning. This method results in the estimated canopy height and canopy cover with normalized root-mean-squared errors of 13% and 15%, respectively [2].…”

Section: Introductionmentioning

confidence: 99%

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Electromagnetic Simulation for Estimation of Forest Vertical Structure Using Polsar Data

Soliman¹,

Hussein²,

Ammar³

2021

PIER B

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A novel method for the estimation of a forest vertical structure using Polarimetric Synthetic Aperture Radar (PolSAR) data only without the need to interferometry data is proposed in the present paper. Electromagnetic (EM) simulation is used to develop the proposed method, where the SAR pulse is simulated as a plane wave incident in the direction of the side looking angle of the SAR. For this purpose, the forest canopy layer is modeled as clouds of randomly oriented thin straight dipoles which are randomly distributed within an inclined prism volume, whereas the forest soil surface is modeled as a random rough surface. The proposed method aims to estimate the average height of the canopy layer above the soil surface, the canopy layer thickness, and the roughness of the forest ground surface. The proposed method is based on the Radar Vegetation Index (RVI) and the normalized Radar Cross Section (RCS) calculated from the PolSAR data and their relevance to the parameters of the forest vertical structure. Some examples are presented to demonstrate the capability of the proposed method using some PolSAR images obtained through EM simulation of the scattering from forest regions and by applying the theorem of SAR target composition with the Multiple Component Scattering Model (MCSM). The phase differences between the components of scattering obtained from the solution of the SAR target decomposition problem are used in the estimation process. The accuracy of the proposed method is assessed by calculating the percentage error of the estimated vertical structure and ground roughness for each resolution cell of the simulated forest region. It is shown that the percentage errors of the estimated parameters are very low, which reflects the accuracy and efficiency of the proposed method.

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“…However, classification of the composition of tree species and nationwide AGB estimations using only ALS data has proven to be a difficult task. Therefore, airborne and/or spaceborne optical and/or radar data are often used for tree species classification [12,13] and to upscale local or regional estimations to the national level [14,15].…”

Section: Introductionmentioning

confidence: 99%

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

2020

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Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.

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Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

Cited by 23 publications

References 34 publications

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Electromagnetic Simulation for Estimation of Forest Vertical Structure Using Polsar Data

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

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