Forest height estimation and validation using Tandem-X polinsar

Cloude, S.R.; Chen, H.; Goodenough, D.G.

doi:10.1109/igarss.2013.6723172

Cited by 17 publications

(10 citation statements)

References 6 publications

(11 reference statements)

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“…The aforementioned issues of lidar and InSAR potentially may be addressed by combining their complementary observations, where lidar data are used to constrain the forest scattering model and to validate InSAR height inversion while InSAR images are exploited to extend lidar observations (Bergen et al 2009;Goetz and Dubayah 2011;Hall et al 2011;Qi and Dubayah 2016;Sun et al 2011). For example, previous studies have used airborne lidar elevation data to provide the needed external DTM to estimate forest height from TDX single-polarization (single-pol) coherence (Cloude et al 2013;Kugler et al 2014;Schlund et al 2015;Soja and Ulander 2013;Solberg et al 2013). Accurate airborne lidar observations of forest vertical structure have also been used to enhance parameterization of the forest scattering models for improved forest height estimation (Brolly et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Lee

Hancock

et al. 2019

Remote Sensing of Environment

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Interferometric Synthetic Aperture Radar (InSAR) and lidar are increasingly used active remote sensing techniques for forest structure observation. The TanDEM-X (TDX) InSAR mission of German Aerospace Center (DLR) and the upcoming Global Ecosystem Dynamics Investigation (GEDI) of National Aeronautics and Space Administration (NASA) together may provide more accurate estimates of global forest structure and biomass via their synergic use. In this paper, we explored the efficacy of simulated GEDI data in improving height estimates from TDX InSAR data. Our study sites span three major forest types: a temperate forest, a mountainous conifer forest, and a tropical rainforest. The GEDI lidar coverage was simulated for the full nominal two-year mission duration, under both cloud-free and 50%-cloud conditions. We then used these GEDI data to parameterize the Random Volume over Ground (RVoG) model driven by TDX imagery. In particular, we explored the following three strategies for forest structure estimation: 1) TDX data alone; 2) TDX + GEDI-derived digital terrain model (DTM); and 3) TDX + GEDI DTM + GEDI canopy height. We then validated the retrieved forest heights against wall-to-wall airborne lidar measurements. We found relatively large biases at 90 [m] spatial resolution, from 4.2-11.9 [m], and root mean square errors (RMSEs), from 7.9-12.7 [m] when using TDX data alone under 2 constrained RVoG assumptions of a fixed extinction coefficient (σ) and a zero ground-to-volume amplitude ratio (μ=0). Results improved significantly with the aid of a DTM derived from GEDI data which enabled estimation of spatially-varying σ values (vs. fixed extinction) under a μ=0 assumption, with biases reduced to 1.7-4.2 [m] and RMSEs to 4.9-8.6 [m] across cloudy and cloud-free cases. The best agreement was achieved in the third strategy by also incorporating information of GEDI-derived canopy height to further enhance the RVoG parameters. The improved model, when still assuming μ = 0, reduced biases to less than or close to one meter and further reduced RMSEs to 4.0-6.7 [m]. Finally, we used GEDI data to estimate spatially-varying μ in the RVoG model. We found biases of between-0.7-0.9 [m] and RMSEs in the range from 2.6-7.1 [m] over the three sites. Our results suggest that use of GEDI data improves height inversion from TDX, providing heights at more accuracy than can be achieved by TDX alone, and enabling wall-to-wall height estimation at much finer spatial resolution than can be achieved by GEDI alone.

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Section: Introductionmentioning

confidence: 99%

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Lee

Hancock

et al. 2019

Remote Sensing of Environment

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“…Approaches relying on InSAR coherence magnitude (e.g., [19,26]) and phase (e.g., [23,27,28] in the presence of external DTM), as well as their combination (as suggested in [24]) were found useful in estimating forest variables. Promising results for model-based forest height estimation were demonstrated over a variety of frequency bands and sites from boreal [17,[29][30][31]] to tropical forests [32][33][34]. However, practically all of these studies were performed only over relatively small test sites and typically required auxiliary data.…”

Section: Introductionmentioning

confidence: 99%

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

et al. 2016

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Abstract:In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios.

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“…In the literature, many methods based on SAR interferometry have been proposed to estimate forest height and can be summarized into three main categories: (a) the Pol-In-SAR methodology [27][28][29]; (b) coherence-based methodology [30][31][32]; and (c) interferometric phase-based methodology [19,20]. Unfortunately, S1 data are not suitable for (a) due to the lack of quad-pol channels that would be required [21,33].…”

Section: Sentinel 1 Datamentioning

confidence: 99%

Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry

Petris¹,

Sarvia²,

Mondino³

2022

Earth

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Forest height is a key parameter in forestry. SAR interferometry (InSAR) techniques have been extensively adopted to retrieve digital elevation models (DEM) to give a representation of the continuous variation of the Earth’s topography, including forests. Unfortunately, InSAR has been proven to fail over vegetation due to low coherence values; therefore, all phase unwrapping algorithms tend to avoid these areas, making InSAR-derived DEM over vegetation unreliable. In this work, a sensitivity analysis was performed with the aim of properly initializing the relevant operational parameters (baseline and multilooking factor) to maximize the theoretical accuracy of the height difference between the forest and reference point. Some scenarios were proposed to test the resulting “optimal values”, as estimated at the previous step. A simple model was additionally proposed and calibrated, aimed at predicting the optimal baseline value (and therefore image pair selection) for height uncertainty minimization. All our analyses were conducted using free available data from the Copernicus Sentinel-1 mission to support the operational transfer into the forest sector. Finally, the potential uncertainty affecting resulting height measures was quantified, showing that a value lower than 5 m can be expected once all user-dependent parameters (i.e., baseline, multilooking factor, temporal baseline) are properly tuned.

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Forest height estimation and validation using Tandem-X polinsar

Cited by 17 publications

References 6 publications

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry

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