Canopy Height Estimation in Mediterranean Forests of Spain With TanDEM-X Data

Gómez, Cristina; López-Sánchez, Juan M.; Romero-Puig, Noelia; Zhu, Jianjun; Fu, Haiqiang; He, Wenjie; Xie, Yanzhou; Xie, Qinghua

doi:10.1109/jstars.2021.3060691

Cited by 21 publications

(28 citation statements)

References 36 publications

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“…confirms results from higher latitude forests(Chen et al, 2016;Gómez et al, 2021;Olesk et al, 2016;Schlund et al, 2019), in which the simplified models have only been used so far. Similar simplified…”

supporting

confidence: 81%

“…As mentioned above, it was found that these assumptions of the simplifications are often not valid and thus empirical parameters in the simplified models are suggested to accurately estimate canopy height (Gómez et al, 2021;Olesk et al, 2016;Schlund & Boehm, 2021), the cumulative density function saturated (Chen et al, 2016). Note that a was independent from the model, resulting in a similar value for linear and sinc models.…”

Section: Canopy Height Estimation With Tandem-xmentioning

confidence: 99%

“…A similar approach achieved highest accuracies in Qi et al (2019), which used simulated GEDI data. Existing studies have only applied the simple models in higher latitude forests (Chen et al, 2016(Chen et al, , 2021Gómez et al, 2021;Olesk et al, 2016), whereas we study the models in a dynamic tropical landscape with different types of land use such as forest, mono-cultural rubber and oil palm plantations. In contrast to other studies, in which ALS data were used for training and reference (Chen et al, 2021;Gómez et al, 2021;Guliaev et al, 2021;Qi et al, 2019), the training of the empirical parameters in our study is conducted with GEDI data.…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies have only applied the simple models in higher latitude forests (Chen et al, 2016(Chen et al, , 2021Gómez et al, 2021;Olesk et al, 2016), whereas we study the models in a dynamic tropical landscape with different types of land use such as forest, mono-cultural rubber and oil palm plantations. In contrast to other studies, in which ALS data were used for training and reference (Chen et al, 2021;Gómez et al, 2021;Guliaev et al, 2021;Qi et al, 2019), the training of the empirical parameters in our study is conducted with GEDI data. GEDI data are substantially different compared to ALS data in terms of acquisition concept, spatial coverage, accuracy and spatial detail (Adam et al, 2020;Dubayah et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

et al. 2022

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1. Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall-to-wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM-X data, simplifications are necessary to estimate canopy height with semi-empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).2. We trained the semi-empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM-X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. 3. Our results indicated that the empirical parameters substantially improved the estimation from a root-mean-square-error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land-use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.4. We demonstrate the potential of the combined exploitation of the TanDEM-X and GEDI missions for a wall-to-wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation

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supporting

confidence: 81%

Section: Canopy Height Estimation With Tandem-xmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

et al. 2022

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“…The wide availability of airborne lidar data triggered a number of attempts to use lidar measurements to compensate for the underdetermination of the forest height inversion problem when addressed in terms of single-polarimetric TanDEM-X acquisitions. Besides of using the lidar-derived DTM to directly enable the forest height inversion [22][23], [25][26][27], lidar data have also been used to constrain individual model parameters reducing, in this way, the dimensionality of the forest height inversion problem [28][29][30][31][32][33]. More recently, the direct use of the lidar waveforms to define the full X-band vertical reflectivity profile has been proposed [34].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data

Choi

Cazcarra-Bes²,

Guliaev

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The present study addresses the development, implementation and validation of a forest height mapping scheme based on the combination of TanDEM-X interferometric coherence and GEDI waveform measurements. The very general case where only a single polarisation TanDEM-X interferogram, a set of spatially discrete GEDI waveform measurements and no DTM are available is assumed. The use of GEDI waveforms to invert the TanDEM-X interferometric measurements is described together with a set of performance criteria implemented to ensure a certain performance quality. The emphasis is set on developing a methodology able to invert forest height at large scales. Combining 595 TanDEM-X scenes and about 15 million GEDI waveforms, a spatially continuous 25 m resolution forest height map covering the whole of Tasmania Island is achieved. The derived forest height map is validated against an airborne lidarderived canopy height map available across the whole island.

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