TanDEM-X elevation model data for canopy height and aboveground biomass retrieval in a tropical peat swamp forest

Schlund, Michael; Poncet, Felicitas von; Kuntz, Steffen; Boehm, Hans-Dieter Viktor; Hoekman, D.H.; Schmullius, Christiane

doi:10.1080/01431161.2016.1226001

Cited by 25 publications

(18 citation statements)

References 73 publications

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“…TDX DEM has been used extensively to derive digital canopy models (DCM) for estimation of tree height and above ground biomass in urban and natural environments ( [23,[47][48][49]. The TDX DEM elevations of the mangrove forest on the southeast Florida coast exhibit the strongest correlation with LiDAR DSM elevations among all forested samples, generating an R-squared value of 0.95 (Table 1) which is comparable to previous studies.…”

Section: Forested Areasupporting

confidence: 76%

“…The TDX DEM dataset provides a consistent and global source for estimating the elevations of terrain, vegetation, and buildings by covering all of the Earth's surface at least twice during the mission [12,18].In 2014, the DLR released the intermediate TDX DEM (IDEM) data to the scientific community for experimental research, based on the first-pass of the TDX DEM mission [19]. The IDEMs were subsequently verified by ICESat, airborne LiDAR and GPS measurements at many study sites [20][21][22][23] and have been used to map canopy heights of boreal forests [24], urban tree heights [25], and building heights [26]. In 2016, the DLR released the final TDX DEM product to the scientific community [27].…”

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confidence: 99%

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Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

et al. 2019

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The TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces.

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Section: Forested Areasupporting

confidence: 76%

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confidence: 99%

Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

et al. 2019

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“…Advanced SAR techniques (e.g., interferometry (InSAR), polarimetry (PolSAR), polarimetric interferometry (PolInSAR), tomography (TomoSAR)) show promising results for estimating vegetation structure parameters [18][19][20][21][22][23]. For instance, studies based on TanDEM-X single-pass interferometer (e.g., [18,19,24,25]) reported promising sensitivity of the InSAR height to canopy height and thus to stem volume and AGB in boreal and tropical forests. Since the temporal baseline of the TanDEM-X mission is quasi zero, temporal decorrelation of the interferometric coherence can be excluded, and the InSAR coherence is primarily impacted by volume decorrelation.…”

Section: Introductionmentioning

confidence: 99%

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

et al. 2018

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Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model's predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model's predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

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“…Only a few studies investigated the transferability of methods to other environments and the impact of forest stratification on model performance [34]. The majority of studies was on boreal [8,14,16,18,19,21,24] and tropical [15,22,25,28,33] forests. In addition, the quality and accuracy of a model depend on the definition of the reference forest height that is investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to these simplified scattering models, the second approach is dependent on the availability of ancillary data, e.g., a digital terrain model to derive information about the ground height. If this information is available, e.g., from national topographical surveys, the subtraction approach provides a simple but robust means to produce the InSAR height which is widely used as a proxy for the canopy height [21][22][23]. A comparison of the InSAR to the dominant (top) height of the canopy reveals that InSAR height underestimates the actual height of the forest [24,25].…”

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confidence: 99%

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

et al. 2019

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Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests' vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey's height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type. and ranging (LiDAR), and synthetic aperture radar (SAR) data. A comprehensive summary of studies that aim at quantifying forest biomass from satellite sensors was compiled by [3]. AGB estimates from optical remote sensing data mostly rely on vegetation indices that parameterize the photosynthetic activity of vegetation. They imply a relationship between the foliage and the total AGB of a vegetation stand, which is further used to estimate AGB. Since the major part of AGB in forests is composed of non-photosynthetic (woody) components, such approaches imply high uncertainties. The major challenge in AGB mapping from optical data is the saturation of the signal after canopy closure which especially hampers the estimation of high-level AGB [4]. However, recent studies confirmed the general ability of optical systems like Landsat to map AGB at biomass levels around 70 Mg/ha [5].The saturation problem can be overcome by sensors, which are able to penetrate through the canopy and interact with the forest constituents. For instance, LiDAR and interferometric SAR (InSAR) systems generally provide the necessary technique because they are able to penetrate the canopy of a forest and deliver information about the vertical structure of a forest stand. LiDAR data produce high-accuracy canopy height models (CHM) but the main drawback ...

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TanDEM-X elevation model data for canopy height and aboveground biomass retrieval in a tropical peat swamp forest

Cited by 25 publications

References 73 publications

Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

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