Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data

Lindgren, Nils; Persson, Henrik J.; Nyström, Mattias; Nyström, Kenneth; Grafström, Anton; Muszta, Anders; Willén, Erik; Fransson, Johan E. S.; Ståhl, Göran; Olsson, Håkan

doi:10.1080/07038992.2017.1356220

Cited by 14 publications

(29 citation statements)

References 23 publications

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“…where x i is the SAR variable (γ Vol ; γ 0 ) and y i the forest parameter to be predicted for the plot i, α is the intercept (bias), β is the slope, and ε i is the error term of the linear regression for that plot. A linear relation between the SAR variables and forest parameters was observed e.g., by [45,46], and other studies. Since it requires a linear scaling of the input variables the gamma naught data were not logarithmically scaled to dB values.…”

Section: Discussionsupporting

confidence: 61%

“…Best results in terms of model significance were achieved with a height of ambiguity that is considerably above the top tree height (65.8 m). If the height of ambiguity is in the range of the top tree height, the phase cannot be unwrapped without ambiguity [57], on the other hand, very small baselines and higher height of ambiguity lead to limitations in sensitivity of the phase to forest vertical structure [45,58,59]. Thus, the optimal height of ambiguity for forest vertical structure assessment from X-band InSAR data is above the maximum stand height and should not exceed 100 m [45].…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…The ISH was normalized with a digital elevation model obtained from ALS. Both the normalized ISH and COH have been shown to have a strong correlation with forest attributes in previous studies [7,24,25], and thus both these characteristics were extracted for the field plots with the same procedure as for the SPOT-data.…”

Section: Tandem-x Datamentioning

confidence: 99%

“…Studying recent developments in forestry applications of DA, promising results have been obtained in simulation studies [5]. However, the empirical results presented by [6,7] pointed out problems to fully realize the theoretical potential of DA in practice. In the latter studies, making use of only the last measurement for estimating the current state of key forest characteristics was sometimes almost as good as making use of the entire time series through DA.…”

Section: Introductionmentioning

confidence: 99%

“…In the latter studies, making use of only the last measurement for estimating the current state of key forest characteristics was sometimes almost as good as making use of the entire time series through DA. However, all these studies [5][6][7] assumed the estimates to be uncorrelated between subsequent time periods, as is the practice in standard DA through Kalman filtering [3,8,9]. However, using a certain kind of RS data repeatedly, such as data from airborne laser scanning (ALS), e.g., [10], it is likely that certain conditions of a given plot or stand will tend to make the estimates always deviate in a certain direction from the true value.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

et al. 2018

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Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures.

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Canopy height estimation with TanDEM-X in temperate and boreal forests

Schlund

Magdon

Eaton³

et al. 2019

International Journal of Applied Earth Observation and Geoinfor

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Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data

Cited by 14 publications

References 23 publications

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

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

Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

Canopy height estimation with TanDEM-X in temperate and boreal forests

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