Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests

Persson, Henrik J.; Fransson, Johan E. S.

doi:10.1080/02827581.2016.1220618

Cited by 40 publications

(46 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the overall better correspondence with the field plot data (lower standard error) and decreased variance as the larger radii were evaluated, it was determined that a 40 m radius was more robust to use, despite the smaller sample radius. This also agrees with the findings in [16].…”

Section: Data Extractionsupporting

confidence: 83%

“…The residual phase trend is caused by small errors in the satellite positions, which result in baseline errors. Similar trends have been reported by other authors [6,15,16,21,49]. The error is easily observed in overlapping areas between adjacent swaths and can be corrected, to first order, by fitting an error model to the data.…”

Section: Scenes With Phase Trendssupporting

confidence: 68%

“…The error is easily observed in overlapping areas between adjacent swaths and can be corrected, to first order, by fitting an error model to the data. Hence, to date, one solution to handle this was to fit a two-dimensional polynomial in the regions corresponding to open ground, in the form of correction = α 0 + α 1 · az + α 2 · rg to the scene, where α i with i ∈ (0, 2), described the coefficients for the pixel number in the azimuth (az) and range (rg) directions (Figure 9c), a solution that was used also in other studies [16,21]. In these open regions, the ALS DTM should coincide with the registered phase height, and hence, a reasonable correction could be found.…”

Section: Scenes With Phase Trendsmentioning

confidence: 99%

See 2 more Smart Citations

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

View full text Add to dashboard Cite

This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%-25% (27-30 tons/ha and 52-65 m 3 /ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level.

show abstract

Section: Data Extractionsupporting

confidence: 83%

Section: Scenes With Phase Trendssupporting

confidence: 68%

Section: Scenes With Phase Trendsmentioning

confidence: 99%

See 1 more Smart Citation

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

View full text Add to dashboard Cite

show abstract

“…From the scatter plots it can be concluded that estimations at higher volume/biomass possess considerably higher standard deviations, which might indicate a limited applicability for denser forest and southern forests with higher volumes/biomasses. Experience from using field data from these test sites together with other sensors (for example TanDEM-X radar data [36]) has shown that the standard deviations can remain constant along the entire evaluated range. In the current study, the heights generally had to be transformed to achieve significance in the regression modelling, and in Krycklan the heights were raised to the power of 2.3 while at Remningstorp the height was raised to the power of 2.6 and 2.7 for VOL and AGB, respectively (Tables 6 and 7).…”

Section: Discussionmentioning

confidence: 99%

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m 2 ·ha −1 (22%) for basal area, 66 m 3 ·ha −1 (27%) for stem volume, and 26 tons·ha −1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.

show abstract

“…These factors, as well as information on the elevation of the ground, need to be considered when estimating biomass from the InSAR height. Several research papers demonstrated that simple linear relationships could predict biomass from estimates of InSAR height of single-pass datasets [35][36][37][38][39][40][41][42]. Nevertheless, it is unclear whether such linear models that were validated at a number of test sites in boreal and savannah forest apply in other forest ecosystems as well [42].…”

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Santoro

Cartus

2018

Remote Sensing

View full text Add to dashboard Cite

Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued.

show abstract

Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests

Cited by 40 publications

References 44 publications

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Contact Info

Product

Resources

About