Temporal stability of InSAR height in a tropical rainforest

Solberg, Svein; Lohne, Tor-Peder; Karyanto, Oka

doi:10.1080/2150704x.2015.1026953

Cited by 11 publications

(11 citation statements)

References 24 publications

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“…It has also been demonstrated that AGB changes could be mapped and quantified based on changes in InSAR height, with results corresponding fairly well to changes measured by field inventory and airborne LiDAR, although a certain bias remained apparently due to errors in the input InSAR DEMs [37]. A second requirement is that the relationship between AGB and InSAR height needs to be the same at the points of time of the InSAR acquisitions, and although a certain temporal stability has been found in TanDEM-X height [38,39], one should avoid severe differences in weather conditions and effects of leaf-on versus leaf-off in deciduous forests, if possible [40,41].…”

Section: Methodsmentioning

confidence: 99%

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

et al. 2018

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Abstract:Monitoring changes in forest height, biomass and carbon stock is important for understanding the drivers of forest change, clarifying the geography and magnitude of the fluxes of the global carbon budget and for providing input data to REDD+. The objective of this study was to investigate the feasibility of covering these monitoring needs using InSAR DEM changes over time and associated estimates of forest biomass change and corresponding net CO 2 emissions. A wall-to-wall map of net forest change for Uganda with its tropical forests was derived from two Digital Elevation Model (DEM) datasets, namely the SRTM acquired in 2000 and TanDEM-X acquired around 2012 based on Interferometric SAR (InSAR) and based on the height of the phase center. Errors in the form of bias, as well as parallel lines and belts having a certain height shift in the SRTM DEM were removed, and the penetration difference between X-and C-band SAR into the forest canopy was corrected. On average, we estimated X-band InSAR height to decrease by 7 cm during the period 2000-2012, corresponding to an estimated annual CO 2 emission of 5 Mt for the entirety of Uganda. The uncertainty of this estimate given as a 95% confidence interval was 2.9-7.1 Mt. The presented method has a number of issues that require further research, including the particular SRTM biases and artifact errors; the penetration difference between the X-and C-band; the final height adjustment; and the validity of a linear conversion from InSAR height change to AGB change. However, the results corresponded well to other datasets on forest change and AGB stocks, concerning both their geographical variation and their aggregated values.

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Section: Methodsmentioning

confidence: 99%

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

et al. 2018

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“…The term in the conversion factor (12) AGB/phase-height. This term is proportional to the derivative of AGB with respect to phase-height, if a polynomial model is used as in the second line of (11). The red points are from InSAR phase-heights calibrated with visual ground finding in another study [10].…”

Section: Model For Deriving Phase-height-rate To Agb Rate Conversion mentioning

confidence: 99%

Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry

Treuhaft

Yang

Gonçalves

et al. 2017

Forests

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Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO 2 , which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes in vertical structure and AGB, with sub-hectare and monthly spatial and temporal resolution, respectively. The phase-height observation is described, with a focus on how it is related to vegetation-density, radar-power vertical profiles, and mean canopy heights, which are, in turn, related to AGB. The study site covers 18 × 60 km in the Tapajós National Forest in the Brazilian Amazon. Phase-heights over Tapajós were measured by DLR's TanDEM-X radar interferometer 32 times in a 3.2 year period from 2011-2014. Fieldwork was done on 78 secondary and primary forest plots. In the absence of disturbance, rates of change of phase-height for the 78 plots were estimated by fitting the phase-heights to time with a linear model. Phase-height time series for the disturbed plots were fit to the logistic function to track jumps in phase-height. The epochs of clearing for the disturbed plots were identified with ≈1-month accuracy. The size of the phase-height change due to disturbance was estimated with ≈2-m accuracy. The monthly time resolution will facilitate REDD+ monitoring. Phase-height rates of change were shown to correlate with LiDAR RH90 height rates taken over a subset of the TanDEM-X data's time span (2012)(2013). The average rate of change of phase-height across all 78 plots was 0.5 m-yr −1 with a standard deviation of 0.6 m-yr −1 . For 42 secondary forest plots, the average rate of change of phase-height was 0.8 m-yr −1 with a standard deviation of 0.6 m-yr −1 . For 36 primary forest plots, the average phase-height rate was 0.1 m-yr −1 with a standard deviation of 0.5 m-yr −1 . A method for converting phase-height rates to AGB-rates of change was developed using previously measured phase-heights and field-estimated AGB. For all 78 plots, the average AGB-rate was 1.7 Mg-ha −1 -yr −1 with a standard deviation of 4.0 Mg-ha −1 -yr −1 . The secondary-plot average AGB-rate was 2.1 Mg-ha −1 -yr −1 , with a standard deviation of 2.4 Mg-ha −1 -yr −1 . For primary plots, the AGB average rate was 1.1 Mg-ha −1 -yr −1 with a standard deviation of 5.2 Mg-ha −1 -yr −1 . Given the standard deviations and the number of plots in each category, rates in secondary forests and all forests were significantly different from zero; rates in primary forests were consistent with zero. AGB-rates were compared to change models for Tapajós and to LiDAR-based change measurements in other tropical forests. Strategies for improving AGB dynamical monitoring with X-band interferometry are discussed.

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“…However, a large portion of overlap occurred between the regions (Figure 10a). Since the coherence varied from acquisition to acquisition due to weather conditions, ground temperature (with frozen conditions, the coherence increased and the range of coherence decreased drastically) and possibly more reasons, a relative threshold was applied [50,51]. The reference coherence was defined as the scene average coherence, after the water mask was applied.…”

Section: Scenes With Phase Trendsmentioning

confidence: 99%

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

et al. 2017

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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.

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Temporal stability of InSAR height in a tropical rainforest

Cited by 11 publications

References 24 publications

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry

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

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