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

Abstract

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.

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

“…If the ground elevation is known, we can derive the phase height above ground, or InSAR height, which depends on the height and the density of the canopy. This InSAR height is close to the power-averaged mean canopy height [24]. InSAR height has been correlated to canopy height, growing stock and AGB [22,[25][26][27][28][29][30].…”

“…InSAR height has been correlated to canopy height, growing stock and AGB [22,[25][26][27][28][29][30]. It has been shown that forest growth and logging can be monitored as increases and decreases, respectively, in InSAR height, and this does not require a DTM [24,31]. However, a conversion from InSAR height changes to AGB changes in areas without a DTM, there are certain requirements that must be met.…”

“…One is that AGB must vary proportionally to InSAR height, or at least be close to this. From a theoretical point of view, it has been argued that the relationship between AGB (or growing stock) and InSAR height follows a power-law model, AGB ∝ H α , where α takes a value in the range 1-2 [24,32]. Studies of InSAR height and field-measured growing stock or AGB have shown both proportional and curve-linear relationships, as well as relationships having too large of a residual variation to clarify this [30,[33][34][35][36].…”

Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches

“…Uncertainty in X-band-derived tropical forest heights (ii) has been reported to lie in a range from 1 to 6 m [72,80]. The precise numbers may vary depending on the sensor configuration, the height metrics and the spatial scale considered.…”

“…Uncertainty in the prediction models can be reduced-for example, by considering data at finer horizontal resolution, the full vertical signal distribution, the multitude of variables that can describe forest structure and individual-tree-based approaches [26]. The uncertainty in height estimations due to the variability in the remote sensing data can be reduced by fitting trends to dense time series of multiple acquisitions [80]. Future research should also try to disentangle the processes and compartments behind biomass dynamics (including also belowground biomass) by quantifying the contributions of wood productivity, mortality, foliage and fine root turnover [81].…”