Biomass estimation in a tropical wet forest using Fourier transforms of profiles from lidar or interferometric SAR

Abstract

Tropical forest biomass estimation based on the structure of the canopy is a burgeoning and crucial remote sensing capability for balancing terrestrial carbon budgets. This paper introduces a new approach to structural biomass estimation based on the Fourier transform of vertical profiles from lidar or interferometric SAR (InSAR). Airborne and field data were used from 28 tropical wet forest stands at La Selva Biological Station, Costa Rica, with average biomass of 229 Mg‐ha−1. RMS scatters of remote sensing biomass estimates about field measurements were 58.3 Mg‐ha−1, 21%, and 76.1 Mg‐ha−1, 26%, for lidar and InSAR, respectively. Using mean forest height, the RMS scatter was 97 Mg‐ha−1, ≈34% for both lidar and InSAR. The confidence that Fourier transforms are a significant improvement over height was >99% for lidar and ≈90% for InSAR. Lidar Fourier transforms determined the useful range of vertical wavelengths to be 14 m to 100 m.

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

“…In agreement with our findings for stands at La Selva Biological Station, Costa Rica [20], co-location error dominated the overall uncertainty in the field biomass, except in early-successional forests where the application of the growth model resulted in larger errors on average (Table 4). The results illustrated in Figure 7 are consistent with the expectation of lower co-location error with increasing Lidar/field overlap, as well as lower errors for secondary forests compared to primary forests, given their lower species diversity and more homogeneous canopy structure (cf.…”

“…Second, the plot-level estimates of biomass are related to co-located remote sensing estimates of structure (e.g., mean canopy height) using a statistical model. The model is then applied together with remote sensing data to predict biomass in locations where ground measurements are not available [17][18][19][20][21]. When the 3-D measurements are spatially discontinuous, as is usually the case with Lidar, the resulting biomass predictions can be further integrated with radar and/or passive optical imagery (typically using machine learning algorithms) to produce wall-to-wall maps of biomass or carbon [5,6], although often with poorer resolution and unknown accuracy.…”

“…This co-location error, σ C , was introduced because of positional errors associated with both data sets [49,50], and because the size, shape, and orientation of the field and the GLAS samples did not exactly coincide ( Figure 3). We took a Monte Carlo approach, based on binomial statistics [20], to estimate the difference in biomass between what was measured in each 50 m × 50 m field plot and what was actually present in the area covered by the GLAS footprint ( Figure 3). The binomial-statistical approach constructs an ensemble of possible tree masses (M) based on those measured in the field.…”

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

“…When grouping studies according to the set of frequencies at which the radar data were acquired, it was evident that longer wavelengths were preferred in studies that were exploiting the radar backscattered intensities, given the stronger sensitivity of the SAR backscattered intensity to biomass [5][6][7]. Studies involving the use of interferometric SAR (InSAR) observables focused on single-pass datasets or short repeat-pass intervals because of the direct and accurate measurement of vertical structural properties by interferometry, and, thus, the strong sensitivity to biomass as well [8][9][10]. L-band data were used in 71% of the papers, followed by C-band (36%), P-band (21%), X-band (19%), VHF (3%), and S-band (1%).…”

“…In [38], it was argued, however, that coherence from TanDEM-X interferograms would only contribute 7% to a combined estimated of biomass. Further along the line of exploiting primarily InSAR phase information, the use of individual Fourier Transform frequency components of the vertical profile was suggested to be estimating biomass more accurately compared to using the mean InSAR height for the reference unit (as used on most studies here cited) [10]. This appears to be a promising approach to be further evaluated.…”