Carbon budget is a popular topic which addresses the rate of which cut down the vegetation of the Earth and how much vegetation is left. There is a major imbalance between current carbon budget and the carbon budget a decade ago - 26% reduction compared to the previous decade, which is significant. There can be two possibilities causing this: underestimation of the carbon sinks or over-estimation of the carbon sources (or both). Determining the carbon sources are not a trivial task, and the authors of the reviewed paper did exactly that. There are couple of methods for determining measuring the vegetation of the Earth. Most of the current large scale vegetation measurements have been performed with multispectral refelctance and/or radar backscattering coefficient imaging. The latter methods lacks the accuracy because they are inherently inaccurate and depend on many different factors. Light detection and ranging (LIDAR) and polarimetric SAR interferometry (PolInSAR) provides much more accurate data, but they typically lack the capability to cover large land masses. Such missions as Global Ecosystem Dynamics Investigation (GEDI) provide large scale LIDAR and PollnSAR measurements that are available to public. However, even these measurements and this data tends to be faulty. The authors if the paper tries to address the measurement inaccuracies caused by slopes in mountainous areas. Different slope adaptive methods have been developed and simulated and the data from the GEDI provides a chance to validate the simulated data.
The authors of the paper chose specific test sites and applied airborne laser scanner (ALS) at low altitudes. For this case the Goddard’s LiDAR, Hyperspectral & Thermal Imager (G-LiHT) was used. Considering that this obtained data is very accurate, it was used as a reference for the data obtained from the GEDI mission. The key link between the two datasets are the location; thus the GEDI footprint geolocations were optimized, which allowed to acquire a better alignment between the two datasets. Afterwards, the proposed slope-adaptive metrics were applied on the GEDI waveforms as well as other methods. Afterwards the data was analyzed and compared to the reference. The slopes in the obtained data did not exceed 45 degrees. Authors were able to show that the GEDI waveforms provide good estimation of forest above-ground biomass (AGB) for areas that has slope elevation below 15 degrees. Compared to the other methods the proposed adaptive slope metrics showed better results. However, the method is limited when the forest footprint is too complicated.
Papers:
- Wenjian Ni, Zhiyu Zhang, Guoqing Sun, "Assessment of Slope-Adaptive Metrics of GEDI Waveforms for Estimations of Forest Aboveground Biomass over Mountainous Areas", Journal of Remote Sensing, vol. 2021, Article ID 9805364, 17 pages, 2021. https://doi.org/10.34133/2021/9805364
- Gadow, Klaus. (2001). Characterizing forest spatial structure and diversity.
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