Characterization of 4D Forest Structure by Integrating LiDAR and InSAR Measurements

Kurzfassung

Forest structure is the result of forest dynamics and biophysical processes that affect their function and diversity. It can be understood as the arrangement of trees and their components in space, but also as the 3D distribution of biomass [1]. The challenge remains in the definition of 3D forest structure optimized for remote sensing measurements. In this sense, this contribution aims at establishing a framework for the joint exploitation of two remote sensing techniques known for their sensitivity to 3D forest structure and dynamics: LiDAR and SAR data. LiDAR sensors provide high resolution but discrete measurements of vegetation reflectance profiles (i.e. waveforms) acquired in a nadir-looking geometry. SAR systems, however, provide lower (though still high) resolution, continuous measurements in a side-looking geometry that allows large-scale coverage and short revisit times. They measure interferometric coherences (InSAR) and radar reflectivity profiles (TomoSAR) related to the physical vegetation structure. The combination of LiDAR and SAR data requires a physical or statistical link between them at different scales and spatial resolutions [2]. Here, different applications and methods aiming at characterizing forest structure at different scales by exploiting the synergies and complementarities of these two types of information are presented and discussed. The need for spatial correlation between vertical reflectivity profiles becomes crucial to capture structural heterogeneity present in disturbed forests. Natural growth versus logging or fire forest scenarios can be simulated with prognostic ecosystem models, e.g. FORMIND [3], and evaluated through multi-scale analysis e.g. by using a wavelet frame [4] with X-band InSAR data. The sensitivity of both LiDAR and SAR data to forest structure has also been proven by using structural horizontal and vertical indices derived from correlating vertical reflectivity profiles [5]. Using LiDAR GEDI waveforms in combination with TanDEM-X interferometric coherence allows enhanced large-scale forest height estimation [6], which can be then used to analyze relative height changes of different temporal periods. At last, GEDI waveforms have proven suitable for the generation of a basis representative of forest structure information that allows the reconstruction of X-band reflectivity profiles [7]. [1] T. A. Spies, P. A. Stine, R. A. Gravenmier, J. W. Long, M. J. Reilly, “Synthesis of science to inform land management within the Northwest Forest Plan area,” Gen. Tech. Rep. PNW-GTR-966, Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 1020, p. 3 vol., 2018, DOI: 10.2737/PNW-GTR-966. [2] M. Pardini, J. Armston, W. Qi, S. K. Lee, M. Tello, V. Cazcarra-Bes, C. Choi, K. P. Papathanassiou, R. O. Dubayah, L. E. Fatoyinbo, “Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization”, Surveys in Geophysics, vol. 40, no. 4, pp. 803–837, 2019, DOI: 10.1007/S10712-019-09553-9/TABLES/2. [3] R. Fischer, F. Bohn, M. Dantas de Paula, C. Dislich, J. Groeneveld, A. G. Gutiérrez, M. Kazmierczak, N. Knapp, S. Lehmann, S. Paulick, S. Pütz, E. Rödig, F. Taubert, P. Köhler, A. Huth, “Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests”, Ecological Modelling, vol. 326, pp. 124–133, 2016, DOI: 10.1016/j.ecolmodel.2015.11.018. [4] L. Albrecht, A. Huth, R. Fischer, K. Papathanassiou, O. Antropov and L. Lehnert, “Estimating forest structure change by means of wavelet statistics using TanDEM-X datasets”, in Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR, pp. 658-662, VDE, April 2024, Munich, Germany. [5] M. Tello, V. Cazcarra-Bes, M. Pardini and K. Papathanassiou, “Forest Structure Characterization from SAR Tomography at L-Band,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 10, pp. 3402-3414, Oct. 2018, DOI: 10.1109/JSTARS.2018.2859050. [6] C. Choi, M. Pardini, J. Armston, K. Papathanassiou, “Forest Biomass Mapping Using Continuous InSAR and Discrete Waveform Lidar Measurements: A TanDEM-X / GEDI Test Study”, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 7675-7689, 2023, DOI: 10.1109/JSTARS.2023.3302026. [7] R. Guliaev, M. Pardini, K. Papathanassiou, “Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis”, Remote Sensing, vol. 16, no. 2146, 2024, DOI: 10.3390/rs16122146.