Speaker
Description
Remote sensing represents a powerful tool for an effective monitoring at a global scale of vegetated areas [1-3]. In particular, given the daylight independence and the capability to penetrate clouds, space-borne synthetic aperture radar (SAR) systems represent a unique solution for the mapping and monitoring of forests.
Sentinel-1, with its large coverage and short revisit-time, is a breakthrough technology, ideal for the generation of a constantly updated forest coverage map product and for the rapid monitoring of large-scale areas, aiming at detecting ongoing deforestation activities and forest disturbance.
Even though the detected SAR backscatter already provides useful information on forest coverage and structure, the use of SAR interferometry adds valuable and reliable information to the classification method [4-5]. In this work, we investigate the evolution in time of the interferometric coherence for different land cover types and exploit its potentials for classification purposes.
In particular, the exponential decorrelation model presented in [6] is used to model the temporal evolution of Sentinel-1 stacks. The model parameters, experimentally retrieved from the data, show a highly informative content and can be used as input observables for the derivation of an ad-hoc classification algorithm based on machine learning.
In the final paper we will present the preliminary results for mapping forested areas over the Amazon rainforest by exploiting Sentinel-1 interferometric time-series. The test site is located in the Rondonia state, Brazil, where constantly deforestation takes place and an up-to-date mapping is therefore highly recommended. Moreover, we will assess advantages and drawbacks of using Sentinel-1 repeat-pass interferometry for classification purposes. Finally, we will address the potential of combining this approach with high-resolution forest/non-forest maps from bistatic TanDEM-X data for an effective monitoring of on-going deforestation [7].
[1] M.C. Hansen, P.V. Potapov, R. Moore, M. Hancher, S.A. Turubanova, A. Tyukavina, D. Thau, S.V. Stehamn, S.J. Goetz, T.R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C.O. Justice, and J.R.G. Townshend: High-resolution global maps of 21st century forest coverage change. Science, vol. 342, pp. 850-853, 2013.
[2] R.O. Dubayah and J. B. Drake: Lidar Remote Sensing for Forestry. Journal of Forestry, vol. 98, no. 6, pp. 44-46, Jun. 2000.
[3] Schmullius, C., Thiel, C., Pathe, C., Herold, M. and Avitabile, V.: DUE GlobBiomass-Estimates of Biomass on a Global Scale.
[4] Hagberg, J. O., Ulander, L. M., and Askne, J. : Repeat-pass SAR interferometry over forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 331-340, 1995.
[5] J. I. H. Askne, P. B. G. Dammert, L. M. H. Ulander, and G. Smith: C-Band Repeat-Pass Interferometric SAR Observations of the Forest. IEEE Trans. On Geoscience and Remote Sensing, vol. 35, n. 1, pp. 25-35, Jan. 1997.
[6] F. Rocca. Modeling interferogram stacks. IEEE Trans. On Geoscience and Remote Sensing, vol. 45, n. 10, pp. 3289-3299, Oct. 2007.
[7] M. Martone, F. Sica, C. Gonzalez, J.-L. Bueso-Bello1, P. Valdo, and P. Rizzoli: High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering, Remote Sensing MDPI, under review.
