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Cartus O.,Friedrich - Schiller University of Jena | Santoro M.,Gamma Remote Sensing | Schmullius C.,Friedrich - Schiller University of Jena | Li Z.,Chinese Academy of Forestry
Remote Sensing of Environment | Year: 2011

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~1.5millionkm2) to map four stem volume classes (0-20, 20-50, 50-80, and >80m3/ha). © 2010 Elsevier Inc.


Teatini P.,University of Padua | Teatini P.,Tesa | Tosi L.,Tesa | Strozzi T.,Gamma Remote Sensing
Journal of Geophysical Research: Solid Earth | Year: 2011

Deltas are highly dynamic coastal systems that over the last few decades have generally experienced a substantial area loss caused by trapping of river sediments in upland drainage basins as well as land subsidence due to natural and anthropogenic causes. A major example is the Po Delta in the Mediterranean in northeastern Italy. This area has experienced as much as 3 m of land subsidence from the 1930s to the 1970s primarily because of the extraction of gas-bearing waters. However, present subsidence rates are largely unknown and the ground settlement is supposedly controlled by natural long-term deep processes. We have combined radar Interferometric Point Target Analysis (IPTA) with previous geomorphological investigations on aerial/satellite images and seismic surveys, and geochronological data from core samples and geomechanical in situ tests, to assess the current sinking of the delta and to understand the processes controlling the vertical movement. The high density of the measurable point targets (more than 15,000) allows characterization of the spatial variation in the vertical land motions (VLM), ranging from -1 to -15 mm/yr. We find that subsidence rates are significantly correlated with the age of highly compressible Holocene deposits that compose the shallowest 30-40 m of the sedimentary sequence. A typical log-type consolidation equation applicable at the scale of the entire delta has been obtained. We conclude that the consolidation of late Holocene sediments is the major cause of the present land subsidence in the Po River delta. This finding has significant impact on the understanding of many other modern deltas that were formed in the lower Holocene epoch. Copyright © 2011 by the American Geophysical Union.


Pantze A.,Swedish University of Agricultural Sciences | Santoro M.,Gamma Remote Sensing | Fransson J.E.S.,Swedish University of Agricultural Sciences
Remote Sensing of Environment | Year: 2014

Long-wavelength Synthetic Aperture Radar (SAR) satellite systems have the potential to increase the efficiency of forest mapping and monitoring, which today are based primarily on optical satellite systems. Here, we evaluate the effectiveness of using L-band SAR satellite images to detect and delineate clear-cuts in Swedish boreal forest. A set of computationally efficient techniques are combined in a fully automated unsupervised bi-temporal change detection approach that detects changes in SAR backscatter intensities. For radiometric normalization and initial change classification, we propose an iterative procedure consisting of successive polynomial based histogram matching and thresholding. Recently proposed methods for automatic SAR amplitude ratio thresholding and final change classification are adopted. The latter is a Markov random field based change detection method that exploits both spectral and spatial information from one or multiple SAR polarization channels. The change detection approach was applied to SAR images from the Japanese Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) acquired in Fine Beam Dual (FBD) mode (HH- and HV-polarizations) with a pixel size of 20. m (path data). Clear-cuts that took place between image acquisitions were clearly detected, and most errors were due to imperfect delineations of clear-cut edges. Pixel-wise clear-cut detection accuracies above 90% could be reached, with false alarm rates of approximately 10% or less. The results indicate that ALOS PALSAR path data are well suited for operational clear-cut detection in Swedish boreal forest. © 2014 Elsevier Inc.


Askne J.I.H.,Chalmers University of Technology | Santoro M.,Gamma Remote Sensing
IEEE Geoscience and Remote Sensing Letters | Year: 2015

Boreal forests play an important part in the climate system, and estimates of the biomass are important also from an economic point of view. In this letter, forest aboveground biomass is estimated from bistatic TanDEM-X data, a Lidar digital elevation model (DEM), and the interferometric water cloud model, without using training samples to calibrate the model. The forest was characterized by allometric relations for area fill (vegetation fraction) and height versus stem volume, and stem volume versus biomass. Biomass was estimated for 202 forest stands at least 1 ha large at the forest test site of Remningstorp, Sweden, from 18 bistatic TanDEM-X acquisitions with a relative root-mean-square error (RMSE) of 16%-32%. TanDEM-X acquisitions with a height of ambiguity around 80 m resulted in the best results. A multitemporal combination resulted in a relative RMSE of 17%. This result is comparable with the retrieval error obtained in a previous study when training the model using a set of known forest stands. © 2004-2012 IEEE.


Cartus O.,Woods Hole Oceanographic Institution | Santoro M.,Gamma Remote Sensing | Kellndorfer J.,Woods Hole Oceanographic Institution
Remote Sensing of Environment | Year: 2012

A method for regional scale mapping of aboveground forest biomass with Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data is presented. A fully automated algorithm, exploiting the synergy of SAR and optical remote sensing for the calibration of a semi-empirical model (Santoro et al., 2011; Cartus et al., 2011), was adopted to map forest aboveground biomass with L-band HH and HV intensity in the northeastern United States. In the retrieval algorithm, a semi-empirical model is calibrated and inverted for each HH and HV image to estimate biomass at the pixel level. Where possible, biomass estimates for single images in a multi-temporal stack were combined in a weighted manner. A comparison with the National Biomass and Carbon Dataset, NBCD 2000 (Kellndorfer et al., in preparation), at different spatial scales indicated the feasibility of the automated biomass retrieval approach and confirmed previous findings that the retrieval accuracy for HV intensity is consistently better than that for HH intensity and depends on the imaging conditions. The weighted combination of the biomass estimates from each intensity image in a multi-temporal stack significantly improved the retrieval performance. Because of regional differences in the multi-temporal coverage with ALOS PALSAR dual polarization data (1-5 images for 2007/08) and the pronounced dependence of the retrieval for single images on the imaging conditions, the possibility of producing maps with consistent accuracy at high resolution was found to be limited. Accurate biomass estimates were obtained when aggregating the ALOS biomass maps at county scale and comparing the estimates to Forest Inventory and Analysis (FIA) county statistics (RMSE=12.9t/ha, R2=0.86). © 2012 Elsevier Inc.

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