Estimation of biophysical parameters in boreal forests from ERS and JERS SAR interferometry

The thesis describes investigations concerning the evaluation of ERS and JERS SAR images and repeat-pass interferometric SAR images for the retrieval of biophysical parameters in boreal forests. The availability of extensive data sets of images over several test sites located in Sweden, Finland and Siberia has allowed analysis of temporal dynamics of ERS and JERS backscatter and coherence, and of ERS interferometric phase. Modelling of backscatter, coherence and InSAR phase has been performed by means of the Water Cloud Model (WCM) and the Interferometric Water Cloud Model (IWCM); sensitivity analysis and implications for the retrieval of forest biophysical parameters have been thoroughly discussed. Model inversion has been carried out for stem volume retrieval using ERS coherence, ERS backscatter and JERS backscatter, whereas for tree height estimation the ERS interferometric phase has been used. Multi-temporal combination of ERS coherence images, and to a lesser extent of JERS backscatter images, can provide stem volume estimates comparable to stand-wise ground-based measurements. Since the information content of the interferometric phase is strongly degraded by phase noise and uncorrected atmospheric artefacts, the retrieved tree height shows large errors

Remote sensing of snow-cover for the boreal forest zone using microwave radar

This doctoral dissertation describes the development of an operationally feasible snow monitoring methodology utilizing spaceborne synthetic aperture radar (SAR) imagery, intended for hydrological applications on the boreal forest zone. The snow-covered area (SCA) estimation methodology developed is characterized using extensive satellite-based datasets, including SAR-based estimation and optical reference data gathered during the snow-melt seasons of 1997-1998, 2000-2002 and 2004-2006 from northern Finland. The methodology applies satellite-based C-band SAR data for snow monitoring during the spring snow-melt season. The SCA information can be utilized for river discharge forecasting and flood predictions and for the optimization of hydropower production. The development efforts included 1) demonstration of a forest compensation algorithm, 2) establishing the use of wide-swath SAR data 3) development of a weather station assimilation procedure and 4) creation of an enhanced reference image selection algorithm for the SCA estimation methodology. The feasibility of a proposed, non-boreal forest specific, SAR-based SCA estimation method was evaluated for the boreal forest zone. The acquired results were compared with the characteristics determined for the boreal-forest specific methodology developed within this dissertation. These results can be used when selecting appropriate SCA estimation approaches for future snow monitoring systems whether conducted in different regions or intended for larger i.e. continental or global scale purposes. An automatic processing system for SCA estimation was developed and demonstrated as part of this work; the system has been delivered to the Finnish Environment Institute for operational use

Snow monitoring using microwave radars

Remote sensing has proven its usefulness in various applications. For mapping, land-use classification and forest monitoring optical satellite and airborne images are used operationally. However, this is not the case with snow monitoring. Currently only ground-based in situ and weather measurements are used operationally for snow monitoring in Finland. Ground measurements are conducted once a month on special snow courses. These measurements are used to update the hydrological model that simulates the runoff. Recently optical images (NOAA AVHRR) have been tested to derive a map of the areal extent of snow. However, during the snow melt, which is the most important period for hydrology, there are few cloudless days and, therefore, the availability of optical data is limited. That is why microwave remote sensing can play an important role in snow melt monitoring due to its unique capability to provide data independent of sun light and in almost all weather conditions. The synthetic aperture radar (SAR) data may make a significant contribution to satellite observations of snow by bridging the period between the on-set and end of snow melt. Microwave radiometers can be used to retrieve the snow water equivalent of dry snow, but they cannot be used to distinguish wet snow and wet ground during the melting period. The results of the thesis indicate that, even in the presence of forest canopies, (1) wet snow can be distinguished from dry snow and bare ground, (2) snow-free areas can be identified, (3) seasonal evolution of snow cover can be monitored and (4) snow-melt maps showing the fraction of snow-free ground (wet ground) and snow (wet snow) can be derived from SAR images.reviewe

Boreal Forest Properties from TanDEM-X Data Using Interferometric Water Cloud Model and Implications for a Bistatic C-Band Mission

Data from TanDEM-X in single-pass and bistatic interferometric mode together with the interferometric water cloud model (IWCM) can provide estimates of forest height and stem volume (or the related above-ground biomass) of boreal forests with high accuracy. We summarize results from two boreal test sites using two approaches, i.e., 1) based on model calibration using reference insitu stands, and 2) based on minimization of a cost function. Both approaches are based on inversion of IWCM, which models the complex coherence and backscattering coefficient of a homogeneous forest layer, which includes gaps where free-space wave propagation is assumed. A digital terrain model of the ground is also needed. IWCM is used to estimate forest height or stem volume, since the two variables are assumed to be related through an allometric equation. A relationship between the fractional area of gaps, the area-fill, and stem volume is also required to enable model inversion. The accuracy of the stem volume estimate in the two sites varies between 16% and 21% for height of ambiguity <100 m. The results clearly show the importance of using summer-time acquisitions. Based on the TanDEM-X results at X-band, C-band data from the ERS-1/ERS-2 tandem mission are revisited to investigate the potential of a future bistatic C-band interferometric mission. Out of nine ERS-1/ERS-2 pairs, only one pair was found to be acquired at summer temperatures, without precipitation and with high coherence. A simulated bistatic phase height is shown to give approximately the same sensitivity to stem volume as TanDEM-X

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Comparison of snow-covered area estimation methods in northern Finland

An accuracy evaluation of the Nagler & Rott snow-covered area estimation method is presented in this work. The main objective is to determinate the effects of the threshold level, the reference image, as well as the area analysed.The method is studied by conducting the SCA estimation for 14 Radarsat intensity images of northern Finland where is dominated by boreal forest. The results are also compared with the estimations carried out by the current TKK method