Lori White

A Collection of SAR Methodologies for Monitoring Wetlands

Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way for wetland resources because their data can be used to map and monitor changes in surface water extent, saturated soils, flooded vegetation, and changes in wetland vegetation cover. We review a few techniques to demonstrate SAR capabilities for wetland monitoring, including the commonly used method of grey-level thresholding for mapping surface water and highlighting changes in extent, and approaches for polarimetric decompositions to map flooded vegetation and changes from one class of land cover to another. We use the Curvelet-based change detection and the Wishart-Chernoff Distance approaches to show how they substantially improve mapping of flooded vegetation and flagging areas of change, respectively. We recommend that the increasing availability SAR data and the proven ability of these data to map various components of wetlands mean SAR should be considered as a critical component of a wetland monitoring system.

RADARSAT-2 Beam Mode Selection for Surface Water and Flooded Vegetation Mapping

Abstract Synthetic Aperture Radar (SAR) is well known for its ability to map surface water. There are a number of SAR satellites providing data for this application including the Canadian RADARSAT-2 system. RADARSAT-2 has a wide range of beam modes and some users may be intimidated by the variety of choices and have a difficult time deciding on the most appropriate beam mode. This technical note addresses some issues behind beam mode and polarization selection for surface water mapping with RADARSAT-2 and the upcoming RADARSAT Constellation Mission (RCM). This includes the impacts of resolution, wind effects, and the best mode for flooded vegetation detection. The results show that high resolution modes like the single polarized Spotlight are best for accurately delineating the surface water edge and small patches of flooded terrain. The addition of the cross-polarization available in other beam modes can provide useful surface water information in windy or rough surface conditions because there is little effect on the RADAR backscatter compared to the HH single polarization. For accurately delineating flooded vegetation, a polarimetric or compact polarimetric mode is best because the phase is maintained, which allows the user to apply polarimetric decompositions models to help separate the RADAR backscatter.

Change Detection with Compact Polarimetric SAR for Monitoring Wetlands

Abstract. Compact polarimetric synthetic aperture radar (SAR) architecture is an SAR configuration that consists of transmitting a single circular polarization (left or right) or a 45° oriented linear signal while receiving two linear polarizations, horizontal and vertical. In this study we investigate the potential of the compact polarimetric SAR mode for wetland monitoring applications. Whitewater Lake located in Manitoba, Canada, is selected as a case study where simulated compact polarimetric SAR data are obtained using RADARSAT-2 Fine Quad-POL SAR images. The ability of the compact polarimetric data to monitor wetlands using the Wishart-Chernoff distance is studied and compared to the results obtained using fully polarimetric data. Results of this study show that compact polarimetry provides monitoring capabilities for wetlands. Promising change detection mapping results based on the compact polarimetric coherency matrices are obtained using the Wishart-Chernoff distance. This could be useful for flagging change in the wetland environment for further evaluation and action if required. The compact polarimetry mode could be an attractive configuration for future SAR systems due to the combination of swath coverage, moderate resolution, and enhanced information content for monitoring changes in surface water and flooded vegetation. Résumé. L’architecture du radar à synthèse d’ouverture (SAR) en polarimétrie compacte est une configuration SAR qui transmet un seul signal en polarisation circulaire (gauche ou droite) ou un signal linéaire à 45 degrés tout en recevant deux polarisations linéaires, horizontales et verticales. Dans cette étude, nous étudions le potentiel du mode en polarimétrie compacte du SAR pour les applications de surveillance des zones humides. Le lac Whitewater, situé au Manitoba, au Canada, est sélectionné pour une étude de cas où les données SAR en polarimétrie compacte simulées sont obtenues en utilisant les images SAR en mode quad-pol fin du RADARSAT-2. La capacité des données en polarimétrie compacte pour surveiller les zones humides à l’aide de la distance Wishart-Chernoff a été étudiée, puis comparée aux résultats obtenus en utilisant les données en polarimétrie complète. Les résultats de cette étude montrent que la polarimétrie compacte offre des capacités de surveillance pour les zones humides. Des résultats prometteurs de la cartographie de détection du changement basée sur les matrices de cohérence de la polarimétrie compacte ont été obtenus en utilisant la distance Wishart-Chernoff. Ceci pourrait être utile pour signaler les changements dans les zones humides pour une évaluation supplémentaire et une action le cas échéant. Le mode en polarimétrie compacte pourrait être une configuration attrayante pour les futurs systèmes SAR en raison de la combinaison de la largeur de la couverture au sol, de la résolution moyenne et du contenu d’information effective pour la surveillance des changements dans les eaux de surface et la végétation inondée.

Seasonal Change in Wetland Coherence as an Aid to Wetland Monitoring

Water is an essential natural resource, and information about surface water conditions can support a wide variety of applications, including urban planning, agronomy, hydrology, electrical power generation, disaster relief, ecology and preservation of natural areas. Synthetic Aperture Radar (SAR) is recognized as an important source of data for monitoring surface water, especially under inclement weather conditions, and is used operationally for flood mapping applications. The canopy penetration capability of the microwaves also allows for mapping of flooded vegetation as a result of enhanced backscatter from what is generally believed to be a double-bounce scattering mechanism between the water and emergent vegetation. Recent investigations have shown that, under certain conditions, the SAR response signal from flooded vegetation may remain coherent during repeat satellite over-passes, which can be exploited for interferometric SAR (InSAR) measurements to estimate changes in water levels and water topography. InSAR results also suggest that coherence change detection (CCD) might be applied to wetland monitoring applications. This study examines wetland vegetation characteristics that lead to coherence in RADARSAT-2 InSAR data of an area in eastern Canada with many small wetlands, and determines the annual variation in the coherence of these wetlands using multi-temporal radar data. The results for a three-year period demonstrate that most swamps and marshes maintain coherence throughout the ice-/snow-free time period for the 24-day repeat cycle of RADARSAT-2. However, open water areas without emergent aquatic vegetation generally do not have suitable coherence for CCD or InSAR water level estimation. We have found that wetlands with tree cover exhibit the highest coherence and the least variance; wetlands with herbaceous cover exhibit high coherence, but also high variability of coherence; and wetlands with shrub cover exhibit high coherence, but variability intermediate between treed and herbaceous wetlands. From this knowledge, we have developed a novel image product that combines information about the magnitude of coherence and its variability with radar brightness (backscatter intensity). This product clearly displays the multitude of small wetlands over a wide area. With an interpretation key we have also developed, it is possible to distinguish different wetland types and assess year-to-year changes. In the next few years, satellite SAR systems, such as the European Sentinel and the Canadian RADARSAT Constellation Mission (RCM), will provide rapid revisit capabilities and standard data collection modes, enhancing the operational application of SAR data for assessing wetland conditions and monitoring water levels using InSAR techniques.

Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM.

Polarimetric Decompositions of Temperate Wetlands at C-Band

C-band SAR is well established as a useful sensor for water resources applications. It is commonly accepted that the backscatter from wetlands that consist of many emergent stems over open water (swamps and marshes) is dominated by a double-bounce scattering mechanism. However, recent observations with fully polarimetric data from Radarsat-2 over the extensive wetlands of the Everglades and numerous small wetlands in Ontario appear to be inconsistent with this interpretation of the backscatter physics. In this paper, we use several forms of polarimetric analysis and decomposition. All of these indicate that the backscatter from small marshes and swamps in Ontario is dominated by polarimetric characteristics normally attributed to the odd-bounce mechanism. This anomalous result might be explained as a consequence of changes in the double-bounce reflectance properties of vegetation as a function of the incidence angle. However, detailed electromagnetic backscatter modeling will be needed to provide a more complete and reliable understanding of the details of backscattering from wetlands with emergent vegetation. Additional observational and theoretical work will be required to document and understand the unusual results we report here. If these results are substantiated, the SAR community must re-interpret the generally accepted meanings of the popular decomposition variables, and introduce new terminology to describe them. This would lead to an improved understanding of the backscatter physics and better use of polarimetric SAR for wetland management applications.

A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Random Forests variable importance measures are often used to rank variables by their relevance to a classification problem and subsequently reduce the number of model inputs in high-dimensional data sets, thus increasing computational efficiency. However, as a result of the way that training data and predictor variables are randomly selected for use in constructing each tree and splitting each node, it is also well known that if too few trees are generated, variable importance rankings tend to differ between model runs. In this letter, we characterize the effect of the number of trees (ntree) and class separability on the stability of variable importance rankings and develop a systematic approach to define the number of model runs and/or trees required to achieve stability in variable importance measures. Results demonstrate that both a large ntree for a single model run, or averaged values across multiple model runs with fewer trees, are sufficient for achieving stable mean importance values. While the latter is far more computationally efficient, both the methods tend to lead to the same ranking of variables. Moreover, the optimal number of model runs differs depending on the separability of classes. Recommendations are made to users regarding how to determine the number of model runs and/or trees that are required to achieve stable variable importance rankings.

Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images.

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.

Mapping and monitoring flooded vegetation and soil moisture using simulated compact polarimetry

This paper shows that the m-chi decomposition, the Shannon-Entropy model and the Wishart-Chernoff distance can be used to map and monitor wetlands. Areas which changed from flooded vegetation to non-flooded vegetation were accurately mapped using the m-chi decomposition, and areas that changed from saturated soil to unsaturated soil were visible with the Shannon-Entropy model. In addition, the Wishart-Chernoff distance was able to map wetland areas which had changed to a different land cover type over time.