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This paper describes an approach for combining Landsat and Radarsat satellite images to generate national statistics for urban ecosystem accounting. These accounts will inform policy related to the development of mitigation measures for climatic and hydrologic events in Canada. Milton, Ontario was used as a test case for the development of an approach identifying urban ecosystem types and assessing change from 2001 to 2019. Methods included decomposition of Radarsat images into polarimetric parameters to test their usefulness in characterizing urban areas. Geographic object-based image analysis (GEOBIA) was used to identify urban ecosystem types following an existing classification of local climate zones. Three supervised classifiers: decision tree, random forest and support vector machine, were compared for their accuracy in mapping urban ecosystems. Ancillary geospatial datasets on roads, buildings, and Landsat-based vegetation were used to better characterize individual ecosystem assets. Change detection focused on the occurrence of changes that can impact ecosystem service supply – i.e., conversions from less to more built-up urban types. Results demonstrate that combining Radarsat polarimetric parameters with the Landsat images improved urban characterization using the GEOBIA random forest classifier. This approach for mapping urban ecosystem types provides a practical method for measuring and monitoring changes in urban areas. [ABSTRACT FROM AUTHOR]

This work proposes a method for detecting inundation between semi-diurnal low and high water conditions in the northern Gulf of Mexico using high-resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood-prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high-resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high-resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Remote sensing is the most practical method available to managers of flood-prone areas for quantifying and mapping flood impacts. This study explored large inundation areas in the Maghna River Basin, around the northeastern Bangladesh, as determined from passive sensor LANDSAT data and the cloud-penetrating capabilities of the active sensors of the remote imaging microwave RADARSAT. This study also used passive sensor LANDSAT wet and dry images for the year 2000. Spatial resolution was 30 m by 30 m for comparisons of the inundation area with RADARSAT images. RADARSAT images with spatial resolution of 50 m by 50 m were used for frequency analysis of floods from 2000 to 2004. Time series images for 2004 were also used. RADARSAT remote sensing data, GIS data, and ground data were used for the purpose of flood monitoring, mapping and assessing. A supervised classification technique was used for this processing. They were processed for creating a maximum water extent map and for estimating inundation areas. The results of this study indicated that the maximum extent of the inundation area as estimated using RADARSAT satellite imaging was about 29, 900.72 km in 2004, which corresponded well with the heavy rainfall around northeast region, as seen at the Bhairab Bazar station and with the highest water level of the Ganges-Brahmaputra-Meghna (GBM) Rivers. A composite of 5 years of RADARSAT inundation maps from 2000 to 2004, GIS data, and damage data, was used to create unique flood hazard maps. Using the damage data for 2004 and the GIS data, a set of damage maps was also created. These maps are expected to be useful for future planning and flood disaster management. Thus, it has been demonstrated that RADARSAT imaging data acquired over the Bangladesh have the ability to precisely assess and clarify inundation areas allowing for successful flood monitoring, mapping and disaster management. [ABSTRACT FROM AUTHOR]

Abstract: The magnitude, duration, and frequency of forest disturbance caused by the spruce budworm and forest tent caterpillar in northern Minnesota and neighboring Ontario, Canada have increased over the last century due to a shift in forest species composition linked to historical fire suppression, forest management, and pesticide application that has fostered increased dominance of host tree species. Modeling approaches are currently being used to understand and forecast potential management effects in changing insect disturbance trends. However, detailed forest composition data needed for these efforts is often lacking. We used partial least squares (PLS) regression to integrate different combinations of satellite sensor data including Landsat, Radarsat-1, and PALSAR, as well as pixel-wise forest structure information derived from SPOT-5 sensor data (Wolter et al., 2009), to determine the best combination of sensor data for estimating near species-level proportional forest composition (12 types: 10 species and 2 genera). Single-sensor and various multi-sensor PLS models showed distinct species-dependent sensitivities to relative basal area (BA), with Landsat variables showing greatest overall sensitivity. However, best results were achieved using a combination of data from all these sensors, with several C-band (Radarsat-1) and L-band (PALSAR) variables showing sensitivity to the composition and abundance of specific species. Pixel-level forest structure estimates derived from SPOT-5 data were generally more sensitive to conifer species abundance (especially white pine) than to hardwood species composition. Relative BA models accounted for 68% (jack pine) to 98% (maple spp.) of the variation in ground data with RMSE values between 2.46% and 5.65% relative BA, respectively. Receiver operating characteristic (ROC) curves were used to determine the effective lower limits of usefulness of species relative BA estimates which ranged from 5.94% (jack pine) to 39.41% (black ash). These estimates were then used to produce a dominant forest species map for the study region with an overall accuracy of 78%. Most notably, this approach facilitated discrimination of aspen from paper birch as well as spruce and fir from other conifer species which is crucial for the study of forest tent caterpillar and spruce budworm dynamics in the Upper Midwest. We also demonstrate that PLS regression is an effective data fusion strategy for mapping composition of heterogeneous forests using satellite sensor data. [Copyright &y& Elsevier]

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world''s largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for characterizing the existing tundra–taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth.It was found that a tundra–taiga transitional area can be characterized using multi-spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent. [Copyright &y& Elsevier]

This study quantified relationships between forest composition and flooding gradients on the Roanoke River floodplain, North Carolina. Because flooding is highly variable in time and space, the research was designed to determine the specific hydrological parameters that control woody species abundance on the landscape scale. I specifically tested the importance of spring vs. yearly flood duration, as well as flood duration during hydrologically wet vs. dry years. Field vegetation samples of woody species composition were integrated with spatial data from a Landsat Thematic Mapper (TM) classification and a flood simulation model derived in part from synthetic aperture radar (SAR) imagery. Flood simulations were output and summarized for the periods 1912–1950 (before dams were constructed on the river) and 1965–1996 (after all of the dams were completed). Tenth percentile (dry), median, and 90th percentile (wet) hydroperiod (flood duration) regimes were generated for the spring and year, both pre- and post-dam. Detrended correspondence analysis (DCA) was used to ordinate the plot data, and correlation/regression between ordination axis scores and the flood variables were used to explore the relationships between flooding and species composition. Nineteenth percentile hydroperiod (i.e., wet conditions) correlated most strongly with DCA axis 1 ( r>0.9), indicating that inundation during extremely wet years strongly controls species composition on the floodplain. The results were used to quantitatively determine the niche width for both species and mapped vegetation classes in terms of number of days flooded annually and during the spring growth period. The results suggest that spring hydroperiod is an important mechanism that may drive competitive sorting along the flooding gradient, especially during the early years of succession (i.e., pre-dam, which represents the period during which most of the forests sampled were established), and that annual hydroperiod affects the relative dominance of species as the forests mature. [ABSTRACT FROM AUTHOR]

Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for each. Historical open water maps from 202 Landsat scenes spanning the years 1985–2016 generated previously were enhanced to improve flooded vegetation mapping along the Saint John River in New Brunswick, Canada. Open water and flooded vegetation maps were created over the same region from 181 RADARSAT 1 and 2 scenes acquired between 2003–2016. Comparisons of maps from different sensors and hydrometric data were performed to examine consistency and robustness of products derived from different sensors. Simulations reveal that the methodology used to map open water from dual-pol RADARSAT 2 is insensitive to up to about 20% training error. Landsat depicts open water inundation well, while flooded vegetation can be reliably mapped in leaf-off conditions. RADARSAT mapped approximately 8% less open water area than Landsat and 0.5% more flooded vegetation, while the combined area of open water and flooded vegetation agreed to within 0.2% between sensors. Derived historical products depicting inundation frequency and trends were also generated from each sensor’s time-series of surface water maps and compared. [ABSTRACT FROM AUTHOR]