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Integrating Blue Carbon into Sustainable and Resilient Coastal Development
Published in Lisamarie Windham-Myers, Stephen Crooks, Tiffany G. Troxler, A Blue Carbon Primer, 2018
Stephen Crooks, Christine May, Ryan Whisnant, Michelle Orr
Plan conservation and restoration projects in the wider landscape context: When planning to conserve and restore coastal ecosystems it is important to consider how individual projects fit within the wider landscape context (Goals Project 2015). Ecosystem benefits are derived from maintaining and recovering expansive and connected areas rather than a patchwork of isolated fragments. Providing space offers resilience to gradual changes and the capacity to respond to disturbance events such as storms (Pethick and Crooks 2000). Landscape mosaics also offer a degree of ecosystem redundancy, which is important to maintaining resilient populations of species. In urban settings, which do not offer large-scale restoration potential, strategic location, and connection of projects can make the most of space available to maximize ecological benefits (Simenstad et al. 2006) and integration with natural infrastructure approaches for flood risk reduction in channels that transition uplands to coastal waters.
Object based classification using multisensor data fusion and support vector algorithm
Published in International Journal of Image and Data Fusion, 2018
Following two data sets (IEEE GRSS Data Fusion Contest (2014)) acquired over Black Lake area of Thetford Mines, Province of Québec, Canada (46.047927N, 71.366893W), has been used in this study. The region in the mosaic consists of a variety of natural and man-made objects such as trees, vegetation, soil, roads and buildings. The first airborne data set shown in Figure 1 has been acquired using the Telops’ Hyper-Cam, an airborne long-wave infrared hyperspectral imager, which is based on a Fourier-transform spectrometer. The airborne LWIR hyperspectral imagery consists of 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half maximum). The average spatial resolution of LWIR hyperspectral imagery is approximately 1 m.The second airborne data set shown in Figure 2 has been acquired using a digital colour camera (2 Megapixels) mounted on the same platform as that for LWIR Hyperspectral data. The airborne visible imagery consists of uncalibrated, high spatial resolution, digital data with sparse ground coverage over the same area as the LWIR hyperspectral imagery. The average spatial resolution of visible imagery is approximately 0.1 m.