Rigorously quantifying the variability seen in dynamic riparian ecosystems, whether due to natural disturbance or anthropogenic causes, is one of the greatest challenges for forest managers. Even quantifying the area left in riparian leave strips over broad areas can be problematic as such areas are often too small to be detected using traditional satellite remote sensing data (at 30m resolution) or broad scale GIS data. Interpretation of aerial photographs for detailed information on stand structure (species composition, density, etc.) is time-consuming and relies on highly-trained operators (of which we are severely lacking). Furthermore, assessing the structure of riparian zone vegetation directly in the field is problematic, time-consuming and often expensive, and thus often infeasible for large regions. In addition, given that riparian areas are also highly productive this has resulted in intense human use of such areas historically (e.g., for forestry). As a result, our understanding of the range and variability in riparian zone structure is poor, and methodologies to characterize the structure of re-growing stands and make restoration decisions are needed. Techniques to assess riparian zone structure are needed that are rapid, inexpensive, complete, and systematic, allow for repeat monitoring for large areas. Although management of riparian zones is at the forefront of environmental management issues in BC, systematic quantification of changes in vegetation structure in riparian zones remain virtually nonexistent for broad areas. Fortunately, over the past 25 years, the spatial resolution of satellite imagery has significantly increased (Tanaka and Sugimura 2001) whereby today less than 1m spatial resolution imagery is readily available. As this high spatial resolution satellite data becomes more readily available, it is being increasingly used for riparian vegetation studies (Muller 1997). Remote sensing of smaller, distinct landscape features such as riparian areas, benefits greatly from higher resolution data that more accurately captures and describes the variability found within these areas. Our primary goal is to rigorously quantify natural and anthropogenic variability in riparian forest structure (using relevant TEM categories) across a diversity of landscapes using QuickBird high spatial resolution satellite imagery. Using a variety of state of the art techniques to process high spatial resolution imagery, including object based classification, this project will classify riparian forest structure at two sites in coastal British Columbia. The accuracy of our classifications will be tested using ground-truth data derived from a combination of field-work, aerial orthophotography, GIS data, as well as local expertise and collaborators. Our project incorporates a substantive, iterative extension project with First Nations ensuring endusers are involved in all stages of the development process for this emerging technology. Fundamental Challenges in Riparian Mapping and Management Riparian forests lie at the interface between terrestrial and aquatic ecosystems, and as a result, contain a variety of unique attributes extremely important to ecosystem integrity and diversity. Riparian vegetation is responsible for moderating in-stream temperatures, as well as providing a source of large woody debris essential for aquatic habitats needed by salmonids. Distinct micro-climatic conditions (temperature and moisture) result in special conditions that certain species take advantage of (e.g., rare amphibians of concern), thus such areas can act as conduits for organisms, matter and energy through the landscape (Apan et al. 2002). As a result of these unique micro-climatic conditions, riparian areas have distinctive vegetation, making them biodiversity hotspots in the landscape. These unique (but sometimes small) linear riparian systems are notoriously difficult to map systematically over broad areas using existing techniques ...
Gergel, Sarah E., Thompson, Shanley D.; Coops, Nicholas C.; Morgan, Jessica L.; Bater, Christopher W.. 2008. Quickbird high resolution satellite imagery for riparian TEM classification. Forest Investment Account (FIA) - Forest Science Program. Forest Investment Account Report. FIA2008MR159
Topic: FLNRORD Research Program
Keywords: Forest, Investment, Account, (FIA), British, Columbia
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