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An Evaluation and Comparison of LiDAR Remote Sensing Technology and Large Scale Digital Photography for Landscape Level Forest Management Applications in complex multi-aged coniferous forests
Karjala, Melanie
The purpose of this project is to evaluate and compare the use of two relatively new and potentially useful remote sensing technologies to assess high priority forest values in managed and unmanaged spruce/subalpine fir forest types in the SBS wk1 (wet-cool) ecozone. Conventional airphotos require time consuming analysis, and optical satellite imagery may exhibit relatively poor spatial and depth resolution and numerical precision. By comparison, LiDAR (Light Detection And Ranging system) has the potential to assess forest condition efficiently by providing accurate and detailed information describing topography and forest structure; and large scale digital aerial photography combines the advantages of very high spatial resolution (12cm pixel size) with digital stereo viewing for tree height measures and capture of fine topographic detail. The LiDAR system emits intense pulses of light, generated by lasers, from an airplane at high altitude toward the ground. Each signal is reflected from either ground or canopy surfaces to the plane and the time between emission and reception is recorded. Each time is converted to the laser?s travel distance, and each tract of land surveyed produces a set of distances which can be used to determine the ground elevation and density and height of forest canopy layers. This technology generates high resolution data where tens of thousands of pulses are transmitted and returned over each footprint (~15m by 15m area). The potential for this technology as a forest management tool in plantations and deciduous forest types has been recently reviewed, and the need for further application and evaluation in a variety of forest types recommended (Evans et al., 2006). Large scale digital photography allows capture of tree and forest attributes under a broader range of illumination conditions than conventional inventory photography. Imagery processing is eliminated since film is not used, therefore product turnaround time is reduced. Softcopy photogrammetry with stereo viewing at a computer monitor using 3D glasses allows the interpreter to selectively view canopy structure to optimize inventory typing process, while strategically capturing the DEM points to optimize the DEM model using the floating dot process. This study will use recently acquired LiDAR data (Aug., 2006) and large scale digital photography (Oct., 2006) for a ~1250 ha section of the Aleza Lake Research Forest (ALRF). This area was specifically located for its: 1)topographic and vegetative diversity; 2)overlap with two sets of recently measured sample plots - one describing forest carbon stocks the other, forest growth and yield; 4)diversity of management regimes including unharvested old forest, partial cuts, and clearcuts; and 5) overlap with areas targeted for harvest in the next 5 years. We will focus on evaluating the use of the two systems in the context of measuring three forest values: understory conifer vegetation, timber volume, and carbon stocks. Integration and correlation of forest cover information, existing sample plot data and LiDAR and photography measurements detailing forest structure will be derived using regression modelling. Utilizing forest inventory maps, conventional airphotos and regionally calculated yield tables present unique challenges with respect to planning harvest units and estimating their timber volumes. Forests at ALRF are: affected by subtle changes in topography resulting in varied levels of productivity; comprised of dynamic, patchy, multi-aged forest types produced by insects, pathogens, and wind events; and composed of a substantial area in natural stands at the end of their optimal rotation age. These characteristics limit the ability to determine stand productivity and ultimately timber yield within potential harvest areas using current tools and methods. LiDAR may overcome these limitations by using canopy densities and heights to improve harvest planning by identifying areas with low produc ...
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