Combining GIS and lidar data enabled predictive mapping for riparian areas for a portion of the Sierra Nevada mountain range. Riparian areas pose many problems for vegetation modeling because of their narrow width, dendritic pattern, and the sensitivity of plant species to subtle changes in topography that cannot be easily recorded by coarse-scale digital elevation models (DEMs). Vegetation mapping and monitoring in riparian areas have relied heavily on field-based surveys that record the distribution of plant communities along transects perpendicular to the river. Typically, these studies also collect ancillary variables, such as stage elevation or height above the river (HAR), soil texture, and soil moisture, that are used to predict the distribution of vegetation types. However, these methods are extremely time consuming and do not allow for the development of predictive maps because the data collected cannot easily be extrapolated to a larger region. GIS and lidar data provide an opportunity to derive variables, such as HAR, for large areas, making wall-to-wall predictive mapping a possibility.“
Interesting paper, authors: By Thomas E. Dilts, Jian Yang, and Peter J. Weisberg, University of Nevada, Reno
ArcGIS Spatial Analyst extension was used, good example to create a model with ArcGIS from LiDAR dataset.
Link: http://www.esri.com/news/arcuser/0110/files/mapping-with-lidar.pdf
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