Mapping Riparian Vegetation with Lidar Data

Predicting plant community distribution using height above river and flood height 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 spe-cies 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 perpen-dicular 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 de-velopment 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. Deriving HAR from Lidar Data Developing cross section profiles akin to traditional survey methods posed a number of problems. It is time consuming to digitize the thousands of cross sections that would be required to cover the entire 200-mile stretch of the Walker River, which runs from the Sierra Nevada range in eastern California to Walker Lake in western Nevada. However, automated approaches for generating transects also entail problems. If the transects are generated perpendicular to the river, the density of transects is less on the outside of a river bend than on the inside of that river bend. If transects are generated perpendicular to one another, they do not always cross the river at right angles. One-dimensional methods also suffer from problems because points located on different transects may be close to one another geographically yet have very different values because the transects they are on intersect the river at different elevations. When producing a predictive map from suc