Vegetation change detection and quantification: linking Landsat imagery and LIDAR data

Birgit E. Peterson; Kurtis J. Nelson

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USGS · 70157322

Vegetation change detection and quantification: linking Landsat imagery and LIDAR data

Birgit E. Peterson·Kurtis J. Nelson

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Abstract

Measurements of the horizontal and vertical structure of vegetation are helpful for detecting and monitoring change or disturbance on the landscape. Lidar has a unique ability to capture the three-dimensional structure of vegetation canopies. In this preliminary study, we present the results of a series of exploratory data analyses that tested our assumptions about the links between the structural data obtainable from lidar and the change detection products derived from Landsat imagery. Our study area is located in the Sierra National Forest in the Sierra Nevada Mountains of California and covers a wide range of vegetation types. The lidar data used in this study were collected by the Laser Vegetation Imaging System (LVIS) (Blair et al., 1999). LVIS is a largefootprint lidar system optimized to measure canopy structure characteristics. A series of Landsat scenes from 1984 through 2008 was collected for the study area (Path 42, Row 34) and processed to generate maps of disturbance. The preliminary results described here indicate that even simple metrics of height can be useful in assessing changes in structure brought about by disturbance in forest canopies. For example, canopy height values for 2008 were higher on average than those measured for 1999 in undisturbed forest, whereas this trend is not clearly observable for the disturbed forest patches.

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Source-reported bounding extent: 36.756490329505176° to 37.709899354855125° latitude; -119.63287353515624° to -118.42437744140625° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Birgit E. Peterson, Kurtis J. Nelson. 2009. Vegetation change detection and quantification: linking Landsat imagery and LIDAR data. https://pubs.usgs.gov/publication/70157322

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