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Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.

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Title: Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.
Authors: Strunk, Jacob L.1 Jacob.Strunk@oregonstate.edu, Temesgen, Hailemariam1, Andersen, Hans-Erik2, Packalen, Petteri3
Source: Photogrammetric Engineering & Remote Sensing. Feb2014, Vol. 80 Issue 2, p143-150. 8p.
Subjects: Landsat satellites, Linear statistical models, LIDAR, Vegetation mapping, Forest mapping
Geographic Terms: Kenai Peninsula (Alaska)
Abstract: In this study we demonstrate that sample strips of lidar in combination with Landsat can be used to predict forest attributes more precisely than from Landsat alone. While lidar and Landsat can each be used alone in vegetation mapping, the cost of wall to wall lidar may exceed users' financial resources, and Landsat may not support the desired level of prediction precision. We compare fitted linear models and k nearest neighbors (kNN) methods to link field measurements, lidm, and Landsat. We also compare 900 m² and 8,100 m² resolutions to link lidar to Landsat. An approach with ]idar and Landsat together reduced estimates of residual variability for biomass by up to 36 percent relative to using Landsat alone. Linear models generally performed better than kNN approaches, and when linking lidar to Landsat, using 8,100 m resolution performed better than 900 m². [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:In this study we demonstrate that sample strips of lidar in combination with Landsat can be used to predict forest attributes more precisely than from Landsat alone. While lidar and Landsat can each be used alone in vegetation mapping, the cost of wall to wall lidar may exceed users' financial resources, and Landsat may not support the desired level of prediction precision. We compare fitted linear models and k nearest neighbors (kNN) methods to link field measurements, lidm, and Landsat. We also compare 900 m² and 8,100 m² resolutions to link lidar to Landsat. An approach with ]idar and Landsat together reduced estimates of residual variability for biomass by up to 36 percent relative to using Landsat alone. Linear models generally performed better than kNN approaches, and when linking lidar to Landsat, using 8,100 m resolution performed better than 900 m². [ABSTRACT FROM AUTHOR]
ISSN:00991112
DOI:10.14358/PERS.80.2.143-150