Artificial Intelligence

    

Predicting Year of Plantation with Hyperspectral and Lidar Data

Authors: Adrià Descals, Luis Alonso, Gustau Camps-Valls

This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classi-fiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sensible feature rankings.

Comments: 4 Pages.

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Submission history

[v1] 2020-12-19 11:21:13

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