Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery

Author:

Ni-Meister Wenge12ORCID,Albanese Anthony1,Lingo Francesca2ORCID

Affiliation:

1. Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA

2. Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA

Abstract

Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global tree species distribution. Many questions remain unanswered about the best practices for creating a global, general hyperspectral tree species classification model. This study aims to address three key issues in creating a hyperspectral species classification model. We assessed the effectiveness of three data-labeling methods to create training data, three data-splitting methods for training/validation/testing, and machine-learning and deep-learning (including semi-supervised deep-learning) models for tree species classification using hyperspectral imagery at National Ecological Observatory Network (NEON) Sites. Our analysis revealed that the existing data-labeling method using the field vegetation structure survey performed reasonably well. The random tree data-splitting technique was the most efficient method for both intra-site and inter-site classifications to overcome the impact of spatial autocorrelation to avoid the potential to create a locally overfit model. Deep learning consistently outperformed random forest classification; both semi-supervised and supervised deep-learning models displayed the most promising results in creating a general taxa-classification model. This work has demonstrated the possibility of developing tree-classification models that can identify tree species from outside their training area and that semi-supervised deep learning may potentially utilize the untapped terabytes of unlabeled forest imagery.

Funder

NASA

Publisher

MDPI AG

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