A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

Abstract

In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data.

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

“…The data in this study were limited in the sample size of this study is 2024, of which 1720 were used for training, and so the effectiveness and success of artificial intelligence models in modeling big data may not have been obtained sufficiently, or a limited number of data may have negative effects on iteration success. However, while data pools in the forest growth and yield modeling studies such as this study remain limited the sample size, data analysis which may consist of millions or even millions of data, also called as big data, may be involved in applications such as forestry image processing such as Hamdi et al (2019), Fricker et al (2019) and Sylvain et al (2019). In the analysis of forestry image processing data based on big data, the effectiveness of deep learning techniques will be even more apparent.…”

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

“…Hansen et al [30] have achieved the individual pig recognition with accuracy rates of 96.7%. Fricker et al [31] have proposed CNNs for the identification of tree species in mixed-conifer forest from hyperspectral imagery, the results demonstrated that the method can accurately identify tree species and predict their distribution. Altuntaş et al [32] have used CNNs to recognize haploid and diploid maize seeds automatically and Zhang et al [33] have proposed a global pooling dilated CNN to identify six common cucumber plant disease.…”

Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

“…SVM and RF tend to perform similarly in terms of classification accuracy and training time [29][30][31]. Neural networks are increasingly used in ecological remote sensing studies for their ability to identify trends and patterns from data, model complex relationships, accept a wide variety of input predictor data, and produce high accuracies, at the expense of requiring large amounts of training data [13,[32][33][34]. Tree species classification accuracies reported throughout the literature vary widely from approximately 60% to 95%, along with the type and number of sensors used, biodiversity within forests, and classification methods utilized [6].…”

“…NEON data has enabled a new wave of tree detection and classification research, in addition to a need for integrative and reproducible analysis and synthesis [34,37,44,45]. This research has been further accelerated by an ecological data science competition that has tasked research groups with tree crown segmentation, alignment of data, and species classification at the open canopy longleaf pine ecosystem at the Ordway-Swisher Biological Station in Florida [37,46].…”