Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Abstract

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

“…By combining remote sensing data with artificial intelligence techniques, it is possible to properly map tree species and improve accuracy in applications regarding vegetation monitoring. In recent years, multiple frameworks have been implemented to assess the performance of such algorithms to accomplish this task (2,5,15,(35)(36)(37). During the past years, the detection and extraction of trees in high-resolution imagery were performed with more traditional machine learning algorithms, like support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and others (38)(39)(40)(41).…”

“…However, state-of-the-art deep learningbased methods should be capable of identifying single treespecies with an attractive accuracy and computational load even in RGB images. Recently, deep learning-based methods have been implemented in multiple remote sensing, specifically for image segmentation, classification, and object detection approaches (37,(47)(48)(49). Deep learning techniques are among the most recently adopted approaches to process remote sensing data (50)(51)(52).…”

Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

“…The spectral content originates from the images, which are radiometrically calibrated using the mapping camera software package with the aim of creating homogenous looking image blocks and orthophotos [22]. Alternatively, more advanced methods like radiometric block adjustment [23,24] can be done on the individual images. However, when the spectral information is projected to the point cloud it can be done in different ways as each point is seen in more than one aerial image [25].…”

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

“…Mapping and monitoring the tree species composition of Earth's forests using remote sensing technologies is a challenging task that has motivated an active research community over the past three decades [1][2][3][4][5][6][7]. In between the global coverage and long-standing historical records of satellite platforms [8][9][10] and the flexibility and high spatial resolution achieved by unmanned aircraft systems [11][12][13], airborne platforms provide a valuable intermediate scale of local to regional ecosystem monitoring capabilities [3,[14][15][16][17]. Passive optical (multispectral [10] and hyperspectral [18]) and active (such as Light Detection and Ranging (LiDAR) [19]) remote sensing systems have each been used to map tree species on their own with varying levels of success, but combining spectral and structural remote sensing data has been shown to generally improve tree species detection abilities compared to using any of them alone [2,3,14,[20][21][22].…”

“…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].…”