Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

Abstract

Forests are the planet’s main CO2 filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation).

Multi-Species Individual Tree Segmentation and Identification Based on Improved Mask R-CNN and UAV Imagery in Mixed Forests

“…feature points are easy to search, while broad-leaved tree canopies are complex and size, with fewer similar structures, making feature matching more difficult, fo ple, false C exists in Figure 15d. (4) The distribution of the background data in indicates that the loss and misjudgment in tree species canopy are mainly relate presence of background and False segmentation, and it also can be directly obs Figure 15b,d…”

“…According to recent studies [3], there are approximately three trillion trees on Earth, among which most trees are in tropical and subtropical regions (1.39 trillion), successively followed by boreal forests (0.74 trillion) and temperate forests (0.61 trillion). As one of the major sinks of atmospheric CO 2 , these trees also can contribute critical ecosystem services to mitigate climate change [4]. Since forests have a great influence upon the human environment in many ways, it becomes critical to obtain accurate value at the single tree aspect-with key characteristics such as tree species, canopy size and the number of trees.…”

“…In this case, deep learning (DL) and various convolutional neural networks (CNN) give a novel idea in dealing with the segmentation and classification from the multi-species individual trees [1,23]. CNN can reproduce expert observations of individual trees over hundreds of hectares and has become a powerful artificial intelligence tool for analyzing forestry RGB images [4]. The widespread use of DL and CNN in forest research has facilitated analysis of tree detection [24], tree species classification [25,26] and forest disturbance detection [27,28] in detail.…”

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

“…The data should faithfully reflect the practical problem that is being solved and be divided into training/validation and testing sets. The testing set should have absolutely no overlap with the training/validation set and, if at all possible, they should be independent (see [42,43] for discussion on good practices for data collection and subdivision).…”

Improve the Deep Learning Models in Forestry Based on Explanations and Expertise

“…Due to the ability to gain knowledge from large amounts of train data, artificial intelligence technology represented by deep learning models has also been applied in forestry to accomplish diverse tasks (Wang et al, 2021 ) including tree species classification (Wagner et al, 2019 ) and damage assessment (Hamdi et al, 2019 ; Tao et al, 2020 ). In terms of the data types, most studies in forestry have used deep learning models to analyze remote sensing data (Zhu et al, 2017 ; Diez et al, 2021 ), such as unmanned aerial vehicle (UAV) data (Diez et al, 2021 ; Onishi and Ise, 2021 ), high-resolution satellite images (Li et al, 2017 ), and 3-D point cloud data (Zou et al, 2017 ). There are also some studies based on other data types including the images of digital cameras (Liu et al, 2019 ) and the characteristics of individual trees (Ercanlı, 2020 ).…”