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).
Highlights
A key point from this research area is that it requires multi-disciplinary teams in order to achieve contributions that are solid from the point of view of deep learning (DL) and can produce significant advances in the understanding of forests
Based on this forestry-expert analysis, we assigned subjective values representing the level of difficulty for each of the problems addressed in this review
The promising results obtained by Mask R-CNN [36], specially when re-training both the network head and backbone [5], indicate that publicly available databases of challenging tree detection scenarios can help to overcome this challenge
Summary
Forests represent an invaluable source of natural resources as well as one of the main sinks of atmospheric CO2. Climate change exerts positive and negative feedback on forests that are still not well understood. Developing new technologies that allow scientists to study large forest areas with a high level of detail is a crucial step to understand the response of forests to environmental changes. Unmanned Aerial Vehicles (UAVs) are becoming an essential tool in forestry research thanks to their capacity to cover high spatial resolutions and provide a high temporal-frequency analysis [1,2,3] for the required level of detail. UAVs are inexpensive, easy-to-use remotely operated vehicles that can carry a varied array of sensors such as LiDAR, multispectral, hyperspectral and RGB cameras
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