Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

Abstract

Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout a complex, mixed-species forests. These methods are first compared to assessments of crown vigor in the field, to evaluate the potential in supplementing this resource intensive practice. This investigation uses the random forest and support vector machine (SVM) machine learning algorithms to classify the imagery into the five forest health classes. Using the random forest classifier, the UAS imagery correctly classified five forest Health classes with an overall accuracy of 65.43%. Using similar methods, the high-resolution airborne NAIP imagery achieved an overall accuracy of 50.50% for the five health classes, a reduction of 14.93%. When these classes were generalized to healthy, stressed, and degraded trees, the accuracy improved to 71.19%, using UAS imagery, and 70.62%, using airborne imagery. Further analysis into the precise calibration of UAS multispectral imagery, a refinement of image segmentation methods, and the fusion of these data with more widely distributed remotely sensed imagery would further enhance the potential of these methods to more effectively and efficiently collect forest health information from the UAS instead of using field methods.

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

“…Unmanned aerial vehicle (UAV) clusters have been widely used to perform various complex missions in military and civil fields, such as plant protection, mobile signal service, load transportation service, target detection, and strike [1][2][3][4][5][6]. For example, a cluster needs to allocate many subgroups of UAV with various ammunition resources to strike heterogeneous enemy targets in different areas, leading to the problem of UAV cluster cooperative strike mission planning.…”

The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level

“…A healthy tree will produce more chlorophyll than a tree that has been under stress due to, e.g., lack of nutrients and water. A change in a photosynthetic production can be reflected so that this tree will have larger amounts of green and red reflectance [31]. For studying vegetation conditions, many VIs based on spectral bands have been developed, and they form combinations of surface reflectance at two or more wavelengths [32].…”

“…Thus far, spectral data for the analysis of tree vitality in forestry were mostly focused on assessing the vitality of the forest in relation to the dynamics of biotic natural disturbances, such as diseases, insect outbreaks (especially damage and early detection of bark beetles) [31,33,[42][43][44][45][46][47][48], and meteorological disasters, such as damage by hurricanes [49]. In this paper, we wanted to investigate the use of the UAV-acquired multiband images for studying the early detection of trees or parts of forest stands where tree vitality is affected due to geomorphologic abiotic disturbances (e.g., rockfalls, avalanches, and debris slides).…”

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

“…Fortunately, the UAV platform had managed to break through this limitation with its centimeter-level spatial resolution [10]. It made it possible to evaluate the health of individual trees based on UAV remote sensing technology [13]. In recent years, the emergence of consumer multispectral UAVs has provided a new perspective for real-time insight into forest health.…”

“…When using the random forest classifier, the classification accuracy of UAS images is 71.19% and that of aerial images is 70.62%. They think that further improving the accurate calibration of UAS multispectral images and improving the image segmentation method will further improve the accuracy of extracting forest health information from UAS multispectral images [13]. Abdallanejad and Panagiotidis used UAS multispectral images to classify tree species and assessed the health of coniferous and broad-leaved mixed forest.…”