Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.

Eco-hydrological models can be used to support effective land management and planning of forest resources. These models require a Digital Elevation Model (DEM), in order to accurately represent the morphological surface and to simulate catchment responses. This is particularly relevant on low altimetry catchments, where a high resolution DEM can result in a more accurate representation of terrain morphology (e.g., slope, flow direction), and therefore a better prediction of hydrological responses. This work intended to use Soil and Water Assessment Tool (SWAT) to assess the influence of DEM resolutions (1 m, 10 m and 30 m) on the accuracy of catchment representations and hydrological responses on a low relief forest catchment with a dry and hot summer Mediterranean climate. The catchment responses were simulated using independent SWAT models built up using three DEMs. These resolutions resulted in marked differences regarding the total number of channels, their length as well as the hierarchy. Model performance was increasingly improved using fine resolutions DEM, revealing a bR² (0.87, 0.85 and 0.85), NSE (0.84, 0.67 and 0.60) and Pbias (−14.1, −27.0 and −38.7), respectively, for 1 m, 10 m and 30 m resolutions. This translates into a better timing of the flow, improved volume simulation and significantly less underestimation of the flow.

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.

Terracing is widely used as an effective soil and water conservation practice in sloped terrains. Physically based hydrological models are useful tools for understanding the hydrological response of terraced catchments. These models typically require a DEM as input data, whose resolution is likely to influence the model accuracy. The main objective of the present work was to investigate how DEM resolution affects the accuracy of terrain representations and consequently the performance of SWAT hydrological model in simulating streamflow for a terraced eucalypt-dominated catchment (Portugal). Catchment´s hydrological responses were analyzed based on two contrasting topographic scenarios: terraces and no terrace, to evaluate the influence of terraces. To this end, different SWAT models were set up using multi-resolution DEMs (10 m, 1 m, 0.5 m, 0.25 m and 0.10 m) based on photogrammetric techniques and LiDAR data. LiDAR-derived DEMs (terraces scenario) improved topographic surface and watershed representation, consequently increasing the model performance, stage hydrographs and flow-duration curves accuracy. When comparing the contrasting topographic scenarios, SWAT simulations without terraces (10 m and 1 m DEMs) produced a more dynamic and rapid hydrological response. In this scenario, the streamflow was 28% to 36% higher than SWAT simulations accounting for the terraces, which corroborates the effectiveness of terraces as a water conservation practice.

Pest damages in eucalyptus plantations cause significant economic losses for the pulp and paper industry. Longhorned borers (ELB) outbreaks induce mortality in eucalyptus stands. In this study, multispectral imagery was obtained from unmanned aerial vehicles. We attempt to improve the classification process done in previous work. The local maxima of sliding a window and the Large-Scale Mean-Shift segmentation (LSMS) were applied to extract tree crows. Subsequently, the mean of spectral bands and twelve vegetation indices were calculated to characterize each segment. To classify tree canopies into dead and healthy trees, supervised machine learning using Random Forest (RF) and Support Vector Machine (SVM) were applied. The overall accuracy of Random Forests was 98.35% and Support Vector Machine of 97.7%. We concluded that SVM did not perform better than RF. Moreover, adding new vegetation indices in the classification process did not increase accuracy.