Unmanned Aerial System and Machine Learning Techniques Help to Detect Dead Woody Components in a Tropical Dry Forest

A multi-scale approach to detecting standing dead trees in UAV RGB images based on improved faster R-CNN

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future.

UAV-Based Forest Health Monitoring: A Systematic Review

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

New Contributions to the Ericion umbellatae Alliance in the Central Iberian Peninsula

The study of heathlands dominated by Erica australis, E. umbellata and Cistus populifolius in the centre and west of the Iberian Peninsula allows us to separate the eight shrubland communities. The taxonomic analysis of E. australis distinguishes two subspecies: E. australis subsp. australis and E. australis subsp. aragonensis. The statistical treatment confirms the differences between the suballiances Ericenion aragonensis and Ericenion umbellatae. This ecological, bioclimatic, biogeographical and floristic study has allowed us to differentiate three new associations from the remaining five: TCp = Teucrio oxylepis-Cistetum populifolii nova. HEau = Halimio ocymoidis-Ericetum australis nova. DEu = Drosophyllo lusitanicae-Ericetum umbellatae nova. ECp = Erico australis-Cistetum populifolii Rivas Goday 1964. PCp = Polygalo microphyllae-Cistetum populifolii Rivas Goday 1964. HEa = Halimio ocymoidis-Ericetum aragonensis Rivas-Martínez 1979. HEu = Halimio ocymoidis-Ericetum umbellatae Rivas Goday 1964. UEu = Ulici eriocladi-Ericetum umbellatae.