The Application of UASs in Forest Management and Monitoring: Challenges and Opportunities for Use in the Miombo Woodland

Abstract

The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications.

Use of Multi-Date and Multi-Spectral UAS Imagery to Classify Dominant Tree Species in the Wet Miombo Woodlands of Zambia

“…Three UAS images used to classify tree species were acquired on 25 May 2021 at full leaf maturity, 15 August 2021 at senescence for the majority of dominant canopy tree species and early flushing for BL and BS species, and 24 October 2021 at greening of flushed leaves for the majority of dominant species [ 1 , 37 ]. The DJI Phantom 4 RTK Multispectral multi-rotor UAS, equipped with one RGB camera and a multispectral camera array with five cameras covering blue (450 nm ± 16 nm), green (560 nm ± 16 nm), red (730 nm ± 16 nm), red edge (450 nm ± 16 nm), and near-infrared (840 nm ± 26 nm), as well as a D-RTK 2 mobile Global Navigation Satellite System (GNSS) base station [ 38 ], was used to capture imagery for this study.…”

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

“…UAS technologies show potential to strategically fit into the operational framework of this challenging fire environment (Ambrosia and Wegener, 2009;Zajkowski et al, 2016). For example, UAS technology proves to be relatively safe, inexpensive, and low complexity when compared to their manned counterparts (Shamaoma et al, 2022;Keerthinathan et al, 2023). UAS-captured information is also relatively fine resolution and temporally flexible when compared to space-borne and airborne remote sensing methods (Muchiri and Kimathi 2016;Pádua et al, 2017).…”