UAV-assisted seeding and monitoring of reforestation sites: a review

Figures & data

Figure 1. Specific species mapping of Australian shrub swamp communities. True colour orthomosaic (left); near-infrared false colour composite (Band 1: Green, Band 2: Blue and Band 3: Near Infrared) (middle); subset of total swamp with classified Gleichenia microphylla overlaid on true colour composite (right) (Strecha et al. 2012)

Table 1. Summary of uncrewed automated vehicle (UAV) parameters, as reported by enterprises

UAV enterprise	Seed deployment rate per UAV	Flight time	Land coverage rate	UAV technology	Payload capacity	Cost analysis
Flash Forest Canada	75 seedpods min^−1 (10 000–20 000 seedpods day^−1)	30–55 min	50 ha day^−1	Automated mapping UAVs, pneumatic planting UAVs, macro/micro site detection, DJI UAVs: Phantom 4 Pro V2, the Matrice 600 Pro, Matrice 300 RTK	N/A	10 times faster and 80% cheaper than traditional planting
Dendra Systems United Kingdom	120 seedpods min^−1	2 h	60 ha day^−1	Sensors resolve images at 2–3 cm pixel^−1, RGB, 4 band multispectral sensors	22 kg	11 times faster and 3 times cheaper than traditional planting
AirSeed Australia	120 seeds min^−1	10 min	40 ha day^−1	Pneumatic shooting device, AI and GPS, terrain mapping	N/A	25 times faster and 80% cheaper than traditional planting, 80% seed-germination success
Lord Of The Trees Australia	120 seedpods min^−1	N/A	160 000 seeds day^−1 80 ha day^−1	N/A	N/A	>75% seed growth success
CO2 Revolution Spain	N/A	30–55 min	1 ha per 10 min	GPS and mapping systems, spectrographic camera, DJI UAVs, BIG DATA software	N/A	10% of the cost and time of traditional planting, 80% seed growth success
Droneseed United States	800 seeds h^−1	8–18 min	3000 m^2 flight^−1	lidar, spectral cameras, pneumatic shooting	26 kg	6 times faster and 10% the cost of traditional planting
Dronecoria Spain	N/A	42 min without payload	10 ha h^−1	Basic 3D printable design for individuals, aerial seed sowing UAV, fully open source	8 kg	10 times cheaper than traditional planting
Seedcopter India	60 seedpods min^−1	N/A	100 000 trees day^−1 50 ha day^−1	Hyperspectral imaging, machine learning, geotagged flight path	16 kg	10 times cheaper than traditional planting
CAFU United Arab Emirates	20 seedpods min^−1	20–30 min	N/A	Pneumatic shooting, GPS geotagging	N/A	N/A

AI = artificial intelligence; GPS = global positioning system; N/A = not applicable; RGB = red, green, blue.

Table 2. Uncrewed aviation vehicle (UAV) forestry mission parameters

Camera model	UAV model	Forestry application
Ricoh GR3 camera 10 MP, 75% overlap	Gatewing X100 wingspan 100 cm, flight height 100–750 m	Classification of riparian forest species and health condition (Michez et al. 2016)
RGB camera 16 MP, 70–90% overlap	Aeryon Skyranger flight height 100 m	Individual tree estimates for Douglas fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) and lodgepole pine (Pinus contorta var. latifolia Engelm.) (Gómez et al. 2020)
RGB camera 24 MP, 85% overlap	SenseFly eBee X flight height 121.92 m	Finely monitoring individual tree health using UAVs compared with satellite imagery (Fraser and Congalton 2021)
JHP QX3MINI camera 80% overlap	DJI M600 Pro Six rotors, flight height 150–250 m	Delineation of individual trees and extraction of height and crown area of pine plantation forests in China (Zhou and Zhang 2020)
Sony NEXT5T digital camera 16.1 MP, 85% overlap	Multicopter DJI Matrice 600 Pro flight height 40 m	Measuring and identification of individual tree crowns of woody species (Yurtseven et al. 2019)
DJI FC350 Camera and Sony EXMORE 1/2.3 Camera 85% overlap	DJI Inspire1 pro	Single tree point segmentation of red pine forest in Heilongjiang Province, China (Hu and Li 2020)

RGB $=$ red, green, blue.

Table 3. Convolutional neural networks (CNN) object detection methods

Object detection method	Description	Average accuracy (%)	Time taken (s)	Source code link
Faster-RCNN	Feature map generated by a ResNet50 network. Objects detected in two stages: 1. Region proposal network – feature map received and object bounding box anticipated 2. Region of interest pooling layer – extracts features from each bounding box Next, a softmax layer proposes the class of bounding box regions and their offset values	82.48 (±3.94)	0.163 (±0.066)	https://github.com/yhenon/keras-frcnn
YOLOv8	Uses direct logistic regression from pixels to generate bounding box coordinates. Uses Darknet-53 backbone for improved accuracy and decreased operations. Comprises the following: 1. 4 constraints for bounding box coordinates 2. objectiveness score 3. C class probabilities 4. alike to feature pyramid networks	85.88 (±4.03)	0.026 (±0.001)	https://yolov8.com/
RetinaNet	One-stage object detector that reduces class imbalance. Consists of: 1. backbone – feature pyramid network 2. fully convolution network – subnetwork that predicts object presence 3. subnetwork responsible for bounding box regression	92.64 (±2.61)	0.067 (±0.001)	https://github.com/fizyr/keras-retinanet

Strecha C, Fletcher A, Lechner A, Erskine P, Fua P. 2012. Developing species specific vegetation maps using multi-spectral hyperspatial imagery from unmanned aerial vehicles. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 1:311–316. doi:10.5194/isprsannals-I-3-311-2012.

 Google Scholar

Michez A, Piégay H, Lisein J, Claessens H, Lejeune P. 2016. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental Monitoring and Assessment. 188(3):146–146. doi:10.1007/s10661-015-4996-2.

 PubMed Web of Science ®Google Scholar

Gómez C, Goodbody TRH, Coops NC, Álvarez-Taboada F, Sanz-Ablanedo E. 2020. 11 – Forest ecosystem monitoring using unmanned aerial systems. In: Green D, editor. Unmanned aerial remote sensing: uAS for environmental applications. Milton (UK): Taylor & Francis Group; p. 173–196.

 Google Scholar

Fraser BT, Congalton RG. 2021. Monitoring fine-scale forest health using unmanned aerial systems (UAS) multispectral models. Remote Sensing. 13(23):4873. doi:10.3390/rs13234873.

 Web of Science ®Google Scholar

Zhou X, Zhang X. 2020. Individual tree parameters estimation for plantation forests based on UAV oblique photography. IEEE Access. 8:96184–96198. doi:10.1109/ACCESS.2020.2994911.

 Google Scholar

Yurtseven H, Akgul M, Coban S, Gulci S. 2019. Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques. Measurement: Journal of the International Measurement Confederation. 145:651–664. doi:10.1016/j.measurement.2019.05.092.

 Web of Science ®Google Scholar

Hu X, Li D. 2020. Research on a single-tree point cloud segmentation method based on UAV tilt photography and deep learning algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13:4111–4120. doi:10.1109/JSTARS.2020.3008918.

 Web of Science ®Google Scholar