UAV-based Remote Sensing Applications

1. i

2. 1. INTRODUCTION
2. UAV DEPLOYED SENSORS
3. METHODOLOGY
4. REVIEW A
5. REVIEW B
6. PROS AND CONS
7. CONCLUSION
8. REFERENCES

3. INTRODUCTION
Historically, robots have been employed to undertake ‘the three D’s’; tasks
that are considered too dirty, dangerous or dull for humans. Although this
also reflects the focus of early Unmanned Aerial Vehicle (UAV) application
and research, the future direction of UAV work is another four D’s: the
collection of detailed data over difficult or delicate terrain (Buters, 2019).
Satellites and manned aircrafts that are used to collect data remotely are
effective but relatively expensive, and these technologies tend to have low
spatial and temporal resolutions.
To overcome these limitations, unmanned aerial vehicle (UAV) technologies
have been developed for remote sensing applications (Eisenbeiss, 2004).

4. Table 1. International classification of unmanned aerial vehicles (UAVs) Source: Eisenbeiss H. (2004)
Category MTOW [kg] Range [km] Maximum Ceiling [m]
Micro <5 <10 250
Mini <25/30/150 <10 150/250/300
Short Range 25 - 150 10 - 30 3000
Medium Range 50 - 250 30 - 70 3000
Long Range >250 >70 >3000
Rapid technological advancements have led to the creation of smaller, more
affordable drones that can mount a wider range of sensors and more quickly
gather a wider range of data.
MTOW: Maximum Take-off Weight

5. UAV DEPLOYED SENSORS
Optical Cameras.
Thermal Cameras/Sensors.
LiDAR (Light Detection and
Ranging) Sensor.
Hyperspectral Sensors.
Ground Penetrating Radar (GPR)
Sensor.
Lightweight Portable Radiometer
(LPR).
Tetracam Multispectral Camera.
Fig. 1: An example of the rotary-wing UAV-based
remote sensing data acquisition platform.
(Source: Xiang et al., 2019)

6. 1. GENERAL WORKFLOW OF UAV-BASED
REMOTE SENSING
2. GENERAL WORKFLOW OF UAV-BASED
DATA PROCESSING
Application
Image interpretation
Digital elevation model (DEM) and
orthophoto generation
Digital surface model (DSM) generation
Aerial triangulation
Pre-processing
Original images
Data processing and analysis
Data collection and check
UAV flight planning
Selection of appropriate UAV platforms
and sensors

7. FOREST MONITORING USING UAV-BASED REMOTE SENSING
ITEM METHODS
Forest structure 3D structures: LiDAR and profiling radar.
Forest inventory
 Plot-level metrics: canopy points or image classification.
 Tree-level metrics: canopy height model.
Forest biomass
 UAV-based L-band radar.
 Vertical information + L-band radar
Forest biodiversity
 Quantification of canopy spatial structures and gap patterns.
 Fallen trees detection and their spatio-temporal variation analysis.
Forest health
monitoring
Multi- and hyper-spectral remote sensing, dense point clouds.
Forest fire
monitoring
 Before fires: forest prevention, e.g. create fire risk maps, (3D) vegetation maps.
 During fires: detect active fires, locate fires, predict fire propagation.
 After fires: detect active embers, map burned areas and assess fire effects.

9. Fawcett et al. (2020) conducted a study to monitor
spring phenology of individual tree crowns using
drone-acquired NDVI data. They used drone-acquired
normalized difference vegetation index (NDVI) time-
series data with a multi-spectral sensor in Cornwall,
UK, during a period of spring green-up. They used
spring phenological stages: Start-of-spring (SOS),
middle-of spring green-up (MOG) and start-of-peak
greenness (SOP).
They concluded that the capability of drone-mounted
multi-spectral instruments for spatio-temporal
characterization of crown-level phenology shows great
promise for improving the understanding of intra and
inter-species differences in strategy, and offers an
efficient means of doing so over areas of a few
hectares.

10. Fig. 2: False-color infrared representations (A) leaf-off
(22/03/2019) and (B) leaf-on (22/07/2019) conditions.
(C) A map of the digitized tree samples color coded by
species.
Fig. 3: Boxplots of SOS, MOG and SOP of all individual deciduous crowns
grouped by species

11. Yuan et al. (2018) conducted a study using the of UAV
remote sensing imagery data in Chengguan Town,
China. They used 10 vegetation indices and six types of
land objects (including vegetation: grassland, forest
land, crops and non-vegetation: buildings, roads, and
bare land) for further analysis of the performance of
different vegetation indices. For accuracy assessment
they used the bimodal histogram and the histogram
entropy threshold method for each vegetation index to
extract vegetation information.
They concluded that with the development of sensors,
the aerial image resolution has been continuously
improved, but the cost is also high. Researchers are
more likely to use UAV to complete short-term and
small-scale research because its small size and
lightweight.

12. Table. 2: Evaluation of vegetation extraction accuracy
Table. 3: Characteristics of 10 vegetation indices in 6 species of land

13. ADVANTAGES AND DISADVANTAGES OF UAV-BASED
REMOTE SENSING
ADVANTAGES
The main advantage of UAVs is
that they can be used in high-risk
situations without endangering
human life.
Easy controllable or deploy-able.
Cost saving technology.
Flexibility for quick responses.
In-depth and detail data in-place.
DISADVANTAGES
Weather dependent.
Requires knowledge and skill.
Data transfer speed is slow.
Vulnerable to wild animals.
Software issues or malfunction.

14. CONCLUSION
UAV-based remote sensing offers significant benefits for monitoring vegetation
growth and performance, mostly because of the sizes at which monitoring is
required.
Due to their higher spatial resolution compared to satellite imagery, significantly
lower operational costs compared to manned aircraft, and ability to operate in
weather conditions that would prevent both satellites and manned aircraft from
gathering useful data.
UAVs are regarded as the best option for remote sensing at scales up to 250 ha.
Additionally, compared to satellites and manned aircraft, remote sensing utilizing
UAVs offers higher repeatability and quicker responses.

15. REFERENCES
1. Cui, Yuxing, Yishan Ji, Rong Liu, Weiyu Li, Yujiao Liu, Zehao Liu, Xuxiao Zong, and Tao Yang.
(2023). “Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data” Drones 7, no. 6: 378.
https://doi.org/10.3390/drones7060378
2. Eisenbeiss H. (2004) A mini Unmanned Aerial Vehicle (UAV): System overview and image acquisition;
Proceedings of the International Archives of Photogrammetry. Remote Sensing and Spatial Information
Sciences, 36(5/W1); Pitsanulok, Thailand. 18-20.
3. Fawcett, Dominic & Bennie, Jonathan & Anderson, Karen. (2020). Monitoring spring phenology of
individual tree crowns using drone-acquired NDVI data. Remote Sensing in Ecology and Conservation. 7.
10.1002/rse2.184.
4. Image by AzureEyes, Cannon_Fodder, MoritzMess from Pixabay
5. Tian-Zhu Xiang, Gui-Song Xia, and Liangpei Zhang, (2019). Mini-Unmanned Aerial Vehicle-Based
Remote Sensing: Techniques, Applications, and Prospects, Journal of latex class files, 14(8).
6. Todd Michael Buters (2019) Drone-based remote sensing as a novel tool to assess restoration trajectory
at fine-scale by identifying and monitoring seedling emergence and performance. Page: 98.
7. Yuan, Huijie; Liu, Zhengjun; Cai, Yulin; Zhao, Bing (2018). Research on Vegetation Information
Extraction from Visible UAV Remote Sensing Images, International Symposium on Signal Processing and
Information Technology (ISSPIT),1-5.