Citation: Trinh, H.L.; Kieu, H.T.; Pak,
H.Y.; Pang, D.S.C.; Cokro, A.A.; Law,
A.W.-K. A Framework for Survey
Planning Using Portable Unmanned
Aerial Vehicles (pUAVs) in Coastal
Hydro-Environment. Remote Sens.
2022, 14, 2283. https://doi.org/
10.3390/rs14092283
Academic Editor: Jorge Delgado
García
Received: 11 March 2022
Accepted: 4 May 2022
Published: 9 May 2022
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Article
A Framework for Survey Planning Using Portable Unmanned
Aerial Vehicles (pUAVs) in Coastal Hydro-Environment
Ha Linh Trinh
1
, Hieu Trung Kieu
1
, Hui Ying Pak
1,2
, Dawn Sok Cheng Pang
1
, Angel Anisa Cokro
1
and Adrian Wing-Keung Law
1,3,
*
1
Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute,
Nanyang Technological University, Singapore 637141, Singapore; halinh.trinh@ntu.edu.sg (H.L.T.);
trunghieu.kieu@ntu.edu.sg (H.T.K.); pakh0001@e.ntu.edu.sg (H.Y.P.); dawn.pang@ntu.edu.sg (D.S.C.P.);
angelanisa.cokro@ntu.edu.sg (A.A.C.)
2
Interdisciplinary Graduate Programme, Graduate College, Nanyang Technological University,
Singapore 637141, Singapore
3
School of Civil and Environmental Engineering, Nanyang Technological University,
Singapore 637141, Singapore
* Correspondence: cwklaw@ntu.edu.sg
Abstract:
Recently, remote sensing using survey-grade UAVs has been gaining tremendous momen-
tum in applications for the coastal hydro-environment. UAV-based remote sensing provides high
spatial and temporal resolutions and flexible operational availability compared to other means, such
as satellite imagery or point-based in situ measurements. As strict requirements and government
regulations are imposed for every UAV survey, detailed survey planning is essential to ensure safe
operations and seamless coordination with other activities. This study established a comprehensive
framework for the planning of efficient UAV deployments in coastal areas, which was based on recent
on-site survey experiences with a portable unmanned aerial vehicle (pUAV) that was carrying a
heavyweight spectral sensor. The framework was classified into three main categories: (i) pre-survey
considerations (i.e., administrative preparation and UAV airframe details); (ii) execution strategies
(i.e., parameters and contingency planning); and (iii) environmental effects (i.e., weather and ma-
rine conditions). The implementation and verification of the framework were performed using a
UAV–airborne spectral sensing exercise for water quality monitoring in Singapore. The encountered
challenges and the mitigation practices that were developed from the actual field experiences were
integrated into the framework to advance the ease of UAV deployment for coastal monitoring and
improve the acquisition process of high-quality remote sensing images.
Keywords:
unmanned aerial vehicles (UAVs); drones; remote sensing; coastal hydro-monitoring;
survey planning; comprehensive framework; water quality monitoring
1. Introduction
Regular coastal monitoring, which includes the monitoring of coastal erosion and air
and water quality, as well as the mapping of coastal topography, bathymetry or marine habi-
tats, requires a vigilant and practical observation approach. Over the past few decades, the
monitoring of coastal hydro-environment over large spatial areas has been accomplished
primarily via either remote sensing with satellite imagery or in situ point-based measure-
ments. While the former method is susceptible to cloud interference [
1
] and infrequent
acquisition times, the latter method is typically costly and time-consuming for continuous
acquisition. Remote sensing with unmanned aerial vehicles (UAVs), which are also referred
to as uncrewed aircraft systems, holds immense potential in bridging the gap between
satellite observations and traditional point measurements, such as fixed-location sampling
or in situ grab sampling. With high rates of accuracy and refined spatial resolutions, UAV–
airborne sensor systems are efficient tools for the on-demand monitoring of various coastal
Remote Sens. 2022, 14, 2283. https://doi.org/10.3390/rs14092283 https://www.mdpi.com/journal/remotesensing
