# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Airborne Remote Sensing Sensors  
Since 2014, the advancements in unmanned aerial vehicles (UAVs) have resulted in the extensive application of airborne sensors for the detection of inundated areas in complex urban landscapes and hazardous, inaccessible regions. This technology has been successfully deployed in flood monitoring and rescue missions, such as during the floods in Uttarakhand, India [83], the state of Oklahoma, USA [84], and Malaysia [85]. It can be seen that sensors mounted on UAVs can help to achieve higher temporal and spatial resolution for flood monitoring, mapping, and detection, and they play a role in the monitoring of different flood indicators (Table 3). Compared to traditional aerial photogrammetry, UAV photography offers improved spatial accuracy and maneuverability during flood surveys [86; 87]. In previous years, drones were used to carry optical cameras to capture flooded areas [88] but it has taken a long time since then to address the waterproofing characteristics of drones in heavy rainfall environments and to improve the low quality of the data from raindrop-obstructed images. More recently, computer vision techniques have been applied effectively for monitoring water levels in flood warnings using ground-based fixed cameras [89]. Satellites and space shuttles, while valuable, are often limited by their orbital paths, making it challenging to obtain high-quality images; UAVs, with their ability to capture oblique images, can help fill this data gap [90; 91]. These techniques have proven superior in disaster damage assessment and flood mapping, as seen in the 2017 Hurricane Harvey [92] and 2021 Melamchi floods [93]. As technology advances, drones have been recognized as an effective platform for flood management [94]. Some studies used photogrammetric data collection on drones equipped with instruments such as high-resolution cameras, LiDAR, radar, multispectral cameras, and thermal sensors to capture the data [95] to create digital elevation models (DEM) [96] or to employ deep learning algorithms for delineating flood extents [97; 98]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Index** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Optical Camera} & & 1. High-resolution & 1. Affected by weather \\  & surface features, color & 2. Intuitive and easy & 2. Limited performance at night \\  & & 3. Low costs & or in low-light conditions \\ \hline \multirow{3}{*}{Camera} & topographic changes & 1. Real-time & 1. Affected by light and weather \\  & & 2. Intuitive dynamics & 2. Complexity of data processing \\ \hline \multirow{3}{*}{Infrared Cameras} & water body temperature, ground temperature, vegetation health & 1. Capable of monitoring & 1. Low-resolution \\  & vegetation health & temperature and water flow & 2. Affected by weather conditions \\ \hline \multirow{3}{*}{Multi-spectral Cameras} & vegetation type, vegetation health, water body boundaries, flood extent & 1. Analyzing specific bands & 1. High costs \\  & vegetation type, vegetation health, water body & 2. Capable of differentiating & 2. Complex data processing \\ \cline{1-1}  & boundaries, flood extent & between vegetation and water bodies & 3. Limited resolution and \\ \cline{1-1}  & & & spectral range \\ \cline{1-1}  & & 4. Unsuitable for complex features & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantage and disadvantage analysis of flood airborne remote sensing sensors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Index** & **Advantages** & **Disadvantages** \\ \hline Hyperspectral Cameras & water quality, vegetation health & 1. High spectral resolution & 1. High costs \\  & water quality, vegetation health & 2. High-precision & 2. Large volume of data \\  & & 3. Detailed surface analysis & 3. Complex data processing \\ \hline Thermal Infrared Sensors & water body temperature, ground temperatures, flood extent, changes in humidity & 1. Suitable for wide-area monitoring & 1.