# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## Materials and Methods
### UAV Data  
The structure of Balikdami wetland is complex and contains several different classes of wetlands. In order to be able to separate and investigate the differences and relations between the classes, in a leaf-on season, data from UAV with a resolution of 8 cm were collected over the study area, Balikdami. UAV platforms are nowadays a valuable source of data for surveillance, inspection, mapping, and 3D modeling issues. New applications in the short- and close-range domain are introduced, being the UAVs low-cost alternatives to the classical manned aerial photogrammetry (Sakarya, 2017). Today the UAVs images and 3D data are used in many different fields such as forestry and agriculture (Sakarya, 2017), archeology and cultural heritage, environmental surveying, traffic monitoring (Sakarya, 2017), 3D reconstruction etc.  
In this study, a UAV was used for collecting data over the Balikdami wetland area on August 10, 2017. The steps followed for obtaining the UAV data were: (i) Flight Planning; (ii) Taking real images; (iii) Data processing; and (iv) Results. The UAV and the integrated camera, as well as their characteristics, are given in Table 1.  
The data were processed in the Pix4D software. The details for every flight data are given in Table 2 and the orthophoto mosaic of all flight is given in Figure 1.  
Using the high-resolution imagery obtained with the UAV, in consultation with a wetland expert, we were able to distinguish six different classes within the study area. The classes and their characteristics are given below in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline Camera & S.O.D.A. \\ Resolution & 7.00 cm/px \\ Lateral overlap & 70\% \\ Longitudinal overlap & 65\% \\ Area & 850.5 ha/461.5 ha \\ Altitude & 297.6 m \\ Flight lines spacing & 115 m \\ Distance between photos & 89 m \\ Image coverage & \(383\times 255\) m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight planning details.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Flight 1** & **Flight 2** & **Flight 3** & **Flight 4** & **Flight 5** & **Flight 6** & **Flight 1–6** \\ \hline Avg GSD & 7.37 cm & 7.32 cm & 7.35 cm & 7.45 cm & 7.59 cm & 7.72 cm & **7.37 cm** \\ Area Covered & 460.8 ha & 549.7 ha & 547.9 ha & 574.0 ha & 167.4 ha & 162.2 ha & **1891.8 ha** \\ No of Images & 393 & 425 & 451 & 486 & 113 & 81 & **1949** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the flight results.  
Figure 1: Orthophoto of the study area Balikdami.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Description** & **Image** \\ \hline Swamp & Tall, grass-like plants of wetlands (Reed). & \\ Water & Open Water Bodies & \\ \hline Bog & A wetland that accumulates peat, a deposit of dead plant material & \\ Land Mix & Dryland vegetated areas mixed with open water bodies & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classes within the study area viewed from high-resolution unmanned aerial vehicle (UAV) images.  
In order to collect detailed information about every class separately, according to the area of the classes, a number of random points were created and the values from every month from both Landsat-8 and Sentinel-1 satellite images were obtained. A total of twenty-three satellite images were used in this paper (Table 4).  
The Sentinel-1 images were downloaded for every month of the year, while the images from Landsat-8 were downloaded only in the cloud-free condition. Some of the dates of both satellites are close to each other with only a few days' difference. Images from both satellites represent one annual cycle. After the download of the images, NDVI was calculated and LST maps were obtained for the Landsat-8 images, while Sentinel-1 images were pre-processed.