**4. Conclusions**

*Military Engineering*

**98**

**Figure 7.**

**Figure 6.**

(**Figure 8**). In comparing area (a) with area (b) using the NDVI (**Figure 8**), area (b)

Using the SR vegetation index (**Figure 7**), area (a) tends to exhibit a difference with respect to area (b) (**Figure 8**). More specifically, in target area (a) (**Figure 7**), the reflectance response is lower than area (b) (**Figure 8**), which indicates that the

has higher values due to the nonexistence of underground structures, while area (a) (**Figure 7**) has similar vegetation but lower NDVI values due to the existence of underground structures. Likewise, using the EVI, area (a) (**Figure 7**) has lower values due to the existence of underground structures, while area (b) (**Figure 8**)

*Image maps showing NDVI, EVI, and SR field data for area (a) during Head Emerging Stage.*

*Image maps showing NDVI, EVI, and SR field data for area (b) during Flag Leaf Emerging stage.*

has higher values due to nonexistence of structures.

Field spectroscopy can support satellite remote sensing studies for monitoring systematically critical areas of interest including the detection of underground bunkers.

The application of remote sensing in defense and security merges the technological improvements of remote sensing sensors with military needs to improve the quality of information retrieved from remote sensing data. Indeed, decision-making authorities can benefit from such efficient space imaging technology of underground targets.

The advantages of using vegetation indices as proxy variables for intercalibration among existing sensors are the low sensitivity to the uncertainties in atmospheric correction and the variation in the satellite viewing angle [30]. As shown in this paper, vegetation indices can corroborate areas of possible military underground structure.

In comparing the two areas, the spatial distributions of VIs exhibit no table differences (**Figures 7** and **8**). This is clear in **Figure 8**, where image maps illustrate the differences between the two areas using NDVI, EVI and SR Vegetation Indices. Mostly SR vegetation index is used for defining areas where military underground structures are present.

Consequently, the near-infrared (NIR) band of Landsat 8 sensor could be useful to identify the underground structure. It is apparent that the waveband analysis of the Landsat 8 sensor distinguishes between the two study sites. Monitoring variations of the NIR spectrum during the life cycle of vegetation is a key parameter for the field spectroscopy for the detection of military underground structures using remote sensing techniques.

In this chapter, it was demonstrated how remote sensing can be exploited as a monitoring and decision-making tool by any agency in tackling military and security issues related to the presence of underground military structures. Field spectroscopy measurements were used to detect underground military structures through variations in vegetation indices. Indeed, vegetation indices can be used to develop a suitable vegetation index for detecting military underground structures.

Areas covered by natural soil where underground structures are present or absent can easily be detected as a result of the change in the spectral signature of the overlying vegetation; in this respect, vegetation indices, such as the NDVI, SR, and EVI, may be used for this purpose.

It is recommended to collect field spectroradiometric measurements to other types of military underground structures to evaluate the above results and the satellites' spectral sensitivity. The development of a standard model/methodology framework to be produced through the stages of the study for locating military underground structures is an innovation in military operations research. Additionally, an unmanned aerial vehicle (UAV) may be used to survey the area with visible and near-infrared cameras to generate vegetation indices for comparison to the in situ spectroradiometric measurements [31].
