Mohammed Jalal Tazi

Salama Alaikum

For geological lineament extraction, both Landsat imagery and Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs) can be useful, but they provide different types of information and have their own advantages and disadvantages.

1. **Landsat Imagery**:

- Landsat imagery provides multispectral data in the visible, near-infrared, and shortwave infrared regions of the electromagnetic spectrum.

- It is particularly useful for detecting lithological and mineralogical variations, which can help identify lineaments associated with different rock types or geological structures.

- The high spatial resolution (30 meters for most bands) allows for the detection of relatively small-scale lineaments.

- However, Landsat imagery may not always clearly reveal topographic features or lineaments that are not associated with spectral variations.

2. **SRTM DEM**:

- SRTM DEMs provide a representation of the Earth's topography, which can be effective for lineament extraction based on topographic expressions.

- DEMs are particularly useful for identifying lineaments associated with faults, fractures, or other structural features that are expressed in the terrain.

- The shaded relief and slope maps derived from DEMs can enhance the visibility of lineaments, especially in areas with limited vegetation cover.

- However, SRTM DEMs have a relatively coarse spatial resolution (30 meters or 90 meters), which may limit the detection of small-scale lineaments.

In practice, the best approach for geological lineament extraction often involves integrating both Landsat imagery and SRTM DEMs. The combination of spectral information from Landsat and topographic information from SRTM DEMs can provide complementary insights and improve the accuracy of lineament mapping.

Here are some common techniques for lineament extraction using both datasets:

1. **Edge Enhancement Techniques**: Apply edge detection filters (e.g., Sobel, Prewitt, or Canny) to both Landsat imagery and SRTM DEMs to enhance lineaments.

2. **Principal Component Analysis (PCA)**: Perform PCA on Landsat bands and SRTM DEMs to identify the components that best represent lineaments.

3. **Lineament Extraction Algorithms**: Use specialized lineament extraction algorithms, such as Line Segment Detector (LSD), Hough Transform, or Radon Transform, on both datasets and combine the results.

4. **Manual Interpretation**: Visually interpret and digitize lineaments by combining the information from Landsat imagery (for spectral variations) and SRTM DEMs (for topographic expressions).

The choice between Landsat imagery and SRTM DEMs, or the combination of both, ultimately depends on the specific geological setting, the scale of the study area, and the availability of resources. In many cases, integrating both datasets can provide the most comprehensive and accurate lineament mapping results.

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