### What will you learn?

In this tutorial, you will learn how to effectively rotate a GeoTIFF file containing Digital Elevation Model (DEM) data using Python. This process is essential for various geospatial analyses and visualizations.

### Introduction to Problem and Solution

GeoTIFF files are commonly used for storing georeferenced imagery and DEMs, representing surface elevation data. When it becomes necessary to rotate these files while preserving spatial reference information, challenges may arise due to the complexity of handling spatial data accurately.

To address this issue, we will leverage Python libraries such as rasterio and numpy. These libraries empower us to manipulate GeoTIFF files efficiently, including rotation operations while retaining georeferencing details. Our approach involves reading the original GeoTIFF file, rotating the underlying array (image), updating the spatial metadata accordingly, and saving the rotated result into a new GeoTIFF file.

### Code

```
import rasterio
from rasterio.enums import Resampling
import numpy as np
def rotate_geotiff(input_path, output_path, angle):
with rasterio.open(input_path) as src:
image = src.read(1)
affine = src.transform
rotation_matrix = np.array([[np.cos(np.radians(angle)), -np.sin(np.radians(angle))],
[np.sin(np.radians(angle)), np.cos(np.radians(angle))])
rotated_affine = affine * rasterio.Affine.rotation(angle)
rotated_image = np.rot90(image)
out_meta = src.meta.copy()
out_meta.update({"driver": "GTiff",
"height": rotated_image.shape[0],
"width": rotated_image.shape[1],
"transform": rotated_affine})
with rasterio.open(output_path, "w", **out_meta) as dest:
dest.write(rotated_image, 1)
rotate_geotiff('path_to_your_input_file.tif', 'path_to_the_output_file.tif', 90)
# Copyright PHD
```

### Explanation

The code snippet demonstrates how to rotate a GeoTIFF file containing DEM data by a specified angle:

- Import necessary modules: rasterio for working with geospatial raster data; Resampling from rasterio.enums; and numpy for numerical computations.
- The function rotate_geotiff takes input path of the original GeoTIFf file (input_path), output path for the rotated file (output_path), and rotation angle in degrees (angle).
- Read the input file’s data into an array (image) along with its affine transformation matrix (affine) using rasterio.open.
- Calculate a new affine transformation considering rotation by combining existing transformation with rotational component based on the specified angle.
- Rotate the image array using NumPy’s rot90() method.
- Update metadata reflecting changes in dimensions and transformation due to rotation.
- Save the transformed dataset along with updated metadata into a new GeoTIFf file specified by output_path.

By following these steps, you can accurately rotate your DEM within a GeoTIFF file while maintaining precise geo-spatial referencing crucial for GIS applications or further analysis.

#### What is a GEOtiff File?

A GEOtiff is an advanced TIFF format embedding geographic information like spatial metadata aligning images onto maps directly.

#### What is Digital Elevation Model (DEM)?

Digital Elevation Model represents bare-ground surface elevation omitting structures like vegetation or buildings providing elevation insights at specific locations globally.

#### Why Rotate A GeotIFF?

Rotating GeotIFF is essential for alignment purposes in mapping projects where orientation plays a critical role in accurate representation of landscapes or terrains.

#### Can I Rotate Any Angle?

Yes, theoretically any degree from 0 – 360. However, practical applications often involve standard angles like horizontal or vertical flips depending on project requirements.

#### Does Rotation Affect Spatial Accuracy?

When done correctly following outlined steps ensuring metadata updates accurately no significant loss in accuracy expected post-rotation.

By utilizing Python libraries such as RasterIO and NumPy alongside proper management of spatial metadata within GeoTIFF files containing digital elevation models (DEMs), complex operations like rotations can be performed accurately. This ensures that outputs maintain their utility across various fields of study and research. Embrace the power of Python for geospatial data manipulation!