Working with Satellite Data for Land Use Land Cover Mapping and Change Detection.

Practical Guide: Working with Satellite Data for Land Use/Land Cover Mapping and Change Detection

1. Introduction

Satellite data is essential for land use and land cover (LULC) mapping and change detection over time. This guide will walk you through the steps of acquiring satellite imagery, processing it, and analyzing it for LULC mapping and change detection using GIS software like QGIS or ArcGIS.

2. Setting Up Your Environment

• Install GIS Software: Ensure you have QGIS or ArcGIS installed on your computer.
• Open the Application: Launch your GIS software.

3. Acquiring Satellite Data

You can obtain satellite imagery from various sources, such as:

• USGS Earth Explorer: For Landsat imagery.
• Copernicus Open Access Hub: For Sentinel imagery.
• NASA Earthdata: For various satellite datasets.

A. Downloading Data from USGS Earth Explorer

1. Visit the Earth Explorer Website: Go to USGS Earth Explorer.
2. Create an Account: Sign up for a free account if you don’t have one.
3. Define Area of Interest:

• Use the map interface to zoom in and draw a polygon around your area.

1. Select Data Sets:

• Click on the “Data Sets” tab and select the appropriate satellite imagery (e.g., Landsat 8).

1. Set Date Range: Specify the date range for your analysis.
2. Search and Download: Click the “Search” button, review results, and add desired images to your download cart.

4. Preprocessing Satellite Data

Preprocessing steps may include atmospheric correction, cloud masking, and subsetting.

A. Loading Satellite Data

1. Add Raster Layer:

• QGIS: Click Layer > Add Layer > Add Raster Layer and select your downloaded imagery.
• ArcGIS: Click the Add Data button and select your raster files.

B. Cloud Masking

1. Identify Cloud Pixels: Use the quality assessment band to identify clouds.
2. Create a Mask: Generate a mask layer to exclude cloud pixels from your analysis.

C. Atmospheric Correction

• Use tools like Sentinel-2 Toolbox or Landsat Surface Reflectance to perform atmospheric correction if necessary.

5. Land Use/Land Cover Mapping

A. Creating LULC Classes

1. Define Classes: Determine the land cover classes (e.g., forest, water, urban, agricultural).
2. Use Supervised Classification:

• QGIS: Use the Semi-Automatic Classification Plugin to train the model.
• ArcGIS: Use the Image Classification Wizard or ArcGIS Pro for supervised classification.

1. Training Samples: Collect training samples for each class by digitizing polygons on the imagery.
2. Run Classification: Execute the classification algorithm (e.g., Maximum Likelihood, Random Forest).
3. Post-Processing: Refine the classification using tools like majority filtering or smoothing.

6. Change Detection Analysis

Change detection helps identify changes in land cover over time.

A. Using Multi-Temporal Data

1. Load Multi-Temporal Images: Add images from different time periods.
2. Calculate Change:

• Image Differencing: Subtract one classified image from another to highlight changes.
• Normalized Difference Vegetation Index (NDVI): Calculate NDVI for both dates and compare.

1. Change Detection Tools:

• QGIS: Use the Raster Calculator to perform calculations.
• ArcGIS: Use the Change Detection tools in the Spatial Analyst toolbox.

B. Analyzing Change Results

1. Create a Change Map: Classify the change areas (e.g., gain, loss, no change).
2. Quantify Changes: Calculate the area of each change category using the Zonal Statistics tool.

7. Exporting Results

1. Save LULC Map:

• QGIS: Right-click the LULC layer and select Export > Save As.
• ArcGIS: Use Data > Export Data to save your classified raster.

1. Export Change Detection Results: Save your change detection map for reporting and analysis.

8. Conclusion

This practical guide has introduced you to the essential steps for working with satellite data for land use/land cover mapping and change detection. By following these steps, you can analyze and visualize changes in land cover over time effectively.