How do you handle discrepancies between field data and remote sensing information in your GIS projects?

3 Spatial Adjustment

Spatial discrepancies occur when there are mismatches in the geographic locations of data points. To handle this, you'll need to perform spatial adjustments such as georeferencing, which involves aligning the remote sensing data with known geographic locations from your field data. This may require the use of ground control points that serve as reference locations to correct any misalignments in the datasets.

4 Resolution Matching

The resolution of remote sensing data can significantly differ from the granularity of field data. High-resolution satellite images might not match the more detailed observations captured on the ground. To reconcile this, you may need to resample your remote sensing data to a coarser resolution that aligns better with your field data, or alternatively, aggregate your field data to match the broader scale of the remote sensing information.

5 Data Integration

Integrating field data with remote sensing information is a delicate process that requires careful consideration of the discrepancies. You may use various GIS tools and techniques like overlay analysis to see where the data aligns or diverges. The integration process might also involve creating a weighted system that gives preference to one type of data over another based on its reliability and relevance to the project's objectives.

6 Iterative Refinement

Finally, handling discrepancies is not a one-time task but an iterative process. As you work through integrating and adjusting data, new discrepancies may emerge, necessitating further analysis and refinement. This iterative approach ensures that you continually improve the quality of your GIS project, leading to more accurate and reliable results. It's a cycle of evaluation, adjustment, and re-evaluation until the datasets are harmonized.