Context and Justification:
Sustainable management of natural resources and land use planning require accurate and up-to-date data on land use. This project aims to create a spatiotemporal land use map using Google Earth Engine, combined with field surveys conducted using Trimble GPS to ensure data accuracy and validation.
Project Objectives:
Create a spatiotemporal land use map that reflects changes in land use over a specified period.
Utilize Google Earth Engine to analyze satellite imagery and classify land use types.
Conduct field surveys with Trimble GPS devices to collect control points that will validate the classification results.
Methodology:
Data Collection:
Satellite Imagery: Select and download multispectral satellite images (e.g., Landsat, Sentinel-2) covering the study area for multiple years.
Field Data: Use Trimble GPS to perform field surveys in representative areas of each land use type (urban, agricultural, forested, etc.).
Analysis with Google Earth Engine:
Image Preprocessing: Apply radiometric and atmospheric corrections to the satellite images.
Data Classification: Use supervised classification algorithms (such as Random Forest) to classify the images into different land use categories.
Temporal Analysis: Compare classifications over different years to identify changes in land use.
Validation of Results:
Control Points: Use the data collected with Trimble GPS to validate the accuracy of the classification. Compare the land use types derived from the analysis with ground observations.
Accuracy Assessment: Conduct accuracy analyses (confusion matrix, Kappa) to evaluate the reliability of the results.
Production of the Final Map:
Map Creation: Produce a thematic spatiotemporal land use map using mapping software (QGIS, ArcGIS) to visualize the analysis results.
Project Report: Write a detailed report outlining the methodology, obtained results, and recommendations for data use.
Expected Results:
A precise and detailed spatiotemporal land use map illustrating changes in land use.
A validated and accessible database for decision-makers, urban planners, and researchers interested in natural resource management.
Conclusion:
This project, utilizing modern tools such as Google Earth Engine and high-precision equipment like Trimble GPS, will provide essential data for sustainable land management and planning. The results will contribute to a better understanding of land use dynamics and informed decision-making in land development.
