Wheat yield estimation based on remote sensing and GIS
| dc.contributor.author | Bourada, Loubna | |
| dc.date.accessioned | 2025-04-23T10:25:55Z | |
| dc.date.available | 2025-04-23T10:25:55Z | |
| dc.date.issued | 2024-12-18 | |
| dc.description.abstract | This thesis investigates the application of remote sensing and Geographic Information Systems (GIS) for estimating and forecasting wheat yields in the Tipaza region of Algeria. As agriculture faces increasing challenges from climate change and resource limitations, accurate yield estimation becomes critical for enhancing productivity and ensuring food security. Utilizing high-resolution satellite imagery from Sentinel-2 and Landsat 8, this study develops a comprehensive methodology that includes land use mapping, classification, and yield forecasting. The research begins with the acquisition of satellite images and ground truth data, which are processed using Google Earth Engine and QGIS to create detailed land use maps. Classification algorithms, specifically Random Forest and XGBoost are employed to categorize agricultural practices effectively. The classification results are validated against real yield data and agricultural statistics from the Algerian Directorate of Agricultural Services, ensuring the reliability of the findings. Following validation, Random Forest regression and XGBoost regression techniques are applied to predict wheat yields based on the classified land use data. Additionally, the study compares yield dynamics across other crops, including soft wheat, soybeans, and oats, providing a broader perspective on agricultural productivity in the region. The findings demonstrate that remote sensing technologies can significantly enhance yield estimation accuracy and contribute to sustainable agricultural practices. This research not only advances our understanding of wheat production in Tipaza but also proposes a predictive framework that can be adapted for wider application throughout Algeria. By integrating advanced technologies into agricultural monitoring, this thesis aims to support informed decision-making for farmers and policymakers, ultimately contributing to improved food security and resource management in the region | |
| dc.identifier.uri | http://dspace.ensa.dz/handle/123456789/3813 | |
| dc.language.iso | en | |
| dc.subject | Geographic information | |
| dc.subject | Yield estimation | |
| dc.subject | Wheat | |
| dc.subject | Sustainability | |
| dc.title | Wheat yield estimation based on remote sensing and GIS | |
| dc.type | Thesis |