Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia
(2024). Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia. EKB Journal Management System, 16(2), 51-72. doi: 10.21608/ejec.2024.381190
. "Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia". EKB Journal Management System, 16, 2, 2024, 51-72. doi: 10.21608/ejec.2024.381190
(2024). 'Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia', EKB Journal Management System, 16(2), pp. 51-72. doi: 10.21608/ejec.2024.381190
Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia. EKB Journal Management System, 2024; 16(2): 51-72. doi: 10.21608/ejec.2024.381190

Integrating GIS and Remote Sensing Data for Detecting Change in Agricultural Land during 1985-2024 in Qassim Region, Saudi Arabia

The Egyptian Journal of Environmental Change
Article 4, Volume 16, Issue 2, October 2024, Page 51-72  XML PDF (4.53 MB)
Document Type: Peer-reviewed articles
DOI: 10.21608/ejec.2024.381190
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Abstract
The Qassim region is one of the most significant agricultural areas in Saudi Arabia, particularly for date cultivation. This study investigates changes in agricultural land in the Qassim region between 1985 and 2024, utilizing Geographic Information System (GIS) technology and remotely sensed data to analyze and quantify agricultural land transformations. Key factors contributing to these changes are identified, providing valuable insights into environmental dynamics and land cover patterns. This research aims to inform policymakers and land managers, facilitating sustainable agricultural practices and effective land management strategies.
An object-based data analysis approach was employed for thematic mapping, considering both the spectral and spatial properties of the data extracted from the NDVI index. This method enables more accurate classification by leveraging the contextual information of image objects, resulting in improved extraction of agricultural land. Supervised classification was applied to the NDVI indices produced from Landsat images captured in the years 1985, 2000, 2015, and 2024 to identify agricultural lands.
 The results indicate that the agricultural landscape of the Qassim region in Saudi Arabia underwent significant changes over the past two decades. Agricultural lands declined between 2000 and 2015 due to government policies to rationalize water-intensive practices, but more recent data from 2024 shows a 14.4% increase in agricultural area to 2,438.16 square kilometers, attributed to strategic priorities focused on sustainable resource management, food security, rural development, and agricultural productivity. The expansion has been spatially distributed, with the Agricultural Development Fund playing a key role through promoting rational resource use, technology application, and investment facilitation.
Keywords
Remotely Sensed Data; Geographic Information Systems; Qassim; Agriculture Land
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