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Land cover dynamics were analyzed temporally and spatially in Al-Hubail wetland (Al-Ahsa, Kingdom of Saudi Arabia) to determine the evolution of the environmental status of this biologically and ecologically interesting area in Saudi Arabia. Land cover changes were estimated for the next 41 years (1980-2021) using remote sensing data. For this analysis, three images from 1985 (Landsat 5 MSS) 2000 (Landsat 7 ETM+) and 2021 (Sentinel-2) were used to classify and detect changes. A machine learning algorithm was used and the images were classified into four main land cover classes: Water bodies, hydromorphic areas, vegetation and open ground. Change detection was performed for the year pairs 1985 to 2000 and 2000 to 2021. The results of this classification show that there was a significant increase in the area of hydromorphic areas and vegetation. The results were checked with a confusion matrix indicating an overall accuracy between 89.3 and 92.8%. The qualitative trend data show that the Al-Hubail wetland has changed significantly during the study period. Thus, a significant expansion of the wetland was observed in conjunction with an increase in agricultural drainage towards the wetland. This analysis shows the strong anthropogenic pressure on the area and highlights the need to strengthen the existing laws in order to preserve the local biodiversity in the long term. It suggests that more efforts should be made to effectively manage the water resources of the region.
Keywords
Land cover, classification, change detection, Al-Hubail wetland
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Posted 12 Nov, 2021
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Received 11 Nov, 2021
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Editor invited
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This preprint is under consideration at Wetlands Ecology and Management. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Nov, 2021
No community comments so far
Reviews received
Received 11 Nov, 2021
Reviewers invited
Invitations sent on 10 Nov, 2021
Editor invited
On 02 Nov, 2021
Editor assigned
On 02 Nov, 2021
First submitted
On 30 Oct, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Land cover dynamics were analyzed temporally and spatially in Al-Hubail wetland (Al-Ahsa, Kingdom of Saudi Arabia) to determine the evolution of the environmental status of this biologically and ecologically interesting area in Saudi Arabia. Land cover changes were estimated for the next 41 years (1980-2021) using remote sensing data. For this analysis, three images from 1985 (Landsat 5 MSS) 2000 (Landsat 7 ETM+) and 2021 (Sentinel-2) were used to classify and detect changes. A machine learning algorithm was used and the images were classified into four main land cover classes: Water bodies, hydromorphic areas, vegetation and open ground. Change detection was performed for the year pairs 1985 to 2000 and 2000 to 2021. The results of this classification show that there was a significant increase in the area of hydromorphic areas and vegetation. The results were checked with a confusion matrix indicating an overall accuracy between 89.3 and 92.8%. The qualitative trend data show that the Al-Hubail wetland has changed significantly during the study period. Thus, a significant expansion of the wetland was observed in conjunction with an increase in agricultural drainage towards the wetland. This analysis shows the strong anthropogenic pressure on the area and highlights the need to strengthen the existing laws in order to preserve the local biodiversity in the long term. It suggests that more efforts should be made to effectively manage the water resources of the region.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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