نظرة عامة على منصة Google Earth Engine(GEE) واستخداماتها في الدراسات البيئية
الكلمات المفتاحية:
Engine، الدراسات البيئية، Google Scholar، IEEE، منصة (GEE)، مجالات التطبيق، البيانات والأدوات المستخدمة في المنصة
الملخص
تدور فكرة هذا البحث حول استخدام منصة (GEE) Google Earth Engine في الدراسات والأبحاث البيئية، وتبين من هذه الدراسة أنه تم نشر 790 ورقة بحثية خلال المدة ما بين عامي (2015 – 2024) بواقع 787 ورقة بحثيه غير عربية وثلاث أوراق عربية، وجميعها استخدام منصة (GEE) Google Earth Engine في الحصول البيانات المكانية من صور الأقمار الاصطناعية أو البيانات المكانية الأخرى ، واستعرض البحث التوزيع المكاني للمناطق التي تم استخدام (GEE) Google Earth Engine في دراستها وتبين أن قارة آسيا صاحبة المركز الأول تليها قارة أوروبا ثم أمريكا الشمالية، وبين أهم المجالات التي تم استخدام المنصة في معالجة بياناتها وهى مراقبة الغابات والتصحر ، وإدارة الموارد المائية ، ورصد التغيرات المناخية ، والتخطيط العمراني والتنمية المستدامة ، بالإضافة إلى البيانات والأدوات التي استخدمها الباحثين في تلك الأبحاث ما بين صور الأقمار الاصطناعية مثل Landsat وSentinel ومؤشر NDVI وصور القمر الاصطناعي STRM.
المراجع
1. Ali G. Mahmoud (2022): Land Use/Cover Changes in Al-Jouf, KSA in Response to Water Management Strategies Using Multi-Sensor/-Temporal Data in Google Earth Engine, Scientific Journal of Agricultural Sciences, vol. 4, no. 1.
2. Belgacem Amel et al. (2023): Impact of Climatic Factors on the Mekerra watershed (North-West Algeria) via Hydrometeorological Series Provided by The Google Earth Engine Platform, Egyptian Academic Journal of Biological Sciences, vol. 15, no. 1.
3. صبحي عبد الحميد عبد الجواد عبد الحميد (2023): النمذجة الجيومكانية للذكاء الاصطناعي Google Earth Engine لتحليل وتوقع المناطق المهددة بأخطار الجريان السيلي، حوض وادي وتير، جنوب شبه جزيرة سيناء، مصر، مجلة وادي النيل للدراسات والبحوث الإنسانية والاجتماعية والتربوية، مجلد 40، عدد 40.
4. Alejandro Tobón-Marín & Julio Cañón Barriga (2020): Analysis of changes in rivers planforms using google earth engine, International Journal of Remote Sensing, vol. 41, no. 22.
5. Biswajeet Pradhan et al. (2018): Sand dune risk assessment in Sabha region, Libya using Landsat 8, MODIS, and Google Earth Engine images, Geomatics, Natural Hazards and Risk, vol. 9, no. 1.
6. Daniel Paluba et al. (2024): Tracking burned area progression in an unsupervised manner using Sentinel-1 SAR data in Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17.
7. Demuzere, M., Trigo, R. M., Vila-Guerau de Arellano, J., et al. (2021): "Urban Planning and Sustainable Development with Google Earth Engine." Landscape and Urban Planning.
8. Elisabet Walker & Virginia Venturini (2019): Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine, Remote Sensing Letters, vol. 10, no. 10.
9. Gorelick, N., Hancher, M., Dixon, M., et al. (2017): "Long-term Climate Change Monitoring Using Google Earth Engine." Remote Sensing of Environment.
10. Guangyi Wang et al. (2022): Leveraging Google Earth Engine and Semi-Supervised Generative Adversarial Networks to Assess Initial Burn Severity in Forest, Canadian Journal of Remote Sensing, vol. 48, no. 3.
11. Guzinski, R., Nieto, H., Stisen, S., et al. (2020): "Estimating Evapotranspiration Using Remote Sensing and Google Earth Engine." Remote Sensing of Environment.
12. Hansen, M. C., Potapov, P. V., Moore, R., et al. (2013): "Global Forest Change 2000 – 2013." Science.
13. https://earthengine.google.com/datasets/.
14. H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli,and B. Brisco (2020/6): Google Earth Engine for geo-big data applications: A meta-analysis and systematic review,” ISPRS J. Photogrammetry Remote Sens., vol. 164, pp. 152–170.
15. Jiangliu Xie et al. (2022): Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine, IEEE Transactions on Geoscience and Remote Sensing, vol. 60.
16. Jianwen Hua et al. (2021): Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
17. Jiao Hu et al. (2022): Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
vol. 15.
18. Lalit Kumar & Onisimo Mutanga (2018/10): Google Earth Engine Applications Since Inception: Usage, Trends, and Potential, p.2.
19. Masoud Mahdianpari et al. (2020): Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform, Canadian Journal of Remote Sensing, vol. 46, no. 1.
20. M. Amani et al. (2019/4): Canadian wetland inventory using Google Earth Engine: The first map and preliminary results,” Remote Sens., vol. 11, no. 7, Art. no. 842.
21. Meisam Amani et al. (2019): A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing, Big Earth Data, vol. 3, no. 4.
22. Meiling Gao et al. (2021): Use of Google Earth Engine to Generate a 20-Year 1 Km × 1 Km Monthly Air Temperature Product Over Yellow River Basin, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
23. Meinan Zhang et al. (2019): Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine, International Journal of Remote Sensing, vol. 40, no. 24.
24. Mengmeng Wang et al. (2020): An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13.
25. Meyra Fuentes, Koreen Millard & Emil Laurin (2020): Big geospatial data analysis for Canada’s Air Pollutant Emissions Inventory (APEI): using google earth engine to estimate particulate matter from exposed mine disturbance areas, GIScience & Remote Sensing, vol. 57, no. 2.
26. Renzhe Wu et al. (2024): Glacial Lake Extraction Framework Based on Coupling of GEE and Historical Glacial Lake Position, IEEE Geoscience and Remote Sensing Letters, vol. 21.
27. Ritika Srinet et al. (2020): Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine, International Journal of Remote Sensing, vol. 41, no. 18.
28. Ronglong Xu et al. (2021): A Simple Phenology-Based Vegetation Index for Mapping Invasive Spartina Alterniflora Using Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
29. Wei Wang et al. (2022): Automatic Identification of Sand and Dust Storm Sources Based on Wind Vector and Google Earth Engine, IEEE Geoscience and Remote Sensing Letters, vol. 19.
30. Xiangtian Zheng et al. (2024): Coupling Remote Sensing Insights with Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17.
31. Xiucheng Yang, Yang Chen & Jingzhe Wang (2020): Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine, Remote Sensing Letters, vol. 11, no. 7.
2. Belgacem Amel et al. (2023): Impact of Climatic Factors on the Mekerra watershed (North-West Algeria) via Hydrometeorological Series Provided by The Google Earth Engine Platform, Egyptian Academic Journal of Biological Sciences, vol. 15, no. 1.
3. صبحي عبد الحميد عبد الجواد عبد الحميد (2023): النمذجة الجيومكانية للذكاء الاصطناعي Google Earth Engine لتحليل وتوقع المناطق المهددة بأخطار الجريان السيلي، حوض وادي وتير، جنوب شبه جزيرة سيناء، مصر، مجلة وادي النيل للدراسات والبحوث الإنسانية والاجتماعية والتربوية، مجلد 40، عدد 40.
4. Alejandro Tobón-Marín & Julio Cañón Barriga (2020): Analysis of changes in rivers planforms using google earth engine, International Journal of Remote Sensing, vol. 41, no. 22.
5. Biswajeet Pradhan et al. (2018): Sand dune risk assessment in Sabha region, Libya using Landsat 8, MODIS, and Google Earth Engine images, Geomatics, Natural Hazards and Risk, vol. 9, no. 1.
6. Daniel Paluba et al. (2024): Tracking burned area progression in an unsupervised manner using Sentinel-1 SAR data in Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17.
7. Demuzere, M., Trigo, R. M., Vila-Guerau de Arellano, J., et al. (2021): "Urban Planning and Sustainable Development with Google Earth Engine." Landscape and Urban Planning.
8. Elisabet Walker & Virginia Venturini (2019): Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine, Remote Sensing Letters, vol. 10, no. 10.
9. Gorelick, N., Hancher, M., Dixon, M., et al. (2017): "Long-term Climate Change Monitoring Using Google Earth Engine." Remote Sensing of Environment.
10. Guangyi Wang et al. (2022): Leveraging Google Earth Engine and Semi-Supervised Generative Adversarial Networks to Assess Initial Burn Severity in Forest, Canadian Journal of Remote Sensing, vol. 48, no. 3.
11. Guzinski, R., Nieto, H., Stisen, S., et al. (2020): "Estimating Evapotranspiration Using Remote Sensing and Google Earth Engine." Remote Sensing of Environment.
12. Hansen, M. C., Potapov, P. V., Moore, R., et al. (2013): "Global Forest Change 2000 – 2013." Science.
13. https://earthengine.google.com/datasets/.
14. H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli,and B. Brisco (2020/6): Google Earth Engine for geo-big data applications: A meta-analysis and systematic review,” ISPRS J. Photogrammetry Remote Sens., vol. 164, pp. 152–170.
15. Jiangliu Xie et al. (2022): Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine, IEEE Transactions on Geoscience and Remote Sensing, vol. 60.
16. Jianwen Hua et al. (2021): Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
17. Jiao Hu et al. (2022): Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
vol. 15.
18. Lalit Kumar & Onisimo Mutanga (2018/10): Google Earth Engine Applications Since Inception: Usage, Trends, and Potential, p.2.
19. Masoud Mahdianpari et al. (2020): Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform, Canadian Journal of Remote Sensing, vol. 46, no. 1.
20. M. Amani et al. (2019/4): Canadian wetland inventory using Google Earth Engine: The first map and preliminary results,” Remote Sens., vol. 11, no. 7, Art. no. 842.
21. Meisam Amani et al. (2019): A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing, Big Earth Data, vol. 3, no. 4.
22. Meiling Gao et al. (2021): Use of Google Earth Engine to Generate a 20-Year 1 Km × 1 Km Monthly Air Temperature Product Over Yellow River Basin, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
23. Meinan Zhang et al. (2019): Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine, International Journal of Remote Sensing, vol. 40, no. 24.
24. Mengmeng Wang et al. (2020): An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13.
25. Meyra Fuentes, Koreen Millard & Emil Laurin (2020): Big geospatial data analysis for Canada’s Air Pollutant Emissions Inventory (APEI): using google earth engine to estimate particulate matter from exposed mine disturbance areas, GIScience & Remote Sensing, vol. 57, no. 2.
26. Renzhe Wu et al. (2024): Glacial Lake Extraction Framework Based on Coupling of GEE and Historical Glacial Lake Position, IEEE Geoscience and Remote Sensing Letters, vol. 21.
27. Ritika Srinet et al. (2020): Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine, International Journal of Remote Sensing, vol. 41, no. 18.
28. Ronglong Xu et al. (2021): A Simple Phenology-Based Vegetation Index for Mapping Invasive Spartina Alterniflora Using Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14.
29. Wei Wang et al. (2022): Automatic Identification of Sand and Dust Storm Sources Based on Wind Vector and Google Earth Engine, IEEE Geoscience and Remote Sensing Letters, vol. 19.
30. Xiangtian Zheng et al. (2024): Coupling Remote Sensing Insights with Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17.
31. Xiucheng Yang, Yang Chen & Jingzhe Wang (2020): Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine, Remote Sensing Letters, vol. 11, no. 7.
منشور
2024-08-22
كيفية الاقتباس
مرام فيصل الرحيلي. (2024). نظرة عامة على منصة Google Earth Engine(GEE) واستخداماتها في الدراسات البيئية. Journal of Arts, Literature, Humanities and Social Sciences, (111), 232-249. https://doi.org/10.33193/JALHSS.111.2024.1204
إصدار
القسم
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