دورية أكاديمية

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

التفاصيل البيبلوغرافية
العنوان: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
المؤلفون: Kel N. Markert, Calla M. Schmidt, Robert E. Griffin, Africa I. Flores, Ate Poortinga, David S. Saah, Rebekke E. Muench, Nicholas E. Clinton, Farrukh Chishtie, Kritsana Kityuttachai, Paradis Someth, Eric R. Anderson, Aekkapol Aekakkararungroj, David J. Ganz
المصدر: Remote Sensing, Vol 10, Iss 6, p 909 (2018)
بيانات النشر: MDPI AG, 2018.
سنة النشر: 2018
المجموعة: LCC:Science
مصطلحات موضوعية: lower mekong basin, landsat collection, suspended sediment concentration, online application, google earth engine, Science
الوصف: Reservoir construction and land use change are altering sediment transport within river systems at a global scale. Changes in sediment transport can impact river morphology, aquatic ecosystems, and ultimately the growth and retreat of delta environments. The Lower Mekong Basin is crucial to five neighboring countries for transportation, energy production, sustainable water supply, and food production. In response, countries have coordinated to develop programs for regional scale water quality monitoring that including surface sediment concentrations (SSSC); however, these programs are based on a limited number of point measurements and due to resource limitations, cannot provide comprehensive insights into sediment transport across all strategic locations within the Lower Mekong Basin. To augment in situ SSSC data from the current monitoring program, we developed an empirical model to estimate SSSC across the Lower Mekong Basin from Landsat observations. Model validation revealed that remotely sensed SSSC estimates captured the spatial and temporal dynamics in a range of aquatic environments (main stem of Mekong river, tributary systems, Mekong Floodplain, and reservoirs) while, on average, slightly underestimating SSSC by about 2 mg·L−1 across all settings. The operational SSSC model was developed and implemented using Google Earth Engine and Google App Engine was used to host an online application that allows users, without any knowledge of remote sensing, to access SSSC data across the region. Expanded access to SSSC data should be particularly helpful for resource managers and other stakeholders seeking to understand the dynamics between surface sediment concentrations and land use conversions, water policy, and energy production in a globally strategic region.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2072-4292
Relation: http://www.mdpi.com/2072-4292/10/6/909; https://doaj.org/toc/2072-4292
DOI: 10.3390/rs10060909
URL الوصول: https://doaj.org/article/556c3a11f3d3416184a722f2bec926d5
رقم الأكسشن: edsdoj.556c3a11f3d3416184a722f2bec926d5
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20724292
DOI:10.3390/rs10060909