Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

Publication Date:
2018-06-09
Publisher:
MDPI Publishing
Electronic ISSN:
2072-4292
Topics:
Architecture, Civil Engineering, Surveying
Geography
Published by:
http://www.mdpi.com/
_version_ 1836398966115139585
autor 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
beschreibung Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Remote Sensing doi: 10.3390/rs10060909 Authors: 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 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.
citation_standardnr 6280149
datenlieferant ipn_articles
feed_id 124526
feed_publisher MDPI Publishing
feed_publisher_url http://www.mdpi.com/
insertion_date 2018-06-09
journaleissn 2072-4292
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher MDPI Publishing
quelle Remote Sensing
relation http://www.mdpi.com/2072-4292/10/6/909
search_space articles
shingle_author_1 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
shingle_author_2 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
shingle_author_3 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
shingle_author_4 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
shingle_catch_all_1 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Remote Sensing doi: 10.3390/rs10060909 Authors: 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 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.
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
MDPI Publishing
2072-4292
20724292
shingle_catch_all_2 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Remote Sensing doi: 10.3390/rs10060909 Authors: 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 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.
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
MDPI Publishing
2072-4292
20724292
shingle_catch_all_3 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Remote Sensing doi: 10.3390/rs10060909 Authors: 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 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.
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
MDPI Publishing
2072-4292
20724292
shingle_catch_all_4 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Remote Sensing doi: 10.3390/rs10060909 Authors: 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 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.
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
MDPI Publishing
2072-4292
20724292
shingle_title_1 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
shingle_title_2 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
shingle_title_3 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
shingle_title_4 Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
timestamp 2025-06-30T23:35:28.459Z
titel Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
titel_suche Remote Sensing, Vol. 10, Pages 909: Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing
topic ZH-ZI
R
uid ipn_articles_6280149