An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

Poortinga, Ate; Clinton, Nicholas; Saah, David; Cutter, Peter; Chishtie, Farrukh; Markert, Kel; Anderson, Eric; Troy, Austin; Fenn, Mark E.; Tran, Lan Huong; Bean, Brian D.; Nguyen, Quyen; Bhandari, Biplov; Johnson, Gary W.; Towashiraporn, Peeranan

doi:10.3390/rs10050760

Cited by 48 publications

(38 citation statements)

References 33 publications

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“…GEE consists of a cloud-based data catalogue and computing platform. The App Engine framework was used to serve the data to a web browser and communicate with the GEE using the Python API.Figure reusedwith permission from Poortiga et al[63].…”

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confidence: 99%

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

et al. 2018

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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

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confidence: 99%

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

et al. 2018

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“…Fortunately, in many cases the cost and technical expertise needed to apply these technologies and practices to address gaps is generally decreasing. The availability of high-quality remotely sensed and GIS data has steadily increased with processing packages and large public data repositories such as that hosted by Google Earth Engine (GEE) now freely available to academic, NGO, and public sectors (Gorelick et al, 2017;Markert et al, 2018;Poortinga et al, 2018). Many new tools, sites, and organizations have emerged to increase the power and ease of web-mapping and web-based map and GIS services, while reducing the cost and other barriers to entry.…”

Section: Discussionmentioning

confidence: 99%

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Saah

Tenneson²,

Matin

et al. 2019

Front. Environ. Sci.

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Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

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“…GEE consists of a cloud-based data catalogue and computing platform. The App Engine framework was the client used to deliver the data to a web browser and communicate with the GEE using the JavaScript or Python application programming interface (API) [42]. Developers can explore the geospatial data using JavaScript and in a web-based integrated development environment (IDE).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

Zeng

Zhang

et al. 2019

Remote Sensing

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Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall's correlation and sen's Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A "dry gets dryer, wet gets wetter" (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation.

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An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

Cited by 48 publications

References 33 publications

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

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

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

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