A cyber-enabled spatial decision support system to inventory Mangroves in Mozambique: coupling scientific workflows and cloud computing

Tang, Wenwu; Feng, Wenpeng; Jia, Meijuan; Shi, Jiyang; Zuo, Haoyi; Stringer, C. E.; Trettin, Carl C.

doi:10.1080/13658816.2016.1245419

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…A Spatial Decision Support System (SDSS) developed by Tang et al (2016) was used to allocate sampling plots within strata based on the categories of the tree canopy heights and hydrogeomorphic settings while considering logistical, operational and safety constraints. Forty-nine plots (0.0414 ha) were randomly located within the strata to collect vegetation measurements and soil samples within the inventory area.…”

Section: Sampling Designmentioning

confidence: 99%

Soil Carbon within the Mangrove Landscape in Rufiji River Delta, Tanzania

et al. 2022

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Mangroves are among the most carbon-rich terrestrial ecosystems, primarily attributable to the soil pool. There are substantial differences in soil carbon (C) and nitrogen (N) due to the disparities in geomorphic settings and ecological drivers, but this insight is drawn primarily from observational studies. An objective inventory of carbon stocks in mangroves of the Rufiji River Delta, Tanzania was conducted. Seventy-five soil cores were collected within a 12,164 ha inventory area, comprising the northern portion of the delta. Cores were collected from intact and dwarf mangroves, agricultural fields, and mudflats. The spatial mean soil organic carbon (SOC) density in mangroves was 16.35 ± 6.25 mg C cm−3. Mean SOC density in non-vegetated mudflats was 12.16 ± 4.57 mg C cm−3, demonstrating that mangroves develop on soils with a substantial soil C stock. However, long-established mangroves had had a higher C density (17.27 ± 5.87 mg C cm−3). Using a δ13C mixing model, the source of soil organic matter in mudflats was primarily marine, while long-established mangroves was predominantly mangrove. There were small differences in SOC among long-established mangrove sites in different geomorphic settings. The proportion of marine-sourced SOC increased with soil depth in mangroves. The SOC and nitrogen of agricultural sites resemble those of mudflats, suggesting those sites are developed from relatively young forests. The SOC and nitrogen density in dwarf mangrove sites were lower than others, perhaps reflecting past disturbances.

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Section: Sampling Designmentioning

confidence: 99%

Soil Carbon within the Mangrove Landscape in Rufiji River Delta, Tanzania

et al. 2022

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“…While the study of SDSS in early stages focuses on the development of conceptual architecture, cyberinfrastructure-enabled computing technologies such as web and cloud computing have been fostering the implementation and applications of SDSS into different domain studies (Sugumaran & Degroote, 2010; Tang et al, 2017). For example, Mwaura and Kada (2017) presented a web-based SDSS in which a multi-criteria decision making model was used to evaluate potential sites of geothermal wells in Kenya, east Africa.…”

Section: Literature Reviewmentioning

confidence: 99%

A Web-based Spatial Decision Support System of Wastewater Surveillance for COVID-19 Monitoring: A Case Study of a University Campus

Tang

Chen

Slocum

et al. 2022

Preprint

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The ongoing COVID-19 pandemic has produced substantial impacts on our society. Wastewater surveillance has increasingly been introduced to support the monitoring, and thus mitigation, of COVID-19 outbreaks and transmission. Monitoring of buildings and sub-sewershed areas via a wastewater surveillance approach has been a cost-effective strategy for mass testing of residents in congregate living situations such as universities. A series of spatial and spatiotemporal data are involved with wastewater surveillance, and these data must be interpreted and integrated with other information to better serve as guidance on response to a positive wastewater signal. The management and analysis of these data poses a significant challenge, in particular, for the need of supporting timely decision making. In this study, we present a web-based spatial decision support system framework to address this challenge. Our study area is the main campus of the University of North Carolina at Charlotte. We develop a spatiotemporal data model that facilitates the management of space-time data related to wastewater surveillance. We use spatiotemporal analysis and modeling to discover spatio-temporal patterns of COVID-19 virus abundance at wastewater collection sites that may not be readily apparent in wastewater data as they are routinely collected. Web-based GIS dashboards are implemented to support the automatic update and sharing of wastewater testing results. Our web-based SDSS framework enables the efficient and automated management, analytics, and sharing of spatiotemporal data of wastewater testing results for our study area. This framework provides substantial support for informing critical decisions or guidelines for the prevention of COVID-19 outbreak and the mitigation of virus transmission on campus.

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“…The advancement of web and Internet technologies has stimulated the development and use of SDSS over the web (Sugumaran and Degroote 2010). A series of studies have developed SDSS combined with web-based GIS capabilities in different domains, such as natural resource management (Choi, Engel, and Farnsworth 2005;Sugumaran, Meyer, and Davis 2004;Tang et al 2017;Zeng et al 2012), earth science (Mwaura and Kada 2017;Zhang, Zhao, and Weidong 2010), business location (Jung and Sun 2006), and health (Kelly et al 2011;Delmelle et al 2011;Kienberger et al 2013;Delmelle et al 2014). Geoprocessing services play a key role in these web-based SDSS applications.…”

Section: Geoprocessing Web Servicesmentioning

confidence: 99%

“…A set of geoprocessing capabilities can be connected and published into a geoprocessing service to provide complicated spatial analytical functionalities. As the resolution of a spatial problem typically involves multiple spatial data processing and spatial analyses, multiple geoprocessing steps are often chained through scientific workflow technologies as an important cyberinfrastructure capability (see Wang 2010;Tang et al 2017).…”

Section: Geoprocessing Web Servicesmentioning

confidence: 99%

“…The use of geoprocessing services has been stimulated by the development of Web Processing Service (WPS), which is an interoperable geoprocessing service standard by Open Geospatial Consortium (OGC). Geoprocessing services have been applied in various domains for spatial decision support and, as a result, a series of web-based SDSS have been developed(e.g., Burdziej 2012; Li et al 2013;Lin et al 2015;Modica et al 2016;Mwaura and Kada 2017;Tang et al 2017;Zhang et al 2019). Although the application of web-based SDSS with support from geoprocessing services is relatively common for air quality, weather and environmental data -such as the INTAMAP project (Distefano et al 2015;Hiemstra et al 2009;Pebesma et al 2011;Schäjfler et al 2010;Williams et al 2011), it is relatively rare into the study of groundwater quality, particularly, private wells.…”

Section: Geoprocessing Web Servicesmentioning

confidence: 99%

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A web-based spatial decision support system for monitoring the risk of water contamination in private wells

et al. 2020

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Long-term exposure to contaminated water can cause health effects, such as cancer. Accurate spatial prediction of inorganic compounds (e.g. arsenic) and pathogens in groundwater is critical for water supply management. Ideally, environmental health agencies would have access to an early warning system to alert well owners of risks of such contamination. The estimation and dissemination of these risks can be facilitated by the combination of Geographic Information Systems and spatial analysis capabilities-i.e., spatial decision support system (SDSS). However, the use of SDSS, especially web-based SDSS, is rare for spatially explicit studies of drinking water quality of private wells. In this study, we introduce the interactive Well Water Risk Estimation (iWWRE), a web-based SDSS to facilitate the monitoring of water contamination in private wells across Gaston County, North Carolina (US). Our system implements geoprocessing web services and generates dynamic spatial analysis results based on a database of private wells. Environmental health scientists using our system can conduct fine-grained spatial interpolation on 1) a particular type of contaminant such as arsenic, 2) on various subsets through a temporal query. Visuals consist of an estimation map, cross validation information, Kriging variance and contour lines that delineate areas with maximum contaminant levels (MCL), as set by the US Environmental Protection Agency (EPA). Our web-based SDSS was developed jointly with environmental health specialists who found it particularly critical for the monitoring of local contamination trends, and a useful tool to reach out to private well users in highly elevated contaminated areas.

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A cyber-enabled spatial decision support system to inventory Mangroves in Mozambique: coupling scientific workflows and cloud computing

Cited by 13 publications

References 47 publications

Soil Carbon within the Mangrove Landscape in Rufiji River Delta, Tanzania

Soil Carbon within the Mangrove Landscape in Rufiji River Delta, Tanzania

A Web-based Spatial Decision Support System of Wastewater Surveillance for COVID-19 Monitoring: A Case Study of a University Campus

A web-based spatial decision support system for monitoring the risk of water contamination in private wells

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