SOURCES SOUGHT
B -- B--DATASET REFINEMENTS
- Notice Date
- 6/15/2023 9:27:29 AM
- Notice Type
- Sources Sought
- NAICS
- 541690
— Other Scientific and Technical Consulting Services
- Contracting Office
- OFFICE OF ACQUISITON GRANTS SACRAMENTO CA 95819 USA
- ZIP Code
- 95819
- Solicitation Number
- 140G0323Q0164
- Response Due
- 6/22/2023 3:00:00 PM
- Archive Date
- 07/07/2023
- Point of Contact
- Deng, Yangzhi, Phone: 916-278-9326
- E-Mail Address
-
yangzhideng@usgs.gov
(yangzhideng@usgs.gov)
- Description
- DATA REFINEMENTS SOURCES SOUGHT The US Geological Survey (USGS) is conducting market research to determine the availability of qualified businesses capable of providing dataset refinements to define water-use-specific land use attributes and three-dimensional built infrastructures as described in the Statement of Work. This sources sought announcement is not a request for quote or proposal and the Government is not committed to award of a purchase order or contract pursuant to this announcement. The information resulting from this market research is simply for planning purposes to assist the Government in determining its acquisition strategy. The Government will not pay for any costs incurred in the preparation of information for responding to this notice. The North American Industry Classification System (NAICS) code: 541690, Other Scientific and Technical Consulting Services, and associated size standard 16.5 million apply to this announcement, along with Product Service Code: B529, Special Studies/Analysis- Scientific Data. All responsible sources may submit a capability statement detailing the ability to meet the statement of work included with this announcement. Responses to this announcement shall only be accepted through electronic mail addressed to yangzhideng@usgs.gov and must be uploaded and received in their entirety no later than 06/22/2023 at 1500 PST. Proposals submitted by hardcopy or any web portal shall not be accepted or considered. Statement of Work Objectives of this project include development of national water source type datasets that can be used to develop dynamic publicly supplied service area boundaries, estimate self-supplied domestic and industrial water use, and support water use forecasting. Development of a source water type data set will be facilitated by high resolution (10-30 m) Landsat imagery, census data, land use data, and other datasets to be identified during the project period. The amount of water used often relates to the spatial patterns of land use and built-up areas, particularly domestic use dominated by landscaping in suburban and exurban areas, and potential household size and other factors in more urban contexts. Rural houses outside city limits that are too far from town centers are self-supplied and should not be included in estimates of public supply water use, and this work will support better delineation of the threshold of housing density to distinguish self-supplied from publicly supplied water use. The approach for this work includes the incorporation of several satellite data products to delineate built infrastructure using machine learning approaches and spectral data to identify changes in development that could indicate changes in self-versus publicly supplied source water type. Through characterization of features expressed in high resolution imagery that are unique to different types of land cover, these data can be used to monitor change in built infrastructure that could be related to shifts in source water type, and these maps can be updated to represent other years in the historical period being evaluated as part of the water use modeling reanalysis. Satellite data will be used to create additional spatial datasets to estimate the extent of impervious cover, vegetation associated with domestic use between and surrounding buildings using 30 m Landsat, and 10 m Sentinel remote sensing for the period 2016-2020, referred to as �greenscapes.� For commercial land use, area estimates will be refined by relating to the area of commercial use to include the number of commercial units. This is done by mapping the vertical aspects of built-up areas (e.g., number of stories in buildings). Built-up lands will be separated into roads/parking lots and buildings using 10-30 resolution imagery. Land use types of rural, urban, and ex-urban using a housing density also will be included to support development of maps of source water type. Additionally, other ancillary data sets analyzed for information to support water use type modeling, including land pattern metrics, state provided water service area boundaries (where available), and topographic constraints. All these data will be analyzed in context with mapped areas of known source water type to support training of machine learning models. Model training will rely on the USGS Water Use Program�s recently developed national water service areas for training and validating estimated raster maps of source water type. Other water use characteristics will be defined for this modeling, including water use datasets characterizing diversion/source of water for public-supplied, industrial, and domestic water. Well location data for self-supplied domestic wells will be compiled from existing dataset to support this work. Finally, maps will be updated temporally to represent changes that occur in 2-5 year increments for the period 2010-2020 using population changes provided by the US Census Program (e.g., American Community Survey) and National Land Cover Dataset. Scripted workflows for generating new maps in the future will be part of the deliverables for this project to support future work.
- Web Link
-
SAM.gov Permalink
(https://sam.gov/opp/0b8b19eb80be4e08b09395b256d82dfc/view)
- Record
- SN06718161-F 20230617/230616060716 (samdaily.us)
- Source
-
SAM.gov Link to This Notice
(may not be valid after Archive Date)
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