This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

More MetadataThe Ridge Feature Protection layer was developed using best available elevation data and the Geomorphon Landforms geoprocessing tool available thru Esri ArcGIS Pro software. The Geomorphon Landforms geoprocessing tool is based on a powerful algorithm that combines elevation differences and visibility concepts to classify terrain into landform types. A geomorphon is a representation of landscape based on elevation differences within the surrounding area of a target cell. The following parameters and data refinements were used for the Geomorphon Landforms geoprocessing tool in order to provide a general representation of primary and other prominent ridge features in Loudoun County. Parameters:[Flat Angle Threshold = 3 degrees, Search Distance = 2,640 feet (1/2 mile), and Skip Distance = 660 feet (1/8 mile), Landform Values = Peak, Ridge OR Shoulder] Data Refinements:All features outside of the MOD were removed.All features within the MOD but below the critical elevation defined within the MOD were removed.All features within MOD less than 20 acres in size and not contiguous with the County’s boundary were removed.All features within MOD not contiguous with a primary ridge feature were removed.All features within MOD contiguous with a primary ridge feature but 400 feet or more in elevation below the primary ridge feature were removed. The Ridge Feature Protection layer is intended to be used only as a reference. Better site-specific elevation data may improve the precision and accuracy of the Ridge Features layer. To assure that Loudoun County is using the best available elevation data, please provide the Office of Mapping and Geographic Information with information concerning any errors, omissions, or other discrepancies discovered in this data. Source Data Digital Elevation Model/DEM (~ 10 m cell size) USGS 1/3 Arc Second; 3D Elevation Program (3DEP)

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.

A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included. The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. http://creativecommons.org/licenses/by/4.0 Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland. This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset. Open Data Bulk Data Downloads (version date: 23/08/2023) The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. ERSI Shapefiles of the SMR points and SMRZone polygons are also available The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. GIS Web Service APIs (live views): For users with access to GIS software please note that the Archaeological Survey of Ireland data is also available spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below: SMR; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMROpenData/FeatureServer SMRZone; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMRZoneOpenData/FeatureServer Historic Environment Viewer - Query Tool The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders.New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.

This data set is part of the TPB Regional Transportation Data ClearinghouseThe service is a collection of the Regional Automatic Counts, DC Bicycle Counts, and Northern Virginia Bicycle Counts. Below is a description of each Count type included in the service, as well as the data included for each.The 2018 Pedestrian and Bicycle Automatic CountsData represents pedestrian and bicycle trips captured by a regional network of automatic counters (http://www.bikearlington.com/counter-data/) located in Arlington County, City of Alexandria, the District of Columbia, and Montgomery County for calendar year 2018. The raw data was downloaded via Commuter Page’s web services ( http://www.commuterpage.com/pages/tools-resources/tools-for-developers/) and processed, organized and summarized.Layers:2018 Pedestrian Automatic Counts (regional)2018 Bicycle Automatic Counts (regional)Tables:2016 Bicycle Automatic Counts, Daily (regional)2016 Pedestrian Automatic Counts, Daily (regional2016 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2017 Pedestrian Automatic Counts, Daily (regional)2017 Bicycle Automatic Counts, Daily (regional)2017 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2018 Bicycle Automatic Counts, Daily (regional)2018 Pedestrian Automatic Counts, Daily (regional)Disclaimer (taken from BikeArlington): Because the dashboard presents "raw" data direct from the system server and the devices in the field, it will sometimes include errors, or contain gaps or blank periods. As we continue to develop the dashboard, our goal is to provide the most accurate and useful information possible. This may involve presenting both data direct from the system as well as data that have been normalized statistically. We invite your help and active participation in this process. If you find problems with the data, have questions about particular time periods, or would like to share insights or interpretations, please feel free to communicate them to: bikepedcounts@arlingtonva.us.Washington, D.C. Bicycle CountsBicycle Counts collected for Washington, D.C. during AM Peak and PM Peak time periods. Counts were totaled and recorded in 15 minute intervals from 06:00 to 10:00 and 15:00 to 18:00. The Street, between which intersections, date, day of the week, parking, speed limit, bike lanes, one way street, were all recorded for each count location. Count data collected by TPB Staff for DDOT includes: Day Total, Peak Time Period Total, Helmet(yes/no) Total, Male Total, Female Total, Adult, Child by Station. The related table includes Total (Time Period Total), Helmet (yes), Helmet (no), Male, Female, Adult, Child Total, Male Total, Female Total, Adult, Child by Station.Layers:District of Columbia Bicycle Count Stations Totals, Monthly (FY 2014, 2015, 2016, 2017)Tables:District of Columbia Bicycle Counts, Hourly (FY 2014, 2015, 2016, 2017) VDOT Bicycle CountsAs part of TPB's Northern Virginia Technical Assistance program, Travel Monitoring staff conduct bicycle and pedestrian counts for the Virginia Department of Transportation (VDOT) at requested locations on a regular basis. The data was collected utilizing VDOT's Miovision cameras (video data collection). The cameras allow directional observations. The layer data provided was collected at the 24-hour summary level by mode as well as by direction where available. 15-minute increments for the 24-hour period per count location for bicycles and pedestrians by direction where available data is made available in the tables .Layers:VDOT Pedestrian Counts (FY 2013, 2014, 2015, 2016, 2017)VDOT Bicycle Counts (FY 2013, 2014, 2015, 2016, 2017)Tables:VDOT Bicycle & Pedestrian Counts - Hourly (FY 2013, 2014, 2015, 2016, 2017)

The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.NAIP imagery is available for distribution within 60 days of the end of a flying season and is intended to provide current information of agricultural conditions in support of USDA farm programs. For USDA Farm Service Agency, the 1 meter and 1/2 meter GSD product provides an ortho image base for Common Land Unit boundaries and other data sets. The 1 meter and 1/2 meter NAIP imagery is generally acquired in projects covering full states in cooperation with state government and other federal agencies that use the imagery for a variety of purposes including land use planning and natural resource assessment. The NAIP is also used for disaster response. While suitable for a variety of uses, prior to 2007 the 2 meter GSD NAIP imagery was primarily intended to assess "crop condition and compliance" to USDA farm program conditions. The 2 meter imagery was generally acquired only for agricultural areas within state projects.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

1. IntroductionThe lower cost of sensors is making possible the acquisition of big data sets in several applications and research areas [1]. Indoor air quality and commuter exposure to pollutants are some of these areas, which can have impacts on our livelihood. We considered activities that happen outdoors, like public or private transportation while commuting, as an indoor environment that can accumulate CO2, mainly as a result of the metabolic process in humans. This is why CO2 is often considered a good indicator of inefficient ventilation. Concentrations above 1000 ppm fall within this category.Figure 1. Graphical abstractSome studies have been able to register peak concentrations above the recommended 5000 ppm CO2 [2] inside Hong Kong buses [3]. However, these were sustained for short periods of time and do not pose a concerning health risk. Despite this, recent findings suggest that concentrations as low as 950 ppm, sustained for an 8-hour workday, can cause loss of 15% in performance during cognitive tests, although it is recognized that further research is needed to isolate CO2 as the single culprit [4].In order to know if we are exposed to a certain type of pollutant and for how long, we must first be able to quantify it. In air quality monitoring this is not accessible to just anyone as reliable heavy-duty sensors can cost upwards of thousands of euros. These costly sensors offer extremely accurate measurements but suffer from low temporal resolution and low spatial density. This is why in recent years, as technology improves and gets miniaturized, a copious amount of low-cost sensors (LCS) have flooded the market with acceptable performances at very low prices. It is recognized that they are still not on par with high-cost sensors when it comes to accuracy and reliability; however, the gap is being narrowed year by year as advancements both in technology and research are made. Correlation coefficients above 0.75 are not uncommon.2. ObjectivesThe main objective of this exploratory research was to assemble portable equipment along with an in-house prototype, low-cost and easy to replicate in any location worldwide. In the published work [5], in which this summary is based on, it was answered how CO2, noise and energy expenditure compare in transportation modes with indoor environments (metro, bus and car). Assessments on commuting times and search for correlations and trends between the parameters were also made.3. MethodsThe assembled equipment made use of an Arduino Uno, an SD-card module and a low-cost sensor (Sensirion SCD30). These components, along with a smartwatch and a smartphone, were capable of quantifying personal exposure to CO2, noise and also measure heart rate.The exploratory field campaign was conducted on an urban commuting route, in Lisbon city, between Rossio (downtown of Lisbon city) and Campo Grande (near FCUL campus) during 6 weeks.At the end of the campaign, all the usable data, mainly from CO2 and HR, in CSV format, was integrated into a geospatial database using Esri© technology (ArcMap 10.5) with the intent of producing maps that compare modes of transportation, morning versus afternoon and school break versus school period. Considerable quantities of data required the use of ArcMap model builder to speed up the process as seen in figure 2. All the CO2 and HR data was merged from multiple CSV sheets into a single shapefile that was then used to search for hot spots using the Getis and Ord cluster tool. Some experiments with the make route event layer tool were done to try and visualize metro underground data. The utilization of such tool is crucial to recreate the underground trips, where global positioning systems (GPS) cannot reach. This is an extremely useful tool that was tested with and despite our success using it, the maps produced revealed some inherent methodology constraints which will be fixed in future iterations.Nonetheless, for the remainder of the data, all the maps proved invaluable to help corroborate any trend or correlation with observational data as well as categorize the modes of transportation in the study.Figure 2. Model Builder for Bus and Car hot spot analysis (metro was discarded).4. ResultsThe commuter made a total of 35 round-trips, 12 were made by metro, 11 by bus and another 12 by car. This corresponds to a total of 70 one-way measurement trips. Sample sizes for each variable were as follows: 32794 for CO2, temperature and humidity, 27825 for HR and 297521 for noise. Noise and metro measurements were excluded from the results section as the utilization of GIS revealed some aspects of the methodology that could be improved. Next it is highlighted some of the valid GIS results obtained. Figure 3. Bus CO2 hot spots. All morning Figure 4. Bus CO2 hot spots. All afternoon data.In figure 3 and 4, it can be seen that in afternoon commutes, the occurrence of hot spots is more sparse. In the morning commutes however, there is a stronger prevalence of hot spots between Restauradores and Av. da República. It was in these areas that the highest CO2 concentration of 2190 ppm was registered. Even though this concentration was not enough to surpass the 5000 ppm registered in Hong Kong buses [3] or pose a concerning health risk [6], it is still significant and indicates that the bus suffers from inefficient ventilation during certain parts of the commute [7].Figure 5. Heart rate hot spots of all Bus morning data.The map seen in figure 5, was useful to corroborate observational data of overcrowding periods. Along with a correlations table seen in the full article [5], it was realized that these HR hot spots have no direct correlation with CO2 seen in figures 3 and 4, but instead seem related with the specific overcrowding periods.5. ConclusionsThe bus has big portions of its commute time spent in the inefficient ventilation category. It is advisable that the A/C and/or air circulation should be turned to a higher setting during these moments. With the GIS analysis, it could be stated that high concentrations of CO2 were prevalent between Restauradores and Av. da República, something that would not have been possible to affirm by simple using a temporal analysis. As previously stated, bus HR hot spots were correlated with the overcrowding periods and not the CO2 variable [5]. During these periods, the commuter would usually be standing or maneuvering through the crowd that is entering and exiting the bus. As expected, it can be seen more examples of heart rate hot spots while entering and exiting.The car CO2 measurements were considered uneventful for the most part and were mainly dependent on the windows being opened or not. Some indications of traffic light stops trends with CO2 and HR could be seen in some periods, but revised methodology is required to make sure our affirmations are solid. Nonetheless, it was considered the worst mode of transportation in the study as it goes against the idea of a less congested and clean city. Also, the car reached CO2 concentrations close to the values in the bus (2000 ppm) with only one passenger.The expansion of this project is underway and is intend to have more commuters (10 minimum), routes, transportation modes and sensors (Ozone - O3 and Nitrogen Oxides - NOx) collecting personal exposure data. We will also have a prototype replica calibrated for 6 months in-situ versus very high cost sensors (10,000€+) to further test the sensors capabilities.The strength of this project relies on its endless possibilities. The novelty of measuring these particular parameters together and acquiring big datasets fits perfectly with GIS analysis tools. The author adds that there is a strong case to be made about the under-utilization of software technologies, like ArcMap, for these purposes. It was denoted, while conducting research, that many mobile air quality stations researchers don’t use the full potential of GIS technologies or end-up using mobile apps that do a disservice to the quality of the data obtained. The use of such tools made it possible to vastly improve our methodology offering ways to speed up the process of data analysis (e.g. model builder). It also presented new ideas on ways to produce underground metro maps using the make route event layer. The reason the metro was not included in the GIS analysis was because we did not carefully timestamp all underground movements. This is something that will be present in the next iteration of the project as it is very easy to implement. We will be able to produce hot spot analysis of underground measurements with high fidelity and compare it with the other modes of transportation.References1. Kumar, P.; Morawska, L.; Martani, C.; Biskos, G.; Neophytou, M.; Di Sabatino, S.; Bell, M.; Norford, L.; Britter, R. The rise of low-cost sensing for managing air pollution in cities, 2015. doi:10.1016/j.envint.2014.11.019.2. WHO/Europe. WHO guidelines for indoor air quality: selected pollutants. Bonn, Germany: in puncto druck+ medien GmbH 2010. doi:10.1186/2041-1480-2-S2-I1.3. Chan, A.T. Commuter exposure and indoor-outdoor relationships of carbon oxides in buses in Hong Kong. Atmospheric Environment 2003. doi:10.1016/S1352-2310(03)00465-5.4. Vehviläinen, T.; Lindholm, H.; Rintamäki, H.; Pääkkönen, R.; Hirvonen, A.; Niemi, O.; Vinha, J. High indoor CO2 concentrations in an office environment increases the transcutaneous CO2 level and sleepiness during cognitive work. Journal of Occupational and Environmental Hygiene 2016. doi:10.1080/15459624.2015.1076160.5. Soares, A.; Catita, C.; Silva, C. Exploratory Research of CO2, Noise and Metabolic Energy Expenditure in Lisbon Commuting. MDPI 2020. doi:https://doi.org/10.3390/en13040861.6. COHEN, JOEL M.. PETERSON, R.D. COMPLETE GUIDE TO OSHA COMPLIANCE; CRC PRESS, 2019.7. ANSI. ANSI/ASHRAE Standard 62.1-2010, Ventilation for Acceptable Indoor Air Quality. Ashrae 2007.doi:ANSI/ASHRAE Standard 62.1-2004.

The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.

2015 National Agriculture Imagery Program (NAIP) natural color 1 meter pixel resolution. The imagery was collected statewide from August 8 to October 30, 2015. The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile. Credits: USDA-FSA Aerial Photography Field Office, ND GIS Hub

The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.NAIP imagery is available for distribution within 60 days of the end of a flying season and is intended to provide current information of agricultural conditions in support of USDA farm programs. For USDA Farm Service Agency, the 1 meter and 1/2 meter GSD product provides an ortho image base for Common Land Unit boundaries and other data sets. The 1 meter and 1/2 meter NAIP imagery is generally acquired in projects covering full states in cooperation with state government and other federal agencies that use the imagery for a variety of purposes including land use planning and natural resource assessment. The NAIP is also used for disaster response. While suitable for a variety of uses, prior to 2007 the 2 meter GSD NAIP imagery was primarily intended to assess "crop condition and compliance" to USDA farm program conditions. The 2 meter imagery was generally acquired only for agricultural areas within state projects.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

2019 National Agriculture Imagery Program (NAIP) natural color .6-meter pixel resolution. The imagery was collected statewide from June 23, 2019 through September 19, 2019.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders.

New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile. Credits: USDA-FSA Aerial Photography Field Office, ND GIS Hub

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile. NAIP imagery is available for distribution within 60 days of the end of a flying season and is intended to provide current information of agricultural conditions in support of USDA farm programs. For USDA Farm Service Agency, the 1 meter and 1/2 meter GSD product provides an ortho image base for Common Land Unit boundaries and other data sets. The 1 meter and 1/2 meter NAIP imagery is generally acquired in projects covering full states in cooperation with state government and other federal agencies that use the imagery for a variety of purposes including land use planning and natural resource assessment. The NAIP is also used for disaster response. While suitable for a variety of uses, prior to 2007 the 2 meter GSD NAIP imagery was primarily intended to assess "crop condition and compliance" to USDA farm program conditions. The 2 meter imagery was generally acquired only for agricultural areas within state projects.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.NAIP imagery is available for distribution within 60 days of the end of a flying season and is intended to provide current information of agricultural conditions in support of USDA farm programs. For USDA Farm Service Agency, the 60 centimeter GSD product provides an ortho image base for Common Land Unit boundaries and other data sets. The NAIP imagery is generally acquired in projects covering full states in cooperation with state government and other federal agencies that use the imagery for a variety of purposes including land use planning and natural resource assessment. The NAIP is also used for disaster response. While suitable for a variety of uses, prior to 2007 the 2 meter GSD NAIP imagery was primarily intended to assess "crop condition and compliance" to USDA farm program conditions. The 2 meter imagery was generally acquired only for agricultural areas within state projects.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.This layer is part of The Idaho Map (TIM) as described in the NAIP Digital Orthoimagery Standard (S4255).

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones. Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event. Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are:1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

2019 National Agriculture Imagery Program (NAIP) color infrared .6-meter pixel resolution. The imagery was collected statewide from June 23, 2019 through September 19, 2019.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders.

New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.Credits: USDA-FSA Aerial Photography Field Office, ND GIS Hub

2015 National Agriculture Imagery Program (NAIP) color infrared 1 meter pixel resolution. The imagery was collected statewide from August 8 to October 30, 2015. The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile. Credits: USDA-FSA Aerial Photography Field Office, ND GIS Hub

2016 National Agriculture Imagery Program (NAIP) color infrared .6-meter pixel resolution. The imagery was collected statewide from June 20 to August 22. The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile. Credits: USDA-FSA Aerial Photography Field Office, ND GIS Hub

(Link to Metadata) The NAIP_0_6M_CLRIR_2016 dataset is a (60 centimeter) truecolor and infrared (4 band) NAIP imagery product acquired during the summer of 2016 by the USDA-FSA-APFO NAIP program, then reprojected to VT State Plane Meters and cropped to the USGS quarter quad boundary by VCGI. This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.