Possible Changes to NAIP Imagery Licensing Model

As this blog and our book make clear, the world of geospatial data is in a continual state of change.  Much of this change has been toward more data in the public domain, but sometimes, the change may move in the opposite direction. The National Agriculture Imagery Program (NAIP) has been a source for aerial imagery in the USA since 2003 and has been in the public domain, available here.  But recently, the Farm Services Agency (FSA) has proposed to move the data model from the public domain to a licensing model.  The collection of this imagery has been under an innovative model wherein state governments and the federal government share the costs.

One reason for the proposed change is that the states have been $3.1 million short over the past several years, and FSA cannot continue “picking up the tab.”  Furthermore, delays in releasing funding from cost-share partners forces contract awards past “peak agriculture growth” season, which thwarts one key reason why the imagery is collected in the first place–to assess agricultural health and practices.  We have discussed this aspect of geospatial data frequently in this blog–that geospatial data comes at a cost.  Someone has to pay, and sometimes, those payment models need to be re-considered with changing funding and priorities.  In this case, agencies and data analysts that rely on NAIP imagery would suffer adverse consequences, but with the expansion of the types and means by which imagery can be acquired nowadays, perhaps these developments will enable those other sources to be explored more fully.  And, possibly, the model could be adjusted so that the data could be paid for and that all could benefit from it.

For more information, see the report by our colleagues at GIS Lounge, and the presentation housed on the FGDC site, here.

Two samples of NAIP imagery, for Texas, left, and North Dakota, right.

VGI Data Sources: Assessing Completeness and Correctness

In a recent article published in the ISPRS International Journal of Geo-Information, Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany, the authors investigated some of the concerns regarding data quality and data usability often levelled at Volunteered Geographic Information (VGI) data sources.

The objective of the study, based in the Rhine-Neckar region of southern Germany, was to compare OSM data to the authoritative land use and land cover (LULC) data set ATKIS Base DLM version 6.0. published by the LGL mapping agency (Baden-Württemberg State Office for Geoinformation and State development).

The results for the OSM data completeness and correctness comparison were variable across the different classes of land use in the study area. However some general trends emerged including:

• Areas with a high percentage of forest cover were the areas with the highest level of completeness and correctness.
• Other classes (incl. farmland and urban areas) had low levels of completeness but higher levels of correctness; features present were mapped accurately but some features were missing.
• Other areas (incl. quarry and lakes) had high levels of completeness (most features mapped) but had a greater percentage of incorrectly mapped features.
• There was a marked difference between rural and urban areas; the study identified higher OSM coverage and thematic accuracy in densely populated areas (more people available/interested in collecting the data?).
• Some land use classes demonstrated both high levels of completeness and correctness, suggesting they had been mapped for a specific purpose.

Although not intended as a definitive statement of OSM data quality, the study suggested that if full coverage and accurate LULC data was a requirement for a project, then OSM data (at present) may not be the best option. However for certain land use classes, where the LULC information was available it was mostly correct so depending on project requirements OSM data may be a suitable alternative.

As we’ve said many times before on Spatial Reserves, it is not whether the data are good, but rather if they are good enough to meet your requirements.

Ref:
Dorn, H.,Törnros, T. and Zipf, A. (2015). Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany. ISPRS Int. J. Geo-Inf. 4, pp. 1657 – 1670

Spatial Analyst videos describe decision making with GIS

I have created a series of 22 new videos describe decision making with GIS, using public domain data.  The videos, which use the ArcGIS Spatial Analyst extension, are listed and accessible in this YouTube playlist.  Over 108 minutes of content is included, but in easy-to-understand short segments that are almost entirely comprised of demonstrations of the tools in real-world contexts.  They make use of public domain data such as land cover, hydrography, roads, and a Digital Elevation Model.

The videos include the topics listed below.  Videos 10 through 20 include a real-world scenario of selecting optimal sites for fire towers in the Loess Hills of eastern Nebraska, an exercise that Jill Clark and I included in the Esri Press book The GIS Guide to Public Domain Data and available online.

New Spatial Analyst videos explain how to make decisions with GIS.

1)  Using the transparency and swipe tools with raster data.
2)  Comparing and using topographic maps and satellite and aerial imagery stored locally to the same type of data in the ArcGIS Online cloud.
3)  Analyzing land cover change with topographic maps and satellite imagery on your local computer and with ArcGIS Online.
4)  Creating a shaded relief map using hillshade from a Digital Elevation Model (DEM).
5)  Analyzing a Digital Elevation Model and a shaded relief map.

6)  Creating contour lines from elevation data.
7)  Creating a slope map from elevation data.
8)  Creating an aspect (direction of slope) map from elevation data.
9)  Creating symbolized contour lines using the Contour with Barriers tool.
10)  Decision making using GIS:  Introduction to the problem, and selecting hydrography features.

11) Decision making using GIS:  Buffering hydrography features.
12)  Decision making using GIS:   Selecting and buffering road features.
13)  Decision making using GIS:  Selecting suitable slopes and elevations.
14)  Decision making using GIS:  Comparing Boolean And, Or, and Xor Operations.
15)  Decision making using GIS:   Selecting suitable land use.

16)  Decision making using GIS:  Selecting suitable land use, slope, and elevation.
17)  Decision making using GIS:   Intersecting vector layers of areas near hydrography and near roads.
18)  Decision making using GIS:  Converting raster to vector data.
19)  Decision making using GIS:  Final determination of optimal sites.
20)  Creating layouts.

21)  Additional considerations and tools in creating layouts.
22)  Checking Extensions when using Spatial Analyst tools.

How might you be able to make use of these videos and the processes described in them in your instruction?

Geo-Wiki.org: Crowdsourcing to improve global land cover data

In our book The GIS Guide to Public Domain Data, we spend quite a bit of time discussing crowdsourcing, and rightly so:  Over the past few years, crowdsourcing has become a viable way not only to collect data, but also to verify and update existing data.  Reasons include budget constraints in those agencies that provide data and the subsequent need for field verification, a growing recognition that decisions based on spatial data are only as beneficial as the accuracy of the data sets themselves, the rapid expansion of citizen science, and growth in the number and variety of mobile and web-GIS tools that enable citizen scientists to contribute to the global community.

Examples of verifying and updating existing data are numerous, and a noteworthy one is from a group of researchers at the International Institute for Applied Systems Analysis (IIASA) in Austria who lead an effort to improve global land cover/land use data.   This effort, http://www.geo-wiki.org, verifies three land cover data sets, including GlobCover from the ESA, MODIS from NASA, and GLC 2000 from the IES Global Environment Monitoring Unit, through knowledge and photographs from people local to specific areas.

Geo-Wiki.org site.

Besides an improvement of the data and, it is hoped, in the decisions based on those data, some of these efforts feature innovative projects that provide benefit to local people.  For example, Geo-Wiki users were asked to identify the presence of cultivated land and settlements in samples in Ethiopia in a “hackathon” associated with USAID in an effort to improve local food security.

More information can be found on the Geo-Wiki site and in an article describing the project.