ArcGIS for Desktop software provides tools to help you access, read, and manage data formatted in Network Common Data Form (netCDF) and hierarchical data format (HDF). These file formats have been designed to support creating, accessing, and sharing scientific data.
Major organizations, such as the National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and US Geological Survey (USGS), deliver scientific data in netCDF and HDF. Used extensively by the atmospheric, oceanographic, and remote-sensing communities, these formats are built to store data such as temperature, wind speed, and wave height or satellite imagery representing land surface temperature or evapotranspiration.
A GIS user quickly accesses historical precipitation data in netCDF format to create a weather map and uses the Time Slider tool to see changes in weather patterns over time.
ArcGIS supports netCDF in the following ways:
- Read netCDF files natively and access data without converting or importing it.
- Represent netCDF data as a raster layer, feature layer, and table view.
- Use netCDF data in spatial and statistical analysis workflows.
- View a two-dimensional slice of three- or fourdimensional data in the map window.
- Animate a time-aware netCDF file, use the Time Slider tool, and record and save video clips of the animation.
The ArcGIS Multidimension toolbox
provides tools for working with
ArcGIS supports HDF in the following ways:
- Read HDF4 and HDF5 files as raster data, natively, gaining immediate access to the data without the need for converting or importing data.
- Select which subdatasets to use from a multidataset HDF file interactively in ArcMap™ or using a geoprocessing tool.
- Manage huge collections of HDF data using a mosaic dataset.
- Use HDF data in spatial analysis and statistical analysis workflows.
Moderate-resolution imaging spectroradiometer (MODIS) evapotranspiration data in HDF format can be brought directly into the ArcGIS mapping environment.
Transactions in GIS, Volume 16, Issue 1, February 2012
Amy Pocewicz, Max Nielsen-Pincus, Greg Brown and Russ Schnitzer
“Public participation geographic information systems (PPGIS) are an increasingly important tool for collecting spatial information about the social attributes of place. The availability of Internet-based options for implementing PPGIS presents new opportunities for increased efficiency and new modes of access. Here we used a mixed-mode approach to evaluate paper versus Internet mapping methods for the same PPGIS survey in Wyoming. We compared participant characteristics, mapping participation, and the spatial distribution of mapped attributes between participants who responded to the paper versus Internet option.
Locations of Internet versus paper map points for a subset of survey attributes: recreation, open space, residential development, and wind development. Internet points are displayed transparently over top of paper points to better illustrate overlap between the two point types
“The response rate for those who completed the paper version of the survey was nearly 2.5 times the response rate of the Internet version. Paper participants also mapped significantly more places than did Internet participants (43 vs. 18). Internet participants tended to be younger, more likely to have a college degree, and had lived in the region for less time than paper participants. For all but one attribute there was no difference in the spatial distribution of places mapped between Internet and paper methods. Using a paper-based PPGIS survey resulted in a higher response rate, reduced participant bias, and greater mapping participation. However, survey mode did not influence the spatial distribution of the PPGIS data.”
Remote Sensing, 2012, 4(4), 911-933; published online 29 March 2012
“Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough textures, digital numbers (DNs) are quantized into several quantum values. Quantization using several interval widths is applied during segmentation, and for each quantization, areas with homogeneous values are labeled in an image.
Edge completion using filters: (a) non-completed edges, (b) segmentation result using non-completed edges, (c) completed edges, and (d) segmentation result using completed edges. All results were generated with Δdi = 40.
“Edges determined from the homogeneous areas obtained at each quantization are then merged, and frequently observed edges are extracted. By using a “rectangular index”, regions whose shapes are close to being rectangular are thus selected as buildings. Experimental results show that the proposed algorithm generates more practical segmentation results than an existing algorithm does. Therefore, the main factors in successful segmentation of shadowed roofs are (1) combination of different quantization results, (2) selection of buildings according to the rectangular index, and (3) edge completion by the inclusion of non-edge pixels that have a high probability of being edges. By utilizing these factors, the proposed algorithm optimizes the spatial filtering scale with respect to the size of building roofs in a locality. The proposed algorithm is considered to be useful for conducting building segmentation for various purposes.”