Upon completion of module 1, you will be able to identify potential data sources for remotely sensed data, determine availability and cost for those data, and acquire remotely sensed data from the Web.
Written by Doug Miller, except as noted.
John and Frank Craighead, twin brothers and Penn State Class of '39 graduates, were world renowned for their efforts in wildlife studies and habitat conservation. Their long term study of grizzly bears in North America and around the world is lauded for its long-term record and success in understanding the grizzly bear and its habitat requirements. Their research legacy lives on in the form of the Craighead Environmental Research Institute (CERI) [1] where the Craighead family continues the conservation biology efforts initiated by their fathers/grandfathers.
CERI has focused much of its efforts in conservation biology by studying the wildlife habitat connectivity using remotely sensed data. Much of this work has focused on the Greater Yellowstone Ecosystem in Wyoming and portions of Idaho and Montana. In a region with increasing human influence, habitat continues to be fragmented, necessitating continued monitoring to ensure that essential landscape connections are preserved for wildlife. CERI uses remotely sensed data to track changes in land use and land cover in an attempt to understand the impact of these changes on grizzly bear habitat.
CERI, like many modern organizations, has discovered the power of remote sensing to track changes in the environment. The ability to image the same land area at multiple times ensures that geospatial databases can be kept current and that a historic record of these changes can be maintained. The goal of this exercise will be to familiarize you with the challenge of acquiring remotely sensed data for your final project area.
This module is one week in length. Please refer to the course Calendar tab, in ANGEL, for the due date.
Please see the Deliverables section at the end of Part II for this week's readings and action items.
Lesson 4 is one week in length. Please refer to the Calendar in ANGEL for specific time frames and due dates. To finish this lesson, you must complete the actvities listed below. You may find it useful to print this page out first so that you can follow along with the directions.
Step | Activity | Access/Directions |
---|---|---|
1 | Work through Lesson 4 | You are in the Lesson 4 online content now. The overview page is previous to this page, and you are on the Checklist page right now. |
2 | Complete the deliverables for Lesson 4 | Page 4 has this week's deliverables. |
My story:
Centre County has digital orthophotos that are available to the public, but they are becoming dated. I would like to investigate new sources of high resolution satellite imagery to see if these might be a good update for my current geodatabase of the county. I was interested in determining if current data has already been acquired for the county, it's type, format, and cost per square mile. I was also curious about remotely sensed data that could be acquired freely from the Web for my county.
I acquired aerial photography from the county, aerial photography from PASDA, and free satellite data (ETM+,GeoTIFFs) from the Global Land Cover Facility (GLCF) site. If you want to see these various products unzip lesson4files and open lesson4.mxd. As you can imagine, these data are large. I eliminated quite a few images that I wanted to show, but the .zip file is still about 344 mb. I included the images of Beaver Stadium (Penn State's football stadium) because it is a large feature that stands out. It's interesting to compare the different products. I also looked at the sources provided below to get information about acquiring high resolution satellite imagery.
Registered Students download from ANGEL the Lesson 4 data (lesson4files.zip) to a new folder (e.g., C:\MGIS\GEOG488\Lesson4).
I want to thank Doug Miller for his contributions to this lesson.
Doug's story:
In the introduction, you read about a scenario that Doug Miller is interested in. Doug teaches a remote sensing course in the Geography Department on campus and will probably be developing an online remote sensing course for the MGIS Program. The example was given to get you thinking about some scenarios in which remotely sensed data are used. I will be deferring to Doug for some of your more in-depth questions about remote sensing.
Remote sensing is the art and science of acquiring information about an object without being in physical contact with the object. Remotely sensed imagery can be collected at scales ranging from hand-held devices to orbiting satellites. Remote sensors rely on the interaction of energy with the area of interest. Sensors mounted on aircraft or satellite can collect imagery in multiple portions of the spectrum for use in feature identification. A comprehensive discussion of remote sensing is beyond the bounds of this exercise. However, in the "Resources" section you'll find a list of several very good texts that can be consulted for detailed information concerning the remote sensing process.
Satellite remote sensing, as applied to natural resources management, has been around since the early 1970s when NASA launched the first of a series of satellites that was to eventually become the "Landsat" program. In the intervening 30+ years, the number of earth sensing satellites has grown rapidly, with increasing spectral and spatial resolution and opportunities to combine, synergistically, information from multiple sensors. Commercial vendors now vie with the government-sponsored sensor programs for the consumer audience. For a quick, self-guided overview of several current programs and sources of remotely sensed data, explore the following:
You have just completed Part I of this module, which involved getting a refresher on remote sensing and browsing some sites that provide remotely sensed data. In Part II, you will assess remotely sensed data in your local area.
Your story:
Assume that as part of your GIS plan you need to provide information about the acquisition of remotely sensed data for the temporal updating of a variety of data layers in the local enterprise GIS. You've heard that new commercial remote sensing satellites now provide extremely high resolution (0.5 - 1.0 meters) imagery in the visible portion of the spectrum that can be used for applications that previously required aerial photography acquisition. This new generation of satellites has provided a new set of opportunities for organizations who want to update their geospatial information systems. This exercise will introduce you to the challenge of assessing the best option for the acquisition of remotely sensed data for integration into a GIS.
Questions to answer:
Prepare a write-up that outlines your assessment and provides an initial recommendation. Your write-up should include a map of the area and a full discussion of your findings including each of the factors listed above. A complete write-up will indicate a discussion of the logistical challenges, associated costs (on a per square mile basis), and a discussion of the options that are available.
Factors to Consider:
Raster or image data can be used as a source of vector data . There are several ways that raster data can be used to extract vector data or to adjust the vector data. The underlying principle seems to be that a picture is worth a 1000 lines. In other words, people trust pictures more than they do other forms of data. First, it must be remembered that distortion in images, even ortho-rectified ones, is most often greater than that achieved by a competent land surveyor. Secondly, the rendering of a line in an image is not to the same accuracy as it is in a vector due to pixelation, and for course rasters this is much worse of a problem. Thirdly, most raster do not have associated attribute data.
The ways to make vector data are many and will only be very briefly touched here. The most common today is heads up digitizing, i.e., tracing around the edges as they are seen on an image in an editing session. This is what Susan did for Montserrat. This can be quite accurate of it is done carefully and at the same scale as the image was taken, which is the raster resolution. Tests have shown that any form of manually digitizing has error and that rarely will two operators, or even the same operator if they repeat the same segment, end up with the same line work. However, with care and good work practices, this can be very nearly as accurate as the image (it is often hard to tell how accurate an image is so the final accuracy and precision is hard to judge definitively). In Spatial Analyst there is a menu choice to convert from raster to vector. Thus it does so in a "black box" manner. Care must be taken to look at the result critically. Often it is quite good, especially for simple raters, like a simple line drawing map. It is no use at all for actual photographs. Edge extraction can be used on these but is often so messy to be useless even as a drawing guide in heads up digitizing. There is an extension in ArcGIS called ArcScan that can be used to supervise how ArcGIS converts raster data. ArcScan allows you to trace lines manually and the program follows the lines in the raster for you. It has a number of rules about when to bridge a gap in the pixels and when not to jump across a gap; it lines up with the center of a line if it is more than few pixels wide. It also has many parameters to allow scanned material to be automatically converted after it has been suitably prepared. ArcScan was used to convert nautical charts to vector data. The last method is to use an image to spatial adjust vector data. This is the opposite of ortho-rectified Anchor points placed on lines and a linkage made to where the lines have to move to line up with the same visible feature in the image data. A note of caution here, it is necessary to ensure that the image is in the same projection, same datum and is error free -- otherwise you could be ruining a perfectly good dataset to agree to something just because it is pleasing to the eye. This problem often comes up when aerial photography is used as a backdrop to vector data. For example, bridges will appear to be wrong in an aerial because they are above the surface. It is worse if the overpass is high, and even worse when they are off to the edge of an aerial where parallax makes the distortion greater. It is rare that ortho-rectification is done so carefully as to remove all such artifacts or that a DEM is available that exactly follows the surface. Further, ortho-rectification is only as good as the DEM, the height control and exactness of the camera lens mathematical model. That is a long way of saying do not always trust your own or the camera's eyes.
More information on ArcScan can be found in the ESRI Helpfiles.
This module is one week in length. Please refer to the course Calendar tab, above, for the due date.
1. Readings:
Required:
Optional:
2. Post a project write-up including:
3. Discuss the weekly topic on the discussion forum.
4. Begin to finish writing your course paper. Your paper is due in the Dropbox of Lesson 5 Folder next week .
You have just completed module 4.
Don't forget...if you have any questions, feel free to post them to the Lesson 4 Discussion Forum.
Links
[1] http://www.craigheadresearch.org/
[2] http://glcf.umiacs.umd.edu/
[3] http://landsat.org/
[4] https://www.e-education.psu.edu/geog488/sites/www.e-education.psu.edu.geog488/files/downloads/LandSat.pdf
[5] http://www.digitalglobe.com
[6] http://landsat.usgs.gov/index.php
[7] http://gs.mdacorporation.com/SatelliteData/Radarsat2/Price.aspx
[8] http://www.earthexplorer.com/2007-09/geophysics-in-urban-brownfields.asp
[9] https://cms.psu.edu/section/default.asp?id=201112SPWD%5F%5F%5FIGEOG%5F488%5F001&goto=
[10] http://proceedings.esri.com/library/userconf/proc95/to150/p124.html
[11] https://cms.psu.edu/default.asp
[12] http://www.isi.edu/integration/papers/chiang05-acmgis.pdf