Showing posts with label Remote Sensing. Show all posts
Showing posts with label Remote Sensing. Show all posts

Tuesday, November 6, 2012

Free GIS Data & Resources

Utilities

QGIS - Free, open source GIS software

ESRI's ArcExplorer is a free program to use to view GIS data

Minnesota Deptartment of Natural Resources Garmin Data Formatter

EasyGPS
Transfer files between your GPS unit and computer

Google Earth

SeaDAS
Free software for the display and analysis of MODIS and other imagery

BEAM
Open source software for viewing, analyzing, and processing remote sensing imagery



Data


Florida

Florida Department of Environmental Protection's GeoData Directory
Lots of accurate and basemap layers with very well managed metadata

FDEP's Map Direct (Internet Explorer only)
Interactive internet mapping application with DEP's GIS data

FDEP's Land And Boundary INformation System ("LABINS")
Orthophoto imagery, raster basemaps, Digital Elevation Models (DEMS), and other high resolution imagery and surveying data for the state of Florida.

Florida Department of Transportation Aerial Photography
High resolution historical imagery of Florida

FDOT's GIS Directory
Florida traffic information & transportation networks

Florida Geographic Data Library (FGDL)
Lots of Florida data (statewide, or by county), but much of it is antiquated.  I'd look at FDEP's Geodata Directory first.


Beyond Florida

The USGS National Map
Topo maps, historical data, etc.

Minnesota DNR Data Deli (Retired)
Minnesota Geospatial Commons

Lakes Data for Minnesota
Including outlines, DEMs, & contours

MIT's Geodata Repository
Use the navigation on the left to browse through lists of US nationwide, statewide, and worldwide datasets

Stanford University Libraries & Academic Information Resources
Marine Biology GIS Data

GPSFileDepot.com Garmin GPS Maps
Browse for a number of useful layers

Harvard Geospatial Library

Minnesota GIS/LIS Consortium's list of GIS resources

Data.gov - Geospatial One Stop
Browse US maps & data

Bathymetry

NOAA's National Geophysical Data Center
- Maps & Scanned Images
- U.S. Coastal Relief Models

Imagery

OceanColor Web
Search and download various multi-spectral imagery

USGS Global Visualization Viewer
Search, download, and learn about various imagery


ITT Visual Information Solutions' ENVI tutorials

Wednesday, March 17, 2010

Georeferencing / Georectification / Geometric Correction in ArcMap

I've done some geometric correction using other software packages, but have not yet tried to do it in ArcMap.  It turns out that there's a pretty small learning curve in the world of georeferencing in ArcMap.  I recommend simply turning on the Georeferencing toolbar and playing around with the hand full of tools that it holds.  All of the most useful tools are hidden away in either the Georeferencing menu on the left of the toolbar or in the small menu of three manual alteration tools to the right of drop down menu of selectable layers.  In the hour or so that I have been attempting to georeference a couple images, I already want to pull the tools out of the drop down menus and place them on the main toolbar for faster access.


A screenshot of the Georeferencing toolbar in ArcMap


Turn on the Georeferencing toolbar:
To verify, turn on the Georeferenceing toolbar by either right clicking in empty gray space beside tool bars/file menus at the top of the ArcMap window, or by going to Tools :: Customize and on the Toolbars tab, place a check mark in the box next to the Georeferencing toolbar option (then click Close).  The toolbar (see above) will appear.

Add imagery:
Use the Add Data button to add a raw, unreferenced images (.jpg, .png, .bmp, etc.) to an ArcMap project.  Expect to receive an "Unknown Spatial Reference" message that explains that the image cannot be projected.  If there are spatial layers already added to the project, the images will probably be displayed somewhere off in a corner.  Right click on the image layer in the table of contents and select Zoom to Layer to view it.

Here are a few good resources to consider to obtain imagery:

Georeferencing:
I have access to rectified imagery and other reference datasets (transportation networks, hydrography, etc.) so I'll use those to illustrate some techniques (very quickly and roughly).  I'll just be using aerial imagery from 2005-2009 and a coverage of State Routes.
  • Zoom to the area where the image should cover.  This example is looking at an area in Florida around Taylor Creek, just north of Lake Okeechobee:











  • Choose an image to manipulate from the Layer menu on the Georeferencing toolbar

  • Since my .jpg image (in this case) is hiding down at the grid origin, somewhere in the Gulf of Mexico, I will have a hard time matching similar locations in the relation process.  Open the Georeferencing menu on the toolbar and select Fit to Display.  The image will shift into view and be resized to fill your map view.

  • Begin by using the three tools in the menu to the right of the Layer menu to roughly align the image:
    • Primarily, it seems that the images are rotated about 90° clockwise (north is pointing east). Use the Rotate tool to fix this. The original top of the raw JPEG is now facing west:
      (This can also be performed from the Georeferencing menu under Flip or Rotate :: Rotate Left)
Rotated the image 90° anti-clockwise so north
(Click to enlarge images)
    • Next, use the Scale tool to shrink the image a bit:
 
Shrink the image with the scale too
    • Finally, use the shift tool to move the image closer to its correct position:
 Moved the image using the shift tool
  •  Open the Link Table by clicking the button on the far right of the Georeferencing toolbar.  This will keep track of the control points that will be added in the next step.

  • Zoom in to a location that is easily distinguishable in the rectification image as well as any reference data.  Roadways are excellent references, especially when they intersect with each other or with a stream, etc.  A less effective reference feature can be a lake or stream, for example, because the shorelines can change over time.  Roadways often have less variation.  Local knowledge is very beneficial in these situations.

    Below is an example of a roadway intersection that is a decent enough reference point to use.  The red circle indicates where the control points will be placed:
(Broken GIF: the image should be animated
to illustrate the two control point locations)
  • Use the Add Control Points tool to add pairs of control points.  The first click will represent the "from" location that will correspond to a point on the image that is being rectified (the black & white image in this case).  The second click will represent the "to" position, where the image will be moved or stretched.  This pair of control points creates a vector that is used to rotate, stretch, and skew the image.

    Add a few of these control points across the extent of the rectification image.  Each time a control point is completed, the information will appear in the Link Table, and the image will move (if the Auto Adjust option is checked in the Georeferencing menu).

    Note the Total RMS Error that is reported in the lower right corner of the Link Table window.  Root Mean Square Error is "a measure of the difference between locations that are known and locations that have been interpolated or digitized" (Heather Kennedy. 2000. ESRI Dictionary of GIS Terminology, ESRI Press, Redlands, CA.). In general, lower values are desirable.  Read more about RMS Error.


  •  It helps to turn layers on and off in the table of contents pane.  Another useful technique is to use the Swipe Layer tool (found on the Effects toolbar).  Make sure that the image/layer/group layer is selected in the Effects toolbar selected layer menu, then select the Swipe Layer tool and click the map view.  Then simply click and drag the tool across a map to reveal lower layers on the map.
Image rectified with various control points
* Caution! Changing the selected layer on the
Georeferencing toolbar will remove any editing
or work that has been done to an image. Use the
following methods to preserve your referencing
Save image referencing:
  •  From the Georeferencing menu, simply choose Update Georeferencing
This adds two files ( [image name].aux & [image name].JGw ) to the directory where the image is located.  These two files are referenced when the image is added to a project,future

(Optional) Export image:
  •  From the Georeferencing menu, choose Rectify to bring up the Save As dialog

  • Change the Output Location (choose a folder or workspace), and change the name of the image if necessary.  The remaining parameters can be left with the default settings.  Click Save to continue.

  • Use the Add Data button again to add the new image to the project, or distribute as necessary

Remove null values:
After adding the image, you may notice a black box surrounding the image.  This is simply the blank space left when the new image was created from an image that was rotated slightly.  These null values have the value of 0 in the dataset and can be easily removed:
  • Right click on the image layer in the table of contents pane and choose Properties (or double click on the layer)
  • Navigate to the Symbology tab
  • Place a check next to Display Background Value setting.  Ensure that 0 is the denoted value, and that the color selector is set to No Color.  Choose Apply or OK to continue

    Removing null values from the layer

    Several georeferenced images

    One problem remains.  There are still some black outlines on the edges of these images.  Those areas are inherited from the original raw images.  These can be handled in two ways;
    1. The images can be cropped from the original JPEG files before they are georeferenced to remove the outlines.  Be sure to make copies of the original images before any editing is applied.
    2. Manipulate the geographic extent of each layer:
      • Open the layer properties for an image
      • Navigate to the Extent tab
      • Change the "Set the extent to" option to "a custom extent entered below"
      • Increase (Left & Bottom) or decrease (Top & Right) the Visible Extent values by small intervals and click Apply to view the results.

        * Reset the values if necessary by changing the "Set the extent to" option to "the default extent of this layer"

      Several georeferenced images with altered geographic extents

      If this is still not suitable, you may need to pre-process the image with some other graphics software.

      More information:

      Tuesday, January 26, 2010

      Research Needs for Water Resource Applications of GIS

      Water resources are challenging to model in a geographic information system because these features are often so temporally variable. John P. Wilson, et al. discuss in the article, “Water Resource Applications of Geographic Information Systems” many methods of water resource management and assessment. Often, these topics reiterate the relation of data sharing with the increasing level of technology. These advancements allow a greater number of less professionally trained individuals to contribute what can possibly be high quality data with a varying degree of uncertainty. For this reason, future technology will need to incorporate these individuals’ level of training and geographic and scientific understanding into the framework of tool building. Thus interfaces for using such tools must be created to suit a wide audience efficiently. Otherwise, difficulty in using technology directly translates to uncertainty. Scale is addressed often as well when reviewing other issues ranging from the integration of spatial data acquisition technology to data sharing practices.
      Primarily, it is stressed that raster grid resolution selection should attempt to match the size of the smallest features that are being modeled.  Accuracy of these resources is quickly degraded due to excess aggregation of topographic features.  Generalization in this regard minimizes the number possibilities of variation for a given area where a smaller stream feature, for instance, could exist in reality.  Using a raster resolution of 30 meters rather than the 2-10 meter resolutions suggested by several studies mentioned easily smoothes over smaller changes in topography.  Further, a 30 meter cell size yields only 21% - 30% of correct slope gradients.  However, if only larger (major) features are required to be analyzed or communicated, 30 meter resolution can sometimes be quite sufficient.

      Many water resource applications are GIS-based.  The National Hydrography Dataset is a prime example of how to digitally store and communicate a hydrologic network by logically displaying geographic information with its organizational and descriptive parameters embedded within in a GIS environment.  An example of temporal water quality monitoring is the Florida DEP's Watershed Monitoring program and EPA & FDEP’s STORET (“STOrage and RETrieval” database) program, where water quality data from across the state of Florida is collected, managed in databases, and can be served internally or publicly via internet mapping service, while being provided to in-house personnel for GIS analysis via ArcSDE & ArcGIS Server. Advancements in technology such as these increase the dissemination of information to a growing number of skilled as well as improperly trained stewards and contributors of information.  Increased availability must be mirrored by an increased accounting of data collection information in the form of metadata to ensure reliability.  A method of controlling uncertainty for every step of data collection is currently available by reporting appropriate metadata parameters prior to distribution of any data.  Although this information is mainly included on official final-drafts of published datasets, more can be done deeper within an organization to include metadata to aide in the quality assurance of final versions of disseminated data.

      Overall, it will be important to develop efficient models of water resources that can be used by an audience with contrasting degrees of familiarity with geographic information.  By conducting further research on the most effective way to properly incorporate data with varying degrees of uncertainty, as well as investigating the most efficient scales at which modeling hydrologic networks should take place, the GIS community can continue to accurately model water resources in the future.

      Saturday, January 23, 2010

      Multispectral & Hyperspectral Remote Sensing Image Extraction

      Multispectral sensors such as the Landsat series, SPOT, IKONOS, and QuickBird acquire anywhere from three to ten simultaneous bands of information across a scene.  Each of these bands cover a relatively broad spectral range of electromagnetic radiation observation.  Hyperspectral remote sensing, as its name implies, is generally composed of a greater number of spectral bands which observe a more precise (narrow) spectral threshold.  The MODIS sensor uses 36 bands, while NASA's AVIRIS sensor captures as many as 224 bands.

      John R. Jensen continues to describe in his book, Introductory Digital Image processing: A Remote Sensing Perspective, two methods of multi- and hyperspectral image acquisition methods: the whiskbroom system, and the linear and area array technique.  In both methods, instantaneous field-of-view (IFOV) radiant flux – observed reflectance from the Earth's surface – is passed through a spectrometer in order to disperse, or separate the light into separate bands; ranging from blue to near infrared (NIR) and IR wavelengths, which are passed onto a spectrometer where it is dispersed and focused onto a array of detectors which digitally record the field of view.  The whiskbroom method uses a rotating mirror to reflect and direct radiant flux through the spectrometer to a linear array detector which individually measures the value of radiation of each band that has been separated.  This technique is best suited for capturing broad spectral ranges and is utilized by multispectral sensors such as Landsat MMS and SPOT, etc.  These sensors undersample the observed radiant flux by making only a few measurements from wide spectral bands; as wide as several hundred nanometers which may cover more than one color of the spectrum simultaneously.  Although the use of a dispersing element is similarly used in each method to separate incoming light into individual bands, the alternative method of does not use a scanning mirror; thus allowing a longer amount of time for a detector to record the incoming radiant flux of a given area.  This extra duration of detection yields improved geometric and radiometric accuracy.

      These methods have dramatic implications on the type of information that is produced.  The varying techniques influence spectral, temporal, radiometric, and even spatial resolution of a produced image.  Various types of investigation require unique parameters in the type of data that will be used in analysis.  Having an understanding of the different types of information that are produced by multi- and hyperspectral imagery with a respect to ground conditions that are being observed will ensure the most accurate results are obtained during image analysis and exploration.

      The process of extracting information from multispectral and hyperspectral imagery are largely similar, however there are a few preliminary steps one must complete before analyzing hyperspectral datasets.  Various forms of calibration – radiometric, geometric, etc. – are common between these two types of imagery, however the larger volume of highly specific spectral bands associated with hyperspectral imagery permit the construction of "spectra" that closely resemble the quality of spectral signatures captured by spectroradiometers in laboratories.  Further, initial image quality assessment of hyperspectral can be a much more tedious undertaking, although it is occasionally possible to use hyperspectral images with poor quality for atmospheric correction.  Due to this more time consuming and redundant task of visual examination, many image processing packages have animation functions that provide a more efficient means of inspecting up to hundreds of images in a single session.

      Overall, the dimensionality is the most distinguishing characteristic between these two types of remote sensing techniques.  The low data dimensionality of multispectral imagery is significantly more accessible and less difficult to work with in basic research due to the low number of spectral bands that make up an image.  The higher number of bands/images involved with hyper- and ultraspectral imagery generate numerous obstacles, ranging from data storage to processing abilities.  Numerous images of highly specific spectral bands – often representing bandwidths of just 10 nm each – produce certain amounts redundant information.  Statistical analysis aids in identifying, and removing or transforming data in order to reduce the dimensionality of the overall hyperspectral dataset, improving the efficiency of exploration and analysis.  Although there are indeed variations that exist with regard to image processing techniques between these two types of remote sensing data, the causes again are largely due to dimensionality.  An analyst must decide whether few, spectrally broad images or numerous and specific images will best suit his or her analysis.

      Saturday, January 16, 2010

      Remote Sensing for Landscape Level Dugong Habitat Evaluation

      This is a review of an article presented to Dr. Yang's GIS for Environmental Modeling class at Florida State University in the spring of 2008.
      Sheppard, James K., Lawler, Ivan R., Marsh, Helene. (2007). Seagrass as Pasture for Seacows: Landscape-Level Dugong Habitat Evaluation. Estuarine Coastal & Shelf Science, 71(1-2):117-132.




      The dugong is a cousin to the West Indian manatee, which feeds on specific types of seagrasses in area around northern Australia, Indonesia, and along similar latitudes along coastal areas of the Indian Ocean.  This study uses GIS to determine which specific types of seagrasses are preferred by grazing dugongs in waters off of Queensland, Australia.  GIS is used to observe remotely sensed near infrared spectroscopy to quantify the composition and configuration of seagrass communities.

      Dugongs are tagged with GPS tracking devices, and their locations are tracked using the Animal Movement Analyst Extendsion in ArcView.  After three months of tracking, a study area is developed from the many line features created by the movement of the animals to estimate the extent of popular grazing locations in Hervey Bay.  Several field methods are used to capture samples of seagrasses at various locations throughout the study area – ranging from intertidal treks in shallow water to sample plant species by hand, to using submarine video equipment to analyze the plant structure from a boat.  Both methods use GPS to derive latitude and longitude data and to record other information.  Further, a steel sediment grab is used to sample benthic composition and sediment nutrients.

      The information is loaded into a GIS where bathymetry, seagrass cover, and nutrient profiles are created from the GIS point data that was captured.  Each of these layers are interpolated into a raster surface using the Kriging method, as it yields the most precise output for the data captured.  Nutrient analysis is conducted with Near Infrared Reflectance Spectroscopy (NRIS) due to its ability to identify the composition of organic samples very efficiently.

      The analysis sampled five seagrasses, ranging in levels of nitrogen, starch, and fiber.  The dugongs convert nitrogen into protein, and use starch for energy.  Fiber is a tertiary element in the seagrasses that is difficult for the dugongs to digest, therefore it is mainly avoided in large amounts.  The analysis in this study confirms these findings from past research.

      Remote sensing in this study is limited in evaluating biomass and species at low densities.  More valid results are easier to obtain when higher concentrations of any particular species exist over a larger area, though patches can be discovered and accounted for when sampling.  This aligns well with the general structure of seagrass meadows.  Further, remote sensing is limited to shallow water, or where seagrass stalks reach to shallow depths of water were the electromagnetic wave can penetrate and return to the capturing device.  Water quickly absorbs much of the beam emitted from sensing devices.  In addition, turbid water is another limiting factor to using remote sensing in open, flowing water.

      Overall, this is a very interesting article that touches on several powerful uses of GIS and remote sensing technology.  The biological analysis addressed seems to get moderately complex, however the conclusions are clearly represented and are easily interpreted in context with the spatial component of this research.

      Monday, August 17, 2009

      Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast



      Presented 3 February 2009
      Spring 2009 Advanced Remote Sensing
      Florida State University

      Reviewing the work of Everitt, J.H., Yang, C., Sriharan, S., and Judd, F.W. 2008 Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast. Journal of Coastal Research, 24:6, 1582-1586


      This is the (very abbreviated) presentation I gave summarizing the article above. My presentations usually go into illustrative detail about each point, so the information in the Power Point document itself is not as informative. The presentation discusses a brief introduction on the species, their location and salt-tolerant characteristics, importance of mangroves habitat, etc. followed by more technical information regarding remote sensing techniques, methodology and sampling design, accuracy assesment and field verification, and finally a brief discussion (mostly omitted from the presentation) regarding analysis of the results of the study.





      Saturday, April 18, 2009

      Working with .tar files and imagery

      When downloading imagery (Landsat, IKONOS, ASTER, SRTM, etc) I continually encounter the .tar extension/format of compressed datasets. 7-zip is a great (light and free) package to use.



      7-zip.org








      Select all .tar files in the directory to be extracted, right click, and choose "Extract Here" from the 7-Zip menu:


      [Click to enlarge]


      Newer versions of WinZip have .tar support as well, though this utility is pretty quick and efficient.




      These are a few of the resources I have been using to obtain remote sensing imagery:
      Global Land Cover Facility (Free)
      http://glcf.umiacs.umd.edu/data

      USGS Landsat Web Site (Free now)
      http://landsat.usgs.gov

      NASA Earth Sci Grad Student Fellowship Program for RS
      http://nspires.nasaprs.com

      Thursday, February 26, 2009

      Spatial Filters are Neat


      [Click to enlarge]

      A Hydrothermal Composite Enhancement produces a multi-colored, multi-band image that is composed of three ratioed bands of Landsat TM imagery (MIR[1.55μ-1.75μ]/MIR[2.08μ-2.35μ], Red/Blue, and NIR/Red). This image highlights both hydrographic temperature spikes across the area of interest as well as sources of thermal contribution, such as beaches and urban land cover.