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This Section will treat three apparently diverse topics, although a relationship among them will be established. The first reviews the types of ancillary data - those that provide supporting and explanatory information about what is in a remote sensing image - that are often called "ground truth". Then, the concept of the "multi-" approach is discussed, with the various forms involved explained and exemplified. The third covers the principles behind hyperspectral imaging remote sensing. On this first page, the rationale for gathering ground truth, including observations taken on site in the field, is spelled out. Some of the broad variety of surface collected data and information analysis are listed, along with examples of specific field studies that require appropriate measurements. The role of ground truth in picking out training sites and the role of returning to the field to verify classification accuracy are mentioned.


GROUND TRUTH; THE "MULTI" CONCEPT;

IMAGING SPECTROSCOPY

RATIONALE FOR SURFACE OBSERVATIONS AND DATA COLLECTION

We have demonstrated so far in this Tutorial that remote sensing is an efficient way to gather large amounts of information from vast areas without being on the observed surface. But, interpreters will seldom apply this knowledge effectively unless they have first-hand familiarity with the surface of interest, or at least, with models of the surface. They gather this intelligence by several means: from circumspect field observations, judicious investigations at training sites, sophisticated measurements in the laboratory, on the ground, in the air, and from space, and, ultimately, from a rigorous mathematical analysis of the data to test for validity and correlation.

In remote sensing, ground truth is just a jargon term for near-surface observations. As applied to a planetary body, this refers to gathering reference data on-site and deriving information therefrom that properly characterizes states, conditions, and parameters associated with the surface. With appropriate sensors, we can derive aspects of the subsurface and any gaseous envelope (atmosphere) above it, as well. The purpose in acquiring ground truth is ultimately to aid in calibrating and interpreting remotely recorded surveys by checking realities within the scene. Since human interpreters normally experience Earth as ground dwellers, their view of the world from a horizontal or low-angle panorama is the customary frame of reference. In fact, the remote sensing specialist and the novice should retain a surface-based perspective during all phases of data collection, analysis, and applications, because they will implement most interpretations and decisions dealing with natural resources and land use at the ground level.

Among many ground-oriented data sources are field observations, in situ spectral measurements, aerial reconnaissance and photography, descriptive reports and inventory tallies, and maps. The Table below summarizes the types of tasks and operations associated with obtaining and using ground truth data:

Role of Ground and Aircraft Observations in Supporting

Satellite Remote Sensing

Correlate surface features and localities as known from familiar ground perspectives with their expression in satellite imagery

  • Provide input and control during the first stages of planning for analysis, interpreting, and applying remote sensing data (e.g., identifying landmarks, logistics of access. etc.)

  • Reduce data and sampling requirements (e.g., areas of needed coverage) for exploring, monitoring, and inventory activities

  • Select test areas for aircraft and other multistage support missions (e.g., underflights simultaneous with spacecraft passes)

  • Identify classes established by unsupervised classification

  • Select and categorize training sites for supervised classification

  • Verify accuracy of classification (error types and rates) by using quantitative statistical techniques

  • Obtain quantitative estimates relevant to class distributions (e.g. field size; forest acreage)

  • Collect physical samples for laboratory analysis of phenomena detected from remote sensing data (e.g., water quality, rock types, and insect-induced disease)

  • Acquire supplementary (ancillary) non-remote sensing data for interpretive model analysis or for integration into Geographic Information Systems

  • Develop standard sets of spectral signatures by using ground-based instruments

  • Measure spectral and other physical properties needed to stipulate characteristics and parameters pertinent to designing new sensor systems

     

    13-1: Assume you are working on a Landsat scene that is close enough for you to actually go into the field to examine firsthand the features contained within it. What, in your opinion, would be the most important task to carry out 1) before the satellite takes its image and 2) after you receive and process this image? ANSWER

    Examples of typical observations and measurements conducted in the field, commonly as the remote sensing platform passes over, or shortly thereafter, include these:

    1. Meteorological conditions (air temperature, wind velocity, humidity, etc.)
    2. Insolation (solar irradiance)
    3. On-site calibration of reflectance
    4. Soil moisture
    5. Water levels (stream gauge data)
    6. Snow thickness
    7. Siltation in lakes and rivers
    8. Growth stages of vegetation
    9. Distribution of urban subclasses
    10. Soil and rock types

    13-2: Field work is expensive, and often inconvenient. Yet some kinds of data are needed in near real time. Mention three in this category from the above list. How would you go about getting these critical data values if field work is not an option? ANSWER

    Ground truth activities are an integral part of the "multi" approach. This simply means gathering the data under different conditions, such as the use of several sensors simultaneously and repeat coverage over time. We will explore the "payoffs" from this idea later in this Section.

    Probably the most common reasons for conducting field activities are to select training sites prior to supervised classification or to identify key classes after unsupervised classification. The best way to collect field data, if feasible, is simply to spend a few days in the field, examining the terrain for the classification. Obviously, the scale of this effort depends on the size of the area we want to classify. One or more full Landsat scenes may require considerable travel and field time, whereas we can often examine a typical subscene (such as 512 x 512 pixels) in a day or two. If logistics or circumstances (e.g., an inaccessible foreign area or during an off-season such as winter) limit field operations, then we may use instead aerial photography, maps, literature research, interviews with residents (perhaps over the Internet), etc. In practice, to specify training sites generally means integrating the following sources of information: direct observations, photo documentation, a variety of maps, personal familiarity, and others.

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    Primary Author: Nicholas M. Short, Sr. email: [email protected]

    Collaborators: Code 935 NASA GSFC, GST, USAF Academy
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