The idea of the "multi" approach - which simply means "several to many different" - to remote sensing is introduced. For example, "multilevel" refers to a wide range of different altitudes or distances from the surface being observed. This first of three pages devoted to "multi's" concentrates on different altitudes that lead to different scales and resolutions.

### THE "MULTI" APPROACH TO APPLIED REMOTE SENSING

### Multiplatforms and Multilevels

Ground truth activities are an integral part of the "multi" approach. Thus, we should procure data whenever possible from different platforms ( multiplatform), at various altitudes (multistage; multilevel). This gives rise to multiscaled images or classification maps. Ideally, we should aim to employ multisensor systems simultaneously to provide data, commonly at multiresolution, over various regions of the spectrum (multispectral). Often, we obtain data at different times (mutitemporal), whenever seasonal effects or illumination differences are factors or change detection is the objective. Supporting ground observations should come from many relevant, but not necessarily interrelated, sources (multisource). Some types of surface data may correlate with one another and with other types of remote sensing data (multiphase).

13-15: Using your imagination and growing experience, design an experiment using as many of the above "multi's" as seems sensible. ANSWER

Many remote sensing investigations include several of the above "multi" categories but examination both of remote sensing textbooks and Internet sites generally do not highlight examples that include most of these together as applied to one study or application site. This proved the case in preparing this page. So, in order to illustrate the "multi" concept adequately, it is necessary to show images of different parts of the world that don't show the same piece of "real estate" sensed repeatedly by different systems. We will conclude with a multitemporal study of Kuwait that does include some of the idea of using different sensors over time to optimize the integration of related data. And, we mention now that platforms in the last few years have multiple sensors on board operating simultaneously. The best example of that is Terra (page 16-9) which has five different but complementary sensors which often examine the same target; however, no images or studies have as yet been released on Terra's Web site that show the same ground sensed at any given moment.

Earlier in this Tutorial, there have already been individual and isolated examples of some of the "multi" types of images and photos. In the review erected on this page, we will develop one theme: agriculture, especially crop monitoring. We start with an expansion of the analysis of the Delaware/Chesapeake Bay classification shown on page 13-3. Here we will follow the multi-level approach by looking successively at the farmlands around the Chesapeake and Delaware Canal, starting with a Landsat red band subscene and progressing to a low altitude photo.

Next is a high altitude U-2 photograph of part of the above area; locate yourself using the canal.

Now, to zoom in further, consider this medium altitude aerial photo which contains part of the canal.

Finally, here reproduced is a paper print of a low altitude aerial photo that was actually taken into the Delaware test site. The Soil Conservation Service's field agent has made notations showing characteristics and yield for some of the crop acreage.

The writer (NMS) was a participant in this field study. As part of the preparation for the Landsat phase, NASA flew an aircraft mission with a multispectral scanner over fields in the Delmarva Peninsula to the south of the study site. Here are four images designed to simulate the 4 Landsat MSS bands:

From the data, an analog measure (using a photometer operating on a transparency) of the photo-density of selected fields in each of the MSS-equivalent bands led to this plot of relative darkness as a proxy for reflectance coming from the ground features and crops indicated:

Let us turn from this specific study to some more general examples. Many of the photographs taken from the Shuttle by the astronauts have agricultural areas as their subject matter. Often these photos are not particularly good owing mainly to atmospheric problems. But this one covering the land around Enid, Kansas is one of the better.

Other satellites produce excellent near-natural images of farmlands, such as this SPOT scene:

At higher resolution, here are fields in California's Great Valley near Fresno imaged by the IKONOS-2 satellite.

At the other extreme, the AVHRR, as demonstrated on page 3-4, is quite adept at providing small-scale, large area indications of crop and vegetation vigor, often expressed as NDVI. This next image is a black and white plot of the NDVI values (using channels 1 and 2) for the land in and around Dallas, TX. Light tones indicate high NDVIs.

Crop stress results from insufficient soil and/or crop water (drought), improper nutrients; plant disease, insect infestations, and other factors. The next image is of cropland in Colorado's San Luis Valley. It was made by the AVIRIS sensor that will be described in detail on pages 13-9 and 13-10 of this Section. A classification of these Colorado crops is treated on page 13-10. Here we show AVIRIS hyperspectral data that use bands sensitive to crop moisture deficiency.

Soil moisture is one of the critical parameters a farmer needs to know in making decisions about planting conditions and need for irrigation. It is often the precursor indicator of potential or actual crop stress. This can be done through aerial photography, as shown here for some Indiana farms, but the cost of flying for specific water inventory is high.

Thermal scanners are also good at detecting moisture, as indicated in this aerial image of a Wisconsin farm, taken around 9 PM at night shortly after the setting Sun. The bright spots in the upper left are a herd of (warm) cows. The black rectangle in the upper right is a sheet metallic roof on a farm outbuilding, which shows "cool" because of the very low emissivity of metal.

AVHRR thermal bands can also provide useful agricultural information. And so did HCMM when it operated. Here is a HCMM Day Thermal image of much of California, taken in May. Note the farm patterns in the central Valley. Note also the very dark area in the High Sierras - this is spring snow that will eventually provide water for the crops during summer meltdown.

Radar is a good means of imaging farmland, as seen in this low altitude aerial radar mission over the Maricopa area near Phoenix, AZ:

Seasat radar imaged this next scene, in the Great Plains. Some fields are dark, others light, indicative of the stages of growth (light areas reflect more of the radar beam to the receiver). Of particular interest are the two dark patches which represent the effects of soil moisture (reduces returns) following two local thunderstorms passing over the plains.

As was put forth in Section 8, radar images made from different bands disclose information in each not expressed in the same way as in the other(s). Below is a pair of images of the Medicine Lake area in Alaska west if Fairbanks that were fortuitously imaged 18 minutes apart by two different satellites. On the left is a ERS-1 radar C-band image; on the right is a JERS-1 L band image. Note that the ERS-1 image renders some bogs in bright tones; the JERS-1 image highlights creek beds.

On the next two pages, we will finish this "multi" survey, starting with the information obtained when images from different sensors are merged and ending with multitemporal examples.

Primary Author: Nicholas M. Short, Sr. email: nmshort@ptd.net