USDA-FSA Aerial Photography Field Office Fugro EarthData Yazoo

85 Pages·2008·12.83 MB·English

USDA-FSA
Aerial Photography Field Office
Fugro EarthData
Yazoo County, Mississippi
GeoSAR Evaluation
Report

Prepared by
Geospatial Services Branch
Service Center Support Section

December 17, 2008
Table of Contents

Introduction ……………………………………………………….............. 1

Deliverables ………………………………………………………............. 1

Acquisition ……………………………………………………………….. 2

CLU Maintenance ………………………………………………………... 2

Horizontal Accuracy ……………………………………………………… 4

Classification of GeoSAR Data for Crop Indentification ………………… 5

Using the Unsupervised Classification in Erdas Imagine …………….. 5

Subtracting the P band from the X band ……………………………… 10

Comparing X and P band DEMs to the NED ……………………………. 11

Conclusion ……………………………………………………………….. 15

Appendix Documents
Working with GEOSAR Data in ERDAS Imagine
Final Report for USDA J07-0020
Introduction

The GeoSAR data evaluated was collected by Fugro EarthData, under contract with the
USDA Farm Service Agency's Aerial Photography Field Office. GeoSAR is a system of
data capture and is a type of Airborne Interferometric Synthetic Aperture Radar (IFSAR).
It was collected in Yazoo County, Mississippi on August 29 - 31 2007. The purpose of
this collection was to demonstrate the utility of Airborne IFSAR data to replace optical
imagery data collected in the National Agricultural Imagery Program (NAIP). The
GeoSAR data was evaluated on elevation quality versus the NED, all weather acquisition
capabilities, its potential as a CLU creation source, semi-automated crop identification
and any other general applications.

Deliverables

X band Magnitude 3 meter X band DEM 3 meter

P band Magnitude 5 meter P band DEM 5 meter

The deliverables came in three different geographic subsets – quads, swaths and counties.
The data consisted of 32 bit Geotiffs and 8 bit MrSIDs, most analysis was processed
using the 32 bit data. Magnitude refers to the raw return value relative to surface type.
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The DEMs were created from these magnitude values and converted to equal elevation.
No FGDC metadata was supplied for this data. Future deliveries should include this data.

Acquisition

GOES Weather Satellite

Precipitation Weather Map

The GEOSAR data was collected over 3 days of partly cloudy weather conditions and
low light. The quality and usability was excellent considering the collection conditions.
The ability to be collected at any time of day during most any conditions would have
definite benefits to the user.

CLU Maintenance
Analysis for the applicability of creating, editing and maintaining CLU with X and P
band magnitude datasets yielded inconsistent results. The coarseness (3 and 5 meter) of
the data makes determining discrete field boundaries difficult. If 2 CLU polygons
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bordering each other contain a different type of vegetation it is possible to see the
boundary between the areas and would allow for editing or creating CLU. However, if
the vegetation type is the same, it is often impossible to see the small buffer area between
fields which is needed to draw CLU. This would lead to creating large CLU where there
should be more then one CLU to cover the area. The GEOSAR data could be used to
supplement the imagery used to edit CLU, but it would be especially difficult to use
GeoSAR without the imagery. Without experience with the GEOSAR data, it would be
extremely difficult to edit CLU or identify basic features.

2007 NAIP with CLU

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X band Magnitude with CLU

P band Magnitude with CLU

Horizontal Accuracy
The GEOSAR data horizontal accuracy was checked against the 2007 NAIP. This was a
relative accuracy inspection process. Ten points were selected that were easily
identifiable on both the NAIP image and the GEOSAR image. The mean offset was just
over 3 meters with a maximum of 8 meters of difference. These offsets are a bit large,
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but considering that the GEOSAR data resolutions are 3 and 5 meters respectively, the
offsets are somewhat equivalent to a 1 or 2 meter offset of NAIP, well within tolerance.
Based on this, test data derived from the GEOSAR collection would overlay correctly on
NAIP and vice versa.

Classification of GeoSAR Data for Crop Identification

The classification procedures produced patterns which could be seen approximating the
outlines of fields, as represented by the CLU polygons. The analysis used the same
process with four different tiles, in order to compare the results accurately. This was done
while bearing in mind that there could be many potential variables when running the
processes, and many different tests which could be conducted.

Using the Unsupervised Classification in ERDAS Imagine

In the first tile examined, the pattern was surprisingly clear and similar to the Fugro
EarthData report. The imagery was compared to CLU files with crop data collected
through the crop maintenance tool included in the attribute table. This crop description
(in the field DESC_CROP) assigns a [Mississippi] crop to the CLU polygons. It does not
allow for fields which may contain more than one crop. Fields in Yazoo County
contained both harvested and un-harvested crops at the time of data collection, and this
was obviously not recorded in the attribute table. The upland cotton polygons, shown
with magenta outlines in the image below, as well as sweet potatoes (aqua lines) stood
out very clearly as classes 5 and 6 in the unsupervised Classification. These classes are
represented in dark green in the image below. The neighborhood classification also
clearly displayed areas of lower elevation, such as water and road surfaces. The
hypothesis going forward was that other tiles would exhibit the same patterns after the
same processes were run

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Figure 1: Tile G-6 showed upland cotton (in magenta) and sweet potatoes (aqua) very clearly, as darker
green was used to symbolize classes 5 and 6. Corn (yellow) and soybeans (red) had displayed more variety
in the classification.

Tile H-5 displayed the same pattern as tile G-6. Cotton appeared easily, but pixel classes
for soybeans and corn were too mixed to show any pattern. Tile F-2 showed a somewhat
different pattern. Cotton did not appear as clearly as it had in the previous two tiles
examined. The unsupervised classification was run again with only four possible classes,
and this produced an image with cotton displaying more prominently.

Tile G-2 was chosen for a series of more intensive tests because it had a higher number of
CLU polygons than the previous three tiles. Most of the tiles, such as G-6, pictured above
(figure 1), did not have a full range of CLU polygons throughout. In tile G-2, there is a
greater coverage of CLU; figure 2, below, shows CLU for G-2 above the NAIP image.
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Cotton (in magenta) again stands out, since most of the vegetation appears green on the
NAIP image. Some cotton fields in the northeastern quadrant of the image appeared
brown; this was explained by the fact that the NAIP imagery in this flight line was
acquired on September 15, 2007. The adjacent NAIP flight lines were acquired in
August, before the GEOSAR project was flown. The soybeans, outlined in red, appear in
different stages of growing/harvest, as does the corn, outlined in yellow. The green CLU
polygons are CRP areas, and the reddish brown polygons are fallow.

After the unsupervised classification was run on the tile, the neighborhood process was
also run. The upland cotton areas are again visible as darker green tones, symbolizing
pixel classes 5 and 6. The soybean polygons are mixed between darker and lighter greens,
while the corn polygons are often brown, which might indicate a lower elevation. This
would be consistent with the seemingly bare fields seen in the NAIP image.

At this point, it is important to note that the image used for the classification was the X
band, a 32 bit tiff, displaying the magnitude. The pixel values are without units,
measuring the strength of the return signal. As a result, the values may not be indicative
of vegetation height, especially since they may not bounce back from the exact top of the
plant, just as the P band may not bounce back from ground surface of the image. Another
factor to consider is that the ground sample distance of the X band pixels is 3 meters,
while the resolution of the P band pixels is 5 meters. As a result, pixel boundaries for the
two layers would not coincide, and any results would be somewhat coarse when
compared to the NAIP imagery. The classification process would, by its nature, resample
the data to create a new file with 5 meter GSD.

.
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Figure 2: CLU polygons displayed above the NAIP image in the area of GeoSAR tile G-2 showed upland
cotton (in magenta) clearly as compared to the other crops. Corn polygons (yellow) appear to be bare
fields, and soybeans (red) display more variety in the greenish tone.

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