Phase 2 Tutorial: Remote-sensing Supported Digital Soil Mapping in

Cooperative Ecosystem Studies Unit (CESU)
Phase 2
Tutorial: Remote-sensing Supported Digital Soil Mapping in South Florida
PI: Dr. Sabine Grunwald
Co-PI: Dr. Todd Z. Osborne
Collaborators: Rick Robbins, Howard Yamataki, and Tom Weber, Natural Resources
Conservation Service (NRCS)
Graduate student: Jongsung Kim (Ph.D. student)
Project period: 9/2008 to 9/2010
Contract Number: 68-3A75-4-73 Mod 3
Contract No. 00069677
This report summarizes the research activities of the project “Remote-sensing Supported Digital
Soil Mapping in South Florida” funded by Natural Resources Conservation Service (NRCS).
Goals of the Project
To develop procedures for remote-sensing supported digital soil mapping of various soil
properties in Water Conservation Area-2A.
Specific Objective
 Develop models to predict various types of soil properties: (i) soil taxonomic classes; (ii) soil
morphological properties; and (iii) physico-chemical soil properties.
 Assess the usefulness to incorporate remote sensing imagery into soil prediction models.
 Develop a tutorial for remote-sensing based digital soil mapping that enables transfer of the
methods to other soil survey regions.
Overview
This tutorial serves as a comprehensive documentation for the modeling process to predict soil
properties especially soil series and soil properties.
Study Area: Water Conservation Area-2A, Greater Everglades, Florida
Field Data: 108 points of field data (soil series and properties) including X and Y coordinate
Software:
1) ERDAS Imagine 2011 (Earth Resource Data Analysis System Inc., Atlanta, GA) for
remote sensing imagery analysis
2) ArcGIS 10 (Environmental Systems Research Institute, Redlands, CA) for data extraction
3) CART (Salford Systems, San Diego, CA) for tree-based soil prediction models
development
4) ISATIS (Geovariances Inc., France) for the total phosphorus prediction models
development
5) Microsoft Excel (Microsoft Corporation, Redmond, WA) and SAS (SAS Institute Inc.,
Cary, NC) for data preparation and data analysis
Tutorial for Remote Sensing Based Digital Soil Mapping
The development of computers and information technology lead soil scientists to incorporate
remote sensing technologies into soil prediction models to characterize various soil forming
factors. Remote sensing has been widely used to not only identify land cover/land use changes
but also predict soil properties. Because it supports rapid and inexpensive data collection over
large areas, comprehensive research has been focused on predicting soil properties using
geostatistical/hybrid methods at various scales and regions with a variety of remote sensing
imagery. Remote sensing is ideally suited to derive environmental data for wetlands areas that
are complex ecosystems and are difficult to access. It is an important tool to determine land
resources, to observe land surface covers at various spatial scales, and to determine changes that
result from natural and anthropogenic processes (Schmid et al., 2008). It has many advantages
especially in wetland and soil mapping. For instance, remote sensing based monitoring using
temporal sequences of satellite images can capture seasonal patterns in wetlands. Also remote
sensing data provide information on surrounding land uses of wetlands and changes over time
(Ozesmi et al., 2002).
(1) Remote sensing data sources
Remote sensing is a measurement of the electromagnetic energy reflected or emitted from
objects on the Earth’s surface and the amount of radiation, for any given material, that is
reflected at varying wavelength (Jensen, 2005). It means different materials have different
reflectance characteristics (Compare Figure 1-1). For instance, vegetation reflects wavelength
energy in the range of 0.52 to 0.6 μm (green channel) and 0.76 to 0.90 μm (near-infrared
channel) but absorbs wavelength energy in the range of 0.63 to 0.69 μm (red channel). Water
reflects wavelength energy in the range in 0.45 to 0.52 μm (blue channel) and absorbs other
energy. Therefore, remote sensing can play a role in the identification, inventory, and mapping of
vegetation and soils that are on the surface of the Earth, whereby the impact of soil grain size,
organic matter, and water content on soil spectral reflectance are identified (Jensen, 2000).
The data for remote sensing imagery is collected by two types of sensors: passive, and active.
Passive sensors simply use incoming electromagnetic energy from the Sun and collect/record the
electromagnetic radiation energy that is reflected or emitted from the earth (Figure 1-2). In
contrast, active sensors use machine-made electromagnetic energy. The artificial energy washes
the target area (terrain) and then collect/record the amount of radiant flux scattered back toward
the sensor (Jensen, 2005).
Figure 1-1 Reflectance spectrum of five types of land-cover (figure from center for remote
imaging, sensing & processing: http://www.crisp.nus.edu.sg/~research/tutorial/optical.htm)
Figure 2-2 Schematic diagram of remote sensing
There are varieties of satellite platforms which have different remote sensors: Advanced
spaceborne thermal emission and reflection radiometer (ASTER), Landsat multispectral scanner
(MSS), Landsat thematic mapper (TM), Landsat enhanced thematic mapper plus (ETM+),
Moderate resolution imaging spectroradiometer (MODIS) terra and aqua, Satellite pour
l’observation de la terre (SPOT) 4 and 5 high resolution visible infrared sensor, etc. Because of
its differential spectral, spatial, temporal, and radiometric resolution, each remote sensing data
derived from different remote sensors (satellite platforms) has different reflectance information.
The differences of resolutions among some of the widely used remote sensors are shown in
Figure 1-3 and 1-4.
1) Brief definitions of each resolution
a. Spectral resolution: the number and dimension of spectral regions (e.g. blue,
green, red, near-infrared bands)
b. Spatial resolution: the size of a grid cell or pixel (e.g. 10 x 10 m, 30 x 30 m, 250 x
250 m)
c. Temporal resolution: how often the sensor acquires data (e.g. every 2 days, 16
days)
d. Radiometric resolution: the sensitivity of detectors (e.g. 8-bit, 9-bit, 10-bit)
2) Visit the website for detail information for each satellite platform and sensor:
a. ASTER: http://asterweb.jpl.nasa.gov/
b. Landsat: http://landsat.usgs.gov/index.php
c. MODIS: https://lpdaac.usgs.gov/lpdaac/products/modis_overview
d. SPOT: http://www.spot.com/web/SICORP/403-sicorp-spot-images.php
Figure 1-3 Remote sensors spatial resolutions
Figure 1-4 Spatial and spectral resolution of the Landsat Multispectral Scanner, Landsat
Thematic Mapper, and SPOT Sensor System (Figure from Jenson, Introductory Digital Image
Processing: A Remote Sensing Perspective, 2005).
(2) How to download remote sensing images
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS)
Center offers free-downloadable aerial photography, satellite imagery, elevation, and land cover
data through several viewer tools such as EarthExplorer, Glovis. In this tutorial, ‘Global
visualization viewer (Glovis)’ was chosen to show the brief steps how to search available remote
sensing images and how to download them.
Among various remote sensing images Landsat and MODIS are the most heavily used freedownloadable remote sensing images and both of them are available in Glovis.
1) Visit one of the following websites (search and viewer tools).
a. Glovis: http://glovis.usgs.gov/
b. EarthExplorer: http://edcsns17.cr.usgs.gov/NewEarthExplorer/
c. WIST: https://wist.echo.nasa.gov/
d. There are lots of websites where you can access the remote sensing images.
2) Select Satellite platform, sensor, and study area.
a. Satellite platform (A in Figure 2-1): Landsat, MODIS, ASTER etc. ‘Landsat’ was
chosen for the tutorial.
b. Sensor (B in Figure 2-1): Landsat Enhanced Thematic Mapper Plus (ETM+),
Thematic Mapper (TM), Multi-Spectral Scanner (MSS) etc. ‘Landsat 7 ETM+’
was chosen for the tutorial.
c. Select study area (C in Figure 2-1). ‘Florida’ was chosen for the tutorial.
Figure 2-1 U.S. Geological survey global visualization viewer (Glovis)
3) Search available images and download the selected images.
a. Select path/row or latitude/longitude of the study area (A in Figure 2-2).
To search Landsat images users can select path and row. Landsat data uses
Worldwide Reference System (WRS) which is a global notation system. For
instance, path 15 and row 42 contains the Greater Everglades area in Florida.
b. Select maximum cloud cover (B in Figure 2-2).
c. Explore available images. Select a date (month and year).
d. Once you get an image which is suitable for your study, click ‘Add’ then ‘Send to
Cart’ (C in Figure 2-2). You can add as many images as you want.
e. Give brief information (e-mail address) to USGS and then they will send an email to you with a link to download the images.
f. Download the images through the link.
Figure 2-2 USGS global visualization viewer (Glovis) – the Greater Everlgades, FL
(3) How to import/export the downloaded remote sensing images
Once you get the images you need to convert the file format to use for developing soil prediction
models. For instance, Landsat image files which were created by USGS contain data in
Geographic Tagged Image-File Format (GeoTIFF) and MODIS image data are in Hierarchical
Data Format-Earth Observing System (HDF-EOS). Because different satellite images have
different data format, the images need to be imported into ERDAS Imagine to convert file format
into an image (IMG) to proceed with the image analysis.
1) How to read the remote sensing image file name: Different images have different naming
convention.
e.g. Landsat image file: LE70150422010047EDC00
LE7
Platform and sensor: Landsat 7 ETM+
015042
Path/Row (Worldwide Reference System)
2010047
Date of acquisition: Year and accumulated day (YYYYDDD)
(e.g. 2010047 is 47th day of 2010 = February 16th, 2010)
Landsat image file: LT50400362010286EDC00
Platform and sensor: Landsat 5 TM
LT5
040036
Path/Row (Worldwide Reference System)
2010286
Date of acquisition: Year and accumulated day (YYYYDDD)
(e.g. 2010286 is 286th day of 2010 = October 13th, 2010)
MODIS image file: MOD13Q1.A2010049.h10v06.005.2010067103334
MOD13Q1
.A2010049
.h10v06
.005
.2010067103334
Product short name: MOD13Q1 = 16 day Vegetation index (250m)
Date of acquisition: Year and accumulated day
(e.g.2010049 = 49th day of 2010 = February 18th)
Tile identifier (Sinusoidal tiling system): horizontal 10, vertical 06
Collection version
Date of production: Year, accumulated day, hour, minute, and
second (YYYYDDDHHMMSS)
Visit websites of each remote sensing image to know the naming rule in detail.
2) Open the ERDAS Imagine 2011.
a. Go to ‘Manage data’ tab and click ‘Import’ button in conversion group (A in
Figure 3-1)
b. Select file format (B in Figure 3-1). Click and browse to the remote sensing image
that you want to import. ERDAS Imagine 2011 supports a variety of remote
sensing image conversions. For instance, you can choose ‘Landsat 7 from USGS’
as your import file format (C in Figure 3-1).
c. Select input file (‘*.tif’ in case of Landsat, ‘*.hdf’ in case of MODIS) and name
the output file (*.img) (Figure 3-2).
e.g. Input file: LE70150422010047EDC00.tar.gz
Output file:Landsat7_0216 (you name it).img
d. Follow same protocols when you export remote sensing files to other formats.
Figure 3-1 Import/Export data function in ERDAS Imagine 2011
Figure 3-2 ERDAS Imagine: data import/export
♦ Import function works differently between ERDAS Image 2011 and previous version
of ERDAS Imagine.
a. If you are using ERDAS Imagine 2011, simply follow steps described above.
ERDAS Imagine 2011 supports a variety of remote sensing image conversions.
However, if you are using ERDAS 2010 or previous version, you need additional
steps. For example, Landsat data files provided by USGS are in zipped file format
(*.tar.gz) so that you need to extract (unzip) the files before you import a Landsat
image.
b. In case of Landsat data file, each band comes as a separate file (e.g.
LE70150422010047EDC00_B01.tif (Blue band);
LE70150422010047EDC00_B02.tif (Green band);
LE70150422010047EDC00_B03.tif (Red band)). It exits as individual layer after
file conversion (import) so that you need to stack all layers for further image
analysis.
To stack layers in ERDAS Imagine 2010 and previous version: tool bar 
Interpreter  Spectral  Layer Stack  Browse and add all layers that you want
to stack (e.g. blue, green, red, near-infrared, mid-infrared)
c. ERDAS Imagine 2011 supports automatic layer stack for Landsat data file. It
means once you import downloaded file, you can use the *.img file for the image
analysis. Layer Stack function in ERDAS Imagine 2011: Raster tab  Resolution
group: Spectral  Layer Stack
3) Display image.
a. Go to ‘File’ and click ‘Open (A in Figure 3-3)’.
b. Browse to your image file (*.img) and select (B in Figure 3-3).
c. Click ‘Raster options’ tab (C in Figure 3-3) and select layers (bands) to color (red,
green, and blue) (Figure 3-4) and select OK then the image will display.
♦ Color composition (Compare Figure 3-5)
Each pixel of remote sensing image composed with Red, Green, and Blue (RGB)
component except pseudo color and gray scale display when you display remote
sensing images. You can choose which band goes to red, green, or blue. For instance,
if you select band 3 (red wavelength energy) of Landsat data goes to the red
component, band 2 (green wavelength energy) goes to the green component, and band
1 (blue wavelength energy) goes to the blue component, we call it 3-2-1 RGB
composite image or ‘true-color composite image’ display. If you select band 4 (nearinfrared wavelength energy) goes to the red component, band 3 (red wavelength
energy) goes to the green component, and band 2 (green wavelength energy) goes to
the blue component, we call it is 4-3-2 RGB composite image or ‘false-color infrared
image’ display. The color compositions are only for image display.
Figure 3-3 ERDAS Imagine: image display
Figure 3-4 ERDAS Imagine: image display – raster option
Figure 3-5 Color composition (Landsat 7 image): true-color composite (above) and false-color
infrared (below) image
(4) How to find layer information and how to project/reproject the images
Once you downloaded a remote sensing image reviewing metadata (text format), the description
of the data, are important. The metadata provide general information about the satellite platform
and image. Some of them provide ground control points if the image is rectified and information
for each layer (band). However, the metadata of imagery can be seen in table or histogram
format in ERDAS Imagine or ArcGIS.
1) How to find the detailed information of each band
a. Click ‘Metadata’ (exclamation mark) in information group at Home.
b. It provides information about your image. The layer pull down (A in Figure 4-1)
dictates which layer’s information you are looking at.
It provides each layer’s information such as data type, range of pixel values for
each layer, units, projection, datum, and pixel size. Histogram of the image pixel
values is also shown if you click the histogram icon (B in Figure 4-1).
Figure 4-1 Image metadata in ERDAS Imagine
2) How to subset (clip) the study area
a. To subset your study area, go to ‘Home’ tab and click ‘Inquire’ in the information
group (A in Figure 4-2). Click ‘Inquire box’ then square box and information box
will be appear on you window.
b. You can put X and Y values (upper left X; upper left Y; lower right X; lower left
Y) manually into the inquire information window (B in Figure 4-2) or click
‘Select’ (A in Figure 4-2) and then move/drag the box where you wish to select as
your subset area.
c. After you select the subset area, go to ‘Raster’ tab and click ‘Subset & Chip’ in
geometry group. Select ‘Create Subset Image’.
d. Browse where the subset image will be stored and name the output file (A in
Figure 4-3). Click ‘From Inquire Box’ (B in Figure 4-3) and select the data type
of the output file (C in Figure 4-3). Finally select the layers you want to subset (D
in Figure 4-3) and hit ‘OK’.
e. Open the subset file and confirm that your study area is correctly stored or not
(Figure 4-4).
Figure 5-2 Inquire box in ERDAS Imagine
Figure 6-3 Subset window in ERDAS Imagine
Figure 7-4 Subset image for Water Conservation Area 2A, Greater Everglades, FL
3) How to project/reproject the image
a. All remote sensing imagery and geographical information system (GIS) data
layers should have one common map projection to proceed with any spatial
analysis. For instance, if you want to compare three different images which are
Landsat ETM+, SPOT, and MODIS, and they have different projections. Landsat
ETM+ and SPOT images have Universal Transverse Mercator (UTM) projection
but MODIS image has Sinusoidal projection. In this case, first, you need to decide
which projection is suitable as a ‘common map projection’. If you choose UTM
projection as a common projection, then you need to reproject only MODIS
image from Sinusoidal to UTM projection. Projection or reprojection can be done
in ERDAS Imagine or ArcGIS.
b. In ERDAS Imagine, go to ‘Raster’ tab and click ‘Spatial’ in resolution group.
Select ‘Reproject’ and select output projection for the image.
c. In ArcGIS, go to ArcToolbox. Under ‘Data Management Tools’ click ‘Projection
and Transformations’ and then select ‘Project Raster’ under ‘Raster’ category.
♦A map projection is used to portray all or part of the round Earth on a flat
surface. This cannot be done without some distortion. Every projection has its
own set of advantages and disadvantages. There is no ‘best’ projection.
–National mapping division, USGS-
Figure 8-5 Reprojection of MODIS image: Sinusoidal (left) and UTM projection (right)
(5) Image registration/rectification
Raw data of remotely sensed image do not contain data that were already in their proper
geometric x, y locations. Usually remote sensing images could contain a different level of
geometric distortion due to internal and/or external reasons. For instance, the rotation of the
Earth or remote sensing system itself (e.g. scanning variation) could cause internal geometric
errors and variation of spacecraft’s orbit or sun angle changes could cause external geometric
errors (Jensen, 2005). It is necessary to preprocess and remove the geometric distortion so that
features in remote sensing image are put into ‘real-world’ coordinate system.
Usually image to map rectification and image to image registration method is used to correct
geometric distortion.
Image to map rectification: obtain reference points (ground control points, GCP) directly from a
map, digital orthophoto quarter-quad (DOQQ) images, or through GPS.
Image to image registration: obtain reference points from an already geocorrected image.
1) How to rectify the image
a. Open the image which you wish to rectify and a map which you wish to obtain
and ground control points.
b. Click ‘Control Points’ in transform & orthocorrect group (A in Figure 5-1) then a
window should appear that allows you to select the Geometric Model. Select a
geometric model that you want to use (B in Figure 5-1). In this tutorial,
Polynomial geometric model was chosen.
Figure 5-1 Image to map rectification in ERDAS Imagine: Landsat ETM+ (left) and Digital
orthophoto quarter-quad (DOQQ) image (right)
c. Once you select the geometric model, the ground control points tool reference
setup window will appear. Browse and select the map that you wish to obtain, e.g.
road, lake, buildings, ground control points (reference map). Merged DOQQ
image was used for the tutorial.
d. Confirm the projection and datum when the reference map information box
appears then click OK and wait for all windows to position themselves (Figure 52).
e. It is ready to collect GCPs. Click crosshair button on tool bar (A in Figure 5-2).
Use ‘Zoom-in’, ‘Zoom-out’, and ‘Pan’ button to explore the entire image and
select good GCPs. Once you decide good GCP, click crosshair button and select a
point on your image (e.g. Landsat image). Click again the crosshair button and
then select a point on your reference map (e.g. DOQQ). Then each of the x and y
coordinates of your image and map will be added automatically under your GCPs
area (A in Figure 5-3). Collect as many GCPs as you can. Be sure that you need to
collect GCPs spread well throughout the entire area.
Figure 5-2 Image to map rectification in ERDAS Imagine: Geometric correction window
f. After collecting GCPs, click the sigma button on the tool bar for geometric
correction error assessment (rule of thumb: root mean square should be less than
half resolution of the image; B in Figure 5-3).
g. According to your GCPs, the image will be resampled.
Figure 5-3 Image to map rectification in ERDAS Imagine
(6) Band ratioing/Vegetation index
It is essential to understand the meaning of the features or pixel values of the image. What do
high pixel values in red band mean? What do low pixel values in near-infrared band mean? If an
object observes green wavelength energy and reflect red wavelength energy, the pixel values of
the object can be low in the green band and high in the red band. However, understanding the
relationship between pixel values of images and features or phenomena in nature environment is
essential. Each band of imagery has meaning but sometimes a combination of two or more bands
contains even more information. Band ratioing or ratio transformation is a process by which
reflectance values of pixels in one band are divided by the values of their corresponding pixels in
another band (Jenson, 2005). It allows creating a new output image which is informative. These
ratios may enhance or subdue certain attributes found in the image, depending on the spectral
characteristics in each of the two bands chosen. For example, the ratio of red and near-infrared
bands provides vegetation information and blue and near-infrared ratio provides water
information.
Similarly vegetation indices also use the band ratioing concept. Vegetation indices are widely
used to assess vegetation coverage, changes, and condition of plant vigor since the 1960s. Some
important vegetation indices can be derived from MODIS, Landsat ETM+, and SPOT images.
However, some of the indices are sensor specific. For instance, NDVIgreen cannot be derived
from MODIS image because MODIS does not have a green band. Some commonly used
vegetation indices are shown in Table 1.
Table 1. Selected remote sensing vegetation indices.
Formula*
Indices
Normalized difference vegetation
index
Normalized difference vegetation
green index
Transformed vegetation index
Enhanced vegetation index**
References
Rouse et al.,
1974
Gitelson et al.,
1996
Nellis and
Briggs, 1992
Huete et al.,
1997
Normalized Difference Water
Index
Gao, 1996
Reduced Simple Ratio
Chen et al.,
2002
Mid-infrared index
Musick and
Pelletier, 1988
*
NDVI = Normalized difference vegetation index, NIR = Near-infrared, SWIR = Shortwaveinfrared; ** Empirical parameters for enhanced vegetation indix (EVI) of MODIS: C1=6.0;
C2=7.5; G=2.5; L=1.0.
1) How to do band ratioing
a. Go to the ‘Raster’ tab and click ‘Functions’ in scientific group. Select ‘two image
functions’.
b. Browse the input files. Different bands in the same input file (e.g. band 1 and
band 2) or different input files (e.g. Landsat and SPOT) can be used (A in Figure
6-1).
c. Browse to a folder in which the output file is stored.
d. Click drop-down button and select an operator that you want to use (e.g. +, -, *,
/…) and select data type then click OK.
2) How to derive vegetation indices
a. Go to the ‘Raster’ tab and click ‘Unsupervised’ in classification group. Select
‘NDVI’ or ‘Indices’.
b. Browse input file and output file (you name it).
c. Select the vegetation index which you want to calculate (e.g. NDVI, IR/R, VI…)
and data type (A in Figure 6-2).
d. The equation will appear when you select a vegetation index (B in Figure 6-2).
Once you confirmed the equation, click OK then a newly derived image which
contains the vegetation index in each pixel will be added. You may confirm the
ranges of the images using a histogram or metadata (e.g. NDVI values should be
between ‘-1’ and ‘+1’).
Figure 6-1 Band ratioing: two input operators
Figure 6-2 Vegetation indices
Figure 6-3 Vegetation indices: Normalized difference vegetation index
(7) Extract reflectance or ratio/vegetation values for soil sampling locations
It is impossible to sample all soils exhaustively, i.e. at all locations, to analyze their taxonomic or
biogeochemical properties because it is costly, labor and time intensive. Thus, soil sampling can
only be done at sparse locations and models are used to estimate properties at unsampled
locations. Even though remote sensing images can support continuous dense information
throughout the study area, the images should be connected with site-specific soil data and this
can be done through x and y coordinates.
The images can be merged with site-specific soil properties through x and y coordinates in
ERDAS Imagine or ArcGIS. Reflectance values and derived spectral indices from images need
to be extracted for each (soil) sampling point after the merge process for further prediction model
development. The extraction process can be easily done using ArcGIS. ERDAS Imagine also has
a way (e.g. making process models to extract the pixel values using ‘Model Maker’ under
‘Toolbox’) to assign raster pixel values to shape points. However, the steps are complicated and
slow because spatial modeling processes in ERDAS Imagine rasterize the points internally
during the process. Hence, ArcGIS is used for the pixel values extraction in the tutorial.
1) How to merge the image with site-specific soil properties in ERDAS Imagine
a. Open the remote sensing image and click ‘Add’ to add site-specific soil properties
(*.shp).
b. Browse the soil property layer and add. Please make sure that your ‘file type’ is in
shapefile (*.shp) when you browse the soil layer.
c. Once you have the same map-projection, the soil layer will be automatically
displayed above the remote sensing image.
d. You may edit the soil layer (e.g. attribute table) under ‘Vector’ tab.
2) How to merge the image with site-specific soil properties and extract pixel values in
ArcGIS
a. Open a ArcMap and add all images (e.g. Landsat ETM+, SPOT, MODIS, NDVI
derive from Landsat, NDVI derive from SPOT) and GIS layers (e.g. elevation,
geophysical data) and sampling points layer (e.g. from a soil survey) (Figure 7-1).
b. Go to ‘Spatial Analyst Tools’ in ArcToolbox and select ‘Extraction’. There are
two options that you can use to extract raster pixel values to shape points. The
first one is a ‘Extract by Points’ function. It is a function that extracts pixel values
of one raster layer to shape points. For instance, NDVI image can be chosen as
input raster and only the NDVI values which coincide with soil sampling
locations will be extracted. The second option is a ‘Sample’ function. Multiple
rasters can be added as input rasters in ‘Sample’ method. For instance, NDVI,
NDVIgreen, SPOT, MODIS, etc. can be added as input rasters and be extracted at
one time. Note that when you use ‘Sample’ you need to be sure about the order of
the input raster files. An output table will be written in the order of the input
rasters.
c. Add the input rasters (A in Figure 7-2) and point layer (B in Figure 7-2). Name
the output table. Click OK.
d. The output table is in data base format (*.dbf) and it can be opened in Excel or
other spreadsheets (Figure 7-3).
e. Finally confirm what kind of predictor variables are collected and extracted (e.g.
variables shown in Table 2 are used for the prediction models development in this
tutorial). Everything is ready now!
Figure 7-1 A merged image (NDVI is shown as top layer) with site-specific soil properties
Figure 7-2 Pixel value extraction: Sample in ArcMap
Figure 7-3 Output table which contains soil properties and pixel values of spectral indices
Table 2. Example of environmental predictor variables used for development of soil prediction
models.
Property
Land cover
data
Topographic
data
Attributes*

Vegetation

Elevation (m)


Landform
MODIS: NDVI, TVI, EVI

Landsat ETM+: blue, green, red, NIR, MIR, PAN, NDVI, NDVIgreen,
TVI, MidIR, Principal Component Analysis (PCA) 1 to 6, Tasseled Cap
Transformed data (TC) 1 to 6, NDVI 3x3 window, NDVI 7x7 window


SPOT: green, red, NIR, SWIR, PAN, NDVI, NDVIgreen, TVI, NDWI,
RSR, PCA 1 to 4, NDVI 3x3 window, NDVI 9x9 window
Gamma-ray (potassium)

Bouguer gravity


Isostatic residual gravity
x coordinates (m)

y coordinates (m)


xy coordinates
Distance to water control structures (m)
Remote
sensing data
Geophysical
data
Geographic
data
*
Values were extracted from each local pixel.
(8) Conceptual soil-landscape modeling
McBratney et al. (2003) formalized the SCORPAN soil prediction model rooted in Jenny’s
(1941) factorial model which describes soil as a function of five soil forming factors. For
empirical quantitative descriptions of relationships between soil and other spatially referenced
factors, the SCORPAN model includes spatial position and soil itself as a soil forming factor;
Sc or Sa [x,y,~t] = f (S[x,y,~t], C[x,y,~t], O[x,y,~t], R[x,y,~t], P[x,y,~t], A[x,y], N)
where
Sc = soil class
Sa = soil attributes
S = soil, other properties of the soil at a point
Eq. (1)
C = climate
O = organisms including humans
R = relief
P = parent material
A = age (time factor)
N = space
x, y = x and y coordinates
t=time
Figure 8-1 Conceptual soil-landscape modeling: SCORPAN
Based on the SCORPAN model, in this tutorial, prediction models for soil series and
biogeochemical soil properties are developed using remote sensing imagery which can provide
information about the O factor.
(9) Soil series prediction models development
Classification Trees can be used to build predictive soil models. Classification Trees are a type of
regression which is capable to predict classes (e.g. soil series) from various predictor variables
(e.g. GIS and remote sensing data which characterize the study area).
Interpretations and predictions of environmental data and especially soil data are challenging
because of their complexities and interrelationships. Tree-based methods are suited for dealing
with highly complex data that are non-Gaussian (De’ath and Fabricius, 2000). Tree structures are
generated through recursive partitioning the data into a number of groups (McBratney et al.,
2003). Tree-based models can be implemented in single tree mode or as committee trees using
hundreds of trees as described in Grunwald et al. (2009).
The CART software package (Salford Systems, San Diego, CA) is used for tree-based soil series
prediction models development in this tutorial. Please visit the website (http://salfordsystems.com/cart.php) for detail information about CART. The spectral data and derived indices
from different remote sensing images that were collected through step (1) to (7) can be included.
Vegetation, topographic, geophysical, and geographic data along with the site-specific soil series
data are also used to build classification trees.
In this tutorial, soil series that were collected from 108 sites during field sampling by Natural
Resources Conservation Service (NRCS) and UF staff in WCA-2A are used. There are seven soil
series: Gator, Gator with limestone bedrock, Lauderhill, Okeelanta, Pahokee, Terra Ceia, and
Terra Ceia with limestone bedrock. However, there is a limitation to predict bedrock because
there is no available information about parent material and bedrock in WCA-2A, so that Gator
with limestone bedrock is considered as Gator series, and Terra Ceia with limestone bedrock is
considered as Terra Ceia series to develop prediction models for soil series. There are 7
observations for Gators, 5 for Lauderhill, 14 for Okeelanta, 25 for Pahokee, and 57 for Terra
Ceia soil series.
1) How to build a data file for CART
a. CART reads data in various format including delimited ASCII text files, SAS,
SPSS, and Excel files.
b. CART is case insensitive. Variable names have to start with a letter and should
have only letters, numbers, or underscores.
c. CART supports both character and numeric variables.
d. Variable names must not exceed 32 characters.
(Reference: Steinberg and Colla, CART-Classification and Regression Trees,
1997)
2) How to build soil series prediction models
a. Prepare your dataset including GIS, spectral and vegetation indices.
b. Open CART (Figure 9-1) and click on File. Go to ‘Open’ and then ‘Data files’ to
bring in your dataset. Or click open folder button.
c. Browse and select the input data file. The input data file window should appear
(Figure 9-2). You can see the brief information for the input data file and
variables such as how many records does the input file have, how many variables
does it have, how many character or numeric variables does it have (A in Figure
9-2). You may sort the variables by file order or alphabetical order.
d. You may exclude some of the variables from your input dataset by clicking
‘Stats…’, review all individual data by clicking ‘View Data…’, and start to build
your models by clicking ‘Model…’ in the Activity menu (B in Figure 9-2).
e. CART automatically distinguishes character and numeric variables from the input
data file. If the variable name ends with ‘$’ symbol, it means that the variable is
assumed as a character variable (Compare variable name A and B in Figure 9-3)
and others are treated as numeric variables. Also character variables are
automatically treated as categorical variables (C in Figure 9-3). Categorical
valuables also can be selected manually by putting a checkmark inside the
checkbox.
f. Select ‘Target (A in Figure 9-3)’ and ‘Predictor (B in Figure 9-3)’ variables. If no
predictor is selected, CART treats all remaining variables as predictor variables.
In this case, the target variable = soil series; and predictor variables are
SCORPAN variables extracted from remote sensing images and GIS data layers.
g. Select Tree Type (D in Figure 9-3): ‘Classification’, ’Regression’, or
‘Unsupervised’.
Classification tree: Use when your target variable is a ‘Categorical’ variable (e.g.
soil series).
Regression tree: Use when your target variable is a ‘Continuous’ variable (e.g.
soil phosphorus).
h. Select ‘Single (CART) tree’ or ‘Committee (Combine) tree’ (E in Figure 9-3).
Figure 9-1 Opening CART
Figure 9-2 Input data file window
Figure 9-3 Model setup window - Model
i. Select the validation method in ‘Testing’ tab (e.g. V-fold cross-validation,
variable separates test when you have a separate dataset for validation) (Figure 94).
j. Set ‘Standard error rule’ for selection of the best tree (e.g. minimum cost tree
regardless of size, within one standard error of minimum) in ‘Best Tree’ tab
(Figure 9-4).
k. Select ‘Splitting method (e.g. Gini, Entropy, Twoing)’ and ‘Favor even splits
level’ in ‘Method’ tab (Figure 9-5).
Figure 9-4 Model setup window – Testing (left) and Best Tree (right)
Classification Trees
Gini: Gini looks for the largest class in your dataset and costs are incorporated by
adjusting prior probabilities. For instance, Gini splitting method tries to isolate
‘Terra Ceia’ series which have largest number of observation from all other
classes in soil series dataset.
Symmetric Gini: Costs are made symmetric and directly incorporated into the
impurity function at the tree-growing stage.
Entropy: Entropy looks for splits where as many levels as possible are divided
perfectly or near perfectly.
Class Probability: Class probability method builds probability tress instead of
classification trees.
Twoing: Twoing segments the classes of target variable into two groups. It
attempts to make groups that together add up to 50 % of the input dataset.
Ordered Twoing: Ordered towing method can handle ordered target variable.
(Reference: Steinberg and Colla, CART-Classification and Regression Trees, 1997)
Figure 9-5 Model setup window – Method (A: Classification trees, B: Regression trees)
l. Set ‘Minimum node sizes’ for parent node and terminal node in ‘Advanced’ tab.
m. Once the model parameters are set, click ‘Start’. Result navigator should appear
(Figure 9-6).
n. The navigator shows trees with its cost. The trees can be grown or pruned by the
model developer (A in Figure 9-6). The best performing prediction model is the
one with the lowest relative cost (error). Brief model statistics such as number of
predictors, nodes, and minimum node cases are shown right of the navigator.
o. CART supports several options for displays and reports (B in Figure 9-6). In
essence, classification trees document the splitting rules of environmental
predictor (SCORPAN) variables used in a tree to predict a target variable (here:
soil series) in form of terminal nodes.
p. Only splitters are displayed when you select a ‘Splitters…’ (Left in Figure 9-7)
and ‘Tree details…’ shows specific splitting criteria, number of cases in each
class and so on (Right in Figure 9-7). ‘Summary reports…’ shows ‘Gains chart’
for each class (Left in Figure 9-8), terminal nodes information, and variable
importance (Right in Figure 9-8).
Figure 9-6 Classification tree model
Figure 9-7 Classification tree model displays and reports: Splitters (left) and tree details (right)
Figure 9-8 Summary reports: Gains chart (left) and variable importance indicating which of the
predictor variables had the highest importance to predict a variable of interest (right)
3) Error assessment
The confusion error matrix is shown under the ‘Prediction Success’ tab (Figure 9-9). It
can be utilized to assess the uncertainty of predictions. Confusion error matrices show the
amount of average accuracy and overall accuracy. ‘Average accuracy’ is the average of
each class’s accuracy and ‘accuracy in each class’ is determined by dividing the total
number of correctly classified cases in a class (e.g. Gator soil) by the total number of
observed cases in that class (e.g. Gator soil). The ‘overall accuracy’ is determined by
dividing the total number of correctly classified cases (sum of diagonal of confusion
matrices) by the total number of observed cases. Examples of the confusion error matrix
are shown in Table 3-1 and 3-2. These are results of model development for soil series
using remote sensing spectral data in WCA-2A.
Figure 9-9 Error assessment for classification tree model
Table 3-1. Confusion error matrix for single tree soil series prediction model with Landsat
ETM+ image; Twoing classification method; average accuracy = 59.00 %, overall accuracy =
57.41 %.
Predicted (Classified) soil series
Class
Observed
case
Gator
Gator
N=12
Lauderhill
N=22
Okeelanta
N=13
Pahokee
N=0
Terra Ceia
N=61
7
5
0
2
0
0
Lauderhill
5
0
5
0
0
0
Okeelanta
14
0
2
6
0
6
Pahokee
25
3
12
1
0
9
Terra Ceia
57
4
3
4
0
46
Table 3-2. Confusion error matrix for single tree soil series prediction model with SPOT image;
Entropy classification method; average accuracy = 64.55 %, overall accuracy = 59.26 %.
Predicted (Classified) soil series
Class
Observed
case
Gator
Gator
N=12
Lauderhill
N=11
Okeelanta
N=25
Pahokee
N=11
Terra Ceia
N=49
7
5
0
2
0
0
Lauderhill
5
0
5
0
0
0
Okeelanta
14
0
0
8
2
4
Pahokee
25
3
6
5
6
5
Terra Ceia
57
4
0
10
3
40
You could produce a map with the classification model that you developed for soil series using
ArcGIS. The ‘raster calculator’ can be used to produce a map as shown in Figure 9-10 and 9-11.
Figure 9-10 Single tree model (left) and prediction map of soil series (right) using Landsat ETM+ image in WCA-2A
Figure 9-11 Single tree model (left) and prediction map of soil series (right) using SPOT image in WCA-2A
(10) Soil property prediction model development
Comprehensive research has been focused on predicting soil properties using
geostatistical/hybrid methods at various scales and regions with a variety of remote sensing
imagery. Various statistical and geostatistical prediction methods with field observation data and
remote sensing images can be used to predict the spatial distribution of soil properties. In this
tutorial, total phosphorus (TP) data that were collected and analyzed from 108 sites soil cores
during field sampling are used. TP in WCA-2A ranged from 165.4 to 1694.0 mg kg-1 with an
average of 601.4 mg kg-1 at layer 1 (0 to 10 cm), 27.6 to 2288.0 mg kg-1 with an average of 399.1
mg kg-1 at layer 2 (10 to 20 cm).
The ISATIS software package (Geovariances Inc., France) is used to develop the TP prediction
models. Topographic, geophysical, geographic, spectral data and indices from remote sensing
images can be used to assess the trend models for regression kriging. Figure 10-1 provides an
overview of soil property prediction model development.
Figure 10-1 Overview of soil biogeochemical property prediction models development.
1) What is a semivariogram?
Semivariograms measure the spatial dependence of soil properties using the semivariance
(Webster and Oliver, 2007). Goovaerts (1997) mentioned that the semivariogram provides
information on the range of spatial correlation as well as the magnitude of spatial variability.
Nugget variance, range and sill variance of each semivariogram are parameters provided for
interpretation of spatial dependence (McBratney and Pringle, 1999). The nugget effect
represents random variation caused by measurement error or short-range variation (e.g. due
to microbial activities) that could not be detected. Usually, the semivariance increases with
sampling distances and then approaches a constant value called the sill. The distance at
which the sill is achieved is called the range of spatial dependence (Figure 10-2). Typically,
pairs of samples closer together show smaller semivariance values, whereas samples further
away from each other show larger semivariances. It means samples separated by distances
closer than the range are spatially related, while samples separated by greater distances are
not spatially related. The semivariance is described by the formula:
 (h) 
1 m
 {s( xi )  s( xi  h)}2
2m i 1
Eq. (2)
where γ(h) is the average semivariance of the soil property, m is the number of pairs of
sampling points, s is the value of the property S (e.g. soil TP), x is the coordinate of the point
i, and h is the lag or separation distance (Webster and Oliver, 2007).
Figure 10-2 Semivariogram, showing the range, nugget, and sill.
2) What is Kriging?
Kriging is a statistically-based estimator of spatial variables such as soil properties (Bolstad,
2008) and the most commonly used interpolation method in soil science. It provides a
solution to the problem of estimation based on a continuous model of stochastic spatial
variation (Webster and Oliver, 2007). Ordinary Kriging is the most common type of kriging
and it assumes that we do not know the mean a priori (Meul and Van Meirvenne, 2003;
Webster and Oliver, 2007). Information from the semivariograms is used in ordinary kriging
to derive estimates across a defined region. It uses a search neighborhood function including
only neighboring data values within the search window (certain range of window) to predict
values at an un-sampled location. Typically near points from the target carry more weight
than more distant ones and clustered points carry less weight individually than isolated ones
at the same distance (Webster and Oliver, 2007).
Regression Kriging is a combination of traditional statistics’ regression analysis and
geostatistical analysis. The two components of the prediction (trend and kriging) are
computed separately in regression kriging (Odeh et al., 1995). The deterministic component
is modeled using multiple linear regressions and the stochastic component is modeled using
ordinary kriging or simple kriging of the regression residuals (Odeh et al., 1995; Stacey et al.,
2006; Rivero, 2006). The advantage of regression kriging is that kriging after regression may
improve prediction in comparison to when doing regression or kriging only (Odeh et al.,
1995).
3) How to import an input file and build a variogram model in ISATIS
Kriging method is used to develop TP prediction model for the tutorial. ArcGIS supports
kirging through the Geostatistical analyst extension (Figure 10-3). However, ISATIS is used
to do semivariogram fitting and kriging in this tutorial because it affords the user more
control over the geostatistical modeling process than in ArcGIS. ISATIS uses ‘variogram’
instead of ‘semivariogram’.
Figure 10-3 Geostatistical wizard for Kriging in ArcMAP
a. Open ISATIS (Figure 10-4) and create workspace for the model development.
Select ‘Data File Manager’ under ‘File’ menu and then click ‘Create’. ‘Create
study’ window should appear (Figure 10-4). Name the workspace and select
location. Click ‘Create’ button then your workspace is set.
Figure 10-4 ISATIS open window
Figure 10-5 Create study window
Figure 10-6 Import input file in ISATIS
b. To import a prepared input file which contains soil properties, environmental
ancillary data, and remote sensing spectral data and indices, click ‘File’ and
‘Import’. ISATIS supports several kind of data formats such as ASCII, ArcView,
CAD, Excel, etc. (Figure 10-6). Protocols are the same for all data formats. Excel
file is imported as an example in this tutorial. Select ‘Excel…’ and then ‘Excel
import’ should appear.
c. Import the input file (*.xls or *.xlsx; A in Figure 10-7) and click ‘First available
row contains field manes’ if your input file does (B in Figure 10-7).
d. Set directory and file for ISATIS points file (C in Figure 10-7).
e. Click ‘Automatic’ in data files and then variables name should show up (D in
Figure 10-7). Click each variable and select ‘Field type’ (e.g. easting, northing,
numeric) and unit (E in Figure 10-7). Click ‘Preview’ to confirm the data and then
click ‘Import’ (F in Figure 10-7).
f. For the experimental semivariogram set and data analysis, go to ‘Statistics’ and
click ‘Exploratory data analysis’. Click ‘Data file’ (A in Figure 10-8) and select
variable that you want to analyze (soil TP in layer 1 in this tutorial; B in Figure
10-8). ISATIS supports various types of exploratory data analysis such as
histogram and Q-Q plot (C in Figure 10-8). Explore your data and decide what
further steps you need (e.g. transformation, outliers).
Figure 10-7 Import Excel file into ISATIS (left), points file selector window (upper right), and
preview window (lower right)
g. For the experimental semivariogram set, go to ‘Application’ in the variograms
window (D in Figure 10-8) and then the ‘Variogram calculation parameters’
window should appear (Left in Figure 10-9). Set the variogram parameters (A in
Figure 10-9) and click ‘OK’. Go back to variogram window and click
‘Application’ again. Select ‘Save in parameter file’ under application and name
the experimental variogram (e.g. variogram_TP_tutorial). Click ‘Add’ and ‘OK’.
Figure 10-8 Exploratory data analysis window (left) and various graphs (right)
Figure 10-9 Variogram calculation parameters window (left) and variogram with number of data
pairs (right)
h. Go to ‘Statistics’ and click ‘Variogram fitting’. Once the variogram fitting
window appears, bring in the experimental variogram (A in Figure 10-10) which
was saved in step (g). Click ‘Variogram model’ and name the output file (fitted
variogram model; B in Figure 10-10). To see the fitted variogram, check ‘Fitting
window’ in Graphic windows (D in Figure 10-10).
i. Click ‘Edit’ button (C in Figure 10-10) and then the edit window should appear.
In ‘Edit’ window, develop variogram models. Play with several parameters. Try
to find the “best” variogram model for the variable. You may want to add a
‘Nugget effect’ into your variogram. Select ‘Nugget effect’ and click ‘Add’. Set
‘Sill’.
Figure 10-10 Variogram fitting window
Figure 10-11 Variogram fitting edit window (left) and fitted variogram (right)
j. Click ‘Add’ again and select model for variogram structure (e.g. spherical model,
exponential, Gaussian…) and set the range and sill (Figure 10-11).
k. Click ‘Test’ to see your variogram fitting and if you are satisfied, click ‘OK’ and
‘Run (Save)’.
4) How to interpolate soil properties using the variogram model (ordinary and block
kriging)
a. Click ‘Estimation’ under the ‘Interpolate’ menu and then select ‘Kriging’ method
that you wish to use (Figure 10-12). For example, if the data do not follow a
normal distribution or are severely skewed, you can choose the lognormal kriging
method. Lognormal kriging automatically transforms values using natural
logarithm and back-transforms after interpolation.
b. Click ‘(Co-)Kriging’ and select calculation option (e.g. punctual, block; A in
Figure 10-13) and number of variables.
c. Select an input file and output file (e.g. variable name such as ‘TP_prediction’),
and fitted variogram model that was developed in previous stage (B in Figure 1013). You can also edit the variogram in this step. If you want to perform ‘Simple
kriging’ or ‘Factorial kriging’, you can select the method in ‘Special model
options (C in Figure 10-13)’.
d. Click ‘Neighborhood’ and add a neighborhood file for the kriging. Name the new
file (e.g.neighborhood_TP_tutorial) and then click ‘Add’ and ‘OK’.
e. Once the neighborhood file is added, you can set neighborhood parameters such
as ‘search ellipsoid (distance), minimum and optimum number of samples, and
number of sectors. Click ‘Edit (D in Figure 10-13)’ and set neighborhood
parameters.
Figure 10-12 Kriging methods in ISATIS
f. Click ‘Run’.
g. For the error assessment, the cross-validation method is used. Go to ‘Statistics’
and then click ‘Modeling’, select ‘Cross-validation’. The cross-validation window
should appear (Right in Figure 10-13). Set the number of variables and select a
‘Data file’ and variables which will be cross-validated. Bring the variogram
model and neighborhood and then click ‘Test’. The results window should appear
(e.g. Figure 10-14).
h. Try to build several variogram models and neighborhoods. Run your models and
do the test. Try to find a best model for your variable.
i. Display your kriging result under the ‘Display’ menu in ISATIS or export the
output file as ArcView or ASCII format. Go to ‘File’ and click ‘Export’. Select
output format and then bring the output file into ArcMap to display the result (e.g.
Figure 10-15).
Figure 10-13 Kriging (left) and cross-validation (right) in ISATIS
Figure 10-14 Cross-validation results
Figure 10-15 Block kriging result map for TP in WCA-2A
5) Regression kriging
Regression kriging is a combination of traditional statistics’ regression analysis for the
deterministic component (multiple linear regressions for trend) and geostatistical analysis for the
stochastic component (kriging for residuals) (Odeh et al., 1995).
a. Correlation analysis is performed to look at the relationship among predictor
(SCORPAN) variables and then multiple linear regression method is employed
for trend analysis in SAS (Figure 10-15).
b. Once you finish trend analysis, produce a trend map with the formula (e.g. lnTP =
5.28 + (0.0073×Near-infrared reflectance value) + (8.34×NDVI) + …). Simple
way to produce a trend map is to use ‘Raster calculator’ in ArcMap.
Figure 10-15 Correlations and multiple linear regression (above) and correlation result (below)
c. Derive the residuals by comparing the observed values and values derived from
the regression equation for all sampling locations: observation values – trend
value; or soil lnTP observations – soil lnTP predictions from the regression model
d. Then follow the same steps as explained in (4) How to interpolate soil properties
using the variogram model (ordinary and block kriging).
e. Combine the trend map and residual map (e.g. Figure 10-16). For this step the
‘Raster calculator’ can be used in ArcMap.
Our research results show that remote sensing derived properties explained about
50% of the variability of soil TP in the topsoil. Spectral indices infer on the
chlorophyll content, stress, and composition of vegetation, which are correlated
with soil TP content. Also landscape features such as tree islands and hammocks
were captured by the TP model using remote sensing spectral data as a predictor
variable.
The same steps as outlined in this tutorial can be utilized to develop prediction models for
other soil classes (e.g. soil order, soil suborder) or soil properties (e.g. soil organic carbon,
soil total nitrogen).
Figure 10-16 Regression kriging result map for TP in WCA-2A
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