Peer-Reviewed Articles
1225 Using USDA Crop Progress Data for the Evaluation
of Greenup Onset Date Calculated from MODIS 250-Meter Data.
Brian D. Wardlow, Jude H. Kastens, and Stephen L. Egbert
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Identification of the onset of vegetation greenup is a key
factor in characterizing and monitoring vegetation dynamics
over large areas. However, the relationship between greenup
onset dates estimated from satellite imagery and the actual
growth stage of vegetation is often unclear. Herein, we
present an approach for comparing pixel-level onset dates to
regional planting and emergence information for agricultural
crops, with the goal of drawing reliable conclusions regarding the physical growth stage of the vegetation of interest at
the time of greenup onset. To accomplish this, we calculated
onset of greenup using MODIS 250 m, 16-day composite NDVI
time series data for Kansas for 2001 and a recently proposed
methodology for greenup detection. We then evaluated the
estimated greenup dates using the locations of 1,417 large
field sites that were planted to corn, soybeans, or sorghum
in 2001, in conjunction with United States Department of
Agriculture (USDA) weekly crop progress reports containing
crop planting and emergence percentage estimates.
Average greenup onset dates calculated for the three
summer crops showed that the dates were consistent with
the relative planting order of corn, sorghum, and soybeans
across the state. However, the influence of pre-crop vegetation (weeds and “volunteer” crops) introduced an early bias
for the greenup onset dates calculated for many field sites.
This pre-crop vegetation signal was most pronounced for the
later planted summer crops (soybeans and sorghum) and in
areas of Kansas that receive higher annual precipitation.
The most reliable results were obtained for corn in semi-arid
western Kansas, where pre-crop vegetation had considerably
less influence on the greenup onset date calculations. The
greenup onset date calculated for corn in western Kansas
was found to occur 23 days after 50 percent of the crop had
emerged. Corn’s greenup onset was detected, on average, at
the agronomic stage where plants are 15 to 45 cm (6 to 18
inches) tall and the crop begins its rapid growth.
1235 Evaluation of the Horizontal Resolution of SRTM
Elevation Data
Leland Pierce, Josef Kellndorfer, Wayne Walker, and Oton Barros
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The SRTM dataset is available at the USGS seamless website
with one arc-second pixel spacing for the U.S. Recently,
the value for horizontal resolution has been questioned.
One paper (Smith and Sandwell, 2003) suggests that 60
meters may be more accurate, implying that the resolution
is twice the provided spacing. For users of this data, the
horizontal resolution is very important for their analyses.
Hence, this paper addresses this important question by
using two different approaches: coherence spectra and step-response. The coherence spectra approach uses statistical
techniques to compare the SRTM dataset against a more
accurate one, while the step response approach uses the
observed step response in many areas of the dataset to
estimate the width of the averaging function used to produce the SRTM data.
Results from this study show that the resolution is between 1 and 1.6 pixels, depending on the local variability of the elevation data; with higher resolution near sharp edges and corners, and lower resolution in smoother areas.
1245 Fuzzy Multi-temporal Land-use Analysis and Mine
Clearance Application
Florence Landsberg, Sabine Vanhuysse, and Eleonore Wolff
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This paper puts forward a pixel-based and a region-based
method for detecting land-use changes using a multi-temporal pair of satellite KVR panchromatic and aerial
Daedalus multispectral images. These methods, based on
fuzzy logic, provide a confidence level of their post-classification change detection results.
They were applied to support an early stage of mine clearance in Croatia, where mines laid during the latest conflict (1991 to 1995) cause disruption in the land-use evolution. They were used for locating the agricultural land that was abandoned during the war and is therefore considered as a mine contamination indicator. The mere location of the abandoned agricultural land is of limited use for supporting mine clearance. On the other hand, the integration of the location and spatial confidence level can help the mine experts in their decision making by evaluating both unlikelihood and likelihood of a plot of land to be abandoned.
1255 Epipolar Resampling of Space-borne Linear Array
Scanner Scenes Using Parallel Projection
Michel Morgan, Kyung-Ok Kim, Soo Jeong, and Ayman Habib
Abstract Download Full Article
Get color version of figure 7 (high res tif 1Mb) (low res jpg 57kb)
Epipolar resampling aims at generating normalized images where conjugate points are located along the same row. Such a characteristic makes normalized imagery important for many applications such as automatic image matching, aerial triangulation, DEM and ortho-photo generation, and stereo-viewing. Traditionally, the input media for the normalization process are digital images captured by frame cameras. These images could be either derived by scanning analog photographs or directly captured by digital cameras. Current digital frame cameras provide smaller format imagery compared to those of analog cameras. In this regard, linear array scanners are emerging as a viable substitute to two-dimensional digital frame cameras. However, linear array scanners have more complex imaging geometry than that of frame cameras. In general, the imaging geometry of linear array scanners produces non-straight epipolar lines. Moreover, epipolar resampling of captured scenes according to the rigorous model, which faithfully describes the imaging process, requires the knowledge of the internal and external sensor characteristics as well as a Digital Elevation Model (DEM) of the object space. Recently, parallel projection has emerged as an alternative model approximating the imaging geometry of high altitude scanners with narrow angular field of view. In contrast to the rigorous model, the parallel projection model does not require the internal or the external characteristics of the imaging system and produces straight epipolar lines. In this paper, the parallel projection equations are modified for better modeling of linear array scanners. The modified parallel projection model is then used to resample linear array scanner scenes according to epipolar geometry. Experimental results using Ikonos and SPOT data demonstrate the feasibility of the proposed methodology.
1265 Influence of Vegetation, Slope, and Lidar Sampling
Angle on DEM Accuracy
Jason Su and Edward Bork
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Detailed GIS studies across spatially complex rangeland
landscapes, including the Aspen Parkland of western Canada,
require accurate digital elevation models (DEM). Following the
interpolation of last return lidar (light detection and ranging)
data into a DEM, a series of 256 reference plots, stratified by
vegetation type, slope and lidar sensor sampling angle, were
surveyed using a total laser station, differential GPS and
27 interconnected benchmarks to assess variation in DEM
accuracy. Interpolation using Inverse Distance Weighting IDW
resulted in lower mean error than other methods. Across the
study area, overall signed error and RMSE were ± 0.02 m and
0.59 m, respectively. Signed errors indicated elevations were
over-estimated in forest but under-estimated within meadow
habitats. Increasing slope gradient increased vertical absolut
errors and RMSE. In contrast, lidar sampling angle had little
impact on measured error. These results have implications for
the development and use of high-resolution DEM models
derived from lidar data.
1275 Urban Surface Biophysical Descriptors and Land Surface
Temperature Variations
Qihao Weng, Dengsheng Lu, and Bingqing Liang
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In remote sensing studies of land surface temperatures (LST),
thematic land-use and land-cover (LULC) data are frequently
employed for simple correlation analyses between LULC types
and their thermal signatures. Development of quantitative
surface descriptors could improve our capabilities for
modeling urban thermal landscapes and advance urban
climate research. This study developed an analytical
procedure based upon a spectral unmixing model for
characterizing and quantifying the urban landscape in
Indianapolis, Indiana. A Landsat Enhanced Thematic
Mapper Plus image of the study area, acquired on 22 June
2002, was spectrally unmixed into four fraction endmembers, namely, green vegetation, soil, high and low albedo.
Impervious surface was then computed from the high and
low albedo images. A hybrid classification procedure was
developed to classify the fraction images into seven land-use
and land-cover classes. Next, pixel-based LST measurements
were related to urban surface biophysical descriptors derived
from spectral mixture analysis (SMA). Correlation analyses
were conducted to investigate land-cover based relationships
between LST and impervious surface and green vegetation
fractions for an analysis of the causes of LST variations.
Results indicate that fraction images derived from SMA were
effective for quantifying the urban morphology and for
providing reliable measurements of biophysical variables
such as vegetation abundance, soil, and impervious surface.
An examination of LST variations within census block groups
and their relationships with the compositions of LULC types,
biophysical descriptors, and other relevant spatial data
shows that LST possessed a weaker relation with the LULC
compositions than with other variables (including urban
biophysical descriptors, remote sensing biophysical variables, GIS-based impervious surface variables, and population density). Further research should be directed to refine
spectral mixture modeling. The use of multi-temporal
remote sensing data for urban time-space modeling and
comparison of urban morphology in different geographical
settings are also feasible.
1287 The Individual Tree Crown Approach Applied to Ikonos
Images of a Coniferous Plantation Area
François A. Gougeon and Donald G. Leckie
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In forestry, the availability of high spatial resolution (<1
m/pixel) imagery from new earth observation satellites like
Ikonos favours a shift in the image analysis paradigm from
a pixel-based approach towards one dealing directly with
the essential structuring element of such images: the individual tree crown (ITC). This paper gives an initial assessment
of the effects of 1 m and 4 m/pixel spatial resolutions
(panchromatic and multispectral bands, respectively) on
the detection, delineation, and classification of the individual tree crowns seen in Ikonos images. Winter and summer
Ikonos images of the Hudson plantation of the Petawawa
Research Forest, Ontario, Canada were analyzed. The
panchromatic images were resampled to 0.5 m/pixel and
then smoothed using a 3 × 3 kernel mean filter. A valley-following algorithm and rule-based isolation module were
applied to delineate the individual tree crowns. Local
maxima within a moving 3 × 3 window (i.e., Tree Tops)
were also extracted from the smoothed images for comparison. Crown delineation and detection results were summarised and compared with field tree counts. Overall, the
ITC delineation and the local maxima approaches led to tree
counts that were on average 15 percent off for both seasons.
Visual inspection reveals delineation of clusters of two or
three crowns as a common source of error. Crown-based
species spectral signatures were generated for six classes
representing conifer species, plus a hardwood class and a
shrub class. After the ITC-based classification, classification
accuracy was ascertained using separate test areas of known
species. The overall accuracy was 59 percent. Important
confusion exists between red and white spruces, and mature
versus immature white pines, but post-classification regroupings into single spruce and white pine classes led to an
overall accuracy of 67 percent.
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