Peer-Reviewed Articles
1383 Influence of Resolution in Irrigated Area Mapping and
Area Estimation
N.M. Velpuri, P.S. Thenkabail, M.K. Gumma, C. Biradar, V.
Dheeravath, P. Noojipady, and L. Yuanjie
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The overarching goal of this paper was to determine how
irrigated areas change with resolution (or scale) of imagery.
Specific objectives investigated were to (a) map irrigated areas
using four distinct spatial resolutions (or scales), (b) determine
how irrigated areas change with resolutions, and (c) establish
the causes of differences in resolution-based irrigated areas.
The study was conducted in the very large Krishna River basin
(India), which has a high degree of formal contiguous, and
informal fragmented irrigated areas. The irrigated areas were
mapped using satellite sensor data at four distinct resolutions:
(a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m,
(c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The
proportion of irrigated areas relative to Landsat 30 m derived
irrigated areas (9.36 million hectares for the Krishna basin)
were (a) 95 percent using MODIS 250 m, (b) 93 percent using
MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m.
In this study, it was found that the precise location of the
irrigated areas were better established using finer spatial
resolution data. A strong relationship (R2 = 0.74 to 0.95) was
observed between irrigated areas determined using various
resolutions. This study proved the hypotheses that “the finer
the spatial resolution of the sensor used, greater was the
irrigated area derived,” since at finer spatial resolutions,
fragmented areas are detected better. Accuracies and errors
were established consistently for three classes (surface water
irrigated, ground water/conjunctive use irrigated, and nonirrigated)
across the four resolutions mentioned above. The
results showed that the Landsat data provided significantly
higher overall accuracies (84 percent) when compared to
MODIS 500 m (77 percent), MODIS 250 m (79 percent), and
AVHRR 10,000 m (63 percent).
1397 A Case Study of Developing An Olive Tree Database
For Turkey
Nihal Ceylan, Ediz Unal, and Josiane Masson
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This study was conducted to develop an olive tree registration
database using geographic information systems (GIS)
and remote sensing technology. It also provides information
on the methodology used for the database development for
the candidate country during its accession process to the
European Union. Since olive farming has a great importance
to the Turkish economy, an olive registration database
supported by GIS and compatible with the Integrated Administrative
Control System (IACS) is needed for adaptation to
the European Commission Common Agricultural Policy. In
this study, cadastral maps and satellite images were used to
count olive trees using the OLICOUNT software developed
by the Joint Research Center of the European Commission in
order to develop an olive tree database. According to the
analysis of the counting results for the test sites with
2,291 trees, overall omission error was 11.1 percent and
commission error was 2.94 percent. These results indicate
that the determination of olive trees by OLICOUNT within
the area of interest has 90.37 percent accuracy that makes
the method reasonably reliable.
1407 An Object-space Simulation Method for Low-cost
Digital Camera Stability Testing
Derek D. Lichti, Ayman Habib, and Ivan Detchev
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The widespread availability and low cost of digital cameras
has been the impetus for their increased use for photogrammetric
applications. The metric suitability of these cameras
is critically dependent upon the stability of their interior
orientation parameters (IOPs), which can be evaluated by
simulation methods. Focused on aerial photogrammetry, this
paper presents a new method that assesses the impact of
camera stability in terms of the accuracy of object space
terrain reconstruction from a large number of simulations.
The results of this method are compared with those from
two simulation procedures based on single-photo resection
for ten sets of IOPs from three different low-cost digital
cameras and are found to be in close agreement in terms of
the decision about camera stability. Detailed analyses show
the method is relatively insensitive to the distribution of
ground control points used for camera orientation and the
realism of the randomly-generated terrain, but is highly
sensitive to the range of simulated terrain heights and image
point measurement precision.
1415 A New Approach for Pass-point Generation from
Aerial Video Imagery
Benjamin E. Wilkinson, Bon A. Dewitt, Adam C. Watts,
Ahmed H. Mohamed, and Matthew A. Burgess
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This paper presents a novel approach for automatically
finding conjugate points between video images collected by
a small autonomous unmanned aircraft. Our approach
introduces the idea of saving the resampled patch from
successive least-squares matching epochs and using them as
templates for subsequent images. Tests show that this
method is superior to using the first image as a template for
all subsequent matching attempts. We show how the algorithm
performs in terms of retention of points on successive
images, distribution of points on the images, and utility
when used for bundle adjustments in comparison with the
conventional method of using the first image as a template.
Our proposed method is able to match points on an average
of 2.7 times as many images before failure compared with
using the conventional method. This leads to stronger
geometrical configuration, higher redundancy, and ultimately,
significantly better bundle adjustment solutions.
1425 A Simplified Analytical Model for a-priori Lidar Pointpositioning
Error Estimation and a Review of Lidar
Error Sources
Mihaela Triglav- Čekada, Fabio Crosilla, and Mojca
Kosmatin-Fras
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Although various rigorous lidar error models already exist
and examples of a-posteriori studies of lidar data accuracies
verified with field-work can be found in the literature, a
simple measure to define a-priori error sizes is not available.
In this paper, the lidar error contributions are described in
detail: the basic systematic error sources, the flight-missionrelated
error sources, and the target-characteristic-related
error sources. A review of the different error-source sizes is
drawn from the literature in order to define the boundary
conditions for each error size. Schenk’s geolocation equation
is used as a basis for deriving a simplified error model. This
model enables a quick calculation and gives a-priori plausible
values for the average and maximum error size, independent
of the scan and heading angles as well as being independent
of any specific lidar system’s characteristics. Additionally,
some notes are provided for assistance when ordering lidar
data, to enable easier a-posteriori quality control.
1441 Thematic Accuracy Consequences in Cadastre Landcover
Enrichment from a Pixel and from a Polygon
Perspective
P. Serra, G. Moré, and X. Pons
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In this paper, cadastre agricultural cartography was enriched
using crop raster maps obtained from remote sensing images.
The work demonstrates the implications of applying two new
terms: fidelity and purity. Per-pixel classifications and
polygon enrichments were compared taking into account:
(a) the consequences of using a more or less conservative
strategy at the classification stage, using fidelity, and (b) the
consequences of using modal thresholds at the enrichment
stage when deciding which category each polygon is to be
assigned to, using purity. More than 300,000 pixels and
2,800 polygons were used to measure the thematic accuracy
of ten agricultural categories by means of confusion matrices.
These were computed at pixel, polygon, and area level.
Thematic accuracy was calculated in the classical way and
without taking into account unclassified pixels as errors, as
well as by paying special attention to the consequences for
the classified area. The results show that polygon enrichment
is a useful methodology, achieving thematic accuracies
of 95.6 percent, when optimum parameters are used, while
classifying 87.4 percent of the area.
1451 A Two Stage Method to Estimate Species-specific
Growing Stock
Petteri Packalén, Aki Suvanto, and Matti Maltamo
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Information about tree species-specific forest characteristics
is often a compulsory requirement of the forest
inventory system. In Finland, the use of a combination of
ALS data and orthorectified aerial photographs has been
studied previously, but there are some weaknesses in this
approach. First, aerial photographs need radiometric
correction, and second, the ALS points and aerial photographs
are not properly fused due to the radial displacement.
In this study, ALS points are linked to unrectified
aerial photographs of known orientation parameters,
which enables better fusion. Each ALS point is mapped to
several aerial photographs, and the average of DN values
is utilized; this averaging is considered to be a good
substitute for radiometric correction. The new two-stage
method is compared to the approach in which only ALS
data is used. The results show the benefits of using aerial
photographs together with ALS data in order to estimate
tree species-specific characteristics. Compared to earlier
studies, the new two-stage method shows a considerable
improvement in applicability in operational use.