Peer Reviewed Articles
191 Challenging the Cloud-Contamination Problem in Flood Monitoring with
NOAA/AVHRR Imagery
Yongwei Sheng, Yafang Su, and Qianguang Xiao
Abstract
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NOAA/AWHRR (Advanced Very High Resolution Radiometer)
data hove the potential for flood monitoring due to their high
time resolution and low cost. Cloud-free images are quite
rare during flood periods. Therefore, cloud contamination is
one of the main obstacles to flood monitoring with AVHRR
data. Taking into consideration the spectral characteristics of
the main ground-cover types during floods, and satellite signal
components, this paper discusses a conceptually simple
but practically effective method for water identification using
AVHRR data. Water bodies can be identified not only in
cloud-free areas, but also under semi-transparent clouds and
in cloud shadows with this method. This method was applied
successfully in the 1991 flood disaster in the Huaihe
river Basin in China.
199 Extension of Climate Parameters over the Land Surface by the Use of
NOAA-AVHRR and Ancillary Data
Fabio Maselli, Ljiljana Petkov, and Giampiero Maracchi
Abstract
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Climatic estimates relative to the land surface between
ground stations are of utmost importance-to many agricultural
and forestry activities. In the present paper, two methods
are tested for extending the most important parameters
for a climatic classification (mean annual temperature and
length of the arid and cold seasons) over a complex region
in central Italy (Tuscany). The first method is based on conventional
multivariate regressions applied to the environmental
factors most influential on climate variability
(elevation, distance from the sea, and latitude). The second
relies on the extraction of the climatic information from an
integrated NOAA-AVHRR data set composed of NDVI profiles
and thermal infrared images of two growing seasons. In this
latter case, a more flexible approach based on a fuzzy classification
was adopted. The two methods yielded similar results,
with some differences explainable by environmental
considerations. A statistical procedure is applied for the optimal
merging of the climatic estimates from the two methods.
It is finally concluded that the information derived from
suitably processed NQAA-AVHRR data can supplement that
from more conventional sources for the extension of fundamental
climatic parameters over large land surfaces.
207 A Quantitative Comparison of Change-Detection Algorithms for Monitoring
Eelgrass from Remotely Sensed Data
Robb D. Macleod and Russell G. Congalton
Abstract
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The eelgrass (Zostera marina L.) population in Great Bay,
New Hampshire has recently undergone dramatic changes. A
reoccurrence of the 1930s wasting disease and decreasing
water quality due to pollution led to a reduction in the eelgrass
population during the late 1980s. Currently, the eelgrass
populations in Great Bay have experienced a remarkable
recovery from the decline in the late 1980s. Eelgrass is
important in our estuarine ecosystems because it is utilized
as habitat by many commercial and non-commercial organisms
and is a food source for waterfowl. In order to monitor
the eelgrass populations in Great Bay, a change detection
analysis was performed to determine the fluctuation in eelgrass
meadows over time.
Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. A large number of change-detection techniques have been developed, but little has been done to quantitatively assess the accuracies of these techniques.
In this study, post-classification, image differencing, and principal components change-detection techniques were used to determine the change in eelgrass meadows with Landsat Thematic Mapper (TM) data. Low altitude (1,000 m), oblique aerial photography combined with boat surveys were used as reference data. A proposed change-detection error matrix was used to quantitatively assess the accuracy of each change-detection technique. The three different techniques were then compared using standard accuracy assessment procedures. The image differencing change-detection technique performed significantly better than the post-classification and principal components analysis. The overall accuracy of the huge differencing change detection was 66 percent with a Khat value of 0.43.
This study provided an application of Landsat Thematic Mapper to detect submerged aquatic vegetation and the methodology for comparing change detection techniques using a proposed change detection error matrix and standard accuracy assessment procedures. In addition, this study showed that image differencing was better than the post-classification or principal components techniques for detecting changes in submerged aquatic vegetation.
217 Nonparametric Classifier for GIS Data Applied to Kangaroo Distribution
Mapping
Andrew K. Skidmore
Abstract
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A supervised nonparametric classifier, previously applied to
classify remotely sensed data, is used to classify GIS layers.
The algorithm is trained using GIS data layers as the independent
variables. and predicts the spatial distribution of a
dependent variable using a nonparametric technique. A GIS
database of kangaroo distribution in Australia tests the algorithm. Results are satisfactory, with the presence of kangaroos
being mapped with a producers accuracy of 93 percent
for the western grey, and 100 percent for the eastern grey
and red kangaroo. The algorithm appears robust to variations
in training sample size and a priori probabilities.
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