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Master
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2004
Mapping of dry savannah tree species using object oriented classification and high resolution imagery in Serowe, Botswana
Titre : Mapping of dry savannah tree species using object oriented classification and high resolution imagery in Serowe, Botswana
Auteur : Kimani Jacob Ndirangu
Etablissement de soutenance : International Institute for Geo-Information Science and Earth Observation (ITC)
Grade : Master of Science in Geo-Information Science and Earth Observation 2004
Résumé
Reliable and accurate classification of dry savannah tree species, essential for transpiration up scaling
for water management, has become a major challenge in semi arid areas such as Botswana. The major
hurdles identified are pixel based classifiers and low resolution remotely sensed data. Therefore, the
main objectives of this study were to assess the ability of two object-oriented classification
techniques, eCognition and Feature Analyst, in mapping tree species using different high resolutions
of airborne multi-spectral images (30cm, 60cm and 1m) ; and to determine factors associated with the
distribution of trees and other vegetation types ; among fire, grazing and soil. A comparison was also
done between eCognition classifications of Pan-sharpened IKONOS and airborne data, both of 1m
resolution.
Kappa statistics was used to determine the accurate technique and the optimal resolution. For grazing,
herd’s size per cattle post and grazing radius were used to create weighted buffers that were correlated
with NDVI values. The same NDVI values were correlated with fire evidence point map created from
presence or absence of fire data collected from sample plots. Soil, particle size variability of <125μ m
fraction, which determines its water holding capacity was used. The fraction’s variability was tested
in three depths of 0 to 10, 20 to 30 and 60 to 70 (all in cm).
Kappa accuracy values for eCognition ranged from 0.79 to 0.94, averaging at 0.89, 0.86 and 0.87 for
30cm, 60cm and 1m resolution respectively. The highest value, in Feature Analyst technique was 0.74
with the lowest being 0.29 with averages at 0.73, 0.49 and 0.54 for 30cm, 60cm and 1m resolution
respectively. Though a Z-test showed no significance difference between the two relatively high
Kappa values from the techniques, eCognition was more reliable when other factors were considered.
However, in both techniques, 30cm and 60cm resolutions, gave the highest and the lowest Kappa
accuracy values respectively. Though a one-way-ANOVA gave a standard deviation of zero (0) value
among the Kappa accuracy values for the three resolutions in eCognition, other factors considered,
30cm was the most accurate. IKONOS classification yielded an average of 0.54 Kappa accuracy
value. The results revealed a number of factors that determined the accuracy and reliability of the
classification. These included the season of data acquisition ; diversity of tree species to be mapped ;
closeness of different species in their spectral and biophysical parameters ; tree configuration ; and
image preprocessing techniques (e.g. Brovy transformation in case of IKONOS).
While grazing intensity had an inverse relationship with NDVI values, fire occurrence had a poor one ;
being a constant factor affecting the whole area equally. The fine fraction in the sandy soils had a
low
variability (± 2.74%) with an average of 17.6% of the soil. The extremes were 5.5% and 25%. The
results indicated no variation of the fraction within the depths considered
.
Long and medium spatial
variability were 38%, and 28.5% respectively with a 33.5% unexplained short variability. This
fraction decreases quadratically in a north-east direction in the study area. The spatial distribution of
vegetation is shown to be influenced by grazing intensity and fire ; with a possible influence from the
variability in soil particle size
Version intégrale (ITC) -> http://www.itc.nl/library/papers_20...
Page publiée le 6 avril 2018, mise à jour le 15 novembre 2019