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From
Assessment of Mangrove Spatial -Temporal Dynamics and Biomass by Remotely Sensed Data, Case Study Kilifi County: Kenya
Anam Safi Ibrahim, Thomas.G. Ngigi
Journal of Geosciences and Geomatics
.
2017
, 5(1), 24-36 doi:10.12691/jgg-5-1-3
Figure 1
.
Distribution of mangroves in Kilifi County with the different patches highlighted
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Figure 2
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Flow diagram for adopted cloud removal methodology
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Figure 3
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Flow diagram representing methodology adopted for this research
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Figure 4 (a)
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Land use land cover map for the year 1990. Mangrove patches highlighted in black boxes
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Figure 4 (b)
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Land use land cover map for the year 2000. Mangrove patches highlighted in black boxes
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Figure 4 (c)
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Land use land cover map for the year 2010. Mangrove patches highlighted in black boxes
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Figure 4 (d)
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Land use land cover map for the year 2015. Mangrove patches highlighted in black boxes
Full size figure and legend
Figure 5 (a)
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land use land cover classes for 1990
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Figure 5 (b)
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land use land cover classes for 2000
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Figure 5 (c)
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land use land cover classes for 2010
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Figure 5 (d
)
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land useland cover classes for 2015
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Figure 6
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Trend line showing the mangrove trend from the year 1990 to 2015
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Figure 7 (a)
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Areas of Mangrove loss for the years 1990_2000, along Mtwapa creek
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Figure 7(b)
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Areas of Mangrove loss for the years 2000_2010 along Mtwapa creek
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Figure 7
(
c
)
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Areas of Mangrove loss for the years 1990_2000, 2000_2010 and 2010_2015 respectively along Mtwapa creek
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Figure 8
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Field points distributions for biomass estimation
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Figure 9
(a – d). Examples of Transformation Graphs that were generated
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