Environmental Engineering Reference
In-Depth Information
Today, the technology of remote sensing is
used to generate a land-use map of the mangrove
forest and its surrounding. Two steps are usually
followed for generation of the land-use map.
Step 1: A maximum likelihood supervised clas-
si
data collection at various locations before the
image classi
cation.
Data Used
Temporal satellite images of Landsat MSS
(Landsat Multispectral Scanner-path/row: 152/
48; date: 8 January 1977), Landsat TM (Landsat
Thematic Mapper-path/row: 141/49; date: 12
October 1988), Landsat ETM+ (Landsat
Enhanced Thematic Mapper+ path/row: 141/48,
141/49; date: 8 December 2000) and IRS P6
LISS III (Indian Remote Sensing P6 Linear
Imaging Self Scanner-path/row: 103/61; date: 13
December 2005) was used to carry out the study.
The orthorecti
cation is carried using training areas
chosen according to extensive
eld
knowledge, but without any speci
c
reference to the grip sample points.
Step 2: In this step, the raw result of the super-
vised classi
cation is checked to visual
interpretation of the satellite image and
eld visit. We present here the applica-
tion of remote sensing technology to
generate a land-use map of mangrove
ecosystem located around the Gautami-
Godavari estuary (Fig.
3.6
). The study
area extends a subset between 16
ed Landsat data were downloaded
from GLCF Web site (
http://glcf.umiacs.umd.
edu/
). ERDAS Imagine 9.0 software was used for
processing the images.
To obtain the same spatial resolution, the
Landsat TM, ETM+ and IRS P6 LISS III images
was geometrically corrected in relation to the
MSS image (since Landsat multispectral scanner
(MSS) image downloaded at 57 m resolution
from
http://www.glcf.umiacs.umd.edu
), i.e.,
from 30 to 57 m (in case of IRS LISS III from 23.
5 to 57 m).
°
30
′
E and
includes Kakinada Bay and the Coringa
wildlife sanctuary where the most
important stretch of mangrove is found
and also the mangrove forest situated
south of Gautami-Godavari River.
Several different landscapes compose the area,
with paddy
N
-
17
°
05
′
N and 82
°
30
′
E
-
82
°
25
′
elds and coconut tree plantations in
the West and South, mangrove forests, aquacul-
ture ponds for shrimps farming spreading into
mangrove forest, salt pans, casuarinas plantation
along the beach and on Hope Island, village and
urban areas (Kakinada and Yanam).
The present study was undertaken to assess
the three-decade spatial changes in vegetation
dynamics using multitemporal satellite data and
geographical information system (GIS) consid-
ering the importance of Godavari delta and
mangroves.
For the convenience of the reader, the
approach of this study is explained here in points.
Classification
Mapping of vegetation and land cover of the
study area is assessed by supervised digital
classi
cation method. This has been the most
frequent method for remotely sensed data clas-
si
cation. The samples of known identity were
used to classify pixels of unknown identity in
supervised classi
cation. To represent the typical
spectral information of the land cover classes
(dense mangrove, open mangroves, plantations,
agricultural
lands, built-up area, aquaculture,
sand, mud
ats and water bodies), training sites in
the images are generated (Fig.
3.7
). Using max-
imum likelihood in ERDAS Imagine 9.0 soft-
ware, the classi
fl
Field Survey
During April 2006,
eld survey was carried out
in the study area to investigate the vegetation
and other land cover. For ground collection of
data, false colour composite satellite images and
survey of India topographical maps (65H and
65L) were used. GPS was used for ground truth
cation was run on the images
after the selection of training sites. Signature
separability analysis was carried out on the
training signatures for better classi
cation prior
to the classi
cation.
