584
mangrove area make the conventional methods of data collection (through field
survey and measurements) extremely difficult. Under such conditions, data from
earth observation satellites provide a viable and cost effective alternative for creating spatial inventory of area under mangrove cover as well as its canopy density. On
satellite image, mangroves can be differentiated from the other coastal vegetation
based on tone, association, locations etc. (Nayak and Bahuguna 2001). Impact of
spatial and spectral resolutions on the accuracy of mapping of mangrove has been
studied by Gao (1999) in the mangrove forest of Waite Mata harbour, Auckland,
New Zealand. Mangroves were mapped in to lush green and stunted categories from
SPOT HRA and Landsat TM images at 10, 20, 30 m spatial resolutions using MXL
(maximum likely hood) classification technique. Remote sensing satellite data has
been extensively used in mapping and monitoring of the mangrove forests at global,
national and local scales (Kumar et al. 2012a, b, 2016; Ajai et al. 2012, 2013;
Kuenzer et al. 2011; Giri et al. 2007, 2010; Ramasubramanian et al. 2006; Nayak
and Bahuguna 2001; Azipuru et al. 2000; Blasco et al. 1986, 1998; Green et al.
1998; Chaudhary 1990; Dutrieux et al. 1990; Untawale et al. 1982). Satellite data
acquired during the low tide period are used for mapping of mangrove as during low
tide maximum coastal zone gets exposed.
Giri et al. (2010) has carried out inventory of the mangroves of the world by using
Landsat-TM data of 1997–2000 time frame. Details of their findings are discussed
earlier (in Sect. 19.2). Giri et al. (2015) have studied distribution and dynamics of
mangrove forests of south Asia. Nayak and Bahuguna (2001) have mapped the mangrove cover along with its crown density for India using Indian Remote Sensing
Satellite data. Green et al. (1998) have used SPOT- XS and Landsat- TM data for
mapping of mangroves in the Eastern Caribbean Islands. Blasco et al. (1986) and
Gang and Agatsiva (1992) have used remote sensing data for mapping of mangroves
of Kenya coast. Dutrieux et al. (1990) have made inventory of mangrove forest of
Mahakam Delta, East Kalimantan, Indonesia using SPOT images. Everitt et al.
(1996) have used remote sensing data for discriminating and mapping of black mangroves on Texas coast. Satyanarayana et al. (2011) have used very high spatial resolution multispectral data (2.4 m) from QuickBird along with ground truth measurements
for assessment of mangrove vegetation at Tumpat, Kelantan river delta, Malaysia.
In recent past, studies have been carried out to understand the spatial relationship
between mangroves and their immediate environment, viz., ‘community zones’
(Tomlinson 1994; Hogarth 1999), using advanced digital image processing techniques (Nayak and Bahuguna 2001; Blasco et al. 1998). These mangrove zones
exhibit unique spectral signatures. Study by Nayak and Bahuguna (2001) has revealed
the potential of remote sensing data in discerning and mapping mangrove communities. Earth Observation Satellite data has been extensively used for inventory, mapping and monitoring of mangroves and other coastal vegetation at global, national
and local scales (Giri et al. 2010, 2015; Upadhay et al. 2015; Patel et al. 2014; Ajai
et al. 2013; Kumar et al. 2012a, b; Heumann 2011; Satapathy et al. 2007; Kovcas
et al. 2005; Nayak and Bahuguna 2001; Green et al. 1998; Gang and Agatsiva 1992).
Moderate to high resolution multispectral data from earth observation satellites
such as IRS LISS-III/LISS-IV, Landsat-8 Thematic Mapper, SPOT-7and ASTER
are considered as the primary data source for mangrove inventory, mapping and
Ajai and H.B. Chauhan
mangrove area make the conventional methods of data collection (through field
survey and measurements) extremely difficult. Under such conditions, data from
earth observation satellites provide a viable and cost effective alternative for creating spatial inventory of area under mangrove cover as well as its canopy density. On
satellite image, mangroves can be differentiated from the other coastal vegetation
based on tone, association, locations etc. (Nayak and Bahuguna 2001). Impact of
spatial and spectral resolutions on the accuracy of mapping of mangrove has been
studied by Gao (1999) in the mangrove forest of Waite Mata harbour, Auckland,
New Zealand. Mangroves were mapped in to lush green and stunted categories from
SPOT HRA and Landsat TM images at 10, 20, 30 m spatial resolutions using MXL
(maximum likely hood) classification technique. Remote sensing satellite data has
been extensively used in mapping and monitoring of the mangrove forests at global,
national and local scales (Kumar et al. 2012a, b, 2016; Ajai et al. 2012, 2013;
Kuenzer et al. 2011; Giri et al. 2007, 2010; Ramasubramanian et al. 2006; Nayak
and Bahuguna 2001; Azipuru et al. 2000; Blasco et al. 1986, 1998; Green et al.
1998; Chaudhary 1990; Dutrieux et al. 1990; Untawale et al. 1982). Satellite data
acquired during the low tide period are used for mapping of mangrove as during low
tide maximum coastal zone gets exposed.
Giri et al. (2010) has carried out inventory of the mangroves of the world by using
Landsat-TM data of 1997–2000 time frame. Details of their findings are discussed
earlier (in Sect. 19.2). Giri et al. (2015) have studied distribution and dynamics of
mangrove forests of south Asia. Nayak and Bahuguna (2001) have mapped the mangrove cover along with its crown density for India using Indian Remote Sensing
Satellite data. Green et al. (1998) have used SPOT- XS and Landsat- TM data for
mapping of mangroves in the Eastern Caribbean Islands. Blasco et al. (1986) and
Gang and Agatsiva (1992) have used remote sensing data for mapping of mangroves
of Kenya coast. Dutrieux et al. (1990) have made inventory of mangrove forest of
Mahakam Delta, East Kalimantan, Indonesia using SPOT images. Everitt et al.
(1996) have used remote sensing data for discriminating and mapping of black mangroves on Texas coast. Satyanarayana et al. (2011) have used very high spatial resolution multispectral data (2.4 m) from QuickBird along with ground truth measurements
for assessment of mangrove vegetation at Tumpat, Kelantan river delta, Malaysia.
In recent past, studies have been carried out to understand the spatial relationship
between mangroves and their immediate environment, viz., ‘community zones’
(Tomlinson 1994; Hogarth 1999), using advanced digital image processing techniques (Nayak and Bahuguna 2001; Blasco et al. 1998). These mangrove zones
exhibit unique spectral signatures. Study by Nayak and Bahuguna (2001) has revealed
the potential of remote sensing data in discerning and mapping mangrove communities. Earth Observation Satellite data has been extensively used for inventory, mapping and monitoring of mangroves and other coastal vegetation at global, national
and local scales (Giri et al. 2010, 2015; Upadhay et al. 2015; Patel et al. 2014; Ajai
et al. 2013; Kumar et al. 2012a, b; Heumann 2011; Satapathy et al. 2007; Kovcas
et al. 2005; Nayak and Bahuguna 2001; Green et al. 1998; Gang and Agatsiva 1992).
Moderate to high resolution multispectral data from earth observation satellites
such as IRS LISS-III/LISS-IV, Landsat-8 Thematic Mapper, SPOT-7and ASTER
are considered as the primary data source for mangrove inventory, mapping and
Ajai and H.B. Chauhan