Morphologic Features of Kapıdağ Peninsula and its Coasts (NW-Turkey) using by Remote Sensing and DTM

Gazioğlu, Cem; Aipar, Bedri; Yücel, Zeki; Müftüoğlu, Ahmet Edip; Güneysu, Cem; Ertek, Topçu; Demir, Volkan; Kaya, Hakan

doi:10.30897/ijegeo.300739

Cited by 22 publications

(16 citation statements)

References 34 publications

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“…Spectral indices, such as normalized difference vegetation index and band ratios can be effectively used to distinguish mangrove forests from water, mud flats and sand beach. However, terrestrial vegetation and watervegetation mixed pixels may complicate mangrove forest identification by using these spectral indices (GAzioğlu, et al, 2014;Tiezhu et al, 2016). The mangrove density distribution in Pongok Island was analyzed using the Normalized Difference Vegetation Index (NDVI) computed from Landsat data (Umroha, et al, 2016).…”

Section: Mangroves Health Assessment Using Remote Sensingmentioning

confidence: 99%

Multi Criteria Decision Making (MCDM) Approach for Mangrove Health Assessment using Geo-informatics Technology

Vaghela

Parmar

Solanki

et al. 2018

International Journal of Environment and Geoinformatics

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Mangroves are coastal wetland forests established in the intertidal zones of estuaries, backwaters, deltas, creeks, lagoons, marshes and mudflats of tropical and subtropical latitudes. World-wide mangroves are disappearing at an alarming rate. Mangroves form one of the most important ecosystems of coastal areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. It nurtures and safeguards the local ecology of the coastal areas and provides livelihood options to the fishermen and pastoral families. Amongst the maritime States of India, Gujarat has the second highest mangrove cover after West Bengal. Additionally, during last three decades Gujarat has more than doubled its mangrove cover. In Gujarat State, mangroves are well developed in Lakhpat taluka (block) situated in Kachchh district. In recent past, Gulf of Kachchh experienced both natural and anthropogenic changes which made it a distinctive site to analyze how natural processes and anthropogenic activities determine the changes in mangrove vegetation density and health of mangroves in coastal areas.Multi-temporal Landsat TM data covering Lakhpat taluka (block) of February-1995 Sentinel-2 multi-spectral data (spatial resolution 10 m) of April-2017 was analysed. The mangrove vegetation around the coastal areas was identified and classified into dense and sparse density classes based on Normalized Difference Vegetation Index (NDVI) thresholding approach. The health assessment of mangroves in Lakhpat taluka was attempted using Multi Criteria Decision Making (MCDM) approach including various parameters like mangrove density based on NDVI, Distance of mangroves from human settlement, Distance of mangrove from Industries and Ports which have direct impact of growth and health of mangroves, Erosion/ Accretion over the period of last 22 years and availability of Saline water flow during the high tide for good mangrove growth. The buffers layers of various distances for example, 0 to 10 km, 10 to 20 km and 20 to 35 km were generated from the existing mangroves using Sentinel-2 multi-spectral image in GIS environment.The results indicate that the NDVI which is single parameter indicating the mangrove stand / vigour, growth condition and resulting health of mangroves in the area. This factor has been given highest weightage as compared to other parameters. The major anthropogenic factors like human Pressure and presence of Industries and Ports have negative impact on the mangrove health. Therefore, it was observed that presence of human settlements and Industries and Ports with the buffer region of 0 to 10 km distances from mangroves are unhealthy or prone to degradation in this region. The results of health assessment are very useful for sustainable planning and management of mangroves in the coastal areas of Lakhpat Taluka. The mangrove restoration and regeneration activity needs to be carried out as suggested by Upadhyay et al., 2015 with active participation of Community Based Organizations (...

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Section: Mangroves Health Assessment Using Remote Sensingmentioning

confidence: 99%

Multi Criteria Decision Making (MCDM) Approach for Mangrove Health Assessment using Geo-informatics Technology

Vaghela

Parmar

Solanki

et al. 2018

International Journal of Environment and Geoinformatics

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“…Actively, as a source of energy for photosyhthesis, photosynthesizing leaves absorb the red wavelenghts (LANDSAT TM band 3) and reflect the short-wave infrared (LANDSAT TM band 4). The diffrence between the two is proportional to the amount of photosysthesis (Parodi, 2002;Schreiber, 2006;Gazioğlu, et al, 2014;İslam, et al, 2016;Gazioğlu, et al 2016;Avşar, et al, 2016;Osgouei and Kaya, 2017;Kaya et al, 2017). NDVI is unaffected by band sharpening since the relative contributions of the near-infrared and red bands remain unchanged by the fusion method (Wiemker, 1998).…”

Section: Processing Landsat Tm Imagerymentioning

confidence: 99%

Mapping South Tunisian Landscapes Using Remote Sensing and GIS Applications

Faiza¹

2018

International Journal of Environment and Geoinformatics

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Oases of South Tunisia, plays an important role in environmental, social and economic aspects of this region. This research was conducted in ten regions located on the south which are affected by changes in conditions and composition of land cover change. For each study area, LANDSAT imagery, thematic maps and training data were acquired. These images were georeferenced and radiometrically corrected and landscapes product were generated using Decision Tree Classification. The landscapes typology were then evaluated using the extract oases. This study aimed to assess the potential of NDVI (Normalized Difference Vegetation Index) to determine oasis cover using medium spatial resolution remote sensing imagery in the south of Tunisia. It explores the possibility of identifying and mapping oasis landscape types by using remote sensing, Geographic Information Systems and statistical analysis. The superposition of environment types map obtained with oasis extracted from NDVI map has shown six oasis landscapes in southern Tunisia: Gafsa-ElGuettar, El Hamma de Gabes, Douz-Kebili, Rjim Maatoug, Tozeur-Nafta and Tamerza Chebika oases. This research paper allowed to define a methodology for the construction of landscape types, in a computerized way, based on remote sensing and GIS and its result can be a draft work to make an Atlas of the landscapes of our site of study or, it can be used as a basic document to monitor the spatial extension of agricultural perimeters in order to ensure the sustainable development of these particular agro-systems.

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“…Remοte sensing techniques are increasingly playing an impοrtant rοle in geοsciences for mineral exploration and of lithological discrimination (Sabin,1999;Rowan and Mars 2006;Rajendran 2011;Gazioğlu et al, 2014;Avşar et al, 2016;, İncekara et al, 2017;Shirazi et al, 2018;Zoheir et al, 2019). Based οn remοte sensing data such as Landsat TM, ETM+ and Aster have great potentials in geological interpretations.…”

Section: Introductionmentioning

confidence: 99%

Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

Traoré

Çan

Tekin

2020

International Journal of Environment and Geoinformatics

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Morphologic Features of Kapıdağ Peninsula and its Coasts (NW-Turkey) using by Remote Sensing and DTM

Cited by 22 publications

References 34 publications

Multi Criteria Decision Making (MCDM) Approach for Mangrove Health Assessment using Geo-informatics Technology

Multi Criteria Decision Making (MCDM) Approach for Mangrove Health Assessment using Geo-informatics Technology

Mapping South Tunisian Landscapes Using Remote Sensing and GIS Applications

Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

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