Evaluating the impact of coastal land uses on water‐clarity conditions from Landsat TM/ETM+ imagery: Candarli Bay, Aegean Sea

Ucuncuoglu, E.; Arli, Ozgun; Eronat, Atilla Hüsnü

doi:10.1080/01431160500500326

Cited by 9 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PCC procedure detect changes through classifying two or multidates images independently and then comparing the land cover changes between the dates of imaging. Ucuncuoglu et al (2006) has demonstrated application of PCC method in detecting changes in coastal wetland and woody vegetation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Wang

2009

Environ Monit Assess

101

View full text Add to dashboard Cite

This study compared performance of four change detection algorithms with six vegetation indices derived from pre- and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in order to select an optimal remote sensing technique for identifying forestlands disturbed by Hurricane Katrina. The algorithms included univariate image differencing (UID), selective principal component analysis (PCA), change vector analysis (CVA), and postclassification comparison (PCC). The indices consisted of near-infrared to red ratios, normalized difference vegetation index, Tasseled Cap index of greenness, brightness, and wetness (TCW), and soil-adjusted vegetation index. In addition to the satellite imagery, the "ground truth" data of forest damage were also collected through field investigation and interpretation of post-Katrina aerial photos. Disturbed forests were identified by classifying the composite and the continuous change imagery with the supervised classification method. Results showed that the change detection techniques exerted apparent influence on detection results with an overall accuracy varying between 51% and 86% and a kappa statistics ranging from 0.02 to 0.72. Detected areas of disturbed forestlands were noticeable in two groups: 180,832-264,617 and 85,861-124,205 ha. The landscape of disturbed forests also displayed two unique patterns, depending upon the area group. The PCC algorithm along with the composite image contributed the highest accuracy and lowest error (0.5%) in estimating areas of disturbed forestlands. Both UID and CVA performed similarly, but caution should be taken when using selective PCA in detecting hurricane disturbance to forests. Among the six indices, TCW outperformed the other indices owing to its maximum sensitivity to forest modification. This study suggested that compared with the detection algorithms, proper selection of vegetation indices was more critical for obtaining satisfactory results.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Wang

2009

Environ Monit Assess

101

View full text Add to dashboard Cite

show abstract

“…There are iron-steel factories, coal storage yards, fuel storage yards, fertilizer factory, natural gas power plant, electrical substation, small industrial areas, and other medium and small establishments in the region, which cause heavy metal pollution (Sponza and Karaoglu 2002). The population increased ten times in Aliaga due to the developments of petrochemical industry (Ucuncuoglu et al 2006). Nemrut Bay, the neighboring marine environment of Aliaga town, was investigated in February 2005 to provide baseline information about environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of trace metal pollution in surface sediments of Nemrut Bay, Aegean Sea

Esen

Küçüksezgi̇n

Uluturhan

2008

Environ Monit Assess

101

View full text Add to dashboard Cite

Surface sediments collected from nine stations in Nemrut Bay, Aegean Sea were analyzed for trace metals (Hg, Cd, Pb, Cr, Cu, Zn, Mn, Ni, Fe, As, and Mg) and grain sizes. The results were compared with the numerical sediment guidelines used in North America as well as literature values reported for similar studies conducted in Izmit Bay and Izmir Bay. The metal levels were also evaluated according to the enrichment factor and contamination factor analyses. The analyses revealed significant anthropogenic pollution of Hg, Pb, Zn, and As in the surficial sediments of Nemrut Bay.

show abstract

“…Assessment of the immediate damage and monitoring of the long period of vegetation recovery are vital for resource managers and scientists to take short term actions to salvage harvesting and habitat protection, as well as to evaluate the long-term forest ecosystem recovery and wetland restoration. In situations where hurricane disturbance are spread over a larger area and the disturbed vegetation have variable recovery rates, the use of remote sensing is well suited for assessing and monitoring vegetation dynamics [6,[13][14][15][16][17][18]. Wang and Xu [14] compared performance of four change detection algorithms with six vegetation indices derived from pre-and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in identifying Katrina's damage to forests.…”

Section: Introductionmentioning

confidence: 99%

Potential of MODIS EVI in Identifying Hurricane Disturbance to Coastal Vegetation in the Northern Gulf of Mexico

Wang

D’Sa

2009

Remote Sensing

View full text Add to dashboard Cite

Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery composited in MODIS product MOD13Q1 for assessing hurricane damage to vegetation and its recovery. Vegetation in four US coastal states disturbed by five hurricanes between 2002 and 2008 were explored by change imagery derived from pre-and post-hurricane EVI data. Interpretation of the EVI changes within months and between years distinguished a clear disturbance pattern caused by Hurricanes Katrina and Rita in 2005, and a recovering trend of the vegetation between 2005 and 2008, particularly within the 100 km coastal zone. However, for Hurricanes Gustav, Ike, and Lili, the disturbance pattern which varied by the change imagery were not noticeable in some images due to lighter vegetation damage. The EVI pre-and post-hurricane differences between two adjacent years and around one month after hurricane disturbance provided the most likely damage area and patterns. The study also revealed that as hurricanes damaged vegetation in some coastal areas, strong precipitation associated with these storms may benefit growth of vegetation in other areas. Overall, the study illustrated that the MODIS product could be employed to detect severe hurricane damage to vegetation, monitor vegetation recovery dynamics, and assess benefits of hurricanes to vegetation. OPEN ACCESSRemote Sens. 2010, 2 2

show abstract

Evaluating the impact of coastal land uses on water‐clarity conditions from Landsat TM/ETM+ imagery: Candarli Bay, Aegean Sea

Cited by 9 publications

References 25 publications

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Assessment of trace metal pollution in surface sediments of Nemrut Bay, Aegean Sea

Potential of MODIS EVI in Identifying Hurricane Disturbance to Coastal Vegetation in the Northern Gulf of Mexico

Contact Info

Product

Resources

About