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پایش دریاچه هامون با استفاده از داده های لندست و باران سنجی و مقایسه آن با داده های آلتیمتری سد کجکی

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Abstract

تالابها محیط های بسیار ویژه و با اهمیتی هستند، هم برای پایداری محیط و هم برای جانداران و انسانها، به خصوص اگر تالاب دارای آب شیرین باشد که این مسئله به اهمیت و ارزش آن بسیار می افزاید. هامون اصلی ترین دریاچه جنوب شرق کشور، هم از لحاظ وسعت و هم از لحاظ اکولوژیکی است. در این مطالعه تلاش شده تا به بررسی تغییرات، مساحت و حجم تالاب طی دوره ای 30 ساله پرداخته شود و بدین منظور از تصاویر ماهواره ای لندست جهت بررسی تغییرات منطقه و محدوده ی تالاب در کنار داده های باران سنجی و آلتیمتری استفاده شده است. در این مطالعه از سه شاخص مختلف استفاده و پس از مقایسه آنها در نهایت شاخص )MNDWI) به عنوان بهترین شاخص از لحاظ بیشترین انطباق با واقعیت زمینی انتخاب شد. بررسی ها نشان دهنده این است که این دریاچه طی سالهای مختلف تغییرات کامال محسوسی داشته است و این تغییرات با داده های آلتیمتری سد کجکی و داده های باران سنجی، همبستگی شدیدی را نشان میدهد. کاهش بارندگی، گرم شدن هوا، افزایش فشار به دریاچه و سوء مدیریت را میتوان از علتهای اصلی این تغییرات دانست.
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

GISAli.ahmadi2012@ut.ac.ir
 GISArman.samadi@ut.ac.ir
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

MNDWI)
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
MNDWI 

Lausch & )
(Herzog, 2002
 Geist) (et
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
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al., 2006
 .(Ozesmi and Bauer, 2002)
Lausch & )
(Herzog, 2002

.(Davis and Richard, 1994)




Lu, )
.(Mausel, Brondízio, & Moran, 2004

.(Ozesmi & Bauer, 2002)

SPOT,TM
(Guariglia et al., 2006)
(Kurt, Karaburun, & Demirci, 2010)  

(Gong, Gong, Zhao, & Li, 2007) 
.(de Roeck, Miya, Verhoest, Batelaan, & Brendonck, 2007 )


 


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
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


















1) TOPEX / Poseidon historical archive
2) OSTM interim GDR 20hz altimetry
3) Jason-3 interim GDR 20hz altimetry





https://fa.wikipedia.org/wiki

USGS

 






USGS



ENVI





ETMSLC Off
extension(Gapfill)



Ozturk
Sesli 2015
 NDWI(Gautam, Gaurav, Murugan, & Annadurai, 215)
Normalized Difference Water Index NDWI= (Green-NIR) / (Green+ NIR)
(Gautam et al., 2015) MNDWI
Modified Normalized Difference Water Index MNDWI= (Green-MIR) / (Green + MIR)
 (Feyisa, Meilby, Fensholt, & Proud, 2014) AWEI
Automated Water Extraction Index (0.25*NIR+2.75*SWIR2) -SWIR1) -= 4 *(Green
nsh
AWEI
https://earthexplorer.usgs.gov

 


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
MNDWI











ETM+TM
ENVI 



ArcGIS

RGB و







 AWEI, NDWI, MNDWI 
 OLI
 



























 











TOPEX, OSTMJeson1







Jeson1 
OSTM

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References
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Abstract
Wetlands are very special and important environments, both for environmental sustainability and for
animals and humans, especially if the wetland has fresh water, which adds to its importance and value.
Hamoon is thebiggest lake in the south-east of the Iran country, both in terms of its size and ecologically.
In this study, we tried to study the changes, area and volume of the wetland during the 30-year period. For
this purpose, Landsat satellite images were used to study the changes in the area and the area of the
wetland along with rainfall data and altimetry data. In this study, three different indicators were used and
after comparing them, the (MNDWI) index was selected as the best indicator in terms of maximum
synchrony with ground reality. Studies show that the lake has undergone dramatic changes over the years,
and these changes show a strong correlation with the altimetry data of the Kajaki Dam and rainfall data.
Reduced rainfall, global warming, rising pressure on the lake and mismanagement can be considered as
the main causes of these changes.
Keywords
Hamoon Lake, MNDWI, Altimetry data, Landsat images
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The coastal region of Istanbul has experienced significant coastline changes over the last few decades owing to a rapid increase in industrialization and urbanization. This study was aimed at detecting coastline changes in the coastal region of Istanbul between 1987 and 2007 using remotely sensed data. Two Landsat images acquired in 1987 and 2007 with 30 m resolution were classified with the maximum likelihood supervised classification method. The study area was classified into six land cover classes comprising urban areas, agricultural areas, forest, bare soil, brush/grassland, and lakes/ponds. The study provided an in-depth analysis of the coastal changes in the study area and revealed that the coastlines of Istanbul had expanded by 32 km between 1987 and 2007. From the findings of the study, it can be concluded that the largest variations in the position of the coastline over time occurred on the Marmara Sea coast in the south of Istanbul. Consequently a sustainable coastal management plan should be prepared and put in action in order to preserve the coastal regions.
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To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.
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Coastal dynamics are driven by phenomena of exogenous and endogenous nature. Characterizing factors that influence their equilibrium and continuous monitoring are fundamental for effective environmental planning and management of coastal areas. In order to monitor shoreline changes, we developed a methodology based on a multisource and multitemporal approach. A database, related to the Ionian coast of Basilicata region (about 50 km), was implemented by using cartographic data (IGMI data), satellite imagery (SPOT-PX/XS, Landsat-TM, Corona) and aerial data covering the period form 1949 to 2001. In particular, airborne data (1 m spatial resolution) were acquired during a specific campaign we performed in 2000 and 2001. To obtain the best performance from the available data, we applied a data fusion procedure on visible and thermal information. Different algorithms were tested, such as band ratios and clustering for extracting the coastline. The best results from multispectral data were obtained using a threshold algorithm we devised by exploiting the green, red and NIR bands, whereas for panchromatic data we selected clustering as the more suitable method. Moreover, a GPS survey was performed to evaluate the influence of tidal effects.
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Remote sensing is a valuable tool for wetland ecology and conservation. With this study, we aimed at providing relevant information on wetland characteristics, obtained by standard techniques and relatively cheap optical imagery. The number, surface area, distance, and dynamics of temporary and permanent wetlands were determined for the Western Cape, South Africa. These characteristics are important for the metacommunity structure of amphibians and invertebrates. Isolated open water wetlands were classified by supervised maximum likelihood classification on seven Landsat images (1987 -2002). Imagery acquired in summer contained fewer wetlands than those acquired in winter. The number of winter wetlands showed an increasing trend over time, which was not significantly correlated with yearly rainfall. Most classified wetlands were smaller than 1.5 ha. The distance to the nearest-wetland was longer in winter. In comparison to temporary wetlands, fewer, but on average larger permanent wetlands were classified. The relatively high number of wetlands is essential for local and migrating wading birds. The many small observed wetlands could also serve as stepping-stones, important for species conservation. We conclude that through relatively cheap imagery and standard geographical information system (GIS) techniques, basic ecological data can be generated. However, the resolution of Landsat imagery is too low to detect small wetlands. High accuracy images (such as IKONOS) would give more detailed results, but the high cost and the lack of long term data are at present restricting factors for their use by ecologists.
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Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.
Chapter
One of the key activities of the Land-Use/Cover Change (LUCC) project has been to stimulate the syntheses of knowledge of land-use/cover change processes, and in particular to advance understanding of the causes of land change (see Chap. 1). Such efforts have generally followed one of two approaches: broad scale cross-sectional analyses (cross-national statistical comparisons, mainly); and detailed case studies at the local scale. The LUCC project applied a middle path that combines the richness of indepth case studies with the power of generalization gained from larger samples, thus drawing upon the strengths of both approaches. In particular, systematic comparative analyses of published case studies on landuse dynamics have helped to improve our knowledge about causes of land-use change. Principally, two methods exist for comparative analyses of case studies. These methods are sufficiently broad geographically to support generalization, but at a scale fine enough to capture complexity and variability across space and time.
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In Beijing, Wetlands have been playing a crucial role in conserving municipal freshwater and retaining biodiversity. The classification system of Beijing wetland has been set up. The interpretation keys of six main wetland types in Beijing, including River, Reservoir, City Lake, Artificial Canal, Pound and Paddy Field, were built up using TM/ETM+ multi-spectral pseudo-color composition images as the data source. The modern information technology such as RS, GIS and GPS is combined with field investigation for further analysis to obtain the historical change information of wetland area, water quality and landscape pattern, The wetland soil and plant distribution maps in different large scale were sketched, and their attribute databases including hydrology, soil and plant were built up also. From which, its trend of evolution and some driving factors are analyzed successfully. This study indicates that not only have wetland areas been reduced by half, but also their ecological environments have been degraded because of rapid economic development and population increase. Suggestions based on this research are made to reconstruct the ecological environment of the wetlands and return them to their previous state.
Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability
  • A Lausch
  • F Herzog
Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability. Ecological Indicators, 2(1-2), 3-15. https://doi.org/10.1016/S1470-160X(02)00053-5