Abstract
Abstract. Rapid change of Adama wereda during the last three decades has posed a serious threat to the existence of ecological systems, specifically water bodies which play a crucial part in supporting life. Role of Satellite images in Remote Sensing could be more important in investigation, monitoring dynamically and planning of natural surface water resources. Landsat-5(TM) & Landsat 8 (OLI) has high spatial, temporal and multispectral resolution and therefore provides consistent and perfect data to detect changes in surface changes of water bodies. In this paper, a study was conducted to detect the changes in water body extent during the period of 1984, 2000 and 2017 using various water indices such as namely Water Ratio Index (WRI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), supervised classification and wetness component of K-T transformation and the results are Presented. NDWI has been adopted for this study as compared with other indices through ground survey. The results showed an intense decreasing trend in the lakes of chelekleka, kiroftu, lake 1 and lake 3 of surface area in the period 1984–2017, especially between 2000 and 2017 when the lake lost about 1.309 km2 (one third) of its surface area compared to the year 2000, which is equivalent to 76%, 18%, 0.03% and 96%. Interestingly koka lake has shown very erratic changes in its area coverage by losing almost 3.5 km2 between 1984 and 2000 and then climbing back up by 14.8 km2 in 2017. Percentage of increment was observed that 10.6% as compared with previous year.
Highlights
Assessment and monitoring the changes in environment using remote sensing technology is extensively used in various applications, such as land use/cover change (Salmon et al.,2013; Demir et al.,2013), disaster monitoring (Volpi et al.,2013; Brisco et al.,2013), forest and vegetation change (Kaliraj et al, 2012; Markogianni et al.,2013), urban sprawl (Bagan et al.,2012; Raja et al.,2013), and hydrology (Dronova et al.,2011; Zhu et al.,2011). surface water will play an important role in human survival and social development Ridd and Liu (1998)
In order to detect the changes in surface water area of Adama woreda and its surrounding in the period 1984–2015, To analyze the performance of different satellite derived indices including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Modified, Water Ratio Index (WRI), KT Transform and Supervised classification has been examined for extraction of surface water from individual temporal Landsat data
NDWI, MNDWI, WRI, KT Transform and supervised classification were needed to calculate from Landsat 1984 Thematic mapper (TM),2000TM & 2017OLI images to assess their performances for the extraction of surface water
Summary
Assessment and monitoring the changes in environment using remote sensing technology is extensively used in various applications, such as land use/cover change (Salmon et al.,2013; Demir et al.,2013), disaster monitoring (Volpi et al.,2013; Brisco et al.,2013), forest and vegetation change (Kaliraj et al, 2012; Markogianni et al.,2013), urban sprawl (Bagan et al.,2012; Raja et al.,2013), and hydrology (Dronova et al.,2011; Zhu et al.,2011). surface water will play an important role in human survival and social development Ridd and Liu (1998). Developed remote sensing satellites with different spatial, spectral and temporal resolution provide an abundant data that become primary sources and being used for detecting and extracting surface water and its changes in recent decades (Xu,2006; Zhou et al.,2011; Tang et al.,2013; Li et al.,2013; McFeeters,2013). Images from Landsat series was one of the most widely used remote sensing data that adopted for water body detection as well as changes over the periods (Moradi et al.,2017). Several algorithm has been adopted for change detection studies on water body especially on Landsat data that categorized in to four main groups:1- classification and pattern recognition methods it includes supervised (Tulbure et al.,2013) and unsupervised methods (Ko et al.,2015).2-spectral un-mixing (sethre et al.,2005).3-single band threshold (Klein et al.,2014) and 4-spectral water index (Ji et al.,2009).
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