Land-Use Change and Its Impact on Urban Flooding: A Case Study on Colombo District Flood on May 2016

Abstract

Colombo district has become an increasingly congested urban society. It has been reported that the frequent flooding in the Colombo district occurs due to the shrinking of open spaces, illegal constructions, and lack of suitable waste disposal facilities. Therefore, this study focuses on analyzing the impact of land-use change on the flood of Colombo district in May 2016 in comparison to the land-use during the flood in 1989. Accordingly, Landsat images were utilized to identify the land-use by using NDVI, NDBI, and NDWI indices. Out of the several techniques examined, SVM classification was chosen, and change detection techniques in conjunction with remote sensing and GIS environment were adopted. SVM classification showed the highest accuracy for land-use classification, which was 99.0% in 1989 and 99.9% in 2016. The comparison of land-use changes of 1989 and 2016 with similar flood extent of the Colombo district proved that the area of the Kelani river watershed changed into urban area, having a significant impact on flood inundation. The Kelani river watershed includes 23% of the total urban area of the Colombo district. Similarly, the entire area of land-use transformation covered 37.7% of the area within the watershed region of the Colombo district. Eventually, this research identified the significant impact of Colombo district floods in May 2016 on land-use changes.

Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine

“…Landsat 5 images have been atmospherically corrected using LEDAPS, and include a cloud, shadow, water and snow mask produced using CFMASK, as well as a per-pixel saturation mask and Landsat 8 images have been atmospherically corrected using LaSRC and includes a cloud, shadow, water and snow mask produced using CFMASK, as well as a per-pixel saturation mask. For all the selected images, besides the spectral bands, 4 indexes were also calculated; NDVI (Normalized difference vegetation index) (Hu et al, 2016), NDWI (Normalized Difference Water Index) (Li et al, 2013), NDBI (normalized difference built-up index) (Dammalage and Jayasinghe, 2019) and Urban Index (UI) (Table 3). Each index was calculated using the Maximum Valve Composite Algorithm.…”

Intergration Remote Sensing and Hydrologic, Hydroulic Modelling on Assessment Flood Risk and Mitigation: Al-Lith City, Ksa

“…The study also recommends implementing Canal (2), which collects and adjusts the floodwater inflow coming from the east of the Al-Lith valley. This canal is located in the north of the Al-Lith valley and prevents the flood inflow to the urban mass using the maximum inflow of 100 years as the frequency period, estimated at about 599.6 m 3 /s.…”

“…This canal's dimensions are suggested to be 100 meters in width, 2.8 meters in depth, and 6.5 Kilometer in length. Moreover, the suggested Canal (3) should be implemented with the same purpose as canal (2), as this canal is also proposed to be located on the north side.…”

“…Remote sensing also provided a real opportunity to reduce reliance on costly field surveys and barometric measurement stations used to measure flood characteristics, especially in remote areas and developing countries. It is one of the attractive alternatives that has received increasing attention during the past years in improving forecasts for hydrological and hydraulic models [2][3][4].…”

Google Earth Engine ile Arazi Kullanımı Haritalarının Üretimi

“…NDWI(Normalleştirilmiş fark su endeksi) su alanlarının bulunmasında ve bitkilerdeki su içeriğinin tespitinde verimli bir şekilde kullanılan bir indekstir [28]. Uydu görüntülerinde, yapılaşmış alanlar ile toprak alanlarını ayırt etmek genellikle zordur, bu arazi kullanımı türlerinin ayırılmasında doğruluğu arttıran NDBI(Normalleştirilmiş fark oluşturma indeks) ve Ui(Kentsel indeks) gibi indeksler kullanılmaktadır [29,30].…”