Assessing the impacts of land use–land cover changes on direct surface runoff: a remote sensing approach in Khulna City

Abstract

The increased risks of storm flood occurrence in large cities are the resultants of land use changes due to rapid urbanization. This study examines the influence of land-use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 14 years, from 2005 to 2020. Land use-land cover maps for 2005, 2010, 2015, and 2020 were created employing Support Vector Machine (SVM) based supervised image classification using time-series satellite data, and the surface runoff was determined using SCS-CN model. The major land-use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend in impervious urban areas, which rose by 5.44% from 2005 to 2020 (17.00 mm average runoff depth increment) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation LULC class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

Hydrological Approach for Flood Overflow Estimation in Buleleng Watershed, Bali

“…Based on the research results that support the low surface runoff coefficient is the use of mixed garden land because surface runoff is restrained by rapid soil infiltration and canopies that reduce raindrops. The canopy has an impact on the rainfall intercept effect, which will weaken the impact of splashing raindrops on the soil surface and allow the soil to maintain a fast infiltration rate for a long time, thereby delaying the surface runoff time [42][43][44].…”

Perbandingan Mutu Air di Bendungan yang Berada di Provinsi Jawa Timur

“…Parameter yang tidak memenuhi baku mutu yaitu BOD5, Arsen, Kadmiun, Tembaga, dan Amonia, sama halnya dengan bendungan lainnya. Aktivitas perkebunan dan sawah memberi pengaruh terhadap kualitas air [17]. Aktivitas manusia di daerah tangkapan air seperti pemupukan, pemakaian pestisida, dan humus akan terakumulasi di badan air [18].…”

“…Berdasarkan analisis parameter BOD5, Kadmium, Arsen, dan Klorin Bebas tidak memenuhi baku mutu. Bahan organik yang menyebabkan BOD5 tinggi diprediksi berasal dari ketiga guna lahan tersebut, demikian juga cadmium, arsen dan klorin bebas diprediksi berasal dari aktivitas perkebunan dan sawah di daerah tangkapan air [17]. Guna lahan tertinggi di Waduk Kedung Brubus yaitu perkebunan dengan luas sebesar 254,27 Ha, selanjutnya sawah 13,27 Ha, dan pemukiman 3,27 Ha.…”

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

“…In recent years, the use of advanced machine learning techniques has significantly improved the detection of impervious surfaces from satellite imagery [26,27]. Techniques such as Classification and Regression Trees (CARTs) [28][29][30], the Random Forest (RF) method [31,32], Artificial Neural Networks (ANNs) [33][34][35][36], and Support Vector Machines (SVMs) [37][38][39][40] are examples of these methodologies. For instance [41], Lodato et al (2023) employed RF classification on Landsat imagery and, through remote sensing techniques and innovative cloud services, documented the transformation of the northern coastal region of Rome, an important rural area, into new residential and commercial services [42].…”