Ms. Alfiatun Nur Khasanah Profile

Alfiatun Nur Khasanah

at Gadjah Mada University

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Proceedings Article | 29 January 2024 Paper

Gully erosion mapping based on remote sensing data

Alfiatun Nur Khasanah, Projo Danoedoro, Muhammad Anggri Setiawan

Proceedings Volume 12977, 129771F (2024) https://doi.org/10.1117/12.3009755

KEYWORDS: Modeling, Vegetation, Data modeling, Remote sensing, Machine learning, Random forests, Associative arrays, Roads, Agriculture, Systems modeling

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Gully erosion is the most destructive type of soil erosion, induced by soil detachment. As a result, modest to massive incisions are made in the field. The process can degrade the quantity and quality of soil and potentially cause structural damage. Field studies are used to map the position of gullies, but they are inefficient in terms of time and cost, especially on a regional scale. Therefore, another approach is applied to visualize the probability of gully erosion development using geoenvironmental factors. Remote sensing data can be used to examine the condition of the land, leading to an accurate representation of the earth's surface. This research's primary goal is to predict the location of gully erosion using remote sensing data in the upper section of the Sapi Watershed, Banjarnegara, Indonesia. This location primarily consists of mountainous areas used for massive cultivation. Parameters comprising land use and vegetation area derived from SENTINEL 2A, and topographic and hydrological data from DEMNAS. The mapping process considers the actual location of the gully and other geographical characteristics using Random Forest. A total of 85 gully location records were collected and verified using Google Earth and field surveys. Nongully data were obtained using median filters to distinguish between river and mountain top. 70% of the data is used for modelling and the rest for validation of model results. RF-generated prediction maps could provide an essential instrument for planning and land conservation in the early phases of gully formation.

Proceedings Article | 21 November 2019 Open Access

Distribution of diurnal land surface temperature in Yogyakarta urban area

Alfiatun N. K., Dwi Setyo Aji

Proceedings Volume 11311, 1131109 (2019) https://doi.org/10.1117/12.2548980

KEYWORDS: Earth observing sensors, Landsat, Atmospheric corrections, Reflectivity, Remote sensing, Temperature metrology, Climatology, Climate change, Vegetation, Roads

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Yogyakarta urban area has grown throughout the year as the course of migration and its attraction to tourists and students resulting the high demand of living space which leads to the increment of built-up area such as hotels and other supportingtourism-activity accommodation, so-called urban sprawl. The increase of paved-surface causes the increase of land surface temperature (LST) which may impact to micro-climate in the urban area with adverse consequences for instance erratic rainfall and rainstorm in the urban area. Consequently, it triggers new future problems. This paper attempt to present the distribution of diurnal Land Surface Temperature (LST) in Yogyakarta urban area, extracted from remotely-sensed Landsat 8 image acquired from a two—year images. Prior the extraction, several variables are incorporated i.e Normalized Difference Vegetation Index (NDVI) to calculate emissivity, as well as atmospheric correction parameter transmissivity, upwelling and downwelling radiance. In order to obtain NDVI, the reflectance values are also corrected. Land surface temperature is extracted according to the procedures: conversion of digital number of Landsat image to radiance, correction of radiance value, conversion of the corrected radiance value to brightness temperature, then brightness temperature to land surface temperature. The extracted temperature map then presented into 10°C interval. Consecutively, the two-year of temperature maps are then analyzed to obtain the difference of its spatial distribution. The expected result is the expanding high temperature distribution in the urban area. The result shows there is an increase of the average land surface temperature by 3oC from two different image, 2014 and 2018. The majority value of temperature is between 30 – 40°C, dominated with built-up area. Two image shows that the respective area spread from 54% to 70%.

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