The impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans.
To date the cause of saline water in Jakarta area is still debated. One opinion says that salty ground water is caused by sea water intrusion. Other opinions stated that the salty water appears from connate water. The objective of this study is determining the causes of saline water in North Jakarta, especially in Tanjung Priok and Koja. The method used to describe the subsurface fluid flow and resistivity spread is geoelectric method. The method consists of SP (self potential) for fluid flow and resistivity for distribution of subsurface saline water. The data is processed using RES2DINV software and interpreted with processed SP to produce a cross-section map. The results of these two methods are also supported by geological data and wells data samples as well as gravity data in the form of FHD (first horizontal derivative). The results of the resistivity indicate the presence of saline water at a depth of 5-10 meters which is a shallow aquifer. The saline water in this study area most likely caused by the sea intrusion as the SP results show that the subsurface groundwater flows from North to South.
The presence of saline or salt water in the Jakarta groundwater aquifer is still widely debated by various geologists and groundwater experts. This study intends to identify the cause of the high salinity of groundwater at Tanjung Priok and Koja, North Jakarta. The First Horizontal Derivative (FHD) method of gravity data is applied to identify the direction of subsurface fluid flow. It is also supported by groundwater sample data and self potential (SP) data. The direction of fluid flow on the FHD contour map is indicated from low to high FHD value. From gravity data, Bouguer density values obtained in the study area were 2.12 gr/cm3. This study focuses on surface aquifers so that it is necessary to separate regional and residual anomalies. The results show that the direction of fluid flow is from north to south, there is a high FHD value that is distributed around the coordinates (709000, 9322000) which may indicate as the salt depositions of sea water intrusion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.