Developing Spectral Reflectance Measurement System for Environmental Remote Sensing Applications

Salih, Mohammed Mejbel

doi:10.18280/ijdne.160105

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…On the other hand, water is the backbone of life and the essence of human survival. Therefore, according to Salih [57], monitoring the quality of water resources becomes very necessary.…”

Section: Discussionmentioning

confidence: 99%

Identification of Karstification Zoning and Aquifer Channels in Karst Basin at Sendang Biru Beach, Malang-Indonesia: A Case Study

Wahid¹,

Sunaryo²,

Susilo³

et al. 2022

IJDNE

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Karst zoning and channel aquifers in the karst basin at Sendang Biru Beach, Tambak Rejo Village have been investigated. Sendang Biru Beach has a cave appearance as a karst morphological feature and is composed of limestone. Seasonal karst water sources come from springs, surface runoff, underground rivers, channels in valleys, basins, and slopes that often experience drought. The purpose of this study is to identify the presence of karst aquifers and karstification zones that are prone to damage or disasters in the karst environment. The method used is a geophysical combination of Electrical Resistivity Tomography (ERT), Induced Polarization (IP), and Self Potential (SP). The results showed that the eastern part's karstification zone was characterized by moderate to high resistivity and a chargeability zone with a moderate to high range. In addition, the existence of karst aquifer channels in the anomalous zone of low resistivity, high chargeability, and negative natural potential. This zone is located in the Qas Formation, with the water flowing from west to east and from west to south. The inundated karst aquifer that spreads in a circular pattern with different depths interspersed by limestone cracks causes a heterogeneous karstification process.

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“…On the other hand, water is the backbone of life and the essence of human survival. Therefore, according to Salih [57], monitoring the quality of water resources becomes very necessary.…”

Section: Discussionmentioning

confidence: 99%

Identification of Karstification Zoning and Aquifer Channels in Karst Basin at Sendang Biru Beach, Malang-Indonesia: A Case Study

Wahid¹,

Sunaryo²,

Susilo³

et al. 2022

IJDNE

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“…It is defined as a characteristic response pattern as each object on the earth's surface has its interaction with electromagnetic energy. The basis of the classification is to find some areas of the electromagnetic spectrum where the nature of this interaction differs for each object or land cover on the earth's surface (Wang B et al, 2018;Salih, 2021). Understanding the spectral signatures of different objects and features is essential for accurate classification and mapping in remote sensing applications.…”

Section: Signature Reflectance In Different Land Cover Typesmentioning

confidence: 99%

Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques

Trigunasih,

Narka,

Saifulloh

2023

J. Degrade. Min. Land Manage.

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Volcanic eruptions are natural disasters with significant environmental and societal impacts. Timely detection and monitoring of volcanic eruptions are crucial for effective hazard assessment, mitigation strategies, and emergency response planning. Remote sensing technology has emerged as a valuable tool for detecting and assessing the effects of volcanic eruptions. One of the challenges in remote sensing image processing is handling large data dimensions that are difficult to address using traditional methods. Machine learning approaches offer a suitable solution to tackle these challenges. Machine learning demonstrates increasing computational capabilities, the ability to handle big data and automation. This study aimed to compare different machine learning classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), and K-Nearest Neighbors (KNN). The data utilized in this study was derived from Sentinel-2A Multi-Spectral Instrument (MSI) imagery, which was tested in areas affected by the eruption of Mount Agung, Bali Province, in 2017. The results indicated that the GMM algorithm performed the best among the machine learning classifiers, achieving an Overall Accuracy (OA) value of 82.04%. It was followed by RF (78.86%) and KNN (77.55%). The areas affected by volcanic eruptions were determined by overlaying disaster-prone regions with areas mapped using the machine learning approach. The total affected area was measured as 29.89 km<sup>2</sup>, with an additional 3.31 km<sup>2</sup> outside the designated zone. The findings of this study serve as a guideline for governmental entities, stakeholders, and communities to implement effective mitigation efforts for disaster risk reduction.

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“…The emerging digital technology provides a rational and objective method to mine and quantify the endogenous laws of landscape architecture, making the landscape architecture research much more scientific and efficient. Spatial information technology has quickly permeated every aspect of the teaching, scientific research, and engineering of landscape architecture [1][2][3][4][5][6][7][8][9][10]. Representative spatial information techniques, including remote sensing (RS), spatial big data, earth observation monitoring, and spatial positioning and simulation, directly drive the synergistic development between the teaching of RS in landscape architecture and the teaching of landscape architecture design, and stimulate the continued breakthroughs and innovations of relevant teaching platforms, methods, and experimental means [11].…”

Section: Introductionmentioning

confidence: 99%

Teaching of Remote Sensing Technology for Landscape Architecture in the Context of Spatial Information Technology

Zhang

2021

Int. J. Emerg. Technol. Learn.

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The research of digital landscape architecture springs up in recent years. The emerging digital technology provides a rational and objective method to mine and quantify the endogenous laws of landscape architecture. Remote sensing (RS) technology has become a new growth point in the current research and design of landscape spatial information. To develop the professional teaching of landscape architecture, it is important to fully integrate the RS technology into the teaching system of spatial information technology, carry out systematic spatial information quantification and research-based teaching of landscape architecture, and collaboratively promote the teaching of landscape architecture design. This paper firstly analyzes the integration and application potential of RS technology into landscape architecture. Considering the demand and trend of information-based teaching of landscape architecture, the authors integrated the relevant technologies into an RS teaching platform for landscape architecture, and summarized an application model of RS technology in the teaching of landscape architecture theories and practices. Moreover, a landscape spatial information chain, which is question-oriented, task-driven, and exploration-based, was constructed to promote the synergistic development between the students’ research and practice ability under spatial information integration.

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Developing Spectral Reflectance Measurement System for Environmental Remote Sensing Applications

Cited by 8 publications

References 20 publications

Identification of Karstification Zoning and Aquifer Channels in Karst Basin at Sendang Biru Beach, Malang-Indonesia: A Case Study

Identification of Karstification Zoning and Aquifer Channels in Karst Basin at Sendang Biru Beach, Malang-Indonesia: A Case Study

Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques

Teaching of Remote Sensing Technology for Landscape Architecture in the Context of Spatial Information Technology

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