Exploring the Relationship Between Remote Sensing Research and the Sustainable Development Goals

Abstract

The Sustainable Development Goals (SDGs), adopted by the 193 member states of the United Nations in September 2015, aim to address the challenges and barriers to sustainable development for the betterment of humanity and the planet Earth by 2030, and involve 169 targets and 231 one-of-a-kind indicators related to human and physical geography. The field of remote sensing (RS) / Earth observation (EO) has great potential to contribute to the realization of the SDGs by monitoring the indicators and evaluating the targets and tracking the policies in this area. Despite its limitations, the RS technologies present a synoptic view, repeatable and multiscale observations, and time-series data for monitoring the SDG indicators directly or indirectly. This study aims to explore the use of RS technologies in advancing the SDGs and analyze the existing literature using bibliometric techniques to identify research trends, gaps, and key contributors in the field of RS/EO in relation to the SDGs. In this study, the Web of Science (WoS) Core Collection database was used to explore SDG-related RS studies in a broad perspective, from bibliometric investigations to gender distribution in this domain. Subsequently, using the publications indexed in the same database, the relationship between RS/EO research and SDG 11 (Sustainable Cities and Communities) has been analyzed in detail with a more comprehensive methodology. This thesis has revealed the increasing trends in research on these topics, the most significant publications and journals in the field, the top-contributing countries, and international scientific collaborations. According to the results, carbon storage (linked to SDG 12), ecological quality (linked to SDGs 15, 14), and impervious surface (linked to SDG 11) have been found to be trending topics in remote sensing studies related to SDGs. In terms of gender analysis, it was observed that researchers are predominantly male. Through conceptual analyses, it was revealed that machine learning, a subset of artificial intelligence, serves as both a driving theme and a fundamental research area in this field. The results of the analysis are discussed extensively in the thesis and are expected to shed light for scientists working in these domains and policy makers, as well as for researchers who plan to work in this research area. Also, the results are expected to contribute to the existing body of knowledge and foster interdisciplinary collaboration in pursuit of the SDGs.