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The application of blockchain technology and Non-Fungible Tokens (NFTs) into geology offers potential for the preservation, management, and dissemination of geological data. This perspective paper explores the feasibility, benefits, and challenges of utilizing NFTs in managing geological data, particularly focusing on geology research materials. NFTs provide immutable, decentralized records that enhance data integrity, accessibility, and provenance, addressing long-standing issues in geological data management. This study outlines the key advantages of NFTs, including immutable record-keeping, enhanced accessibility, clear provenance and ownership, and interoperability across platforms. Specific use cases are highlighted, such as the creation of digital specimen collections, the development of interactive educational resources such as museums, and novel funding mechanisms for research. While the potential applications are promising, the discussion also addresses current limitations, including technical complexity, environmental concerns, and regulatory uncertainties. The opinion concludes with prospects, emphasizing the need for further research and technological advancements to fully realize the benefits of NFTs in geological data management, potentially revolutionizing the field of geology by making data more accessible, reliable, and secure.

In pursuit of the Sustainable Development Goals (SDGs)’s objective of eliminating slum cities, the government of Indonesia has initiated a survey-based slum mapping program. Unfortunately, recent observations have highlighted considerable inconsistencies in the mapping process. These inconsistencies can be attributed to various factors, including variations in the expertise of surveyors and the intricacies of the indicators employed to characterize slum conditions. Consequently, reliable data is lacking, which poses a significant barrier to effective monitoring of slum upgrading programs. Remote sensing (RS)-based approaches, particularly those employing deep learning (DL) techniques, have emerged as a highly effective and accurate method for identifying slum areas. However, the reliance on RS alone is likely to encounter challenges in complex urban environments. A substantial body of research has previously identified the merits of integrating land surface data with RS. Therefore, this study seeks to combine remote sensing imagery (RSI) with street view imagery (SVI) for the purpose of slum mapping and compare its accuracy with a field survey conducted in 2024. The city of Bandung is a pertinent case study, as it is facing a considerable increase in population density. These slums collectively encompass approximately one-tenth of Bandung City’s population as of 2020. The present investigation evaluates the mapping results obtained from four distinct deep learning (DL) networks: The first category comprises FCN, which utilizes RSI exclusively, and FCN-DK, which also employs RSI as its sole input. The second category consists of two networks that integrate RSI and SVI, namely FCN and FCN-DK. The findings indicate that the integration of RSI and SVI enhances the precision of slum mapping in Bandung City, particularly when employing the FCN-DK network, achieving an accuracy of 86.25%. The results of the mapping process employing a combination of the FCN-DK network, which utilizes the RSI and SVI, indicate the presence of 2294 light slum points and 29 medium slum points. It should be noted that the outcomes are contingent upon the methodological approach employed, the accessibility of the dataset, and the training data that mirrors the distribution of slums in 2020 and the specific degree of its integration within the FCN network. The FCN-DK model, which integrates RSI and SVI, demonstrates enhanced performance in comparison to the other models examined in this study.

Nieuwerkerk Volcano, located in the Banda Sea, Indonesia, is a submarine volcano whose entire edifice lies beneath sea level. Its proximity to several inhabited islands raises significant concerns regarding potential impacts from future volcanic hazards. Despite historical unrest recorded in 1925 and 1927, a comprehensive geological and geophysical understanding of Nieuwerkerk remains notably limited, with the last research expedition being in 1930. This study seeks to advance our understanding of the geomorphological structure and subsurface characteristics of the region, contributing to a preliminary hazard assessment and delineating key directions for future geoscientific investigation. The data were obtained during our most recent expedition conducted in 2022. High-resolution multibeam bathymetry data were analyzed to delineate the volcano’s morphology, while marine magnetic survey data were processed to interpret magnetic anomalies associated with its structure beneath volcano. Our updated morphological analysis reveals the following: (1) Nieuwerkerk Volcano is among the largest submarine volcanic edifices in the Banda Sea (length = 80 km, width = 30 km, height = 3460 m); (2) there is the presence of twin peaks (depth~300m); (3) there are indications of sector collapse (diameter = 10–12 km); (4) there are significant fault lineaments; and (5) there are landslide deposits, suggesting a complex volcanic edifice shaped by various constructive and destructive processes. The magnetic data show a low magnetic anomaly beneath the surface, where one of the indications is the presence of active magma. These findings significantly enhance our understanding of Nieuwerkerk’s current condition and volcanic evolution for an initial assessment of potential hazards, including future eruptions, edifice collapse, and landslides, which could subsequently trigger tsunamis. Further investigation, including comprehensive geophysical surveys covering the entire Nieuwerkerk area, rock sample analysis, visual seafloor observation, and seawater characterization, is crucial for a comprehensive understanding of its magmatic system and a more robust hazard assessment. This research highlights the critical need for detailed investigations of active submarine volcanoes, particularly those with sparse historical records and close proximity to populated areas, within tectonically complex settings such as the Banda Sea.

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

The study of Land Use Land Cover (LULC) is essential to understanding how land has been altered in recent years and what has caused the processes behind the change. This is significant for the future development of the area, particularly on the campus of the Universitas Padjadjaran Jatinangor. The purpose of this study was to apply remote-sensing techniques to map a university campus and vicinity by comparing the area of urban green space (UGS) and floor area ratios (FARs) of the campus in 2015 and 2017. Additionally, surface runoff analysis was also conducted. For our research, we used WorldView-2’s high-resolution satellite imagery with a resolution of 0.46 m in the Universitas Padjadjaran (Padjadjaran University, or Unpad) Jatinangor campus, Jawa Barat, Indonesia. Our approach was to interpret the imagery by running the normalized difference vegetation index (NDVI) to distinguish UGS and FAR and using digital elevation model (DEM) interferometric synthetic aperture radar (SAR) data with hydrologic analysis to identify the direction of surface runoff. The results obtained are as follows: the UGS remained more extensive compared with FAR, but the difference decreased over time owing to infrastructure development. Surface runoff has tended to flow toward the southeast in direct relation to the slope configuration.

A new lava field was formed at Holuhraun in the Icelandic Highlands, north of Vatnajökull glacier, in 2014–2015. It was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 covering an area of ~84 km2. Satellite-based remote sensing is commonly used as preliminary assessment of large scale eruptions since it is relatively efficient for collecting and processing the data. Landsat-8 infrared datasets were used in this study, and we used dual-band technique to determine the subpixel temperature (Th) of the lava. We developed a new spectral index called the thermal eruption index (TEI) based on the shortwave infrared (SWIR) and thermal infrared (TIR) bands allowing us to differentiate thermal domain within the lava flow field. Lava surface roughness effects are accounted by using the Hurst coefficient (H) for deriving the radiant flux ( Φ rad ) and the crust thickness (Δh). Here, we compare the results derived from satellite images with field measurements. The result from 2 December 2014 shows that a temperature estimate (1096 °C; occupying area of 3.05 m2) from a lava breakout has a close correspondence with a thermal camera measurement (1047 °C; occupying area of 4.52 m2). We also found that the crust thickness estimate in the lava channel during 6 September 2014 (~3.4–7.7 m) compares closely with the lava height measurement from the field (~2.6–6.6 m); meanwhile, the total radiant flux peak is underestimated (~8 GW) compared to other studies (~25 GW), although the trend shows good agreement with both field observation and other studies. This study provides new insights for monitoring future effusive eruption using infrared satellite images.

The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes.

Sustainable Development Goal (SDG) number 14 pertains to the preservation of sustainable marine ecosystems by establishing marine protected areas (MPAs). However, studies have reported massive damage to Indonesian marine ecosystems due to shipping pollution, anchors, and fishing nets. Thus, this study estimated the potential loss of ecosystem service value due to vessel activity expansion in the MPAs of Indonesia. This study was divided into three stages. The first stage is vessel activity expansion zone modeling based on kernel density. The second stage is marine ecosystem service value modeling through semantic harmonization, reclassification, and spatial harmonization. The last stage is the overlay of the vessel expansion zone model, marine ecosystem service value model, and the MPA of Indonesia. The results of this study indicate that the marine neritic zone of Indonesia has an ecosystem service value of USD 814.23 billion, of which USD 159.87 billion (19.63%) are in the MPA. However, the increase in vessel activity that occurred in 2013–2018 could potentially lead to the loss of the ecosystem service value of USD 27.63 billion in 14 protected areas. These results can assist policymakers in determining priority conservation areas based on the threat of vessel activity and value of ecosystem services.

This paper presents a remote sensing approach for rapidly and automatically generating maps of surface disturbances caused by landslides on the global scale. Our approach not only identifies the locations of these disturbances but also pinpoints the estimated time of their occurrence. Using the Continuous Change Detection and Classification (CCDC) algorithm within the Google Earth Engine (GEE) platform, we analyzed two decades of Landsat 5, 7, and 8 surface reflectance data. We tested this approach in five landslide-prone regions: Iburi (Japan), Kashmir (Pakistan), Karnataka (India), Porgera (Papua New Guinea), and Pasang Lhamu (Nepal). The results were promising, with R2 values ranging up to 0.85, indicating a robust correlation between detected disturbances and actual landslide events compared to manually made inventories. The accuracy metrics further validated our method, with a producer’s accuracy of 75%, a user’s accuracy of 73%, and an F1 score of 75%. Furthermore, the method proved well transferable across different locations. These findings demonstrate the method’s potential as a valuable tool for near real-time and historical analysis of landslide activity, thereby contributing to global disaster management and mitigation efforts.