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 (T_h) 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 $\left({\mathsf{\Phi }}_{\mathrm{rad}}\right)$ 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.

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.

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.

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.