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We report on spectral reflectance measurements of basaltic lava flows on Tenerife Island, Spain. Lava flow surfaces of different ages, surface roughness and elevations were systematically measured using a field spectroradiometer operating in the range of 350–2500 nm. Surface roughness, oxidation and lichen coverage were documented at each measured site. Spectral properties vary with age and morphology of lava. Pre-historical lavas with no biological coverage show a prominent increase in spectral reflectance in the 400–760 nm range and a decrease in the 2140–2210 nm range. Pāhoehoe surfaces have higher reflectance values than ʻaʻā ones and attain a maximum reflectance at wavelengths < 760 nm. Lichen-covered lavas are characterized by multiple lichen-related absorption and reflection features. We demonstrate that oxidation and lichen growth are two major factors controlling spectra of Tenerife lava surfaces and, therefore, propose an oxidation index and a lichen index to quantify surface alterations of lava flows: (1) the oxidation index is based on the increase of the slope of the spectral profile from blue to red as the field-observed oxidation level strengthens; and (2) the lichen index is based on the spectral reflectance in the 1660–1725 nm range, which proves to be highly correlated with lichen coverage documented in the field. The two spectral indices are applied to Landsat ETM+ and Hyperion imagery of the study area for mapping oxidation and lichen coverage on lava surfaces, respectively. Hyperion is shown to be capable of discriminating different volcanic surfaces, i.e., tephra vs. lava and oxidized lava vs. lichen-covered lava. Our study highlights the value of field spectroscopic measurements to aid interpretation of lava flow characterization using satellite images and of the effects of environmental factors on lava surface evolution over time, and, therefore, has the potential to contribute to the mapping as well as dating of lava surfaces.

The main objective of this study is to assess the spatio-temporal dynamics of land cover/land use changes in the lower Mekong Delta over the last 40 years with the coastal Tran Van Thoi District of Ca Mau Province, Vietnam as a case study. Land cover/land use change dynamics are derived from moderate to high spatial resolution (Landsat and SPOT) satellite imagery in six time intervals ranging from 1973 to 2011. Multi-temporal satellite images were collected, georeferenced, classified using per-pixel method, validated, and compared in post classification for the land use/land cover change detection in decades. Seven major land cover/land use classes were obtained, including cultivated lands, aquaculture ponds, mangrove forest, melaleuca forest, built up areas, bare lands, and natural water bodies. The accuracies of the land cover/land use maps for 1973, 1979, 1989, 1995, 2004, and 2011 were 81%, 82%, 86%, 87%, 89%, and 89%, respectively. The results show that the area of cultivated lands reduced over the period 1973–2011, however, it still represents the dominant land use in the case study. Aquaculture ponds were almost absent in 1973 but greatly increased from 1995 to 2004, to represent 20% of the land surface in 2011. Overall, from 1973 to 2011, bare lands, cultivated lands, mangrove forest, and melaleuca forest decreased by 104 km2, 77 km2, 61 km2, and 5 km2, respectively. In contrast, aquaculture lands and built up areas increased by 123 km2 and 120 km2, respectively. Temporal analysis highlights that these changes took place mostly between 1995 and 2004. This study is a first step to identify the main drivers of land use changes in this delta region, which include economical policies as well as demographic, socio-economic, and environmental changes.

Accurate mapping of population distribution is essential for policy-making, urban planning, administration, and risk management in hazardous areas. In some countries, however, population data is not collected on a regular basis and is rarely available at a high spatial resolution. In this study, we proposed an approach to estimate the absolute number of inhabitants at the neighborhood level, combining data obtained through field work with high resolution remote sensing. The approach was tested on Ngazidja Island (Union of the Comoros). A detailed survey of neighborhoods at the level of individual dwellings, showed that the average number of inhabitants per dwelling was significantly different between buildings characterized by a different roof type. Firstly, high spatial resolution remotely sensed imagery was used to define the location of individual buildings, and second to determine the roof type for each building, using an object-based classification approach. Knowing the location of individual houses and their roof type, the number of inhabitants was estimated at the neighborhood level using the data on house occupancy of the field survey. To correct for misclassification bias in roof type discrimination, an inverse calibration approach was applied. To assess the impact of variations in average dwelling occupancy between neighborhoods on model outcome, a measure of the degree of confidence of population estimates was calculated. Validation using the leave-one-out approach showed low model bias, and a relative error at the neighborhood level of 17%. With the increasing availability of high resolution remotely sensed data, population estimation methods combining data from field surveys with remote sensing, as proposed in this study, hold great promise for systematic mapping of population distribution in areas where reliable census data are not available on a regular basis.

The identification of subtle thermal anomalies (i.e., of low-temperature and/or spatial extent) at volcanoes by satellite is of great interest for scientists, especially because minor changes in surface temperature might reveal an unrest phase or impending activity. A good test case for assessing the sensitivity level of satellite-based methods is to study the thermal activity of Oldoinyo Lengai (OL) (Africa, Tanzania), which is the only volcano on Earth emitting natrocarbonatite lavas at a lower temperature (i.e., in the range 500–600 °C) than usual magmatic surfaces. In this work, we assess the potential of the RSTVOLC multi-temporal algorithm in detecting subtle hot spots at OL for comparison with MODLEN: A thermal anomaly detection method tailored to OL local conditions, by using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Our results investigating the eruptive events of 2000–2008 using RSTVOLC reveal the occurrence of several undocumented thermal activities of OL, and may successfully integrate MODLEN observations. In spite of some known limitations strongly affecting the identification of volcanic thermal anomalies from space (e.g., cloud cover; occurrence of short-lived events), this work demonstrates that RSTVOLC may provide a very important contribution for monitoring the OL, identifying subtle hot spots showing values of the radiant flux even around 1 MW.