Mangrove forests have been suffering with the impact of anthropogenic activities (i.e. fishing, deforestation) in the past years. Imaging data with high spatial and spectral resolution is a tool to assist in the conservation management of these ecosystems, besides allowing the in situ characterization of mangrove trees. Systems onboard UAVs have been improving the applications of remote sensing for this purpose, delivering high resolution products with lower costs in comparison to airborne systems. The aim of this study is to investigate the applications of coupled UAV hyperspectral and LiDAR data for the generation of ultra-high-resolution images of mangrove forests. Here we present the preprocessing steps and preliminary results of the combined data.

In geology, the surface temperature is assumed to arise from the collective effect of surface and subsurface processes of which the latter is of fundamental importance. Several methods have been proposed to suppress the contribution of surface processes. However, none has tried to use physically-based modeling techniques to correct surface temperature data for diurnal and terrain effects. The present work introduces a single-source energy balance model named SkinTES developed for modeling and correcting high-resolution surface temperature data aiming to uncover the subtle temperature anomalies arising from subsurface geologic sources. Here, we present the theoretical basis of the model, its parameterization schemes, and the first results obtained by applying the model to point-scale and thermal remote sensing datasets. The potential application of the model in resource exploration and geologic studies is highlighted.

This study focus on the evaluation of scientific and industry-grade hyperspectral airborne sensors for the mapping of methane (CH4) emissions in the SWIR range. An imaging dataset from areas with known CH4 emissions was processed using the classic matched filter technique, and a new CH4 index. The airborne sensors were evaluated based on sensor design (spectral sampling and band centers), effectiveness of image processing, and impact of the signal-to-noise ratio (SNR) on CH4 mapping. The gas plume was mapped only in the images acquired with scientific-grade sensors. Results demonstrated that superior performance could be achieved when the position of band centers are closely located to the center of diagnostic CH4 absorption features. The impact of SNR was examined using a noise simulation, adding white noise to simulate images with varying SNR levels. Results indicate that the noisier signal of the industry-grade sensor is probably what prevented mapping the CH4 plume in this dataset. Simulations also demonstrated that as densest the plumes lower is the impact of SNR. Combined, results indicate that an imaging spectrometer with a scientific-grade SNR and band centers properly positioned to match the main CH4 features would improve the mapping of CH4 plumes with airborne sensors operating in the SWIR range.

This brief review focuses on the use of optical remote sensing for oil prospecting and monitoring in vegetated regions. Major advances have been achieved in this field during the last decade. Recent studies demonstrated that oil can be detected and quantified by exploiting vegetation reflectance from airborne- and satellite-embedded sensors. Various methods coupling vegetation indices or radiative transfer models with classification or regression algorithms have been proposed for this purpose. High spatial and spectral resolutions greatly increase the accuracy of oil detection and quantification. However, important limits remain in regions with sparse or highly diversified vegetation. Oil spills become also more difficult to map when other environmental stressors occur. Hence, an important effort remains to move toward operational applications, especially on upcoming drone- and satellite-embedded hyperspectral sensors.

The detection of fugitive emissions of hydrocarbon (HC) gases steamed from geological reservoirs and leaks in pipelines and refineries has a twofold application: (i) as an exploration and monitoring tool in the fossil fuel industry, and (ii) as a way to improve the estimative of global methane (CH4) budget emissions. HC gases display spectral features at short-, mid- and longwave infrared ranges, which can be used for the detection of gas plumes with remote sensing methods. Here, we present an overview and evaluation of the main methodologies developed for the mapping and quantification of HC gas plumes with airborne hyperspectral sensors.

In this work, we used hyperspectral remote sensing and a simplified three-dimensional radiative transfer approach to retrieve structural and chemical properties of individual tree crowns (ITCs) from a tropical forest area. First, a Look-Up-Table of simulated ITC reflectance was built by randomly varying parameters of the DART and PROSPECT models. Then, simulated and experimental reflectance of ITCs were compared in terms of spectral similarity. Finally, model parameters that yielded simulations spectrally similar to experimental data were related to sub-pixel fractions and narrow-band vegetation indices computed from the hyperspectral images. DART canopy structural parameters were related to the proportion of non-photosynthetic vegetation (NPV) (R2=0.65), green photosynthetic vegetation (GV) (R2=0.72) and shade (R2=0.34) estimated within ITCs. PROSPECT parameters describing foliar chemical traits such as Chlorophyll a+b (Cab) and Carotenoids (Cxc) were related to the ratio of TCARI/OSAVI (R2=0.77) indices and to the simple ratio between reflectance at 515 nm and 570 nm (R515/R570) (R2=0.42), respectively.

This works aims to study tar sand outcrops in the Paraná basin, Brazil, using multi -scale spectral sensing technologies. Sites in this basin comprise well-exposed outcrops of tar sands for which a wealth of orbital, airborne, field and lab spectral data is currently available, including WorldView-3 (WV-3; DigitalGlobe) over the entire study area. The area was also surveyed by the AisaFENIX hyperspectral system both from field-based and airborne platforms. This was followed by outcrop studies and sampling in the field and close-range spectroscopy using both hyperspectral imaging (i.e. Specim's sisuCHEMA) and nonimaging (ASD`s FieldSpec) instruments in the laboratory. The aim of the work is twofold: (i) to detect the tar sands at multiple scales, and (ii) to describe the alteration mineralogy associated with bitumen emplacement in sandstone beds, drawing an analogy between this phenomenon and the microseepage-induced alteration.

In this paper, we introduce a robust method coined iterative curve fitting to estimate the wavelength position and depth from spectral absorption features. The technique iteratively fits a curve to a continuum-removed spectrum and subsets the bands based on the minimum of the previous fit until fulfilling a specified threshold for residual error. The minimum of the latest iteration and its substituted reflectance value are then retrieved as the feature wavelength and depth. Two variants of the technique named iterative Gaussian fitting and iterative polynomial fitting (IPF) are presented. The superiority of these algorithms over current methods is demonstrated using four different absorption features between 400 and 2500 nm collected from an array of sandstones in the laboratory. The methods can achieve rmse values of ±1.0 nm for the wavelength and 1% for the feature depth. The estimated wavelength position in a hyperspectral sensor with less than 10-nm sampling interval is demonstrated to be in error by at most ±3 nm at 95% confidence level. Experiments with varying signal-to-noise ratios (SNRs) indicate the robustness of the technique against noise. The IPF is able to estimate the wavelength of narrow features with an rmse of ±2.7 nm at an SNR of 150 : 1 and broad features with an rmse of ±4.2 nm at an SNR of 400 : 1. The method, which is embedded in a package named Automated Absorption-based Mineral Spectral Analyzer (AMISA), enables the simultaneous calculation of width, area, and asymmetry of spectral data acquired from imaging and nonimaging sensors.

This paper aims to use unique features of hyperspectral data on an automatic process for outlining individual tree crowns (ITCs) in a tropical forest area, with special focus on semi-deciduous species. In order to enhance biophysical and biochemical properties of canopy species, a set of vegetation indices were computed. These indices served as input for a region growing segmentation algorithm that takes into account mutual similarity of pixels and spectral separability between neighbor segments. Segmentation output was evaluated on the basis of a score computed with the proportion of the area of the segments located within manually delineated ITCs. Results show that the segmentation approach is able to automatically delineate up to 70% of the control ITCs.

This study comprises a comparison between reflectance spectra of soils impregnated or not with hydrocarbons in a pilot area, located inside an area with soils impregnated with hydrocarbons. The spectra investigated here were derived by reflectance spectroscopy in laboratory conditions with the FieldSpec Hi-Res sensor (2150 channels in the VNIR-SWIR range) and extracted from imagery pixels generated by imaging spectroscopy, using the ProspecTIR hyperspectral sensor (with 357 channels in VNIR-SWIR range). The results indicate a substantial equivalence between the compared spectra, not only as regards a wealth of diagnostic absorption features of mineral components, but also some specific features related to the presence of hydrocarbon impregnated in the soils. These observations suggest that this technology has great potential for use in environmental monitoring of bare soils along pipelines and refineries.

Láscar volcano (Chile) has undergone at least 4 vulcanian eruptions and other smaller events between May 2000 and April 2009. Multispectral, thermal infrared images taken during this time by ASTER are used to calculate surface temperatures. The spatial extent and intesity of thermal anomalies are interpreted as varying due to fluctuations in the quantity and temperature of gas being emitted from active fumarole sites. The volcano continues to show a different relationship between thermal radiance and eruptive behaviour to that observed before 1993, the year of the volcano's greatest historical eruption, indicating that a change in degassing style has persisted since that time. Monitoring the size of thermal anomalies is shown to provide a new perspective for observing and interpreting volcanic processes and may be useful in future predictive models.