Francesco Fava

Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations

In this paper, we evaluate the impact of mineral dust (MD) on snow radiative properties in the European Alps at ground, aerial, and satellite scale. A field survey was conducted to acquire snow spectral reflectance measurements with an Analytical Spectral Device (ASD) Field Spec Pro spectroradiometer. Surface snow samples were analyzed to determine the concentration and size distribution of MD in each sample. An overflight of a four-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB digital camera sensor was carried out during the field operations. Finally, Landsat 8 Operational Land Imager (OLI) data covering the central European Alps were analyzed. Observed reflectance evidenced that MD strongly reduced the spectral reflectance of snow, in particular, from 350 to 600 nm. Reflectance was compared with that simulated by parameterizing the Snow, Ice, and Aerosol Radiation radiative transfer model. We defined a novel spectral index, the Snow Darkening Index (SDI), that combines different wavelengths showing nonlinear correlation with measured MD concentrations (R2 = 0.87, root-mean-square error = 0.037). We also estimated a positive instantaneous radiative forcing that reaches values up to 153 W/m2 for the most concentrated sampling area. SDI maps at local scale were produced using the UAV data, while regional SDI maps were generated with OLI data. These maps show the spatial distribution of MD in snow after a natural deposition from the Saharan desert. Such postdepositional experimental data are fundamental for validating radiative transfer models and global climate models that simulate the impact of MD on snow radiative properties.

Remote estimation of grassland gross primary production during extreme meteorological seasons

a b s t r a c t Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grass- land equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, includ- ing one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formula- tions. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PARpotential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors.

DEM extraction from archive aerial photos: accuracy assessment in areas of complex topography

Abstract The aim of this study is to analyze the accuracy of a Digital Elevation Model (DEM) created with photogrammetric techniques from stereoscopic pairs of aerial photos in areas with complex geomorphologic characteristics. The evaluation of DEM and derived geomorphometric parameters was conducted by comparison with other standard DEM products (i.e. TINITALY/01 and ASTER GDEM-V2) and by accuracy assessment based on Check Points (CPs). The validation process includes the comparison of elevation profiles, the calculation of DEM accuracies, and the evaluation of the effect of slope and aspect on the DEM accuracy. The produced DEM accurately represent complex terrain (RMSE = 4.90 m), thus providing information suitable for local-scale geomorphometric analysis. The obtained accuracy resulted slightly worse than TINITALY/01 (RMSE = 2.53 m), but significantly better than ASTER GDEM (RMSE = 12.95 m). These results confirm that photo-based DEM extraction can be a very competitive and precise methodology if other expensive high-resolution data are not accessible.

Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest

Airborne hyperspectral remote observations, characterized by high spatial and spectral resolution, allow the estimation of quantitative vegetation variables useful in forest condition assessment. In this research, total chlorophyll (a +  b) concentration (C ab), a biochemical variable describing crown discoloration rate, was mapped to assess oak (Quercus robur L.) condition in the Ticino Regional Park. A simulation experiment was conducted to evaluate the error in C ab estimation due to ecological variables (i.e. canopy leaf area index and understorey characteristics) and to sun-sensor configurations when optical indices are used. Canopy reflectance was simulated by means of the PROSPECT leaf radiative transfer model (Jacquemoud and Baret 1990) coupled with the SAILH canopy radiative transfer model, a variation of the SAIL (Scattering by Arbitrarily Inclined Leaves) model modified to include the hot spot effect (Verhoef 1984, Kuusk 1991). The vegetation was modelled as a two layer medium with oak canopy as the top layer and the understorey as the bottom layer. Simulations were performed for varying leaf C ab and canopy Leaf Area Index (LAI) of the top layer, θl (mean leaf inclination angle) and LAI of the bottom layer (LAIu) and sun-sensor geometry. Optical indices were calculated and used in C ab retrieval. Simulations demonstrated that errors in C ab estimation were negligible when MTCI (MERIS Terrestrial Chlorophyll Index) was used, thus indicating that MTCI was the most reliable index in mapping C ab in this forest environment. Empirical models based on optical indices were developed to map C ab from Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images acquired by an airborne survey on the Park forested area. A regression analysis between C ab concentration measured in leaves sampled in field and optical indices computed from hyperspectral MIVIS data was conducted. The MTCI index showed the highest performances and was therefore used to map C ab concentration of the Ticino Park oak forest. The C ab map was then used to assess crown discoloration level.

Post-fire resilience in the Alpine region estimated from MODIS satellite multispectral data

Abstract In this study, a methodology based on the analysis of MODIS (MODerate-resolution Imaging Spectroradiometer) time series was developed to estimate post-fire resilience of Alpine vegetation. To this end, satellite images of two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were used. The analysis was conducted on wildfire affected areas in the Lombardy region (Italy) between 2003 and 2007. Some land surface (LS) descriptors (i.e. mean and maximum VI, growing season start, end and length) were extracted to characterize the time evolution of the vegetation. The descriptors from a burned area were compared to those from an undisturbed adjacent control site by means of analysis of variance (one-way ANOVA). Post-fire resilience was estimated on the basis of the number of subsequent years exhibiting a statistical difference between burned area and control site. The same methodologies were also applied to events aggregated by main land cover (broadleaf forest, prairies and mixed forest). The averaged resilience of broadleaf forest was 5–6 years, whereas prairie ecosystems exhibited a faster response of 0–2 years. Phenological analysis revealed that fire induces a shift of the start and end of growing season in forest ecosystems but has no effect on prairies. The method provides a useful and quantitative insight into complex post-fire vegetation dynamics in the Alps from a remote sensing perspective; results can apply to post-fire forest management and to multi-risk analysis.

Inter-comparison of hemispherical conical reflectance factors (HCRF) measured with four fibre-based spectrometers

We describe the results of an experiment designed to compare the radiometric performance of four different spectroradiometers in ideal field conditions. A carefully designed experiment where instruments were simultaneously triggered was used to measure the Hemispherical Conical Reflectance Factors (HCRF) of four targets of varying reflectance. The experiment was in two parts. Stage 1 covered a 2 hour period finishing at solar noon, where 50 measurements of the targets were collected in sequence. Stage 2 comprised 10 rapid sequential measurements over each target. We applied a method for normalising full width half maximum (FWHM) differences between the instruments, which was a source of variability in the raw data. The work allowed us to determine data reproducibility, and we found that lower-cost instruments (Ocean Optics and PP Systems) produced data of similar radiometric quality to those manufactured by Analytical Spectral Devices (ASD -here we used the ASD FieldSpec Pro) in the spectral range 400-850 nm, which is the most significant region for research communities interested in measuring vegetation dynamics. Over the longer time-series there were changes in HCRF caused by the structural and spectral characteristics of some targets.

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove forests was addressed. Satellite data were acquired from Landsat-8; for the purpose of validation Rapid-Eye data were also used. In situ data supporting image processing were collected in a field campaign performed from 28 February to 4 March 2015 at the time of sensors overpasses. A hybrid approach based on bio-optical modeling and supervised classification techniques was applied to atmospherically-corrected Landsat-8 data. Bottom depth estimations, to be used in the classification process of shallow waters, were in good agreement with depth soundings (R2 = 0.87). Corals were classified with producer and user accuracies of 58% and 77%, while a lower accuracy (producer and user accuracies of 50%) was found for the seagrass due to the patchy distribution of meadows; accuracies more than 88% were obtained for mangrove forests. The classification indicated the presence of 18 mangroves sites with extension larger than 5 km2; for 15 of those the coexistence of corals and seagrass were also found in the fronting bays, suggesting a significant rate of biodiversity for the study area.

Forest species mapping using airborne hyperspectral APEX data

Abstract The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.

Assessment of Mediterranean pasture condition using MODIS normalized difference vegetation index time series

The aim of this study was to evaluate the potential of MODIS normalized difference vegetation index hypertemporal data analysis for assessing Mediterranean pasture conditions in North Western Sardinia (Italy). During the seasons 2006 to 2007 and 2007 to 2008, field observations were carried out to classify 67 pasture sites in three condition classes based on expert knowledge. The local net primary productivity scaling (LNS) method was applied, and its potential for discriminating the pasture condition classes was evaluated by logistic regression models (LRM). Yearly and average LNS maps were generated for the period 2000 to 2008, and analyzed to identify areas that exhibited persistently low LNS values (hotspots). The LNS method proved useful to discriminate pastures in different conditions (LRM bootstrapped Nagelkerke pseudo R 2 = 0.52 ). The analysis of persistence of low LNS values allow identifying regional hotspots of degradation. A qualitative evaluation of the main hotspots on aerial photographs revealed that approximately 62% of the hotspots were clearly characterized by pasture degradation patterns, whereas the remaining were associated to highly fragmented landscapes or to errors in the land cover map. This result emphasizes the importance of using multiscale approaches by integrating the LNS regional assessment with high spatial resolution remote sensing data analysis.

Remote sensing-based assessment of the 2005-2011 bamboo reproductive event in the arakan mountain range and its relation with wildfires

Pulse ecological events have major impacts on regional and global biogeochemical cycles, potentially inducing a vast set of cascading ecological effects. This study analyzes the widespread reproductive event of bamboo (Melocanna baccifera) that occurred in the Arakan Mountains (Southeast Asia) from 2005 to 2011, and investigates the possible relationship between massive fuel loading due to bamboo synchronous mortality over large areas and wildfire regime. Multiple remote sensing data products are used to map the areal extent of the bamboo-dominated forest. MODIS NDVI time series are then analyzed to detect the spatiotemporal patterns of the reproductive event. Finally, MODIS Active Fire and Burned Area Products are used to investigate the distribution and extension of wildfires before and after the reproductive event. Bamboo dominates about 62,000 km2 of forest in Arakan. Over 65% of the region shows evidence of synchronous bamboo flowering, fruiting, and mortality over large areas, with wave-like spatiotemporal dynamics. A significant change in the regime of wildfires is observed, with total burned area doubling in the bamboo-dominated forest area and reaching almost 16,000 km2. Wildfires also severely affect the remnant patches of the evergreen forest adjacent to the bamboo forest. These results demonstrate a clear interconnection between the 2005–2011 bamboo reproductive event and the wildfires spreading in the region, with potential relevant socio-economic and environmental impacts.