Mattia Callegari

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

A Pol-SAR Analysis for Alpine Glacier Classification and Snowline Altitude Retrieval

In this study, we investigated the use of synthetic aperture radar (SAR) polarimetry (Pol-SAR) and a supervised classification technique, support vector machine (SVM), for the classification of bare soil, ice, and snow, over the Ortles-Cevedale massif, (Eastern Italian Alps). We analyzed the importance of topographic correction on the backscattering and polarimetric SAR signature and the advantage of quad-pol with respect to dual-pol data. When backscattering values only are employed, the incidence angle used as input feature of the SVM classifier assures the best classification accuracy, 9.9% higher than the accuracy obtained with cosine corrected γ0 backscattering. The introduction of polarimetric features and decomposition parameters (such as Cloude-Pottier or Touzi decomposition parameters) increases the classification accuracy by 5.2% with respect to the backscattering case. The simulation of RADARSAT-2 data as Sentinel-1 like for dual-pol data shows a decrease of accuracy equal to 7.8% with respect to the fully polarimetric case (93.5%). The first Sentinel-1 image acquired on our test area was also employed for classification. We then tested the capability of C-band SAR to detect accumulation and ablation zones of the glaciers under the winter dry snow by setting up a multi-incidence angle and fully polarimetric SVM classifier, exploiting ascending and descending RADARSAT2 data. In this case, the accuracy increased by 14.7% combining different geometric acquisitions (88.9%) with respect to the single geometry case. Finally, from the resulting classification maps, we extracted the snowline altitude for a sample of three glaciers, using both optical and SAR data, comparing the different products.

Synergy of Cassini SAR and altimeter acquisitions for the retrieval of dune field characteristics on Titan

This work focuses on the retrieval of Titan’s dune field characteristics addressing different radar modes. The main purpose of the proposed work is to exploit a possible synergy between SAR and altimeter acquisitions modes to provide information about dune field. Cassini has performed 86 Titan flybys in which several observations of dune fields have been collected in altimetry mode. There are several cases in which SAR and altimeter have been acquired over same areas covered by dune fields, such as during T28 (SAR) and T30 (altimeter) flybys. Altimetry together with SAR data have been used to derive the rms slopes of dunes (large scale) over Fensal area, this information has been employed to calculate SAR incidence angle with respect to dunes. We extracted backscattering coefficients of bright and dark areas detected in the analyzed SAR image in order to evaluate the angular response of scattering. Through the Geometric Optics model we retrieve roughness values (small scale rms slope) for both dune bright and dark areas.

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., “Retrieval of wet snow by means of multitemporal SAR data,” in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., “Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions”, EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., “A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., “Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Dune Height Estimation on Titan Exploiting Pairs of Synthetic Aperture Radar Images With Different Observation Angles

Widespread longitudinal dunes have been identified on Titan thanks to the 2.2-cm wavelength Cassini Synthetic Aperture Radar (SAR) instrument. Understanding the properties of these surface features, such as material composition and dune height, is very important for giving new clues about the Titan geology and climate. One of the major difficulties in the estimation of dune heights using SAR occurs when the material composition of the dunes is heterogeneous. In this paper, we propose a novel method for dune height estimation, which takes into account material heterogeneity, and in particular, the case in which the interdune exhibits different dielectric properties with respect to the remaining part of the dune. Paired data acquisitions with orthogonal observations are considered for separating the dielectric from the geometric effect on the backscattering coefficients in order to retrieve the slope and thus the height of the dunes. The results for a test area located in the Fensal region indicate that the slopes of the dune faces are generally lower than 5° and the heights range between 40 and 110 m.

A novel topographic correction for high and medium resolution images by using combined solar radiation

On mountain areas, one main important pre-processing step for optical satellite imagery is the topographic correction. The illumination condition strongly depends on the area topography, sun elevation and azimuth and acquisition geometry of the sensor. These elements generate unequal light distribution over the observed surface. This effect needs to be corrected to improve classification and parameters retrieval performances. In this paper, a novel empirical approach for topographic correction is presented using the combined (direct and diffuse) solar radiation. The combined solar radiation has the advantage to take into account the brightening effect of topography on scene radiance. The approach aims at defining correction coefficients depending on the different land cover. We tested our approach using both high and medium resolution images and terrain effects in both data sets were considerably reduced.

Spatiotemporal variations of alpine climate, snow cover and phenology

Understanding the relationships between vegetation phenology and its seasonal drivers under varying site conditions is of high interest in mountain areas, since alpine ecosystems are assumed to be particularly sensitive to climatic changes. Through the joint analysis of NDVI, snow metrics, and climate data at 250 m and 2 km spatial resolution, respectively, we aim at identifying their temporal and spatial variability and statistical inter- and intra-annual relationships on an alpine-wide scale. Apart from clear patterns in the vegetation and snow metrics related to topography, a negative relationship of mean March NDVI to snow cover duration (SCD) in the preceding months was detected, indicating a high sensitivity of green-up to snow accumulation and melt. In contrast, positive correlations between early winter SCDs and late summer NDVI indicate a lagged water storage effect. On the local scale of South Tyrol, climate variability interacted with topography could explain on average 30% of NDVI variations from late-October till late-May.

Multi-temporal and multi-source alpine glacier cover classification

This work presents a multi-temporal and multi-source approach for glacier cover classification, i.e. bare soil, glacier ice, firn, and snow. The method is based on Hidden Markov Model (HMM) and Support Vector Machine (SVM) and can handle different kinds of satellite virtual constellations composed of high-resolution optical and/or SAR platforms. The proposed method is tested on a Sentinel-1 time series acquired over the Ortler Alps and the obtained classification map time series is used to extract the temporal behavior of the snowline and estimate the equilibrium line altitude (ELA) of the glaciers.

A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as glacier ice in a following date) we here propose an innovative post classification correction based on SVM classification probabilities. Optical data, i.e. Landsat-8 and Sentinel-2, have been employed, when available, for training sample collection. Detailed field observations from two glaciers located in the Ortles-Cevedale massif (Eastern Italian Alps) have been employed for validation.

Quantitative monitoring of surface movements on active landslides by multi-temporal, high-resolution X-Band SAR amplitude information: Preliminary results

Multi-temporal image cross-correlation is a method for tracking moving features and can there-fore be used for quantitative assessments of surface displacements. Accuracies of up to 1/8th of the original image geometric resolution can be achieved. We present the results of an analysis carried out on Corvara landslide located in the Italian Dolomites. Image offset-tracking was applied to CosmoSky-Med amplitude images acquired between October 2013 and August 2015. The presence of a validation dataset consisting of periodical GPS surveys carried out on 16 benchmarks represents an ideal opportunity to test the applicability of SAR-based image cross-correlation for landslide monitoring. Despite the relative low accuracy of the results amplitude-based offset-tracking proved to be beneficial due to the ability of this method to capture large displacements. In particular the results evidence its complementarity with respect to multi-temporal interferometry that is confined to slow displacements along E-W directions. 2 CASE STUDY AND DATASET 2.1 Corvara landslide Corvara landslide is an active slow-moving landslide located up-slope the homonym village in the Dolomites area (NE Italy). Since it is a widely investigated phenomenon whose geometries have been clearly defined by land surveys and instrumental monitoring (Corsini et al. 1999, Corsini et al. 2001, Corsini et al. 2005), it represents the ideal case study for testing novel monitoring techniques and technologies (Corsini et al. 2012, Mulas et al. 2012, Mulas et al. 2015b). The landslide has been classified as an active, slow-moving deep-seated rotational earthslide-earthflow with active slip surfaces located at depths of up to 48 m. The bedrock of the area consist of weak formations of the Triassic characterized by the alternation between sub meter strata of hard rocks and weak rocks (Corsini et al. 2000). 2.2 SAR and D-GPS dataset Monitoring data collected in the period 2001– 2008 (Corsini et al. 2012) highlight a wide range