Ground Truth Campaigns Research Articles

The Volcanic Relief within the Kos-Nisyros-Tilos Tectonic Graben at the Eastern Edge of the Aegean Volcanic Arc, Greece and Geohazard Implications

The active Kos-Nisyros-Tilos volcanic field is located in the eastern sector of the Aegean Volcanic Arc resulting from the subduction of the African plate beneath the Aegean plate. The volcanic activity is developed since Middle Pleistocene and it occurs within a tectonic graben with several volcanic outcrops both onshore and offshore. Data obtained from previous offshore geophysical surveys and ROV exploration, combined with geospatial techniques have been used to construct synthetic maps of the broader submarine area. The volcanic relief is analyzed from the base of the volcanic structures offshore to their summits onshore reaching 1373 m of height and their volumes have been computed with 24.26 km3 for Nisyros Island and a total volume of 54.42 km3 for the entire volcanic area. The volcanic structures are distinguished in: (1) volcanic cones at the islands of Nisyros (older strato-volcano), Pergousa, Yali and Strongyli, (2) volcanic domes at the islands of Pachia, East Kondeliousa and Nisyros (younger Prophitis Ilias domes), (3) submarine volcanic calderas (Avyssos and Kefalos). Submarine volcanic debris avalanches have been also described south of Nisyros and undulating features at the eastern Kefalos bay. Submarine canyons and channels are developed along the Kos southern margin contrary to the Tilos margin. Ground truth campaigns with submarine vessels and ROVs have verified the previous analysis in several submarine volcanic sites. The geohazards of the area comprise: (1) seismic hazard, both due to the activation of major marginal faults and minor intra-volcanic faults, (2) volcanic hazard, related to the recent volcanic structures and long term iconic eruptions related to the deep submarine calderas, (3) tsunami hazard, related to the seismic hazard as well as to the numerous unstable submarine slopes with potential of gravity sliding.

Mapping trees along urban street networks with deep learning and street-level imagery

Planning and managing urban forests for livable cities remains a challenge worldwide owing to sparse information on the spatial distribution, structure and composition of urban trees and forests. National and municipal sources of tree inventory remain limited due to a lack of detailed, consistent and frequent inventory assessments. Despite advancements in research on the automation of urban tree mapping using Light Detection and Ranging (LiDAR) or high-resolution satellite imagery, in practice most municipalities still perform labor-intensive field surveys to collect and update tree inventories. We present a robust, affordable and rapid method for creating tree inventories in any urban region where sufficient street-level imagery is readily available. Our approach is novel in that we use a Mask Regional Convolutional Neural Network (Mask R-CNN) to detect and locate separate tree instances from street-level imagery, thereby successfully creating shape masks around unique fuzzy urban objects like trees. The novelty of this method is enhanced by using monocular depth estimation and triangulation to estimate precise tree location, relying only on photographs and images taken from the street. Experiments across four cities show that our method is transferable to different image sources (Google Street View, Mapillary) and urban ecosystems. We successfully detect >70% of all public and private trees recorded in a ground-truth campaign across Metro Vancouver. The accuracy of geolocation is also promising. We automatically locate public and private trees with a mean error in the absolute position ranging from 4 to 6 m, which is comparable to ground-truth measurements in conventional manual urban tree inventory campaigns.

Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2 = 0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal0 (σT_RHo, σT_RVo) and σveg0 (σV_RHo, σV_RVo) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg0. The present study is a first step towards retrieving crop parameters from hybrid-polarized data and thus possesses the potential to serve as a reference for further research initiatives.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps

In this study, the Land Use/Cover Area frame statistical Survey (LUCAS) of 2009 was used as a reference dataset for validating a Land Cover Map of Greece for 2007, produced with remote sensing by the Greek Office of the World Wildlife Fund (WWF Hellas). First, all class definitions were decomposed in terms of four vegetation parameters (type, height, density, and composition), considered as critical in indicating unconformities between LUCAS and the WWF Hellas map; their inter-class relations were described in a table of correspondence. Then, a two-tier methodology was applied: an “automated” process, where thematic agreement was based exclusively on the main land cover attribute of LUCAS (LC1); and a “supervised” process, where thematic agreement was based on the reinterpretation of LUCAS ground photos and use of ancillary earth observation imagery; non-square error matrix was deployed in both processes. For the supervised process specifically, a decision-tree was designed, using the critical vegetation parameters (mentioned above) as quantified criteria, thus allowing objective labelling of testing points in both systems. The results show that only a small proportion of the reassessed points verified the WWF Hellas map predictions and that the overall accuracy of the supervised process was reduced compared to that of the automated process. In conclusion, the LUCAS point database was found to be supportive, but not fully efficient, for identifying the various sources of error in country-scale land cover maps derived with remote sensing. Synergy with very high resolution satellite images and air photos, or a dedicated ground truth campaign, seems to be inevitable in order to validate their thematic accuracy, especially in highly heterogeneous environments. In this direction, LUCAS could be used as a verification, rather than a validation, dataset.

Mt. Etna sulfur dioxide flux monitoring using ASTER-TIR data and atmospheric observations

This work is aimed at estimating the sulfur dioxide emission of Mt. Etna volcano (Sicily, Italy) using the thermal infrared images remotely sensed by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). A new procedure (named FUN) is presented based on approximating functions which represent the atmospheric terms of the radiative transfer equation: transmittance, up-welling and down-welling radiances. The parameters of the approximating functions have been computed through the MODTRAN simulations performed using atmospheric monthly mean profiles measured at Trapani (1989–2003). Trapani (WMO station 16429) is located at the western tip of Sicily and is the closest station to Mt. Etna volcano. With these functions MODTRAN is no longer necessary to compute the SO 2 abundance in the plume emitted from Mt. Etna and seen by ASTER. Only an atmospheric radiosounding and a digital elevation model (DEM) registered to the ASTER image are required. This new procedure is compared with the known look-up tables (LUT) procedure proposed by other authors. An interesting aspect of the FUN procedure derives from its combined use with a split window (SW) algorithm. In this case only radiosounding is required to compute the SO 2 map. The FUN procedure presented in this paper is a rapid, simple and accurate means of generating SO 2 map estimates from the ASTER view of Mt. Etna. The proposed scheme can easily be adapted to another sensor and another volcano. In this paper the results from two ASTER images, 19 July 2003 and 29 July 2001, are presented. In July 2003, a ground truth campaign was deployed, SO 2 emission was very low (10–25 kg s − 1 ; Etna baseline is about 50 kg s − 1 ). In July 2001, Mt. Etna erupted and a strong SO 2 emission (about 140 kg s − 1 ) was measured.

Soil moisture estimation from ERS/SAR data: toward an operational methodology

Previous studies have shown the possibility of using European Remote Sensing/synthetic aperture radar (ERS/SAR) data to monitor surface soil moisture from space. The linear relationships between soil moisture and the SAR signal have been derived empirically and, thus, were a priori specific to the considered watershed. In order to overcome this limit, this study focused on two objectives. The first one was to validate over two years of data the empirical sensitivity of the radar signal to soil moisture, in the case of three agricultural watersheds with different soil compositions and land cover uses. The slope of the observed relationship was very consistent. Conversely, the offset could change, making the soil moisture retrieval only relative (and not absolute). The second one was to propose an "operational" methodology for soil moisture monitoring based on ERS/SAR data. The implementation of this methodology is based on two steps: the calibration period and the operational period. During the calibration period, ground truth campaigns are performed to measure vegetation parameters (to correct the SAR signal from the vegetation effect), and the ERS/SAR data is processed only once a field land cover map is established. In contrast, during the operational period, no vegetation field campaigns are performed, and the images are processed as soon as they are available. The results confirm the relevance of this operational methodology, since no loss of performance (in soil moisture retrieval) is observed between the calibration and operational periods.

Remote Sensing And GIS As Tools For The Hydro-geomorphological Modeling Of Soil Erosion In Semi-arid Mediterranean Regions

2 3 Abstract This contribution highlights the advantages of using a multidisciplinary modelling approach to the characterisation of the environmental dynamics of a typical semi-arid Mediterranean region, the Flumendosa river basin located in central Sardinia (Italy). Here, the widespread presence of geomorphological active processes interacts with the consequences of intense human activities on crop cultivation and forest management to produce a fragile environment the equilibrium of which must be carefully surveyed and maintained. To this end, an hydro-geomorphological soil erosion hazard model was devised and applied using remote sensing and G.I.S. techniques as support tools. Radiometric enhancement and supervised classification processing of multitemporal Landsat TM images were used to derive land cover mformation, allowing to understand the seasonal evolution of the area and to monitor the presence and movement of organic materials. These data were then combined with those derived from ground truth and ground survey campaigns concerning geology and geomorphology. At the same time, rainfall simulations were carried out in order to evaluate the principal parameters involved in the soil erosion processes. On these data a hydro-geomorphological model based on the spatial prevalence of the different erosion processes, was applied. Six main parameters (i.e. soil erosivity, land use, hillslope gradient and curvature, contributing area and soil infiltration capacity) were used and combined with a matrix linear combination. Results show that the model could represent, if routinely applied at a local administrative level, a valuable tool for forecasting the relative risk of soil erosion in the framework of a correct environmental management.

Potential applications of 1–5 GHz radar backscatter measurements of seasonal land snow cover

Lower frequency microwave radar observations can help to measure the properties of snow cover on land by providing information about the soil‐snow boundary condition. In this theoretical study, we examine the sensitivities of microwave radar measurements to soil and snow characteristics, and we compare a simple model with previously published data. Depending on the surface roughness, copolarized ratios or single polarization time ratios of radar backscattering may be affected only by the incidence angle and the dielectric contrast at the soil‐snow boundary. These measurements can reduce the number of unknowns in any corresponding higher‐frequency observations that are also affected by the lower boundary condition. The copolarized ratio is sensitive, first of all, to the snow density, which offers the possibility of measuring this parameter directly if a separate measurement of the soil temperature from a passive microwave system is also available. The thermal insulation provided by snow cover can have a powerful effect on the soil‐snow boundary by altering the soil temperature and therefore changing the dielectric contrast. Because the microwave response of snow cover is so sensitive to the conditions of the underlying frozen soil, it is important for future ground truth campaigns that measure snow conditions to also collect data on the temperature, water content, and texture of the soil.

An aureole-corrected Langley-plot method developed for the calibration of HiRES grating spectrometers

Ground-based sensors for the vicarious calibration of spaceborne Earth remote-sensing spectrometers must be Sun-calibrated for measurements of direct solar irradiance spectra. The best results for Sun calibration control gives the classical refined Langley-plot analysis at high altitude clean air sites. This analysis fails in general for near sea-level measurements due to unstable atmosphere and inhomogeneous layers during a calibration phase. Adding solar aureole measurements to the Langley analysis, the Sun calibration can be realized even at near sea-level locations and during ground-truth campaigns. The first measurements for such an aureole-corrected Langley-plot analysis have been realised with the HiRES ( Hi gh R esolution E nvironment S pectrometer) grating spectrometers. Results of aureole-corrected Langley-plot measurements carried out in Berlin (60 m above sea level, a.s.l.), Germany, are compared with a reference classical Langley calibration using data measured at the Jungfraujoch observatory (3580 m a.s.l.) in the Swiss Alps.