Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome

In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3–4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations

Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

On the accuracy of integrated water vapor observations and the potential for mitigating electromagnetic path delay error in InSAR

Abstract. A field campaign was carried out in the framework of the Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects (METAWAVE) project sponsored by the European Space Agency (ESA) to investigate the accuracy of currently available sources of atmospheric columnar integrated water vapor measurements. The METAWAVE campaign took place in Rome, Italy, for the 2-week period from 19 September to 4 October 2008. The collected dataset includes observations from ground-based microwave radiometers and Global Positioning System (GPS) receivers, from meteorological numerical model analysis and predictions, from balloon-borne in-situ radiosoundings, as well as from spaceborne infrared radiometers. These different sources of integrated water vapor (IWV) observations have been analyzed and compared to quantify the accuracy and investigate the potential for mitigating IWV-related electromagnetic path delay errors in Interferometric Synthetic Aperture Radar (InSAR) imaging. The results, which include a triple collocation analysis accounting for errors inherently present in every IWV measurements, are valid not only to InSAR but also to any other application involving water vapor sensing. The present analysis concludes that the requirements for mitigating the effects of turbulent water vapor component into InSAR are significantly higher than the accuracy of the instruments analyzed here. Nonetheless, information on the IWV vertical stratification from satellite observations, numerical models, and GPS receivers may provide valuable aid to suppress the long spatial wavelength (>20 km) component of the atmospheric delay, and thus significantly improve the performances of InSAR phase unwrapping techniques

Tropospheric Products from High-Level GNSS Processing in Latin America

ARTÍCULO PUBLICADO EN REVISTA EXTERNA. The present geodetic reference frame in Latin America and the Caribbean is given by a network of about 400 continuously operating GNSS stations. These stations are routinely processed by ten Analysis Centres following the guidelines and standards set up by the International Earth Rotation and Reference Systems Service (IERS) and International GNSS Service (IGS). The Analysis Centres estimate daily and weekly station positions and station zenith tropospheric path delays (ZTD) with an hourly sampling rate. This contribution presents some attempts aiming at combining the individual ZTD estimations to generate consistent troposphere solutions over the entire region and to provide reliable time series of troposphere parameters, to be used as a reference. The study covers ZTD and IWV series for a time-span of 5 years (2014–2018). In addition to the combination of the individual solutions, some advances based on the precise point positioning technique using BNC software (BKG NTRIP Client) and Bernese GNSS Software V.5.2 are presented. Results are validated using the IGS ZTD products and radiosonde IWV data. The agreement was evaluated in terms of mean bias and rms of the ZTD differences w.r.t IGS products (mean bias 1.5 mm and mean rms 6.8 mm) and w.r.t ZTD from radiosonde data (mean bias 2 mm and mean rms 7.5 mm). IWV differences w.r.t radiosonde IWV data (mean bias 0.41 kg/m2 and mean rms 3.5 kg/m2).Sitio de la revista: https://link.springer.com/chapter/10.1007/1345_2020_12

Remote Sensing Techniques for Urban Heating Analysis: A Case Study of Sustainable Construction at District Level

In recent years, many new districts in urban centres have been planned and constructed to reshape the structure and functions of specific areas. Urban regeneration strategies, planning and design principles have to take into account both socioeconomic perspectives and environmental sustainability. A district located in the historical city centre of Terni (Italy), Corso del Popolo, was analysed to assess the construction effects in terms of surface urban heat island (SUHI) mitigation. This district is an example of urban texture modification planned in the framework of the regeneration of the ancient part of the town. The changes were realised starting from 2006; the new area was completed on June 2014. The analysis was carried out by processing Landsat 7 ETM+ images before and after the interventions, retrieving land surface temperature (LST) and albedo maps. The map analysis proved the SUHI reduction of the new area after the interventions: as confirmed by the literature, such SUHI mitigation can be ascribed to the presence of green areas, the underground parking, the partial covering of the local roadway and the shadow effect of new multi-storey buildings. Moreover, an analysis of other parameters linked to the impervious surfaces (albedo, heat transfer and air circulation) driving LST variations is provided to better understand SUHI behaviour at the district level. The district regeneration shows that wisely planned and developed projects in the construction sector can improve urban areas not only economically and socially, but can also enhance the environmental impact

A Stable Gaussian Fitting Procedure for the Parameterization of Remote Sensed Thermal Images

An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand its intensity trend and also to perform quantitative comparisons among different images in time and space. The proposed procedure is computationally rapid and stable, executing an initial guess parameter estimation by a multiple regression before the iterative nonlinear fitting. The Gaussian fit was applied to both low and high resolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization shown. As expected, a reduction of the correlation coefficient between the map values and the Gaussian surface was observed for the image with the higher spatial resolution due to the greater variability of the SUHI values. Since the fitting procedure provides a smoothed Gaussian surface, it has better performance when applied to low resolution images, even if the reliability of the SUHI pattern representation can be preserved also for high resolution images

Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery

Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this work deals with the SUHI pattern identification within the different land use categories of Bangkok city plan. This study first provides an overview of the SUHI phenomenon in Bangkok, then singles out the surface heating behavior in each land use category. To describe the SUHI dynamics within the different classes, the main statistics of the SUHI intensity (mean, standard deviation, maximum and minimum) are computed. Overall, the analysis points out that the categories placed in the city core (high-density residential; commercial; historical and military classes) exhibit the highest mean SUHI intensities (around 4 °C); whilst the vegetated pixels exert a less cool effect with respect to the greenery of categories mainly placed farther from the city center. The proposed analysis can help to identify if the land use plan requires targeted future actions for the SUHI mitigation; or if the maintenance of the current urban development model is in line with the environmental sustainability