Chapter Pyrgi: analysis of possible climatic effects on a coastal archaeological site

This work refers to an interdisciplinary study on the archaeological site of Pyrgi, an Etruscan harbour still under excavation, located on the Lazio’s coast in Santa Severa, in the province of Rome. The objective of the research is to assess the main cause of the floods and the time the water stays in the site to determine if the floods are periodic phenomena over time or random events for guarantee a correct conservation of the site . The study is based on the combined use of geomatic technologies, meteorological and climatic models, and hydrogeological knowledge

Pyrgi. Analysis of possible climatic effects on a coastal archaeological site

This work refers to an interdisciplinary study on the archaeological site of Pyrgi, an Etruscan harbour still under excavation, located on the Lazio’s coast in Santa Severa, in the province of Rome. The site in question is subject to frequent flooding which compromises its accessibility and delays the archaeological excavation operations. The study is based on the combined use of geomatic technologies, meteorological and climatic models, and hydrogeological knowledge of the examined site, to have a global view of the hazard to which it is exposed. Different geomatic techniques at different scales are used in the analysis. Large scale surveys are carried out to define the water networks and to monitor the site using satellite images. On a small scale, drone photogrammetry techniques are used to assess the morphology of the territory and eventual protection from natural hazards present in the site. Using these images, a detailed digital surface model (DSM) has been generated. The objective of the research is to assess the main cause of the floods and the time the water stays in the site and to determine if the floods are periodic phenomena over time or random events. The study was conducted using images captured by Sentinel 2 satellites processed at level 2-A. These images enabled the identification of the flooding periods of the site for the years of monitoring. The study was conducted by comparing the captured images with rainfall data, paying attention to extreme weather phenomena that occurred from 2012 to date. The rainfall data are provided by the National Department of Civil Protection to CNR-ISAC by an agreement between the two institutions. The same images have been compared with the wind data recorded by the anemometer located in the Civitavecchia harbour and the wave height data available from ERA5 reanalysis. Knowledge of the main cause of the floods and a possible periodicity will allow to plan correct conservation of the site through specific protection measures designed according to the hazards to which it is exposed

New sensors benchmark report on Sentinel-2A

Geometric benchmarking for Sentinel-A2 sensor over Maussane test site for CAP purposesJRC.H.6-Digital Earth and Reference Dat

Monitoring vegetation using remote sensing time series data: a review of the period 1996-2017

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources.Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources

REMOTE SENSING ANALYSIS IN THE ARCHAEOLOGICAL CONTEXT OF LICODIA EUBEA (CATANIA)

Archaeological research conducted at the site of Marineo, in the territory of Licodia Eubea (CT, Sicily, Italy), has revealed the existence of a group of evidence dating back to various periods, from the Neolithic to the Middle Ages. Particularly important is the presence of caves, documented through archaeological excavations at the end of the 1980s, subsequently resumed from 2017 to today. These caves were used for ritual activities, especially during the Middle Bronze Age (1450–1250 BC). The existence of numerous combustion structures associated with remains of a meal, as evidenced by remains oan f animal, and human bones in a secondary position, suggests the funerary value of the caves. Until now, however, data were missing on the identification of the settlement inhabited by communities that used caves. During the last archaeological excavation campaign, images and orthophotos were acquired through the use of drones. In this way, through the study of these images, it was possible to identify new anomalies in areas not yet investigated and placed in the vicinity of the caves. Surveys carried out in the area, have confirmed the presence of remains of walls belonging to curvilinear and oval structures. These structures are probably parts of the settlement connected to the caves whose exact location was not known until today. To support the excavation activity, GIS and remote sensing applications were implemented for predictive and postdictive analysis using only free and open source software and satellite images

A lunar reconnaissance drone for cooperative exploration and high-resolution mapping of extreme locations

An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed. This hinders the planning and successful implementation of prospecting missions and poses a high risk for the traverse of robots and humans, diminishing the potential overall scientific and commercial return of any mission. We herein present the design of a lightweight, compact, autonomous, and reusable lunar reconnaissance drone capable of assisting other ground-based robotic assets, and eventually humans, in the characterization and high-resolution mapping (~0.1 m/px) of particularly challenging and hard-to-access locations on the lunar surface. The proposed concept consists of two main subsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service station, enabling almost 9 km of accumulated flight distance. The deployment of such a system could significantly impact the efficiency of upcoming exploration missions, increasing the distance covered per day of exploration and significantly reducing the need for recurrent contacts with ground stations on Earth

Land use classification from Sentinel-2 imagery

[EN] Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improve-ment with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those bands from S2 closer to the four bands from SPOT. In all the cases, between 10%-50% of samples were used to train the classifier while remaining the rest for validation. As a result, a land use map was generated from the best classifier, according to the Kappa index, providing scientifically relevant information such as the area of each land use class.[ES] Sentinel-2 (S2) es un nuevo satélite de la ESA que cuenta con 13 bandas proporcionando imágenes de alta calidad radiométrica y excelente resolución espacial (10 y 20 m) ideal para trabajos de clasificación. En este trabajo se han abordado dos objetivos: determinar el mejor método de clasificación con S2, y cuantificar su mejora respecto a otras misiones operativas, como SPOT. Para ello se han seleccionado cuatro clasificadores (LDA, RF, Árboles de decisión, K-NN) que se han aplicado en dos zonas agrarias: una en la huerta de Valencia (España) y otra en la región de Buenos Aires (Argentina). Se han probado todos los clasificadores usando, por una parte, todas las bandas de S2, y por otra usando sólo las cuatro que coinciden con SPOT. En todos los casos se han aplicando porcentajes entre el 10 y el 50% de datos de entrenamiento y usado el resto de datos como validación. Como resultado se ha generado un mapa de usos del suelo a partir del mejor clasificador, basándose en el índice Kappa, proporcionando información científicamente relevante como es el área ocupada por cada una de las clases.Borràs, J.; Delegido, J.; Pezzola, A.; Pereira, M.; Morassi, G.; Camps-Valls, G. (2017). Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección. (48):55-66. doi:10.4995/raet.2017.7133.SWORD556648Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37-46. doi:10.1177/001316446002000104Comber, A., Fisher, P., & Wadsworth, R. (2005). You know what land cover is but does anyone else?…an investigation into semantic and ontological confusion. International Journal of Remote Sensing, 26(1), 223-228. doi:10.1080/0143116042000274032Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Sensors, 11(7), 7063-7081. doi:10.3390/s110707063Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300. doi:10.1016/j.patrec.2005.08.011Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer Series in Statistics. doi:10.1007/978-0-387-84858-7Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data. Remote Sensing, 4(9), 2661-2693. doi:10.3390/rs4092661Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159. doi:10.2307/2529310Mena, A.J. 2014. Procesamiento de imágenes satelitales multiespectrales. Proyecto final de carrera, Facultad de Informática, Universidad del País Vasco.Quinlan, J.R. 1993. Programs for Machine Learning. 1st ed. San Mateo, CA, Morgan.Rees, G. 2005. The Remote Sensing Data Book. Cambridge University Press, 262 pp.Rodríguez-Galiano, V., Chica-Rivas, M. 2012. Clasificación de imágenes de satélite mediante software libre: Nuevas tendencias en algoritmos de Inteligencia artificial. Departamento de Geodinámica, Universidad de Granada

Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable

Spatiotemporal Analysis Of Lake Water Quality Indicators On Small Lakes, Lake Bloomington And Evergreen Lake In Central Illinois, Using Satellite Remote Sensing

This research explores the use of Sentinel-2 satellite to determine the spatiotemporal patterns of lake water quality indicators (e.g. chlorophyll a) in Lake Bloomington and Evergreen Lake. Lake water quality issues related to algal blooms is a serious problem in basins with abundant agricultural lands causing harmful effects to freshwater ecosystems such as pollution of beaches, taste and odor problems in drinking water, depletion of oxygen levels causing fish kills and the issue of water exceeding safe drinking water standards. Developing monitoring techniques using various water quality indicators of algal blooms is crucial. In this project, remote sensing and field sampling methods were employed to assess the state of water quality of two small lakes, Lake Bloomington and Evergreen Lake, in Central Illinois. Water samples were collected from selected locations from the lakes to test for various water quality variables including nitrate, phosphorus and chlorophyll a. An exo sonde instrument and secchi disk was used to measure additional water quality parameters such as turbidity, secchi depth, and temperature. Concurrent satellite images obtained from Sentinel-2 with flyover with ±5 days were processed and analyzed, and the results were compared with field sampling data. Single and multiple pixel analyses were conducted on various algorithms such as Bottom-of-Atmosphere (BOA), Maximum Chlorophyll Index (MCI), and band ratios. These algorithms were tested to identify the best algorithm for estimating water quality parameters using satellite data for the two lakes. A regression analysis was conducted to derive a linear model which was used to create water quality indicator maps that showed the spatial pattern of algae in the lakes. From the results of the research, Lake Bloomington was more turbid and had higher concentrations of chlorophyll a than Evergreen Lake. Except for band ratio of B1/B2 of Sentinel-2 data, a poor regression relationship between satellite and field water quality values was observed for Lake Bloomington. This poor relationship could be due to the high turbidity of the lake. Evergreen Lake, on the other hand, showed a stronger relationship between satellite values and chlorophyll a. Generally, spatial analysis reveals that chlorophyll a distribution was heterogeneous, and it increased from downstream areas to upstream areas

Satellite-derived bathymetry for shallow water hydrographic mapping

Satellite-Derived Bathymetry (SDB), a new method which derives bathymetric data from multi-spectral satellite imagery, has yet to be recognised as a new acquisition method for shallow water hydrographic survey mapping. Currently, SDB has received substantial attention from researchers worldwide, but most of the studies primarily focused on remote sensing environments. The questions about precision and accuracy are always the subject of interest in the surveying field but went unreported in most of the studies. Hence, this study aims to develop an improved SDB algorithm model which is capable of delivering better accuracy for shallow water hydrographic survey mapping application in a tropical environment. High resolution multi-spectral satellite imageries from the Sentinel-2A, Pleiades and WorldView-2 of Tawau Port, Sabah and Pulau Kuraman, Labuan were derived. Both places have diverse seabed topography parameters. A conceptual model of Multi-Layer Optimisation Technique (M-LOT) was developed based on Stumpf derivation model. Accuracy assessment of M-LOT was carried out against derivation models of Lyzenga and Sumpf. Two types of accuracy assessment were involved: Statistical Assessment and International Hydrographic Organization (IHO) Survey Standard evaluation. The findings showed M-LOT model managed to achieve up to 1.800m and 1.854m Standard Deviation (SD) accuracy for Tawau Port and Pulau Kuraman respectively. In addition, M-LOT has shown a better derivation compared to Stumpf’s, where a total of 13.1% more depth samples meeting the IHO minimum standard for Tawau Port. Furthermore, M-LOT has generated an extensive increment up to 46.1% depths samples meeting the IHO minimum standard for Pulau Kuraman. In conclusion, M-LOT has significantly shown improved accuracy compared to Stumpf, which can offer a solution for SDB method in shallow-water hydrographic survey mapping application