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Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.
Maedeh Farokhi; Farid Faridani; Rosa Lasaponara; Hossein Ansari; Alireza Faridhosseini. Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors 2021, 21, 5211 .
AMA StyleMaedeh Farokhi, Farid Faridani, Rosa Lasaponara, Hossein Ansari, Alireza Faridhosseini. Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors. 2021; 21 (15):5211.
Chicago/Turabian StyleMaedeh Farokhi; Farid Faridani; Rosa Lasaponara; Hossein Ansari; Alireza Faridhosseini. 2021. "Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data." Sensors 21, no. 15: 5211.
Unmanned aerial vehicles are currently the most used solution for cultural heritage in the field of close range and low altitude acquisitions. This work shows data acquired by multitemporal and multispectral aerial surveys in the archaeological site of San Vincenzo al Volturno (Molise, Italy). The site is one of the most important medieval archaeological sites in the world. It is a monastic settlement that was particularly rich during the early Middle Ages, and is famous for its two full-frescoed crypts which represent a milestone in the history of medieval art. Thanks to the use of multispectral aerial photography at different times of the year, an area not accessible to archaeological excavation has been investigated. To avoid redundancy of information and reduce the number of data to be analysed, a method based on spectral and radiometric enhancement techniques combined with a selective principal component analysis was used for the identification of useful information. The combination of already published archaeological data and new remote sensing discoveries, has allowed to better define the situation of the abbey during the building phases of the 8th/9th century and 11th century, confirming and adding new data to the assumptions made by archaeologists.
Nicodemo Abate; Alessia Frisetti; Federico Marazzi; Nicola Masini; Rosa Lasaponara. Multitemporal–Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy). Remote Sensing 2021, 13, 2719 .
AMA StyleNicodemo Abate, Alessia Frisetti, Federico Marazzi, Nicola Masini, Rosa Lasaponara. Multitemporal–Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy). Remote Sensing. 2021; 13 (14):2719.
Chicago/Turabian StyleNicodemo Abate; Alessia Frisetti; Federico Marazzi; Nicola Masini; Rosa Lasaponara. 2021. "Multitemporal–Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy)." Remote Sensing 13, no. 14: 2719.
Nicola Masini; Rosa Lasaponara. Remote and Close Range Sensing for the Automatic Identification and Characterization of Archaeological Looting. The Case of Peru. Journal of Computer Applications in Archaeology 2021, 4, 126 -144.
AMA StyleNicola Masini, Rosa Lasaponara. Remote and Close Range Sensing for the Automatic Identification and Characterization of Archaeological Looting. The Case of Peru. Journal of Computer Applications in Archaeology. 2021; 4 (1):126-144.
Chicago/Turabian StyleNicola Masini; Rosa Lasaponara. 2021. "Remote and Close Range Sensing for the Automatic Identification and Characterization of Archaeological Looting. The Case of Peru." Journal of Computer Applications in Archaeology 4, no. 1: 126-144.
In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.
Carmen Fattore; Nicodemo Abate; Farid Faridani; Nicola Masini; Rosa Lasaponara. Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy. Sensors 2021, 21, 1791 .
AMA StyleCarmen Fattore, Nicodemo Abate, Farid Faridani, Nicola Masini, Rosa Lasaponara. Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy. Sensors. 2021; 21 (5):1791.
Chicago/Turabian StyleCarmen Fattore; Nicodemo Abate; Farid Faridani; Nicola Masini; Rosa Lasaponara. 2021. "Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy." Sensors 21, no. 5: 1791.
Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.
Mohamed K. Abdel-Fattah; Elsayed Said Mohamed; Enas M. Wagdi; Sahar A. Shahin; Ali A. Aldosari; Rosa Lasaponara; Manal A. Alnaimy. Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt. Sustainability 2021, 13, 1824 .
AMA StyleMohamed K. Abdel-Fattah, Elsayed Said Mohamed, Enas M. Wagdi, Sahar A. Shahin, Ali A. Aldosari, Rosa Lasaponara, Manal A. Alnaimy. Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt. Sustainability. 2021; 13 (4):1824.
Chicago/Turabian StyleMohamed K. Abdel-Fattah; Elsayed Said Mohamed; Enas M. Wagdi; Sahar A. Shahin; Ali A. Aldosari; Rosa Lasaponara; Manal A. Alnaimy. 2021. "Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt." Sustainability 13, no. 4: 1824.
The currently available tools and services as open and free cloud resources to process big satellite data opened up a new frontier of possibilities and applications including archeological research. These new research opportunities also pose several challenges to be faced, as, for example, the data processing and interpretation. This letter is about the assessment of different methods and data sources to support a visual interpretation of EO imagery. Multitemporal Sentinel 1 and Sentinel 2 data sets have been processed to assess their capability in the detection of buried archeological remains related to some lost sections of the ancient Via Appia road (herein selected as case study). The very subtle and nonpermanent features linked to buried archeological remains can be captured using multitemporal (intra- and inter-year) satellite acquisitions, but this requires strong hardware infrastructures or cloud facilities, today also available as open and free tools as Google Earth Engine (GEE). In this study, a total of 2948 Sentinel 1 and 743 Sentinel 2 images were selected (from February 2017 to August 2020) and processed using GEE to enhance and unveil archeological features. Outputs obtained from both Sentinel 1 and Sentinel 2 have been successfully compared with in situ analysis and high-resolution Google Earth images.
Rosa Lasaponara; Nicodemo Abate; Nicola Masini. On the Use of Google Earth Engine and Sentinel Data to Detect ``Lost'' Sections of Ancient Roads. The Case of Via Appia. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleRosa Lasaponara, Nicodemo Abate, Nicola Masini. On the Use of Google Earth Engine and Sentinel Data to Detect ``Lost'' Sections of Ancient Roads. The Case of Via Appia. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleRosa Lasaponara; Nicodemo Abate; Nicola Masini. 2021. "On the Use of Google Earth Engine and Sentinel Data to Detect ``Lost'' Sections of Ancient Roads. The Case of Via Appia." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
The Middle Ages have been traditionally considered a crisis period due to the demographic decrease and economic deterioration occurred in Western Europe. Nevertheless, the historical reconsideration has been focused not only on decline and decay, but also on resilience and recovery which characterized the Europe of the fourteenth and fifteenth centuries. So, today the main open question is as follows: how can we explain the diverse attitude (namely recovery versus decline) and the reasons why some settlements were more (or less) resilient than others? To provide a contribution to this issue, we focused on two medieval villages which are located very close to each other (in the Basilicata Region Southern Italy) and selected because they are characterized by diverse vicissitudes: Irsi abandoned in the fourteenth century and Montepeloso (still “existing” and renamed Irsina) where the population of Irsi moved to. To improve our current knowledge on Irsi, we reused and integrated multiscale LiDAR datasets in order to cope with the lack of documentary source. The use of LiDAR data enabled (i) the reconstruction of the potential urban fabric of Irsi, along with its temporal development and the transformation of the surrounding landscape, and (ii) the definition of a hypothesis about the causes of its desertification based on the inter-site analysis between Irsi and Montepeloso. The main results from the LiDAR-based analysis were as follows: (i) the diachronic reconstruction of the building phases of the village and (ii) the identification of a significant indicator obtained as the ratio between the amount of cultivatable land (close to the settlement area) and the population to characterize the resilience behavior in hilly landscape. This approach has been also successfully applied to another similar case study. Outputs from our analyses pointed out that LiDAR data can fruitfully improve medieval archaeological investigations and facilitate knowledge improvement from intra to- inter-site scale analyses and from local up to a landscape perspective.
Nicola Masini; Rosa Lasaponara. On the Reuse of Multiscale LiDAR Data to Investigate the Resilience in the Late Medieval Time: the Case Study of Basilicata in South of Italy. Journal of Archaeological Method and Theory 2020, 1 -28.
AMA StyleNicola Masini, Rosa Lasaponara. On the Reuse of Multiscale LiDAR Data to Investigate the Resilience in the Late Medieval Time: the Case Study of Basilicata in South of Italy. Journal of Archaeological Method and Theory. 2020; ():1-28.
Chicago/Turabian StyleNicola Masini; Rosa Lasaponara. 2020. "On the Reuse of Multiscale LiDAR Data to Investigate the Resilience in the Late Medieval Time: the Case Study of Basilicata in South of Italy." Journal of Archaeological Method and Theory , no. : 1-28.
Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.
Ahmed A. El Baroudy; Abdelraouf. M. Ali; Elsayed Said Mohamed; Farahat S. Moghanm; Mohamed S. Shokr; Igor Savin; Anton Poddubsky; Zheli Ding; Ahmed M.S. Kheir; Ali A. Aldosari; Abdelaziz Elfadaly; Peter Dokukin; Rosa Lasaponara. Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta. Sustainability 2020, 12, 9653 .
AMA StyleAhmed A. El Baroudy, Abdelraouf. M. Ali, Elsayed Said Mohamed, Farahat S. Moghanm, Mohamed S. Shokr, Igor Savin, Anton Poddubsky, Zheli Ding, Ahmed M.S. Kheir, Ali A. Aldosari, Abdelaziz Elfadaly, Peter Dokukin, Rosa Lasaponara. Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta. Sustainability. 2020; 12 (22):9653.
Chicago/Turabian StyleAhmed A. El Baroudy; Abdelraouf. M. Ali; Elsayed Said Mohamed; Farahat S. Moghanm; Mohamed S. Shokr; Igor Savin; Anton Poddubsky; Zheli Ding; Ahmed M.S. Kheir; Ali A. Aldosari; Abdelaziz Elfadaly; Peter Dokukin; Rosa Lasaponara. 2020. "Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta." Sustainability 12, no. 22: 9653.
The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.
Elsayed Said Mohamed; A. A El Baroudy; T. El-Beshbeshy; M. Emam; A. A. Belal; Abdelaziz Elfadaly; Ali A. Aldosari; Abdelraouf. M. Ali; Rosa Lasaponara. Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt. Remote Sensing 2020, 12, 3716 .
AMA StyleElsayed Said Mohamed, A. A El Baroudy, T. El-Beshbeshy, M. Emam, A. A. Belal, Abdelaziz Elfadaly, Ali A. Aldosari, Abdelraouf. M. Ali, Rosa Lasaponara. Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt. Remote Sensing. 2020; 12 (22):3716.
Chicago/Turabian StyleElsayed Said Mohamed; A. A El Baroudy; T. El-Beshbeshy; M. Emam; A. A. Belal; Abdelaziz Elfadaly; Ali A. Aldosari; Abdelraouf. M. Ali; Rosa Lasaponara. 2020. "Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt." Remote Sensing 12, no. 22: 3716.
This paper is focused on investigating the capabilities of SAR S-1 sensors for burned area mapping. To this aim, we analyzed S-1 data focusing on a fire that occurred on August 10th, 2017, in a protected natural site. An unsupervised classification, using a k-mean machine learning algorithm, was carried out, and the choice of an adequate number of clusters was guided by the calculation of the silhouette score. The ΔNBR index calculated from optical S-2 based images was used to evaluate the burned area delimitation accuracy. The fire covered around 38.51 km2 and also affected areas outside the boundaries of the reserve. S-1 based outputs successfully matched the S-2 burnt mapping.
Giandomenico De Luca; Giuseppe Modica; Carmen Fattore; Rosa Lasaponara. Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12253, 63 -77.
AMA StyleGiandomenico De Luca, Giuseppe Modica, Carmen Fattore, Rosa Lasaponara. Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12253 ():63-77.
Chicago/Turabian StyleGiandomenico De Luca; Giuseppe Modica; Carmen Fattore; Rosa Lasaponara. 2020. "Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm." Transactions on Petri Nets and Other Models of Concurrency XV 12253, no. : 63-77.
There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.
Farid Faridani; Sirus Bakhtiari; Alireza Faridhosseini; Micheal Gibson; Raziyeh Farmani; Rosa Lasaponara. Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins. Sustainability 2020, 12, 7371 .
AMA StyleFarid Faridani, Sirus Bakhtiari, Alireza Faridhosseini, Micheal Gibson, Raziyeh Farmani, Rosa Lasaponara. Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins. Sustainability. 2020; 12 (18):7371.
Chicago/Turabian StyleFarid Faridani; Sirus Bakhtiari; Alireza Faridhosseini; Micheal Gibson; Raziyeh Farmani; Rosa Lasaponara. 2020. "Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins." Sustainability 12, no. 18: 7371.
The authors wish to make the following corrections to this paper [1]: Due to mislabeling, replace: Figure 6Typical Neolithic settlements in the Tavoliere delle Puglie: (a) settlement of Masseria Schifata; (b) settlement of Passo di Corvo [36]
Abdelaziz Elfadaly; Nicodemo Abate; Nicola Masini; Rosa Lasaponara. Correction: Elfadaly, A.; Abate, N.; Masini, N.; Lasaponara, R. SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area. Remote Sens. 2020, 12, 2611. Remote Sensing 2020, 12, 2878 .
AMA StyleAbdelaziz Elfadaly, Nicodemo Abate, Nicola Masini, Rosa Lasaponara. Correction: Elfadaly, A.; Abate, N.; Masini, N.; Lasaponara, R. SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area. Remote Sens. 2020, 12, 2611. Remote Sensing. 2020; 12 (18):2878.
Chicago/Turabian StyleAbdelaziz Elfadaly; Nicodemo Abate; Nicola Masini; Rosa Lasaponara. 2020. "Correction: Elfadaly, A.; Abate, N.; Masini, N.; Lasaponara, R. SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area. Remote Sens. 2020, 12, 2611." Remote Sensing 12, no. 18: 2878.
The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km2) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features.
Abdelaziz Elfadaly; Nicodemo Abate; Nicola Masini; Rosa Lasaponara. SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area. Remote Sensing 2020, 12, 2611 .
AMA StyleAbdelaziz Elfadaly, Nicodemo Abate, Nicola Masini, Rosa Lasaponara. SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area. Remote Sensing. 2020; 12 (16):2611.
Chicago/Turabian StyleAbdelaziz Elfadaly; Nicodemo Abate; Nicola Masini; Rosa Lasaponara. 2020. "SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area." Remote Sensing 12, no. 16: 2611.
Most of the artistic heritage in the Mediterranean basin is hosted in rupestrian hypogeum whose peculiarity is given by the presence of at least one open side, which makes them particularly sensitive to meteorological conditions. This makes mandatory the monitoring of both indoor and outdoor environmental parameters to analyze the cause–effect relationship between microclimatic inside and outside the hypogeum. The paper proposes a spatial and temporal multi-scale methodological approach applied to a rupestrian church in Matera, which hosts precious wall paintings, particularly vulnerable to the effects of environmental parameters. The approach is based on the analysis of data acquired by three platforms: indoor, close-range outdoor, and outdoor data from a meteorological station and weather forecast from the COSMO 5 model. The method allowed to characterize the relationships between the indoor and outdoor parameters at different spatial and temporal scales. The results showed a significant correlation between the parameters, thus opening new opportunities for the monitoring of the rupestrian heritage based on the use of data systematically available, such as those from meteorological stations and meteorological forecast.
M. Sileo; F. T. Gizzi; A. Donvito; R. Lasaponara; F. Fiore; N. Masini. Multi-Scale Monitoring of Rupestrian Heritage: Methodological Approach and Application to a Case Study. International Journal of Architectural Heritage 2020, 1 -16.
AMA StyleM. Sileo, F. T. Gizzi, A. Donvito, R. Lasaponara, F. Fiore, N. Masini. Multi-Scale Monitoring of Rupestrian Heritage: Methodological Approach and Application to a Case Study. International Journal of Architectural Heritage. 2020; ():1-16.
Chicago/Turabian StyleM. Sileo; F. T. Gizzi; A. Donvito; R. Lasaponara; F. Fiore; N. Masini. 2020. "Multi-Scale Monitoring of Rupestrian Heritage: Methodological Approach and Application to a Case Study." International Journal of Architectural Heritage , no. : 1-16.
One of the most complex challenges of heritage sciences is the identification and protection of buried archaeological heritage in urban areas and the need to manage, maintain and inspect underground services. Archaeology and geophysics, used in an integrated way, provide an important contribution to open new perspectives in understanding both the history of cities and in helping the decision makers in planning and governing the urban development and management. The problems of identification and interpretation of geophysical features in urban subsoil make it necessary to develop ad hoc procedures to be implemented and validated in significant case studies. This paper deals with the results of an interdisciplinary project in Cusco (Peru), the capital of Inca Empire, where the georadar method was applied for the first time in the main square. The georadar method was successfully employed based on knowledge of the historical evolution of Cusco and the availability of archaeological records provided by some excavations nearby the study area. Starting from a model for the electromagnetic wave reflection from archaeological structures and pipes, georadar results were interpreted by means of comparative morphological analysis of high amplitude values observed from time slices with reflectors visualized in the radargrams.
Nicola Masini; Giovanni Leucci; David Vera; Maria Sileo; Antonio Pecci; Sayri Garcia; Ronald López; Henry Holguín; Rosa Lasaponara. Towards Urban Archaeo-Geophysics in Peru. The Case Study of Plaza de Armas in Cusco. Sensors 2020, 20, 2869 .
AMA StyleNicola Masini, Giovanni Leucci, David Vera, Maria Sileo, Antonio Pecci, Sayri Garcia, Ronald López, Henry Holguín, Rosa Lasaponara. Towards Urban Archaeo-Geophysics in Peru. The Case Study of Plaza de Armas in Cusco. Sensors. 2020; 20 (10):2869.
Chicago/Turabian StyleNicola Masini; Giovanni Leucci; David Vera; Maria Sileo; Antonio Pecci; Sayri Garcia; Ronald López; Henry Holguín; Rosa Lasaponara. 2020. "Towards Urban Archaeo-Geophysics in Peru. The Case Study of Plaza de Armas in Cusco." Sensors 20, no. 10: 2869.
This paper is focused on the use of satellite Sentinel-2 data for assessing their capability in the identification of archaeological buried remains. We selected the “Tavoliere delle Puglie” (Foggia, Italy) as a test area because it is characterized by a long human frequentation and is very rich in archaeological remains. The investigations were performed using multi-temporal Sentinel-2 data and spectral indices, commonly used in satellite-based archaeology, and herein analyzed in known archaeological areas to capture the spectral signatures of soil and crop marks and characterize their temporal behavior using Time Series Analysis and Spectral Un-mixing. Tasseled Cap Transformation and Principal Component Analysis have been also adopted to enhance archaeological features. Results from investigations were compared with independent data sources and enabled us to (i) characterize the spectral signatures of soil and crop marks, (ii) assess the performance of the diverse spectral channels and indices, and (iii) identify the best period of the year to capture the archaeological proxy indicators. Additional very important results of our investigations were (i) the discovery of unknown archaeological areas and (ii) the setup of a database of archaeological features devised ad hoc to characterize and categorize the diverse typologies of archaeological remains detected using Sentinel-2 Data.
Nicodemo Abate; Abdelaziz Elfadaly; Nicola Masini; Rosa Lasaponara. Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy. Remote Sensing 2020, 12, 1309 .
AMA StyleNicodemo Abate, Abdelaziz Elfadaly, Nicola Masini, Rosa Lasaponara. Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy. Remote Sensing. 2020; 12 (8):1309.
Chicago/Turabian StyleNicodemo Abate; Abdelaziz Elfadaly; Nicola Masini; Rosa Lasaponara. 2020. "Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy." Remote Sensing 12, no. 8: 1309.
In recent years, very high-resolution satellite remote-sensing tools have been progressively used in archaeological prospecting to acquire information and improve documentation. Satellite remote sensing has also benefited from technical improvements, including better spectral and spatial resolution of sensors, which have facilitated the detection and discovery of unknown archaeological areas. This paper focuses on investigations conducted using multi-spectral satellite remote-sensing data of the ancient canal systems of the Wadi el Melah Valley (WMV) in southern Tunisia. The area used to be part of a huge military defense system along the desert border. This paper describes the use of GeoEye-1 and Ziyuan-3 satellite remote-sensing data to reveal ancient Roman canals, which were part of an advanced hydraulic system devised to capture runoff water and cope with the lack of water in the area. In general, this research provides new information on some essential sections of the Roman walled defense system Limes (Fossatum) in the southern part of the empire, where we study previously undetected sites.
Nabil Bachagha; Lei Luo; Xinyuan Wang; Nicola Masini; Tababi Moussa; Houcine Khatteli; Rosa Lasaponara. Mapping the Roman Water Supply System of the Wadi el Melah Valley in Gafsa, Tunisia, Using Remote Sensing. Sustainability 2020, 12, 567 .
AMA StyleNabil Bachagha, Lei Luo, Xinyuan Wang, Nicola Masini, Tababi Moussa, Houcine Khatteli, Rosa Lasaponara. Mapping the Roman Water Supply System of the Wadi el Melah Valley in Gafsa, Tunisia, Using Remote Sensing. Sustainability. 2020; 12 (2):567.
Chicago/Turabian StyleNabil Bachagha; Lei Luo; Xinyuan Wang; Nicola Masini; Tababi Moussa; Houcine Khatteli; Rosa Lasaponara. 2020. "Mapping the Roman Water Supply System of the Wadi el Melah Valley in Gafsa, Tunisia, Using Remote Sensing." Sustainability 12, no. 2: 567.
Historic Jeddah is located on the eastern shore of the Red Sea. Historic Jeddah was designated as a UNESCO world heritage site in 2014. The new urban development for the city of Jeddah has resulted in different spatial patterns. The southern part of Jeddah city falls within the moderate zone, because this area is well developed in regard to infrastructure with rainstorm and sewage networks. The middle area of the city falls within high vulnerability risk due to its high population, shallow water depth, flat slopes, and various incomplete network services (i.e., leakage from septic tanks and water pipes). The western and northwestern parts of the city are subject to very high pollution risk, due to the highly permeable area with coralline formation, very shallow water depth, and depressions. Unfortunately, historic Jeddah has been affected by the unplanned development and shallow water depth. Most of the construction and decoration of the ancient buildings are suffering from deterioration. The paper aims to detect the environmental changes, assessing the geo-environmental status, and creating some of the innovative solutions while using the integration between remote sensing and GIS techniques. The combination of SRTM, Corona 1966, Spot 1986, Landsat 1987, Orbview 2003, and Sentinel2A 2017 data will help in monitoring the changes around the study area. The Bands combination and the spatial statistical analysis are considered to be the most effective methods in the examination of the new built-up indices. GIS techniques and some models would be suggested as solutions to protect the archaeological area, according to UNESCO recommendations.
Abdelaziz Elfadaly; Ayaat Shams Eldein; Rosa Lasaponara. Cultural Heritage Management Using Remote Sensing Data and GIS Techniques around the Archaeological Area of Ancient Jeddah in Jeddah City, Saudi Arabia. Sustainability 2019, 12, 240 .
AMA StyleAbdelaziz Elfadaly, Ayaat Shams Eldein, Rosa Lasaponara. Cultural Heritage Management Using Remote Sensing Data and GIS Techniques around the Archaeological Area of Ancient Jeddah in Jeddah City, Saudi Arabia. Sustainability. 2019; 12 (1):240.
Chicago/Turabian StyleAbdelaziz Elfadaly; Ayaat Shams Eldein; Rosa Lasaponara. 2019. "Cultural Heritage Management Using Remote Sensing Data and GIS Techniques around the Archaeological Area of Ancient Jeddah in Jeddah City, Saudi Arabia." Sustainability 12, no. 1: 240.
In this letter, we propose an approach based on the use of Sentinel-2 spectral indices and self-organizing map (SOM) to automatically map burned areas and burned severity. These analyses were performed on a test area in Chania, located in Crete, affected by a fire (around 200 ha) that occurred from July 13, 2018 to July 28, 2018. The investigated area is characterized by heterogeneous land cover types made up of natural and agricultural lands. To identify different levels of fire severity without using fixed thresholds, we applied SOM to the three spectral indices normalized difference vegetation index (NDVI), normalized burn ratio (NBR), and burned area index for sentinel (BAIS) used to enhance burned areas. This is a particular critical issue because fixed threshold values are generally not suitable for fragmented landscapes, vegetation types, and geographic regions different from those for which they were devised. To cope with this issue, the methodological approach herein proposed is based on three steps: 1) indices computation; 2) maps of the difference of the three indices computed using the data acquired from prefire and postfire occurrences; and 3) unsupervised classification obtained processing all the difference maps using the SOM. The obtained results were validated using an independent data set, which showed high correlation with satellite-based fire severity.
R. Lasaponara; A. M. Proto; A. Aromando; G. Cardettini; V. Varela; M. Danese. On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data. IEEE Geoscience and Remote Sensing Letters 2019, 17, 854 -858.
AMA StyleR. Lasaponara, A. M. Proto, A. Aromando, G. Cardettini, V. Varela, M. Danese. On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (5):854-858.
Chicago/Turabian StyleR. Lasaponara; A. M. Proto; A. Aromando; G. Cardettini; V. Varela; M. Danese. 2019. "On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data." IEEE Geoscience and Remote Sensing Letters 17, no. 5: 854-858.
The primary objective of this study is to leverage the integration of surface mapping data derived from optical, radar, and historic topographical studies with archaeological sampling to identify ancient settlement areas in the Northern Nile Delta, Egypt. This study employed the following methods: digitization of topographic maps, band indices techniques on optical data, the creation of a 3D model from SRTM data, and Sentinel-1 interferometric wide swath (IW) analysis. This type of study is particularly relevant to the search for evidence of otherwise hidden ancient settlements. Due to its geographical situation and the fertility of the Nile, Egypt witnessed the autochthonous development of predynastic and dynastic civilizations, as well as an extensive history of external influences due to Greek, Roman, Coptic, Islamic, and Colonial-era interventions. Excavation work at Buto (Tell el-Fara’in) in 2017–18, carried out by the Kafrelsheikh University (KFS) in cooperation with the Ministry of Antiquities, demonstrated that remote sensing data offers considerable promise as a tool for developing regional settlement studies and excavation strategies. This study integrates the mission work in Buto with the satellite imagery in and around the area of the excavation. The results of the initial Buto area research serve as a methodological model to expand the study area to the North Delta with the goal of detecting the extent of the ancient kingdoms of Buto and Sakha. The results of this research include the creation of a composite historical database using ancient references and early topographical maps (1722, 1941, 1950, and 1997), Optical Corona (1965), Landsat MSS (Multispectral Scanner System) (1973, 1978, and 1988), TM (Thematic Mapper) (2005) data, and Radar SRTM (2014) and Sentinel1 (2018 and 2019) data. The data in this study have been analyzed using the ArcMap, Envi, and SNAP software. The results from the current investigation highlight the rapid changes in the land use/land cover in the last century in which many ancient sites were lost due to agriculture and urban development. Three potential settlement areas have been identified with the Sentinel1 Radar data, and have been integrated with the early maps. These discoveries will help develop excavation strategies aimed at elucidating the ancient settlement dynamics and history of the region during the next phase of research.
Abdelaziz Elfadaly; Mohamed A. R. Abouarab; Radwa R. M. El Shabrawy; Wael Mostafa; Penelope Wilson; Christophe Morhange; Jay Silverstein; Rosa Lasaponara. Discovering Potential Settlement Areas around Archaeological Tells Using the Integration between Historic Topographic Maps, Optical, and Radar Data in the Northern Nile Delta, Egypt. Remote Sensing 2019, 11, 3039 .
AMA StyleAbdelaziz Elfadaly, Mohamed A. R. Abouarab, Radwa R. M. El Shabrawy, Wael Mostafa, Penelope Wilson, Christophe Morhange, Jay Silverstein, Rosa Lasaponara. Discovering Potential Settlement Areas around Archaeological Tells Using the Integration between Historic Topographic Maps, Optical, and Radar Data in the Northern Nile Delta, Egypt. Remote Sensing. 2019; 11 (24):3039.
Chicago/Turabian StyleAbdelaziz Elfadaly; Mohamed A. R. Abouarab; Radwa R. M. El Shabrawy; Wael Mostafa; Penelope Wilson; Christophe Morhange; Jay Silverstein; Rosa Lasaponara. 2019. "Discovering Potential Settlement Areas around Archaeological Tells Using the Integration between Historic Topographic Maps, Optical, and Radar Data in the Northern Nile Delta, Egypt." Remote Sensing 11, no. 24: 3039.