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ORCIDSalvatore Eugenio Pappalardo
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Journal Articles
- 2024
[j6]David Govi
, Salvatore Eugenio Pappalardo, Massimo De Marchi, Franco Meggio:
From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture. Remote. Sens. 16(5): 735 (2024)- 2023
- [j5]Nathalia Celis, Alejandro Casallas, Ellie Anne Lopez-Barrera, Martina Felician, Massimo De Marchi, Salvatore Eugenio Pappalardo:
Climate Change, Forest Fires, and Territorial Dynamics in the Amazon Rainforest: An Integrated Analysis for Mitigation Strategies. ISPRS Int. J. Geo Inf. 12(10): 436 (2023) - 2022
- [j4]Valeria Todeschi, Salvatore Eugenio Pappalardo, Carlo Zanetti, Francesca Peroni, Massimo De Marchi:
Climate Justice in the City: Mapping Heat-Related Risk for Climate Change Mitigation of the Urban and Peri-Urban Area of Padua (Italy). ISPRS Int. J. Geo Inf. 11(9): 490 (2022) - 2021
- [j3]Guglielmo Pristeri, Francesca Peroni, Salvatore Eugenio Pappalardo, Daniele Codato, Antonio Masi, Massimo De Marchi:
Whose Urban Green? Mapping and Classifying Public and Private Green Spaces in Padua for Spatial Planning Policies. ISPRS Int. J. Geo Inf. 10(8): 538 (2021) - [j2]Pietro Mattivi, Salvatore Eugenio Pappalardo, Nebojsa Nikolic, Luca Mandolesi, Antonio Persichetti, Massimo De Marchi, Roberta Masin:
Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy. Remote. Sens. 13(10): 1869 (2021) - 2020
- [j1]Andrea Semenzato, Salvatore Eugenio Pappalardo, Daniele Codato, Umberto Trivelloni, Silvano De Zorzi, Sabrina Ferrari, Massimo De Marchi, Matteo Massironi:
Mapping and Monitoring Urban Environment through Sentinel-1 SAR Data: A Case Study in the Veneto Region (Italy). ISPRS Int. J. Geo Inf. 9(6): 375 (2020)
Conference and Workshop Papers
- 2022
- [c1]Daniele Michelini, Michele Dalponte, Angelo Carriero, Erico Kutchartt, Salvatore Eugenio Pappalardo, Massimo De Marchi, Francesco Pirotti:
Hyperspectral and LiDAR Data for the Prediction via Machine Learning of Tree Species, Volume and Biomass: A Contribution for Updating Forest Management Plans. ASITA 2022: 235-250
Informal and Other Publications
- 2022
- [i1]Daniele Michelini, Michele Dalponte, Angelo Carriero, Erico Kutchartt, Salvatore Eugenio Pappalardo, Massimo De Marchi, Francesco Pirotti:
Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans. CoRR abs/2209.15248 (2022)
Coauthor Index
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