DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS

Kutchartt, E.; Hernández, J.; Corvalán, P.; Promis, Á.; Pirotti, F.

doi:https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-913-2022

Estimating canopy fuels across Europe with satellite data and allometric equations

Erico Kutchartt, José Ramón González-Olabarria, Núria Aquilué, Antoni Trasobares, and Francesco Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 279–285, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-279-2024,https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-279-2024, 2024

Exploring Ground-Level Ozone Distribution Across Different Land Covers in the Veneto Region, Italy

Luka Mamić, Gordana Kaplan, and Francesco Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 331–335, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-331-2024,https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-331-2024, 2024

Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought conditions

Francesco Pirotti and Felix Enyimah Toffah

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 413–420, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-413-2024,https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-413-2024, 2024

Do greener childhoods mean fewer eating disorders? Using satellite imagery to uncover insights

Perla Rivadeneyra, Angela Favaro, Paolo Meneguzzo, and Francesco Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 355–362, https://doi.org/10.5194/isprs-annals-X-3-2024-355-2024,https://doi.org/10.5194/isprs-annals-X-3-2024-355-2024, 2024

CLASSIFICATION OF TREES IN HYPERSPECTRAL CANOPY DATA USING MACHINE LEARNING: COMPARATIVE ANALYSIS OF FOREST STRUCTURE COMPLEXITY

F. Galdames, P. González, F. Magni-Pérez, S. M. Funk, F. Lepín, R. Saavedra, and H. J. Hernández

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1737–1742, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1737-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1737-2023, 2023

UPDATING ABOVEGROUND BIOMASS AT A PAN-EUROPEAN SCALE THROUGH SATELLITE DATA AND ARTIFICIAL INTELLIGENCE

F. Pirotti, J. R. González-Olabarria, and E. Kutchartt

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1763–1769, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1763-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1763-2023, 2023

AUTOMATIC COARSE CO-REGISTRATION OF POINT CLOUDS FROM DIVERSE SCAN GEOMETRIES: A TEST OF DETECTORS AND DESCRIPTORS

F. Pirotti, A. Guarnieri, S. Chiodini, and C. Bettanini

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 581–587, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-581-2023,https://doi.org/10.5194/isprs-annals-X-1-W1-2023-581-2023, 2023

INFORSAT: AN ONLINE SENTINEL-2 MULTI-TEMPORAL ANALYSIS TOOL SET USING R CRAN

F. Pirotti, E. Kutchartt, and M. Piragnolo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 365–370, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-365-2022,https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-365-2022, 2022

ISPRS-SHY – OPEN DATA COLLECTOR FOR SUPPORTING GROUND TRUTH REMOTE SENSING ANALYSIS

F. Pirotti, M. Piragnolo, M. Yoshimura, J. P. Hernandez, M. A. Brovelli, B. Leblon, and M. Yamashita

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2022, 45–50, https://doi.org/10.5194/isprs-archives-XLIII-B5-2022-45-2022,https://doi.org/10.5194/isprs-archives-XLIII-B5-2022-45-2022, 2022

COMPARING ACCURACY OF ULTRA-DENSE LASER SCANNER AND PHOTOGRAMMETRY POINT CLOUDS

F. Pirotti, M. Piragnolo, A. Vettore, and A. Guarnieri

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 353–359, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-353-2022,https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-353-2022, 2022

A COMPARATIVE ASSESSMENT OF LAND USE-LAND COVER DYNAMICS BETWEEN BANGLADESH AND INDIAN SUNDARBANS FROM 1975–2020: A GEOSPATIAL AND STATISTICAL-BASED APPROACH

A. H. Kanan and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 625–632, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-625-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-625-2022, 2022

SENTINEL-5P NO2 DATA: CROSS-VALIDATION AND COMPARISON WITH GROUND MEASUREMENTS

F. Tonion and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 749–756, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-749-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-749-2022, 2022

SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING

F. Tonion and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1011–1017, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022, 2022

HISTORIC LAND COVER CHANGE ASSESSMENT OF CHILEAN MEDITERRANEAN COAST: DID FOREST PLANTATIONS REALLY CAUSED FRAGMENTATION?

L. Cortés, H. J. Hernández, and P. Silva

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 383–388, https://doi.org/10.5194/isprs-annals-V-3-2022-383-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-383-2022, 2022

ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

E. Kutchartt, M. Pedron, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 431–438, https://doi.org/10.5194/isprs-annals-V-3-2022-431-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-431-2022, 2022

ASSESSMENT OF VOLUME AND ABOVE-GROUND BIOMASS IN ARAUCARIA FOREST THROUGH SATELLITE IMAGES, COMPARING DIFFERENT METHODS IN THE SOUTH OF CHILE

F. Pirotti, E. Kutchartt, and E. Csaplovics

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 331–336, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-331-2020,https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-331-2020, 2020

TRAINING IN INNOVATIVE TECHNOLOGIES FOR CLOSE-RANGE SENSING IN ALPINE TERRAIN – 3RD EDITION

M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020,https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020

SPATIOTEMPORAL DYNAMICS OF FOREST PLANTATION CLEARCUTTING AT LANDSCAPE LEVEL

L. Cortés, M. P. Acuña, and H. J. Hernández

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 969–973, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-969-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-969-2020, 2020

APPLICATION OF TREE DETECTION METHODS OVER LIDAR DATA FOR FOREST VOLUME ESTIMATION

F. Pirotti, C. Paterno, and M. Pividori

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1055–1060, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1055-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1055-2020, 2020

MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

H. Bernsteiner, N. Brožová, I. Eischeid, A. Hamer, S. Haselberger, M. Huber, A. Kollert, T. M. Vandyk, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 431–437, https://doi.org/10.5194/isprs-annals-V-3-2020-431-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-431-2020, 2020

A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

F. Tonion, F. Pirotti, G. Faina, and D. Paltrinieri

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 565–570, https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020, 2020

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Giovanni Forzieri, Matteo Pecchi, Marco Girardello, Achille Mauri, Marcus Klaus, Christo Nikolov, Marius Rüetschi, Barry Gardiner, Julián Tomaštík, David Small, Constantin Nistor, Donatas Jonikavicius, Jonathan Spinoni, Luc Feyen, Francesca Giannetti, Rinaldo Comino, Alessandro Wolynski, Francesco Pirotti, Fabio Maistrelli, Ionut Savulescu, Stéphanie Wurpillot-Lucas, Stefan Karlsson, Karolina Zieba-Kulawik, Paulina Strejczek-Jazwinska, Martin Mokroš, Stefan Franz, Lukas Krejci, Ionel Haidu, Mats Nilsson, Piotr Wezyk, Filippo Catani, Yi-Ying Chen, Sebastiaan Luyssaert, Gherardo Chirici, Alessandro Cescatti, and Pieter S. A. Beck

Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020,https://doi.org/10.5194/essd-12-257-2020, 2020

Short summary

URBAN GEO BIG DATA

M. A. Brovelli, P. Boccardo, G. Bordogna, A. Pepe, M. Crespi, M. Munafò, and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 23–30, https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019,https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019, 2019

CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW

F. Pirotti and F. Tonion

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1105–1111, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019, 2019

DSM AND DTM FOR EXTRACTING 3D BUILDING MODELS: ADVANTAGES AND LIMITATIONS

E. Fissore and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1539–1544, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019, 2019

DETECTION OF BUILDING ROOFS AND FACADES FROM AERIAL LASER SCANNING DATA USING DEEP LEARNING

F. Pirotti, C. Zanchetta, M. Previtali, and S. Della Torre

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 975–980, https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019, 2019

EXTRACTION AND VISUALIZATION OF 3D BUILDING MODELS IN URBAN AREAS FOR FLOOD SIMULATION

C. E. Kilsedar, F. Fissore, F. Pirotti, and M. A. Brovelli

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 669–673, https://doi.org/10.5194/isprs-archives-XLII-2-W11-669-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W11-669-2019, 2019

PLANNING HARVESTING OPERATIONS IN FOREST ENVIRONMENT: REMOTE SENSING FOR DECISION SUPPORT

M. Piragnolo, S. Grigolato, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W1, 33–40, https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019,https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019, 2019

PILOT STUDY ON THE RETRIEVAL OF DBH AND DIAMETER DISTRIBUTION OF DECIDUOUS FOREST STANDS USING CAST SHADOWS IN UAV-BASED ORTHOMOSAICS

T. Kattenborn, J. Hernández, J. Lopatin, G. Kattenborn, and F. E. Fassnacht

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 93–99, https://doi.org/10.5194/isprs-annals-IV-1-93-2018,https://doi.org/10.5194/isprs-annals-IV-1-93-2018, 2018

INITIAL EVALUATION OF 3D RECONSTRUCTION OF CLOSE OBJECTS WITH SMARTPHONE STEREO VISION

A. Masiero, F. Fissore, M. Piragnolo, A. Guarnieri, F. Pirotti, and A. Vettore

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 289–293, https://doi.org/10.5194/isprs-archives-XLII-1-289-2018,https://doi.org/10.5194/isprs-archives-XLII-1-289-2018, 2018

MIGRATION OF DIGITAL CARTOGRAPHY TO CITYGML; A WEB-BASED TOOL FOR SUPPORTING SIMPLE ETL PROCEDURES

F. Fissore and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 193–200, https://doi.org/10.5194/isprs-archives-XLII-4-193-2018,https://doi.org/10.5194/isprs-archives-XLII-4-193-2018, 2018

COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

M. Piragnolo, G. Lusiani, and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1381–1387, https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018,https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018, 2018

OPEN SOURCE WEB TOOL FOR TRACKING IN A LOWCOST MOBILE MAPPING SYSTEM

F. Fissore, F. Pirotti, and A. Vettore

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 99–104, https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017, 2017

A COMPARISON OF TREE SEGMENTATION METHODS USING VERY HIGH DENSITY AIRBORNE LASER SCANNER DATA

F. Pirotti, M. Kobal, and J. R. Roussel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 285–290, https://doi.org/10.5194/isprs-archives-XLII-2-W7-285-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W7-285-2017, 2017

URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

H. J. Hernández, M. A. Gutiérrez, and M. P. Acuña

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 873–877, https://doi.org/10.5194/isprs-archives-XLI-B8-873-2016,https://doi.org/10.5194/isprs-archives-XLI-B8-873-2016, 2016

HOW MUCH CARBON IS STORED IN DESERTS? AN APPROACH FOR THE CHILEAN ATACAMA DESERT USING LANDSAT-8 PRODUCTS

H. J. Hernández, T. Acuña, P. Reyes, M. Torres, and E. Figueroa

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 879–882, https://doi.org/10.5194/isprs-archives-XLI-B8-879-2016,https://doi.org/10.5194/isprs-archives-XLI-B8-879-2016, 2016

BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE

F. Pirotti, F. Sunar, and M. Piragnolo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 335–340, https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016, 2016

KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

F. Pirotti, D. Travaglini, F. Giannetti, E. Kutchartt, F. Bottalico, and G. Chirici

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 17–22, https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016, 2016

DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS

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