Andrea Nascetti - Academia.edu (original) (raw)

Papers by Andrea Nascetti

ISPRS Journal of Photogrammetry and Remote Sensing

GEOmedia, 2017

Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High... more Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High Performance Computing infrastructures allows Geomatics to widen its application scope, by stimulating new challenging investigations related to the modeling of the observations provided by these new tools. In this review, some methodologies and applications, developed at the Geodesy and Geomatics Division (DICEA) of University of Rome "La Sapienza", are shortly presented. Directly related to the mentioned software and hardware new availability, they are already ready for industrial applications and hopefully can broaden the interaction between Geomatics and other scientific and technological disciplines.

This special issue features a collection of ten contributions focusing on urban remote sensing ap... more This special issue features a collection of ten contributions focusing on urban remote sensing applications. This special issue reflects the thematic diversity and variety of urban remote sensing applications and underlines the importance of this research field. Based on the 5th EARSeL Joint Workshop "Urban Remote Sensing – Challenges & Solutions" held in Bochum, Germany in 2018 the participants were invited to contribute to this special issue. The EARSeL Joint Workshop is a new format that was first initiated in 2006 in Berlin, Germany. Further EARSeL Joint Workshops followed in 2008 in Bochum, Germany, 2010 in Ghent, Belgium, 2012 in Mykonos, Greece and 2014 in Warsaw, Poland. The composition of the participating EARSeL Special Interest Groups varied from workshop to workshop. For 2018 the EARSeL Special Interest Groups Urban Remote Sensing, 3D Remote Sensing, Developing Countries and Radar Remote Sensing agreed to organize this workshop together. High resolution data ar...

Remote Sensing

Accurate burned area information is needed to assess the impacts of wildfires on people, communit... more Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performan...

The EGU General Assembly, 2017

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020

Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena im... more Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena impacting the Earth's surface (e.g. landslides, glacier motions, subsidence, and volcano deformations) and infrastructures (e.g. bridges, dams, buildings). The analysis is generally performed using the Differential SAR Interferometry (DInSAR) technique that exploits the phase information of SAR data. However, this technique suffers for lack of coherence among the considered stack of images, and it can only be adopted to monitor slow deformation phenomena. In the field of geohazards monitoring and glacier melting, the Offset Tracking technique has been also widely investigated. This approach is based on the amplitude information only but it reaches worse accuracy compared to DInSAR. To overcome the limitations of DIn-SAR and Offset Tracking, in the last decade, a new technique called Imaging Geodesy has been investigated exploiting the amplitude information and the precise orbit of the ...

International Journal of Remote Sensing, 2016

Nowadays, the availability of novel high-resolution synthetic aperture radar (SAR) spaceborne sen... more Nowadays, the availability of novel high-resolution synthetic aperture radar (SAR) spaceborne sensors offers new interesting potentialities for the acquisition of data useful for the generation of secondary products as digital surface models (DSMs), orthoimages, and displacement maps. SAR technology provides for low-cost, fast data acquisition and processing, independence from logistic difficulties, and night-and-day and all-weather functionality. These features are of crucial importance for the timely monitoring and management of disasters and emergencies such as geological, hydrological, and geophysical hazards. However, one of the most critical aspects in extracting useful and reliable information from SAR data is the image processing. Although several commercial software suites are available, in recent years the open source technology has confirmed a reliable and effective alternative for SAR processing and in general for geospatial information management. For instance, projects...

Archaeological small finds provide a variegated myriad of data of crucial importance to the study... more Archaeological small finds provide a variegated myriad of data of crucial importance to the study of their finding contexts. Anyway, only a close all-around examination can give a full comprehension of their multiple functions. The production of reliable documentation is thus an essential process and this paper illustrates a fast, reliable and easy tool to collect documentation during the excavation season. The tool, named Penguin 3.0, was developed at the Geodesy and Geomatics Division - Sapienza University, exploits the potentialities of the Occipital Structure Sensor, a low-cost sensor able to rapidly generate reliable 3D models of small objects. This sensor can be connected directly to a mobile device (i.e. smartphone or tablet) and it collects the 3D information of the scanned object in real-time. The aim of this work is to perform a methodological presentation of the acquisition procedure in order to highlight the pros and cons of using this 3D scanning technology to capture 3...

Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High... more Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High Performance Computing infrastructures allows Geomatics to widen its application scope, by stimulating new challenging investigations related to the modeling of the observations provided by these new tools. In this review, some methodologies and applications, developed at the Geodesy and Geomatics Division (DICEA) of University of Rome “La Sapienza”, are shortly presented. Directly related to the mentioned software and hardware new availability, they are already ready for industrial applications and hopefully can broaden the interaction between Geomatics and other scientific and technological disciplines.

Accurate burned area information is needed to assess the impacts of wildfires on people, communit... more Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performan...

Remote Sensing of Environment

Abstract Wildfires are increasing in intensity and frequency across the globe due to climate chan... more Abstract Wildfires are increasing in intensity and frequency across the globe due to climate change and rising global temperature. Development of novel approach to Monitor wildfire progressions in near real-time is therefore of critical importance for emergency responses. The objective of this research is to investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR and Sentinel-2 MSI time series for increasing the frequency and accuracy of wildfire progression mapping. In this study, optical-based burned areas prior to each SAR acquisition (when available) were accumulated into SAR-based pseudo progression masks to train a deep residual U-Net model. Unlike multi-temporal fusion of SAR and optical data, the temporal fusion of progression masks allows us to track as many wildfire progressions as possible. Specifically, two approaches were investigated to train the deep residual U-Net model for continuous learning: 1) Continuous joint training (CJT) with all historical data (including both SAR and optical data); 2) Learning without forgetting (LwF) based on newly incoming data alone (SAR or optical). For LwF, a mean squared loss was integrated to keep the capabilities learned before and prevent it from overfitting to newly incoming data only. By fusing optical-based burned areas, SAR-based progression pseudo masks improve significantly, which benefits both data sampling and model training, considering the challenges in SAR-based change extraction attributed to the variability in SAR backscatter of the surrounding environments. Pre-trained ResNet was frozen as the encoder of the U-Net model, and the decoder part was trained to further refine the derived burned area maps in a progression-wise manner. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019–2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also observed that the SAR cross-polarization ratio (VH/VV) shows good capability in suppressing multiplicative noise and detecting burned areas when VH and VV have diverse temporal behaviors.

IEEE Geoscience and Remote Sensing Letters

Urbanization is progressing rapidly around the world. With sub-weekly revisits at global scale, S... more Urbanization is progressing rapidly around the world. With sub-weekly revisits at global scale, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imager (MSI) data can play an important role for monitoring urban sprawl to support sustainable development. In this letter, we proposed an urban change detection (CD) approach featuring a new network architecture for the fusion of SAR and optical data. Specifically, a dual stream concept was introduced to process different data modalities separately, before combining extracted features at a later decision stage. The individual streams are based on U-Net architecture that is one of the most popular fully convolutional networks used for semantic segmentation. The effectiveness of the proposed approach was demonstrated using the Onera Satellite CD (OSCD) dataset. The proposed strategy outperformed other U-Net-based approaches in combination with unimodal data and multimodal data with feature level fusion. Furthermore, our approach achieved state-of-the-art performance on the urban CD problem posed by the OSCD dataset. Our Sentinel-1 SAR data and code are available on https://github.com/SebastianHafner/DS_UNet.

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

In recent years, wildfires have become major devastating hazards that affect both public safety a... more In recent years, wildfires have become major devastating hazards that affect both public safety and the environment. Thus, agile detection of the wildfires is desirable to suppress wildfires in the early stage. Owing to the high temporal resolution, GOES-R satellites offer capabilities to obtain images every 15 minutes enabling a near real-time monitoring of wildfires. In this research, a time-series-based deep learning framework, composed of Gated Recurrent Units (GRU), is proposed to capture the emerging of the wildfire at early stage. By feeding the embedding of the coarse satellite imagery to Deep GRU network, the active fires are segmented out from the remote sensing imagery. The preliminary results show that proposed network can detect the wildfires earlier than the state-of-the-art fire product for 2020 wildfires in California and British Columbia, at the same time provide sufficiently high accuracy on the burned areas.

Computers in Earth and Environmental Sciences

International Journal of Applied Earth Observation and Geoinformation

Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial a... more Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial and temporal coverage for monitoring biomass burning which could complement other coarse active fire detection products. This paper aims to investigate the use of reflective wavelength Sentinel-2 data to classify unambiguous active fire areas from inactive areas at 20 m spatial resolution. A multi-criteria approach based on the reflectance of several bands (i.e. B4, B11, and B12) is proposed to demonstrate the boundary constraints in several representative biomes. It is a fully automatic algorithm based on adaptive thresholds that are statistically determined from 11 million Sentinel-2 observations acquired over corresponding summertime (June 2019 to September 2019) across 14 regions or countries. Biome-based parameterizations avoid high omission errors (OE) caused by small and cool fires in different landscapes. It also takes advantage of the multiple criteria whose intersection could reduce the potential commission errors (CE) due to soil dominated pixels or highly reflective building rooftops. Active fire detection performance was mainly evaluated through visual inspection on eight illustrative subsets because of unavailable ground truth. The detection results revealed that CE and OE could be kept at a low level with 0.14 and 0.04 as an acceptable trade-off. The proposed algorithm can be employed for rapid active fire detection as soon as the image is obtained without the requirement of using multi-temporal imagery, and can even be adapted to onboard processing in the future.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

The glaciers are a natural global resource and one of the principal climate change indicator at g... more The glaciers are a natural global resource and one of the principal climate change indicator at global and local scale, being influenced by temperature and snow precipitation changes. Among the parameters used for glacier monitoring, the surface velocity is a key element, since it is connected to glaciers changes (mass balance, hydro balance, glaciers stability, landscape erosion). The leading idea of this work is to continuously retrieve glaciers surface velocity using free ESA Sentinel-1 SAR imagery and exploiting the potentialities of the Google Earth Engine (GEE) platform. GEE has been recently released by Google as a platform for petabyte-scale scientific analysis and visualization of geospatial datasets. The algorithm of SAR off-set tracking developed at the Geodesy and Geomatics Division of the University of Rome La Sapienza has been integrated in a cloud based platform that automatically processes large stacks of Sentinel-1 data to retrieve glacier surface velocity field tim...

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google... more The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a largescale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed.The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon.

Remote Sensing

Producing accurate land cover maps is time-consuming and estimating land cover changes between tw... more Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed ...

ISPRS Journal of Photogrammetry and Remote Sensing

GEOmedia, 2017

Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High... more Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High Performance Computing infrastructures allows Geomatics to widen its application scope, by stimulating new challenging investigations related to the modeling of the observations provided by these new tools. In this review, some methodologies and applications, developed at the Geodesy and Geomatics Division (DICEA) of University of Rome "La Sapienza", are shortly presented. Directly related to the mentioned software and hardware new availability, they are already ready for industrial applications and hopefully can broaden the interaction between Geomatics and other scientific and technological disciplines.

This special issue features a collection of ten contributions focusing on urban remote sensing ap... more This special issue features a collection of ten contributions focusing on urban remote sensing applications. This special issue reflects the thematic diversity and variety of urban remote sensing applications and underlines the importance of this research field. Based on the 5th EARSeL Joint Workshop "Urban Remote Sensing – Challenges & Solutions" held in Bochum, Germany in 2018 the participants were invited to contribute to this special issue. The EARSeL Joint Workshop is a new format that was first initiated in 2006 in Berlin, Germany. Further EARSeL Joint Workshops followed in 2008 in Bochum, Germany, 2010 in Ghent, Belgium, 2012 in Mykonos, Greece and 2014 in Warsaw, Poland. The composition of the participating EARSeL Special Interest Groups varied from workshop to workshop. For 2018 the EARSeL Special Interest Groups Urban Remote Sensing, 3D Remote Sensing, Developing Countries and Radar Remote Sensing agreed to organize this workshop together. High resolution data ar...

Remote Sensing

Accurate burned area information is needed to assess the impacts of wildfires on people, communit... more Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performan...

The EGU General Assembly, 2017

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020

Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena im... more Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena impacting the Earth's surface (e.g. landslides, glacier motions, subsidence, and volcano deformations) and infrastructures (e.g. bridges, dams, buildings). The analysis is generally performed using the Differential SAR Interferometry (DInSAR) technique that exploits the phase information of SAR data. However, this technique suffers for lack of coherence among the considered stack of images, and it can only be adopted to monitor slow deformation phenomena. In the field of geohazards monitoring and glacier melting, the Offset Tracking technique has been also widely investigated. This approach is based on the amplitude information only but it reaches worse accuracy compared to DInSAR. To overcome the limitations of DIn-SAR and Offset Tracking, in the last decade, a new technique called Imaging Geodesy has been investigated exploiting the amplitude information and the precise orbit of the ...

International Journal of Remote Sensing, 2016

Nowadays, the availability of novel high-resolution synthetic aperture radar (SAR) spaceborne sen... more Nowadays, the availability of novel high-resolution synthetic aperture radar (SAR) spaceborne sensors offers new interesting potentialities for the acquisition of data useful for the generation of secondary products as digital surface models (DSMs), orthoimages, and displacement maps. SAR technology provides for low-cost, fast data acquisition and processing, independence from logistic difficulties, and night-and-day and all-weather functionality. These features are of crucial importance for the timely monitoring and management of disasters and emergencies such as geological, hydrological, and geophysical hazards. However, one of the most critical aspects in extracting useful and reliable information from SAR data is the image processing. Although several commercial software suites are available, in recent years the open source technology has confirmed a reliable and effective alternative for SAR processing and in general for geospatial information management. For instance, projects...

Archaeological small finds provide a variegated myriad of data of crucial importance to the study... more Archaeological small finds provide a variegated myriad of data of crucial importance to the study of their finding contexts. Anyway, only a close all-around examination can give a full comprehension of their multiple functions. The production of reliable documentation is thus an essential process and this paper illustrates a fast, reliable and easy tool to collect documentation during the excavation season. The tool, named Penguin 3.0, was developed at the Geodesy and Geomatics Division - Sapienza University, exploits the potentialities of the Occipital Structure Sensor, a low-cost sensor able to rapidly generate reliable 3D models of small objects. This sensor can be connected directly to a mobile device (i.e. smartphone or tablet) and it collects the 3D information of the scanned object in real-time. The aim of this work is to perform a methodological presentation of the acquisition procedure in order to highlight the pros and cons of using this 3D scanning technology to capture 3...

Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High... more Nowadays, the increasing availability of low-cost sensors, Free and Open Source Software and High Performance Computing infrastructures allows Geomatics to widen its application scope, by stimulating new challenging investigations related to the modeling of the observations provided by these new tools. In this review, some methodologies and applications, developed at the Geodesy and Geomatics Division (DICEA) of University of Rome “La Sapienza”, are shortly presented. Directly related to the mentioned software and hardware new availability, they are already ready for industrial applications and hopefully can broaden the interaction between Geomatics and other scientific and technological disciplines.

Accurate burned area information is needed to assess the impacts of wildfires on people, communit... more Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performan...

Remote Sensing of Environment

Abstract Wildfires are increasing in intensity and frequency across the globe due to climate chan... more Abstract Wildfires are increasing in intensity and frequency across the globe due to climate change and rising global temperature. Development of novel approach to Monitor wildfire progressions in near real-time is therefore of critical importance for emergency responses. The objective of this research is to investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR and Sentinel-2 MSI time series for increasing the frequency and accuracy of wildfire progression mapping. In this study, optical-based burned areas prior to each SAR acquisition (when available) were accumulated into SAR-based pseudo progression masks to train a deep residual U-Net model. Unlike multi-temporal fusion of SAR and optical data, the temporal fusion of progression masks allows us to track as many wildfire progressions as possible. Specifically, two approaches were investigated to train the deep residual U-Net model for continuous learning: 1) Continuous joint training (CJT) with all historical data (including both SAR and optical data); 2) Learning without forgetting (LwF) based on newly incoming data alone (SAR or optical). For LwF, a mean squared loss was integrated to keep the capabilities learned before and prevent it from overfitting to newly incoming data only. By fusing optical-based burned areas, SAR-based progression pseudo masks improve significantly, which benefits both data sampling and model training, considering the challenges in SAR-based change extraction attributed to the variability in SAR backscatter of the surrounding environments. Pre-trained ResNet was frozen as the encoder of the U-Net model, and the decoder part was trained to further refine the derived burned area maps in a progression-wise manner. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019–2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also observed that the SAR cross-polarization ratio (VH/VV) shows good capability in suppressing multiplicative noise and detecting burned areas when VH and VV have diverse temporal behaviors.

IEEE Geoscience and Remote Sensing Letters

Urbanization is progressing rapidly around the world. With sub-weekly revisits at global scale, S... more Urbanization is progressing rapidly around the world. With sub-weekly revisits at global scale, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imager (MSI) data can play an important role for monitoring urban sprawl to support sustainable development. In this letter, we proposed an urban change detection (CD) approach featuring a new network architecture for the fusion of SAR and optical data. Specifically, a dual stream concept was introduced to process different data modalities separately, before combining extracted features at a later decision stage. The individual streams are based on U-Net architecture that is one of the most popular fully convolutional networks used for semantic segmentation. The effectiveness of the proposed approach was demonstrated using the Onera Satellite CD (OSCD) dataset. The proposed strategy outperformed other U-Net-based approaches in combination with unimodal data and multimodal data with feature level fusion. Furthermore, our approach achieved state-of-the-art performance on the urban CD problem posed by the OSCD dataset. Our Sentinel-1 SAR data and code are available on https://github.com/SebastianHafner/DS_UNet.

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

In recent years, wildfires have become major devastating hazards that affect both public safety a... more In recent years, wildfires have become major devastating hazards that affect both public safety and the environment. Thus, agile detection of the wildfires is desirable to suppress wildfires in the early stage. Owing to the high temporal resolution, GOES-R satellites offer capabilities to obtain images every 15 minutes enabling a near real-time monitoring of wildfires. In this research, a time-series-based deep learning framework, composed of Gated Recurrent Units (GRU), is proposed to capture the emerging of the wildfire at early stage. By feeding the embedding of the coarse satellite imagery to Deep GRU network, the active fires are segmented out from the remote sensing imagery. The preliminary results show that proposed network can detect the wildfires earlier than the state-of-the-art fire product for 2020 wildfires in California and British Columbia, at the same time provide sufficiently high accuracy on the burned areas.

Computers in Earth and Environmental Sciences

International Journal of Applied Earth Observation and Geoinformation

Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial a... more Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial and temporal coverage for monitoring biomass burning which could complement other coarse active fire detection products. This paper aims to investigate the use of reflective wavelength Sentinel-2 data to classify unambiguous active fire areas from inactive areas at 20 m spatial resolution. A multi-criteria approach based on the reflectance of several bands (i.e. B4, B11, and B12) is proposed to demonstrate the boundary constraints in several representative biomes. It is a fully automatic algorithm based on adaptive thresholds that are statistically determined from 11 million Sentinel-2 observations acquired over corresponding summertime (June 2019 to September 2019) across 14 regions or countries. Biome-based parameterizations avoid high omission errors (OE) caused by small and cool fires in different landscapes. It also takes advantage of the multiple criteria whose intersection could reduce the potential commission errors (CE) due to soil dominated pixels or highly reflective building rooftops. Active fire detection performance was mainly evaluated through visual inspection on eight illustrative subsets because of unavailable ground truth. The detection results revealed that CE and OE could be kept at a low level with 0.14 and 0.04 as an acceptable trade-off. The proposed algorithm can be employed for rapid active fire detection as soon as the image is obtained without the requirement of using multi-temporal imagery, and can even be adapted to onboard processing in the future.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

The glaciers are a natural global resource and one of the principal climate change indicator at g... more The glaciers are a natural global resource and one of the principal climate change indicator at global and local scale, being influenced by temperature and snow precipitation changes. Among the parameters used for glacier monitoring, the surface velocity is a key element, since it is connected to glaciers changes (mass balance, hydro balance, glaciers stability, landscape erosion). The leading idea of this work is to continuously retrieve glaciers surface velocity using free ESA Sentinel-1 SAR imagery and exploiting the potentialities of the Google Earth Engine (GEE) platform. GEE has been recently released by Google as a platform for petabyte-scale scientific analysis and visualization of geospatial datasets. The algorithm of SAR off-set tracking developed at the Geodesy and Geomatics Division of the University of Rome La Sapienza has been integrated in a cloud based platform that automatically processes large stacks of Sentinel-1 data to retrieve glacier surface velocity field tim...

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google... more The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a largescale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed.The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon.

Remote Sensing

Producing accurate land cover maps is time-consuming and estimating land cover changes between tw... more Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed ...