Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (AL... more Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clou...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
This work aims to test the effectiveness of artificial intelligence for correcting water refracti... more This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very highresolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths.
Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisi... more Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner to access the capability of different spectral bands and spectral vegetation indices, elevation, and texture features in the classification of land cover and detection of the wet riparian area in the case study in two different epochs. There are many existing methods for the classification of land cover based on UAS images, but very high-resolution centimeter-level data are of main importance in this analysis. Outstanding results have been produced in both epochs considering three extremely accurate performance analysers. Additionally, in this research, the most decisive and effective features have been discovered to compromise accuracy and the number of effectual features.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The health assessment of strategic infrastructures and bridges represents a critical variable for... more The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but timeconsuming model, the UAV-LiDAR resulting point cloud's georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5-10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.
Communications in Computer and Information Science, 2022
Bridges are among the most important structures of any road network. During their service life, t... more Bridges are among the most important structures of any road network. During their service life, they are subject to deterioration which may reduce their safety and functionality. The detection of bridge damage is necessary for proper maintenance activities. To date, assessing the health status of the bridge and all its elements is carried out by identifying a series of data obtained from visual inspections, which allows the mapping of the deterioration situation of the work and its conservation status. There are, however, situations where visual inspection may be difficult or impossible, especially in critical areas of bridges, such as the ceiling and corners. In this contribution, the authors acquire images using a prototype drone with a low-cost camera mounted upward over the body of the drone. The proposed solution was tested on a bridge in the city of Turin (Italy). The captured data was processed via photogrammetric process using the open-source Micmac solution. Subsequently, a...
South of the Sahara, flood vulnerability and risk assessments at local level rarely identify the ... more South of the Sahara, flood vulnerability and risk assessments at local level rarely identify the exposed areas according to the probability of flooding or the actions in place, or localize the exposed items. They are, therefore, of little use for local development, risk prevention, and contingency planning. The aim of this article is to assess the flood risk, providing useful information for local planning and an assessment methodology useful for other case studies. As a result, the first step involves identifying the information required by the local plans most used south of the Sahara. Four rural communities in Niger, frequently flooded by the Sirba River, are then considered. The risk is the product of the probability of a flood multiplied by the potential damage. Local knowledge and knowledge derived from a hydraulic numerical model, digital terrain model, very high resolution multispectral orthoimages, and daily precipitation are used. The assessment identifies the probability ...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors ... more The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors have kept up with it, providing more efficient and effective solutions. One of the most sought-after characteristics of on-board sensors is the low costing associated to good quality of the collected data. This paper proposes a very low-cost multiband sensor developed on a Raspberry device and two Raspberry Pi 3 cameras that can be used in photogrammetry from drone applications. The UAV-integrated radiometric sensor and its performance were tested in in two villages of Southwest Niger for the detection of temporary surface water bodies (or Ephemeral water bodies): zones of seasonal stagnant water within villages threatening the viability and people's health. The Raspberry Pi 3 cameras employed were a regular RGB Pi camera 2 (Red, Green, Blue) and a NoIR Pi 3 camera v2 (regular RGB without IR filter) with 8MPX resolution. The cameras were geometrically calibrated and radiometrically tested before the survey in the field. The results of the photogrammetry elaborations were 4 orthophotos (a RGB and NoIRGB orthophoto for each village). The Normalized Difference Water Index (NDWI) was calculated. The index allowed the localization and the contouring of the temporary surface water bodies present in the villages. The data were checked against the data collected with a Sony (ILCE-5100). Very high correspondence between the different data was detected. Raspberry-based sensors demonstrated to be a valid tool for the data collection in critical areas.
In Sahelian countries, a vast number of people are still affected every year by flood despite the... more In Sahelian countries, a vast number of people are still affected every year by flood despite the efforts to prevent or mitigate these catastrophic events. This phenomenon is exacerbated by the incessant population growth and the increase of extreme natural events. Hence, the development of flood management strategies such as flood hazard mapping and Early Warning Systems has become a crucial objective for the affected nations. This study presents a comprehensive hazard assessment of the Nigerien reach of the Sirba River, the main tributary Middle Niger River. Hazard thresholds were defined both on hydrological analysis and field effects, according to national guidelines. Non-stationary analyses were carried out to consider changes in the hydrological behavior of the Sirba basin over time. Data from topographical land surveys and discharge gauges collected during the 2018 dry and wet seasons were used to implement the hydraulic numerical model of the analyzed reach. The use of the p...
Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial world... more Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping ...
Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (AL... more Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clou...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
This work aims to test the effectiveness of artificial intelligence for correcting water refracti... more This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very highresolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths.
Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisi... more Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner to access the capability of different spectral bands and spectral vegetation indices, elevation, and texture features in the classification of land cover and detection of the wet riparian area in the case study in two different epochs. There are many existing methods for the classification of land cover based on UAS images, but very high-resolution centimeter-level data are of main importance in this analysis. Outstanding results have been produced in both epochs considering three extremely accurate performance analysers. Additionally, in this research, the most decisive and effective features have been discovered to compromise accuracy and the number of effectual features.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The health assessment of strategic infrastructures and bridges represents a critical variable for... more The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but timeconsuming model, the UAV-LiDAR resulting point cloud's georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5-10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.
Communications in Computer and Information Science, 2022
Bridges are among the most important structures of any road network. During their service life, t... more Bridges are among the most important structures of any road network. During their service life, they are subject to deterioration which may reduce their safety and functionality. The detection of bridge damage is necessary for proper maintenance activities. To date, assessing the health status of the bridge and all its elements is carried out by identifying a series of data obtained from visual inspections, which allows the mapping of the deterioration situation of the work and its conservation status. There are, however, situations where visual inspection may be difficult or impossible, especially in critical areas of bridges, such as the ceiling and corners. In this contribution, the authors acquire images using a prototype drone with a low-cost camera mounted upward over the body of the drone. The proposed solution was tested on a bridge in the city of Turin (Italy). The captured data was processed via photogrammetric process using the open-source Micmac solution. Subsequently, a...
South of the Sahara, flood vulnerability and risk assessments at local level rarely identify the ... more South of the Sahara, flood vulnerability and risk assessments at local level rarely identify the exposed areas according to the probability of flooding or the actions in place, or localize the exposed items. They are, therefore, of little use for local development, risk prevention, and contingency planning. The aim of this article is to assess the flood risk, providing useful information for local planning and an assessment methodology useful for other case studies. As a result, the first step involves identifying the information required by the local plans most used south of the Sahara. Four rural communities in Niger, frequently flooded by the Sirba River, are then considered. The risk is the product of the probability of a flood multiplied by the potential damage. Local knowledge and knowledge derived from a hydraulic numerical model, digital terrain model, very high resolution multispectral orthoimages, and daily precipitation are used. The assessment identifies the probability ...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors ... more The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors have kept up with it, providing more efficient and effective solutions. One of the most sought-after characteristics of on-board sensors is the low costing associated to good quality of the collected data. This paper proposes a very low-cost multiband sensor developed on a Raspberry device and two Raspberry Pi 3 cameras that can be used in photogrammetry from drone applications. The UAV-integrated radiometric sensor and its performance were tested in in two villages of Southwest Niger for the detection of temporary surface water bodies (or Ephemeral water bodies): zones of seasonal stagnant water within villages threatening the viability and people's health. The Raspberry Pi 3 cameras employed were a regular RGB Pi camera 2 (Red, Green, Blue) and a NoIR Pi 3 camera v2 (regular RGB without IR filter) with 8MPX resolution. The cameras were geometrically calibrated and radiometrically tested before the survey in the field. The results of the photogrammetry elaborations were 4 orthophotos (a RGB and NoIRGB orthophoto for each village). The Normalized Difference Water Index (NDWI) was calculated. The index allowed the localization and the contouring of the temporary surface water bodies present in the villages. The data were checked against the data collected with a Sony (ILCE-5100). Very high correspondence between the different data was detected. Raspberry-based sensors demonstrated to be a valid tool for the data collection in critical areas.
In Sahelian countries, a vast number of people are still affected every year by flood despite the... more In Sahelian countries, a vast number of people are still affected every year by flood despite the efforts to prevent or mitigate these catastrophic events. This phenomenon is exacerbated by the incessant population growth and the increase of extreme natural events. Hence, the development of flood management strategies such as flood hazard mapping and Early Warning Systems has become a crucial objective for the affected nations. This study presents a comprehensive hazard assessment of the Nigerien reach of the Sirba River, the main tributary Middle Niger River. Hazard thresholds were defined both on hydrological analysis and field effects, according to national guidelines. Non-stationary analyses were carried out to consider changes in the hydrological behavior of the Sirba basin over time. Data from topographical land surveys and discharge gauges collected during the 2018 dry and wet seasons were used to implement the hydraulic numerical model of the analyzed reach. The use of the p...
Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial world... more Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping ...
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Papers by Elena Belcore