Solar Irradiance Modelling with NASA WW GIS Environment

Piragnolo, Marco; Masiero, Andrea; Fissore, Francesca; Pirotti, Francesco

doi:10.3390/ijgi4020711

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…GIS-supported applications can deploy damage maps by using various sources to infer damaged ground cover (Piragnolo et al, 2015(Piragnolo et al, , 2014Pirotti, 2010a). In this case, we used aerial imagery to estimate damage from windthrow in ten areas, getting a very positive result.…”

Section: Discussionmentioning

confidence: 99%

Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Pirotti

Travaglini

Giannetti

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/1 KEY WORDS: Feature matching, Kernel normalized cross-correlation, Forest, Support Vector Machines ABSTRACT:In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13x13 pixels kernel with a simplified linear-feature representation of a cylinder is applied at different rotation angles (from 0° to 170° at 10° steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (svm) with linear kernel and random forests. The first two methods gave the best results. The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from ~1.8 x10 2 m 3 to ~1.2x10 4 m 3 . Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m 3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow.

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Section: Discussionmentioning

confidence: 99%

Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Pirotti

Travaglini

Giannetti

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In addition the performance of a friendly web tool for its management, has opened potential usage of the data to others, thus initiating what could be seen as a sharing principle and a collaborative potential (Pirotti et al, 2011, Piragnolo et al, 2015.…”

Section: Future Developments and Conclusionmentioning

confidence: 99%

Open Source Web Tool for Tracking in a Lowcost Mobile Mapping System

Fissore

Pirotti

Vettore

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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During the last decade several Mobile Mapping Systems (MMSs), i.e. systems able to acquire efficiently three dimensional data using moving sensors (Guarnieri et al., 2008, Schwarz and El-Sheimy, 2004), have been developed. Research and commercial products have been implemented on terrestrial, aerial and marine platforms, and even on human-carried equipment, e.g. backpack (Lo et al., 2015, Nex and Remondino, 2014, Ellum and El-Sheimy, 2002, Leica Pegasus backpack, 2016, Masiero et al., 2017, Fissore et al., 2018). Such systems are composed of an integrated array of time-synchronised navigation sensors and imaging sensors mounted on a mobile platform (Puente et al., 2013, Tao and Li, 2007). Usually the MMS implies integration of different types of sensors, such as GNSS, IMU, video camera and/or laser scanners that allow accurate and quick mapping (Li, 1997, Petrie, 2010, Tao, 2000). The typical requirement of high-accuracy 3D georeferenced reconstruction often makes such systems quite expensive. Indeed, at time of writing most of the terrestrial MMSs on the market have a cost usually greater than 50000, which might be expensive for certain applications (Ellum and El-Sheimy, 2002, Piras et al., 2008). In order to allow best performance sensors have to be properly calibrated (Dong et al., 2007, Ellum and El-Sheimy, 2002). Sensors in MMSs are usually integrated and managed through a dedicated software, which is developed ad hoc for the devices mounted on the mobile platform and hence tailored for the specific used sensors. Despite the fact that commercial solutions are complete, very specific and particularly related to the typology of survey, their price is a factor that restricts the number of users and the possible interested sectors. This paper describes a (relatively low cost) terrestrial Mobile Mapping System developed at the University of Padua (TESAF, Department of Land Environment Agriculture and Forestry) by the research team in CIRGEO, in order to test an alternative solution to other more expensive MMSs. The first objective of this paper is to report on the development of a prototype of MMS for the collection of geospatial data based on the assembly of low cost sensors managed through a web interface developed using open source libraries. The main goal is to provide a system accessible by any type of user, and flexible to any type of upgrade or introduction of new models of sensors or versions thereof. After a presentation of the hardware components used in our system, a more detailed description of the software developed for the management of the MMS will be provided, which is the part of the innovation of the project. According to the worldwide request for having big data available through the web from everywhere in the world (Pirotti et al., 2011), the proposed solution allows to retrieve data from a web interface Figure 4. Actually, this is part of a project for the development of a new web infrastructure in the University of Padua (but it will be available for external users as well), in order to ease collaboration between researchers from different areas. Finally, strengths, weaknesses and future developments of the low cost MMS are discussed.

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“…Geographic information systems (GIS), location based services, object recognition (El-Sheimy and Schwarz, 1998, Piragnolo et al, 2015, Pfeifer et al, 2004, Masiero et al, 2015b, Facco et al, 2009, Facco et al, 2010. Thanks to the small size, the cheap cost and the easy usability of cameras, independently of the specific considered device in most of the cases a standard camera is used as imaging sensor, and photogrammetry is used in order to obtain reconstructions of the observed scenes (Prosdocimi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Reconstruction With Bayesian Information

Masiero¹,

Fissore²,

Guarnieri³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays photogrammetry and laser scanning methods are the most wide spread surveying techniques. Laser scanning methods usually allow to obtain more accurate results with respect to photogrammetry, but their use have some issues, e.g. related to the high cost of the instrumentation and the typical need of high qualified personnel to acquire experimental data on the field. Differently, photogrammetric reconstruction can be achieved by means of low cost devices and by persons without specific training. Furthermore, the recent diffusion of smart devices (e.g. smartphones) embedded with imaging and positioning sensors (i.e. standard camera, GNSS receiver, inertial measurement unit) is opening the possibility of integrating more information in the photogrammetric reconstruction procedure, in order to increase its computational efficiency, its robustness and accuracy. In accordance with the above observations, this paper examines and validates new possibilities for the integration of information provided by the inertial measurement unit (IMU) into the photogrammetric reconstruction procedure, and, to be more specific, into the procedure for solving the feature matching and the bundle adjustment problems.

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Solar Irradiance Modelling with NASA WW GIS Environment

Cited by 19 publications

References 28 publications

Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Open Source Web Tool for Tracking in a Lowcost Mobile Mapping System

Photogrammetric Reconstruction With Bayesian Information

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