Search results for "google earth"
showing 10 items of 22 documents
Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy
2016
Abstract A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004–2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3 km 2 , respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5 m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Mul…
On the Accuracy of Cadastral Marks: Statistical Analyses to Assess the Congruence among GNSS-Based Positioning and Official Maps
2022
Cadastral marks constitute a dense source of information for topographical surveys required to update cadastral maps. Historically, in Italy, cadastral marks have been the cartographic network for the implementation of mapping updates. Different sources of cadastral marks can be used by cadastral surveyors. In recent years, the cadastre is moving toward a digital world, and with the advancement of surveying technology, GNSS CORS technology has emerged in the positioning of cadastral marks. An analysis of congruence among cadastral marks using GNSS CORS and official maps is missing. Thus, this work aims to analyze the positional accuracy of some cadastral marks, located in Palermo, Italy, wi…
Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)
2012
A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, …
Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
2022
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…
3D MODELING of A COMPLEX BUILDING: From MULTI-VIEW IMAGE FUSION to GOOGLE EARTH PUBLICATION
2019
Abstract. This paper presents a pipeline that aims at illustrating the procedure to realize a 3D model of a complex building integrating the UAV and terrestrial images and modifying the 3D model in order to publish to Google Earth in an interactive modality so as to provide better available models for visualization and use. The main steps of the procedure are the optimization of the UAV flight, the integration of the different UAV and ground floor images and the optimization of the model to be published to GE. The case study has been identified in a building, The Eremo di Santa Rosalia Convent in Sicily which hash more staggered elevations and located in the hills of the hinterland and of w…
INTEGRATED SFM TECHNIQUES USING DATA SET FROM GOOGLE EARTH 3D MODEL AND FROM STREET LEVEL
2017
Abstract. Structure from motion (SfM) represents a widespread photogrammetric method that uses the photogrammetric rules to carry out a 3D model from a photo data set collection. Some complex ancient buildings, such as Cathedrals, or Theatres, or Castles, etc. need to implement the data set (realized from street level) with the UAV one in order to have the 3D roof reconstruction. Nevertheless, the use of UAV is strong limited from the government rules. In these last years, Google Earth (GE) has been enriched with the 3D models of the earth sites. For this reason, it seemed convenient to start to test the potentiality offered by GE in order to extract from it a data set that replace the UAV …
Factor selection procedures in a Google Earthtm aided landslide susceptibility model: application to the Beiro river basin (Spain)
2011
A procedure to select the controlling factors connected to the slope instability has been defined. It allowed to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the north-eastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed to generate a landslide inventory consisting in 127 phenomena. Univariate tests, using both association coefficients and validation results of single parameter susceptibility models, allowed to select among 15 controlling factors the ones that resulted as good predictor variables; these have been combined for unique conditions analysis and susceptibility maps were finally prepared. In…
Global Estimation of Biophysical Variables from Google Earth Engine Platform
2018
This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estim…
Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.
2022
The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C)…