Search results for "Google Earth"
showing 10 items of 22 documents
Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression
2021
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…
3D Image Based Modelling Using Google Earth Imagery for 3D Landscape Modelling
2019
In recent years SfM technique experiments have been innumerable and increasingly refined under metric profiles. The techniques rely on photographic datasets of the objects or landscapes which can require in most cases time consuming and expensive surveys. Recently however there have been increases in the available 3D data of sites worldwide on the Google Earth (GE) platform. This paper presents a unique experimentation that considers integrating readily available datasets from GE and images taken during surveys on ground level for 3D replication without the use of expensive aerial surveys. This will enable practitioners the ability to more easily create 3D models of cultural heritage signif…
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
2021
Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong se…
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
Growing stock volume from multi-temporal landsat imagery through google earth engine
2019
Growing stock volume (GSV) is one of the most important variables for.forest management and is traditionally- estimated from ground measurements. These measurements are expensive and therefore sparse and hard to maintain in time on a regular basis. Remote sensing data combined with national forest inventories constitute a helpful tool to estimate and map forest attributes. However, most studies on GSV estimation from remote sensing data focus on small forest areas with a single or only a few species. The current study aims to map GSV in peninsular Spain, a rather large and very heterogeneous area. Around 50 000 wooded land plots from the Third Spanish National Forest Inventory (NFI3) were u…
Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine
2022
The gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data management, modeling and conversion into predictive maps. This in turn has limited the scope to catchment-scales. In this manuscript, we opt to propose a tool where the whole modeling procedure is unified within the same cloud computing system, allowing one to get rid of potential errors caused by input/output operations but also to extend the study …
Il GIS come strumento di fruizione territoriale e valorizzazione turistica
2013
The goal of this work has been to achieve a Geographic Information System, using innovative cartographic representation of the land and landscape, which can provide to the end users an easier and immediate access regarding tourist, cultural and environmental information. This is an ongoing research, carried out in collaboration with the Department of Civil, Engineering, Environmental, Aerospace, Materials (DICAM) of University of Palermo, with the objective to 548 Atti 17a Conferenza Nazionale ASITA - Riva del Garda 5-7 novembre 2013 achieve a complete integration between software used only by qualified specialists in the field and online platforms display. To experience this work, has been…
Confronto di due approcci statistici non parametrici per la valutazione della suscettibilità da frana nella catena appenninica settentrionale sicilia…
2012
Oggigiorno, la valutazione della diversa importanza delle variabili geoambientali nel determinare le condizioni di suscettibilità da frana di un’area è uno dei problemi più attuali della geologia. L’uso ed il confronto di due differenti approcci statistici, ha consentito di stimare le condizioni di predisposizione all’instabilità gravitativa dei versanti, per un esteso settore settentrionale della catena appenninica siciliana, ricadente all’interno delle tavolette I.G.M.I. nn. 259 I SE “Scillato” e 259 II NE “Caltavuturo”. L’area oggetto della sperimentazione, estesa circa 200 Km2, è stata suddivisa in maniera semi-automatica in 1827 unità idro-morfologiche o unità di versante. Per ciascun’…
Exporting a Google Earth™ aided earthflow susceptibility model: a test in central Sicily
2012
Abstract In the framework of a regional landslide susceptibility study in southern Sicily, a test has been carried out in the Tumarrano river basin (about 80 km2) aimed at characterizing its landslide susceptibility conditions by exporting a ‘‘source model’’, defined and trained inside a limited (about 20 km2) representative sector (the ‘‘source area’’). Also, the possibility of exploiting Google Earth TM software and photo-images databank to produce the landslide archives has been checked. The susceptibility model was defined, according to a multivariate geostatistic approach based on the conditional analysis, using unique condition units (UCUs), which were obtained by combining four selec…
Multitemporal Cloud Masking in the Google Earth Engine
2018
The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these r…