Search results for "TW"

Trends in global research in deforestation. A bibliometric analysis

2018

The main aim of this study was to analyse topics of research, scientific production, collaboration among countries, and most cited papers on deforestation through a bibliometric and social network study of articles found in the Web of Science database. The most productive subject areas corresponded to Environmental Sciences, Ecology and Environmental Studies. The articles were published in 458 different journals. A total of 2051 research articles were obtained. The main challenges identified for deforestation include “land use change” “conservation” “climate change” “rain forest” and “reducing emissions from deforestation and degradation”. Social and economic topics are understudied. An imp…

Exploring Effective Ecosystems in Disaster Management: Case studies of Japan and Nepal

2017

Transferring deep learning models for cloud detection between Landsat-8 and Proba-V

2020

Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…

Planktic foraminiferal changes in the western Mediterranean Anthropocene

2021

The increase in anthropogenic induced warming over the last two centuries is impacting marine environment. Planktic foraminifera are a globally distributed calcifying marine zooplankton responding sensitively to changes in sea surface temperatures and interacting with the food web structure. Here, we study two high resolution multicore records from two western Mediterranean Sea regions (Alboran and Balearic basins), areas highly affected by both natural climate change and anthropogenic warming. Cores cover the time interval from the Medieval Climate Anomaly to present. Reconstructed sea surface temperatures are in good agreement with other results, tracing temperature changes through the Co…

Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

2021

Abstract Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to re…

Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

2017

Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…

Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring

2020

Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…

Potential of Automated Digital Hemispherical Photography and Wireless Quantum Sensors for Routine Canopy Monitoring and Satellite Product Validation

2021

To better characterize the temporal dynamics of vegetation biophysical variables, a variety of automated in situ measurement techniques have been developed in recent years. In this study, we investigated automated digital hemispherical photography (DHP) and wireless quantum sensors, which were installed at two sites under the Copernicus Ground Based Observations for Validation (GBOV) project. Daily estimates of plant area index (PAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) were obtained, which realistically described expected vegetation dynamics. Good correspondence with manual DHP and LAI-2000 data (RMSE = 0.39 to 0.90 for PAI, RMSE = 0.07 for FAPAR) provid…

Empirical and physical estimation of Canopy Water Content from CHRIS/PROBA data

2013

20 páginas, 4 tablas, 7 figuras.

The 2009 Edition of the GEISA Spectroscopic Database

2011

The updated 2009 edition of the spectroscopic database GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques; Management and Study of Atmospheric Spectroscopic Information) is described in this paper. GEISA is a computer-accessible system comprising three independent sub-databases devoted, respectively, to: line parameters, infrared and ultraviolet/visible absorption cross-sections, microphysical and optical properties of atmospheric aerosols. In this edition, 50 molecules are involved in the line parameters sub-database, including 111 isotopologues, for a total of 3,807,997 entries, in the spectral range from 10-6 to 35,877.031cm-1.The successful performances of the new …