Search results for "emot"
showing 10 items of 5695 documents
Vegetation vulnerability to drought in Spain
2014
[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegeta…
Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud
2020
Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implem…
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Sun-induced chlorophyll fluorescence III: benchmarking retrieval methods and sensor characteristics for proximal sensing
2019
[EN] The interest of the scientific community on the remote observation of sun-induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM))…
Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with …
2011
International audience; Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test dataset…
Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture
2013
Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire pro…
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
2017
Made available in DSpace on 2018-12-11T17:11:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-03-01 Suomen Akatemia Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees repr…
Efficient remote sensing image classification with Gaussian processes and Fourier features
2017
This paper presents an efficient methodology for approximating kernel functions in Gaussian process classification (GPC). Two models are introduced. We first include the standard random Fourier features (RFF) approximation into GPC, which largely improves the computational efficiency and permits large scale remote sensing data classification. In addition, we develop a novel approach which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones using a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery.
Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
2021
Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…
Contribution of environmental factors to temperature distribution at different resolution levels on the forefield of the Loven Glaciers (Svalbard)
2007
ABSTRACTThe climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery)…