Search

Your search keyword '"Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG)"' showing total 4 results
Start Over Author "Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG)" Journal remote sensing
4 results on '"Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG)"'

1. Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

Author

Quinton, Félix, Landrieu, Loic, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), and Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)

Subjects
010504 meteorology & atmospheric sciences, Computer Science - Artificial Intelligence, Computer science, Science, Computer Science - Computer Vision and Pattern Recognition, Word error rate, Agricultural engineering, 01 natural sciences, Reduction (complexity), crop rotation, crop mapping, Sentinel-2, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0105 earth and related environmental sciences, 2. Zero hunger, I.2.10, Series (mathematics), business.industry, Deep learning, Training (meteorology), [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 04 agricultural and veterinary sciences, 15. Life on land, Crop rotation, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, General Earth and Planetary Sciences, Satellite, Artificial intelligence, business
Abstract

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics of parcel classification. Along with simple training adjustments, our model provides an improvement of over 6.3 mIoU points over the current state-of-the-art of crop classification. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels., Comment: Published in Remote Sensing

Published
2021
Full Text
View/download PDF

2. Integration of Corner Reflectors for the Monitoring of Mountain Glacier Areas with Sentinel-1 Time Series

Author

Jauvin, Matthias, Yan, Yajing, Trouvé, Emmanuel, Fruneau, Bénédicte, Gay, Michel, Girard, Blaise, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab )

Subjects
InSAR, displacement, moraine, glacier, [SDE.IE]Environmental Sciences/Environmental Engineering, [SDE]Environmental Sciences, radar cross section, Sentinel-1, lcsh:Q, lcsh:Science, corner reflector, ComputingMilieux_MISCELLANEOUS, synthetic aperture radar
Abstract

Glacier flow and slope instabilities in Alpine mountain areas represent a hazard issue. Sentinel-1 satellites provide regular Synthetic Aperture Radar (SAR) acquisitions that are potentially useful to monitor these areas, but they can be affected by temporal decorrelation due to rapid changes in the surface. The application of interferometric synthetic aperture radar (InSAR) therefore seems difficult due to loss of coherence. On the other hand, Corner Reflectors (CR) can be used as coherent targets in SAR images for accurate displacement measurement thanks to their strong backscattering property and temporal stability. The use of CRs in multi-temporal InSAR analysis in Alpine mountain areas can thus be beneficial. In this study, we present a comparison between triangular and rectangular CRs, based on Radar Cross Section (RCS) measurements in an anechoic chamber and on long-term experiments over the Argentière glacier and the surrounding slopes and moraine. The visibility in both summer and winter of 10 CRs installed on the test site was investigated. As this area is exposed to heavy precipitation including snow falls, two perforated CRs were tested. The amplitude stability and the phase error of each CR were estimated. A precise tracking of two CRs installed at the glacier surface was also able to measure the displacement of the Argentière glacier, giving results close to previous GPS measurements. Furthermore, a Persistent Scatterer Interferometry (PSI) study was conducted, using the most stable CR as reference point to estimate slope instabilities, which led to the identification of an area corresponding to a tectonic fault called “Faille de l’angle”. The precise absolute locations of the CRs were successfully estimated and PS heights were compared with a LiDAR-based (Light Detection And Ranging) digital elevation model (DEM) and GPS measurements.

Published
2019
Full Text
View/download PDF

3. Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

Author

Sassan Saatchi, Paula Soares, Antonio Ferraz, Margarida Tomé, Luísa Pereira, Clément Mallet, Carlos A. Silva, Stéphane Jacquemoud, Gil Gonçalves, California Institute of Technology (CALTECH), Institut de Physique du Globe de Paris (IPGP), Centre National de la Recherche Scientifique (CNRS)-Université de La Réunion (UR)-Université Paris Diderot - Paris 7 (UPD7)-IPG PARIS-Institut national des sciences de l'Univers (INSU - CNRS), Université de Montréal (UdeM), Forest Research Centre, School of Agriculture, University of Lisbon, Archaeogenetics Laboratory, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Universidade de Coimbra [Coimbra], University of Idaho [Moscow, USA], Universidade de Lisboa (ULISBOA), and Universidade de Aveiro

Subjects
Synthetic aperture radar, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Tree allometry, airborne laser scanning, 02 engineering and technology, vegetation cover, 01 natural sciences, multi-layered forest structure, Space research, ComputingMilieux_MISCELLANEOUS, lidar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Biomass (ecology), biomass, [SDE.IE]Environmental Sciences/Environmental Engineering, carbon, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 15. Life on land, Field (geography), Lidar, individual tree extraction, Remote sensing (archaeology), [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Spatial variability, 3D point cloud clustering, crown delineation
Abstract

International audience; The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO 2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree-or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO 2 stocks and changes to comply with the UN-REDD policies.

Published
2016
Full Text
View/download PDF

4. Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

Author

Thuy Le Toan, Jean-Paul Rudant, Bénédicte Fruneau, Pierre Louis Frison, Ludovic Villard, Thierry Koleck, Syrine Kmiha, Eric Mougin, Eric Dufrêne, Kamel Soudani, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Ecologie Systématique et Evolution (ESE), Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale du Génie Rural, des Eaux et des Forêts (ENGREF)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), Equipe Observation de la Terre et Information Géographique, Université Paris-Est Marne-la-Vallée (UPEM), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
temperate mixed forest, 010504 meteorology & atmospheric sciences, Science, 0211 other engineering and technologies, radar backscattering coefficient, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, Normalized Difference Vegetation Index, law.invention, law, medicine, Temperate Forest, Precipitation, Radar, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Phenology, interferometric coherence, Vegetation, 15. Life on land, Seasonality, seasonal monitoring, medicine.disease, Deciduous, [SDE]Environmental Sciences, Sentinel images, Sentinel-1, General Earth and Planetary Sciences, Environmental science, Temperate rainforest, SAR
Abstract

In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. Radar backscattering coefficients, σ0 and the amplitude of temporal interferometric coherence profiles in relation to environmental variables are shown, such as in situ precipitation and air temperature. The high temporal frequency of Sentinel-1 acquisitions (i.e., twelve days, or six, if both Sentinel-1A and B are combined over Europe) and the dual polarization configuration (VV and VH over most land surfaces) made a significant contribution. In particular, the radar backscattering coefficient ratio of VV to VH polarization, σ V V 0 / σ V H 0 , showed a well-pronounced seasonality that was correlated with vegetation phenology, as confirmed in comparison to NDVI profiles derived from Landsat-8 (r = 0.77) over stands of deciduous trees. These results illustrate the high potential of Sentinel-1 data for monitoring vegetation, and as these data are not sensitive to the atmosphere, the phenology could be estimated with more accuracy than optical data. These observations will be quantitatively analyzed with the use of electromagnetic models in the near future.

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results