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Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 367 - 374 Titre : Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas Type de document : Article/Communication Auteurs : Benedikt Hiebl, Auteur ; Andreas Mayr, Auteur ; Andreas Kollert, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 367 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] emissivité [Termes IGN] flore alpine [Termes IGN] image RVB [Termes IGN] montagne [Termes IGN] série temporelle [Termes IGN] température au sol [Termes IGN] thermographie [Termes IGN] variation saisonnière Résumé : (auteur) Land Surface Temperature (LST) products from thermal infrared imaging rely on information about the spatial distribution of Land Surface Emissivity (LSE). For portable, broadband thermal cameras for drone- or ground-based measurements with camera to object distances up to a few kilometres and with meter-scale resolution, threshold-based retrieval of LSE from Fractional green Vegetation Cover (FVC) can be used. As seasonal changes in vegetation LSE over the year cannot be accounted for by single satellite images or aerial orthophotos, this study evaluates an approach for FVC retrieval via permanently installed RGB webcams and derived Excess Green vegetation index (ExG) time series at a high-mountain test site in the European Alps. Daily ExG values were derived from the imagery of 27 days between 12/07/2021 and 30/10/2021 and projected to a 0.5 m Digital Surface Model (DSM). FVC reference data from 765 in-situ vegetation plots were used to assess the relationship between ExG and the vegetation cover and to determine the thresholds of ExG for no vegetation cover and full vegetation cover. Despite the bad correlation between ExG and in-field FVC with an R² score of 0.15, an approach using a well-tested orthophoto-retrieved NDVI for FVC retrieval performs just slightly better. The comparison of the remotely sensed data and the field measurements therefore remains complex. Time series analysis of both ExG and FVC for highly vegetated areas showed a significant decrease from summer to autumn, which reflects the seasonal changes of LSE for senescent vegetation. Calculated emissivities for vegetated pixels ranged from the minimum of 0.95 to the maximum of 0.985 over the season, while emissivity values for less vegetated pixels stayed constant during the season. The results of this study will be used as input to a correction model for remote LST measurements in the context of micro-scale investigations of the thermal niche of Alpine flora. Numéro de notice : 2022-428 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-367-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100735 [article]

Downscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)  

Public [article]  inGIScience and remote sensing > vol 58 n° 8 (2021) . - pp 1300 - 1315 Titre : Downscaling MODIS spectral bands using deep learning Type de document : Article/Communication Auteurs : Rohit Mukherjee, Auteur ; Desheng Liu, Auteur Année de publication : 2021 Article en page(s) : pp 1300 - 1315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bande spectrale [Termes IGN] image à basse résolution [Termes IGN] image Terra-MODIS [Termes IGN] image thermique [Termes IGN] rayonnement proche infrarouge [Termes IGN] réduction d'échelle [Termes IGN] résolution multiple Résumé : (auteur) MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation or more advanced statistical modeling. Statistical downscaling methods rely on the functional relationship between the LR spectral bands and HR spatial information, which may vary across different land surface types, making statistical downscaling methods less robust. In this paper, we propose an alternative approach based on deep learning to downscale 500 m and 1000 m spectral bands of MODIS to 250 m without additional spatial information. We employ a superresolution architecture based on an encoder decoder network. This deep learning-based method uses a custom loss function and a self-attention layer to preserve local and global spatial relationships of the predictions. We compare our approach with a statistical method specifically developed for downscaling MODIS spectral bands, an interpolation method widely used for downscaling multi-resolution spectral bands, and a deep learning superresolution architecture previously used for downscaling satellite imagery. Results show that our deep learning method outperforms on almost all spectral bands both quantitatively and qualitatively. In particular, our deep learning-based method performs very well on the thermal bands due to the larger scale difference between the input and target resolution. This study demonstrates that our proposed deep learning-based downscaling method can maintain the spatial and spectral fidelity of satellite images and contribute to the integration and enhancement of multi-resolution satellite imagery. Numéro de notice : A2021-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2021.1984129 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1080/15481603.2021.1984129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99309 [article]

Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016 / Mbongowo J. Mbuh in Geocarto international, vol 36 n° 14 ([01/08/2021])  

Public [article]  inGeocarto international > vol 36 n° 14 [01/08/2021] . - pp 1565 - 1590 Titre : Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016 Type de document : Article/Communication Auteurs : Mbongowo J. Mbuh, Auteur ; Ryan Wheeler, Auteur ; Amanda Cook, Auteur Année de publication : 2021 Article en page(s) : pp 1565 - 1590 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chicago (Illinois) [Termes IGN] données spatiotemporelles [Termes IGN] emissivité [Termes IGN] exitance spectrale [Termes IGN] ilot thermique urbain [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] image thermique [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle [Termes IGN] température au sol Résumé : (auteur) Most major cities worldwide are affected Urban Heat Islands – a condition of relatively higher temperatures being observed in one area compared to another that can be caused by a decrease in greenspace. One of the major reasons attributed to this increase in the warming of urban landscapes is the decrease in green space. This concept has received a lot of attention due to the destruction of vegetation for urban development and has prompted long-term spatial-temporal studies of Urban Heat Islands to understanding local climates. The objective of this study is to use Landsat data to examine the temporal intensification of UHIs and their variability from 1984–2016 for the cities of Chicago and Minneapolis-St. Paul. Landsat L4-5 TM), L7 ETM+), OLI and TIRS from 1984 to 2016 was used to examine land surface temperature (LST). Firstly, we converted the digital number (DN) to spectral radiance (L) and to temperature in Kelvin and from kelvin to Celsius and a conversion from Radiance to Top of the Atmosphere Reflectance and estimation of land surface emissivity. Finally, LST was estimated and Urban Heat Island retrieval and anomalies computed to help examine inconsistencies in our data. Our analysis showed year-to-year fluctuations in surface temperature, intensification of UHIs for both metro areas. Using a defined deductive index to identify environmentally critical areas, estimates of UHIs based on LST showed that both metropolitan areas are UHIs with LST > µ + 0.5 × δ. Higher intensification values were observed in 1988 and 2010 for Chicago and 1984, 1999 and 2016 for Minneapolis-St. Paul from analysis. While both areas have the similar climatic conditions, our analysis show differences in UHIs intensification as observed in their urban growth patterns. Chicago experiences a higher UHI intensity compared to Minneapolis-St. Paul and this could be explained by higher number of tall buildings than Minneapolis-St. Paul. Numéro de notice : A2021-556 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1655802 Date de publication en ligne : 29/08/2019 En ligne : https://doi.org/10.1080/10106049.2019.1655802 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98109 [article]

ComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6662 - 6673 Titre : ComNet: combinational neural network for object detection in UAV-borne thermal images Type de document : Article/Communication Auteurs : Minglei Li, Auteur ; Xingke Zhao, Auteur ; Jiasong Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6662 - 6673 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] image thermique [Termes IGN] piéton [Termes IGN] saillance [Termes IGN] véhicule Résumé : (auteur) We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks. Numéro de notice : A2021-632 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029945 Date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98288 [article]

Anomalous variations of air temperature prior to earthquakes / Irfan Mahmood in Geocarto international, vol 36 n° 12 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 12 [01/07/2021] . - pp 1396-1408 Titre : Anomalous variations of air temperature prior to earthquakes Type de document : Article/Communication Auteurs : Irfan Mahmood, Auteur Année de publication : 2021 Article en page(s) : pp 1396-1408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] anomalie thermique [Termes IGN] Argentine [Termes IGN] Canada [Termes IGN] données spatiotemporelles [Termes IGN] fracture [Termes IGN] ionisation [Termes IGN] risque naturel [Termes IGN] séisme [Termes IGN] télédétection spatiale [Termes IGN] température de l'air [Termes IGN] Turquie Résumé : (Auteur) Earthquakes occur because of increase of stress and rock fracture. Prior to impending earthquake, physical and chemical interactions in the earth’s crust lead to anomalous variations of air temperature (AT). Satellite based remote sensing method allows to determine earthquake precursors over a large tectonic area. Buildup of stresses in a seismically active area manifests as thermal anomaly. In the present study, variations in AT prior to eastern Turkey, Bella Bella (Canada) and Pocito (Argentina) earthquakes were studied by utilizing multi-year background data. The analysis shows strong anomalous variations of AT prior to the seismic events with the highest AT values recorded before the earthquakes. Anomaly plots show that the release of energy was concentrated in the region along epicenter. Descriptive statistics of AT for the earthquakes show significant changes prior to the seismic event. Degassing of gases occur during rock micro-fracturing, which results in air ionization, thereby resulting in AT precursory anomalies. Numéro de notice : A2021-379 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1648565 Date de publication en ligne : 07/08/2019 En ligne : https://doi.org/10.1080/10106049.2019.1648565 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97877 [article]

Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

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Mapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data / Shanshan Chen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

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Des pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)

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Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])  

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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

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Détection d’ouvertures par segmentation sémantique de nuages de points 3D : apport de l’apprentissage profond / Camille Lhenry (2021)  

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Remote sensing and GIS / Basudeb Bhatta (2021)

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Retrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January-1 2021)  

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Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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Quantification of cotton water consumption by remote sensing / Jefferson Vieira José in Geocarto international, vol 35 n° 16 ([01/12/2020])  

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