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An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations / Kai Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 1 - 16 Titre : An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations Type de document : Article/Communication Auteurs : Kai Zhou, Auteur ; Lin Cao, Auteur ; Shiyun Yin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bande spectrale [Termes IGN] coefficient de corrélation [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] Ginkgo biloba [Termes IGN] image à haute résolution [Termes IGN] indice foliaire [Termes IGN] Kiangsou (Chine) [Termes IGN] réflectance végétale Résumé : (auteur) As a key phenolic pigment concentrated in the surface tissues of leaves, flavonoids (Flav) are the major bioactive ingredients in Ginkgo leaf extracts. Flav are also marked natural antioxidants and significant indicators of biotic and abiotic stresses, critical for determining cultivation quality and enhancing Flav yield. In particular, area-based Flav (Flavarea) is related to the shortwave-blue light interaction within leaves per unit leaf area, whereas mass-based Flav (Flavmass) is useful for the quantitative assessment of Flav yield. In order to accurately estimate the contents of Flavarea and Flavmass in leaves of Ginkgo plantations, in this study, we developed an advanced bidirectional reflectance factor (BRF) spectra-based approach by reducing the effects of specular reflection and enhancing the absorption signals of Flav (in the shortwave-blue region of spectrum), using a suite of new spectral indices (SIs) (i.e., flavonoid index (FI), modified flavonoid index (mFI) and double difference index (DD)) calculated from the leaf clip equipped spectrometers-collected data. The results demonstrated that most of the SIs derived from the developed BRF spectra-based approach obtained relatively high performance for Flav estimation by alleviating adverse effects of specular reflection to different extents (CV-R2 = 0.60–0.76). In specific, DDnir434,421 selected from DD-type indices performed (CV-R2 = 0.76 for Flavarea; CV-R2 = 0.69 for Flavmass) better than other indices. These findings represent marked potentials of the developed BRF spectra-based approach for non-destructively estimating leaf Flav content, as well as improving the understanding of the mechanisms of specular effects on Flav estimations in leaves of Ginkgo plantations. Numéro de notice : A2022-744 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.020 Date de publication en ligne : 09/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101727 [article]

Remote Sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model / Sangay Gyeltshen in Geocarto international, Vol 37 n° 21 ([01/10/2022])  

Public [article]  inGeocarto international > Vol 37 n° 21 [01/10/2022] . - pp 6331 - 6350 Titre : Remote Sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model Type de document : Article/Communication Auteurs : Sangay Gyeltshen, Auteur ; Rabindra Adhikarib, Auteur ; Padam Bahadur Budha, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6331 - 6350 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Bhoutan [Termes IGN] distribution spatiale [Termes IGN] effondrement de terrain [Termes IGN] érosion [Termes IGN] modèle RUSLE [Termes IGN] système d'information géographique [Termes IGN] télédétection [Termes IGN] utilisation du sol Résumé : (auteur) The repository of soil by water at a national and basin scale was estimated using the RUSLE empirical model which is the first of its kind in Bhutan. The annual soil loss is estimated and categorized into five categories: very low (800 t/yr). Sakteng and Jaldakha basins contributed the highest soil loss rate of 0.04 and 0.039 t/ha/yr, while considering on landuse pattern, non-built-up and landslide category encountered the highest soil loss of 4.09 and 0.7 t/ha/yr among others. Similarly, Tsirang, Samtse and Haa contributed the major soil loss of 0.03, 0.0298 and 0.02 t/ha/yr, respectively. The research can be used as an authentic instrument enabling the soil conservationist and the policymakers to evaluate the adverse impacts, prioritize the conservation efforts and investigate further to narrow down the causes of soil erosion. Numéro de notice : A2022-718 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1936210 Date de publication en ligne : 22/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1936210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101646 [article]

Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska / Jiang Chen in Geocarto international, vol 37 n° 20 ([20/09/2022])  

Public [article]  inGeocarto international > vol 37 n° 20 [20/09/2022] . - pp 6052 - 6071 Titre : Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska Type de document : Article/Communication Auteurs : Jiang Chen, Auteur ; Weining Zhu, Auteur Année de publication : 2022 Article en page(s) : pp 6052 - 6071 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alaska (Etats-Unis) [Termes IGN] analyse comparative [Termes IGN] Google Earth Engine [Termes IGN] image Landsat-8 [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] latitude [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] observation de la Terre [Termes IGN] réflectance de surface Résumé : (auteur) Combining Landsat-8 and Sentinel-2 images is an effective approach to obtain high spatiotemporal resolution data for Earth observation and remote sensing modeling. The differences between Landsat-8 and Sentinel-2 products, such as the reflectance at the top of atmosphere (TOA) and land surface, should be compared and evaluated to make sure they are spectrally consistent. Their consistency has been evaluated and the differences have been empirically corrected at mid-low latitudes, but in high latitude areas with a higher solar zenith angle (SZA), the similar work has not been explored. In this study, Landsat-8 and Sentinel-2 TOA and surface reflectance in Alaska as well as some surface parameters, such as the normalized difference vegetation index (NDVI) and normalized difference snow index (NDSI), were compared using the massive data distributed on Google earth engine (GEE) online platform, and their consistency was evaluated and the uncertainty was analyzed. Some empirical models were suggested to convert Sentinel-2 products to be consistent with Landsat-8 products at all bands. The results show that TOA reflectance is more consistent than surface reflectance in Alaska. This study suggests that the consistency between Landsat-8 and Sentinel-2 at high latitudes should be paid more attention because their consistency is lower than that at mid-low latitudes. Numéro de notice : A2022-717 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1924295 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1924295 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101642 [article]

Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (15 September 2022)  

Public [article]  inRemote sensing of environment > vol 279 (15 September 2022) . - n° 113134 Titre : Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics Type de document : Article/Communication Auteurs : Jakob Wernicke, Auteur ; Christian Torsten Seltmann, Auteur ; Ralf Wenzel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Allemagne [Termes IGN] analyse comparative [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] inventaire forestier étranger (données) [Termes IGN] semis de points [Termes IGN] stratification Résumé : (auteur) Knowledge about the forest canopy stratification is of essential importance for forest management and planning. Collecting structural information (e.g. natural regeneration) still depends on cost and labour intensive forest inventories with a coarse spatio-temporal resolution. Remote sensing partly overcomes these limitations and particularly active sensors of type light detection and ranging (LiDAR) have proven their great potential of separating forest strata. The applicability of LiDAR metrics for the differentiation of the spruce dominated forest strata in Central Germany has not been tested yet. Additionally, studying the potential of Sentinel-2 metrics for the classification of forest strata is lacking too. In this study, we investigated the capabilities of six different classification approaches for the differentiation of five forest strata that are typical for the study region. Reference data were derived from forest inventory measurements surveyed on a dense 200 × 200 m grid. The six classification approaches were trained with fused and un-fused LiDAR and Sentinel-2 inferred metrics. The classification results were compared using the overall mean accuracy, sensitivity and specificity via receivers operating characteristics of multi-class problems. We were interested in the classification abilities of Sentinel-2 metrics due to the obvious advantages of Sentinel-2 based metrics (free of charge, high spatio-temporal coverage). We assumed that the canopy structure determines the reflection on stand level and thus might facilitate the classification of different canopy strata. Beforehand, it was important to examine the influence of distinctly imbalanced and collinear reference data on the classification results. We found that the Random Forest classifier most accurately separated the five forest strata with a mean overall accuracy of 83.3% (Kappa = 76.2%). These values were achieved from balanced training data and the classification capability was confirmed by classification results from an independent test data set. Fused predictors of active (LiDAR) and passive (Sentinel-2) remote sensing revealed no substantial improvement in the classification accuracy due to the dominant role of LiDAR metrics. Herein, we identified that especially the height variability, top height, portion of LiDAR-returns between 2 m and 10 m and the standard deviation of the return number between the 25th and 50th height percentile, predominately contributed to the classification accuracy. Classification results purely based on Sentinel-2 metrics revealed a rather small overall mean accuracy of 54.7%. The metrics (e.g. median, variance, entropy) were derived from Sentinel-2 indices, covering the visible and near to short infrared spectrum. Variable importance computations unraveled a detectable but minor contribution of MSI, TCG, NDVI to the classification result. Finally, our data driven observations illustrated serious drawbacks associated to data imbalance, collinearity and autocorrelation and presented practical guidance to cope with these issues. Numéro de notice : A2022-510 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113134 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101047 [article]

Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])  

Public [article]  inGeocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452 Titre : Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 5433 - 5452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition d'images [Termes IGN] apprentissage profond [Termes IGN] approche participative [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couleur (variable spectrale) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] étiquette [Termes IGN] image multibande [Termes IGN] OpenStreetMap [Termes IGN] pixel [Termes IGN] plateforme collaborative [Termes IGN] texture d'image [Termes IGN] Worldview Résumé : (auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels ​​of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved. Numéro de notice : A2022-702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1917006 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1917006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561 [article]

Feux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)

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Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)  

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Landsat, le programme fête ses cinquante ans / Laurent Polidori in Géomètre, n° 2205 (septembre 2022)

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Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project / Giles M. Foody in Geo-spatial Information Science, vol 25 n° inconnu ([01/08/2022])  

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Mainstreaming remotely sensed ecosystem functioning in ecological niche models / Adrián Regos in Remote sensing in ecology and conservation, vol 8 n° 4 (August 2022)  

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Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)  

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Remote sensing and phytoecological methods for mapping and assessing potential ecosystem services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria / Amal Louail in Forests, Vol 13 n° 8 (August 2022)  

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Smart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)  

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Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)  

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A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

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