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Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data / Anjana N.J. Kukunuri in Geocarto international, vol 37 n° 10 ([01/06/2022])
[article]
Titre : Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data Type de document : Article/Communication Auteurs : Anjana N.J. Kukunuri, Auteur ; Deepak Murugan, Auteur ; Dharmendra Singh, Auteur Année de publication : 2022 Article en page(s) : pp 2871 - 2892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] évapotranspiration
[Termes IGN] image Terra-MODIS
[Termes IGN] Inde
[Termes IGN] indice de stress
[Termes IGN] indice de végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] précipitation
[Termes IGN] réflectance spectrale
[Termes IGN] sécheresse
[Termes IGN] stress hydriqueRésumé : (auteur) Overall health condition of the vegetation is obtained by combining satellite data derived moisture and thermal stresses present in vegetation condition index (VCI) and thermal condition index (TCI), respectively and improves the accuracy of drought classification. Although vegetation health index fuses the information present in VCI and TCI, the relative contribution of each index depends on prior knowledge of the study area. Therefore, the random weighing method is used to obtain optimal weights of VCI and TCI based on variances of individual indices. The obtained fusion results of a normal and drought year demonstrate that the random weighing fusion achieves better estimation of agriculture drought without requiring apriori information and the obtained drought classification results are in line with the available ground truth precipitation records. In addition, the correlation analysis of the obtained optimal weights and standardized precipitation evapotranspiration index exhibited a strong correlation with a Pearson’s correlation coefficient of above 0.8. The study also showed that the relative contribution of VCI is prevalent in normal conditions while TCI in dry to extreme dry conditions. Numéro de notice : A2022-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837256 Date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101299
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2871 - 2892[article]Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)
[article]
Titre : Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon Type de document : Article/Communication Auteurs : Hermann Tagne, Auteur ; Arnaud Le Bris , Auteur ; David Monkam, Auteur ; Clément Mallet , Auteur Année de publication : 2022 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 673 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Cameroun
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] série temporelleRésumé : (auteur) Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval. Numéro de notice : A2022-426 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-3-2022-673-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-673-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100731
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 673 - 680[article]Deep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Deep learning for the detection of early signs for forest damage based on satellite imagery Type de document : Article/Communication Auteurs : Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; Mirjana Voelsen, Auteur ; Christian Heipke, Auteur ; Sönke Müller, Auteur Année de publication : 2022 Article en page(s) : pp 307 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation de la flore
[Termes IGN] dommage forestier causé par facteurs naturels
[Termes IGN] fonction de perte
[Termes IGN] image Sentinel-MSI
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance forestièreRésumé : (auteur) We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image. Numéro de notice : A2022-432 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-307-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100738
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 307 - 315[article]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)
[article]
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] données de terrain
[Termes IGN] emissivité
[Termes IGN] flore alpine
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] modèle numérique de surface
[Termes IGN] montagne
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] variation saisonnièreRésumé : (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 : A2022-428 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
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 367 - 374[article]Detection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images / Kamal Kant Singh in Geocarto international, vol 37 n° 9 ([15/05/2022])
[article]
Titre : Detection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images Type de document : Article/Communication Auteurs : Kamal Kant Singh, Auteur ; Dhiraj Kumar Singh, Auteur ; Narinder Kumar Thakur, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2561 - 2579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] avalanche
[Termes IGN] Himalaya
[Termes IGN] image Sentinel-MSI
[Termes IGN] matrice de co-occurrence
[Termes IGN] modèle numérique de surface
[Termes IGN] réflectance
[Termes IGN] signature spectraleRésumé : (auteur) Release of snow avalanche from a mountain slope depends on various parameters such as snow cover, terrain and meteorological conditions of the region. The precise information of avalanche occurrence in terms of its location and extent is essentially important for hazard mapping and for avalanche occurrence feedback. In the present study, various techniques have been explored for automatic detection and mapping of snow avalanche debris for a part of Western Himalayan region using Sentinel-2 satellite data. Spectral signatures of avalanche and non-avalanche snow collected from the field spectroradiometer survey are used for identifying suitable spectral bands of Sentinel-2 for avalanche debris detection. Techniques such as Ratio Method, Gray Level Co-occurrence Matrix, a new proposed index, i.e. Avalanche Debris Index and Object-Based Image Analysis (OBIA) are applied on satellite images to retrieve the avalanche debris. Retrieved avalanche debris are further compared with the manually digitized avalanche occurred boundaries. The OBIA method has been found the most suitable for avalanche debris detection and mapping using the medium resolution satellite data. Numéro de notice : A2022-565 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1762762 Date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1762762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101245
in Geocarto international > vol 37 n° 9 [15/05/2022] . - pp 2561 - 2579[article]Réservation
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