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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)
[article]
Titre : Mapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data Type de document : Article/Communication Auteurs : Shanshan Chen, Auteur ; Huazhong Ren, Auteur ; Rongyuan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5464 - 5478 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] désertification
[Termes IGN] détection de changement
[Termes IGN] distribution spatiale
[Termes IGN] ensablement
[Termes IGN] image TASI
[Termes IGN] image Terra-ASTER
[Termes IGN] image thermique
[Termes IGN] sable
[Termes IGN] Sinkiang (Chine)Résumé : (auteur) On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China’s four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China’s sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6×104 km 2 (1.53%) by the end of 2016 compared with that in early 2000. Numéro de notice : A2021-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022772 Date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022772 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97977
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5464 - 5478[article]Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images Type de document : Article/Communication Auteurs : Donato Amitrano, Auteur ; Raffaella Guida, Auteur ; Pasquale Lervolino, Auteur Année de publication : 2021 Article en page(s) : pp 5494 - 5514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse forestière
[Termes IGN] canopée
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image RVB
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] segmentation d'image
[Termes IGN] seuillage d'image
[Termes IGN] texture d'imageRésumé : (auteur) Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. Numéro de notice : A2021-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029841 Date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97978
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5494 - 5514[article]SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images Type de document : Article/Communication Auteurs : Daifeng Peng, Auteur ; Lorenzo Bruzzone, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5891 - 5906 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches. Numéro de notice : A2021-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011913 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011913 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97986
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5891 - 5906[article]Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
[article]
Titre : Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine Type de document : Article/Communication Auteurs : Tongxi Hu, Auteur ; Elizabeth Myers Toman, Auteur ; Gang Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin hydrographique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification bayesienne
[Termes IGN] détection de changement
[Termes IGN] estimation bayesienne
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] précision infrapixellaire
[Termes IGN] série temporelleRésumé : (auteur) Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes. Numéro de notice : A2021-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.008 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97746
in ISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 250 - 261[article]Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park Type de document : Article/Communication Auteurs : Emma L. Davis, Auteur ; Andrew Trant, Auteur ; Robert G. Way, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbuste
[Termes IGN] Arctique
[Termes IGN] Canada
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] écosystème
[Termes IGN] écotone
[Termes IGN] géostatistique
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] parc naturel national
[Termes IGN] régression logistique
[Termes IGN] surveillance de la végétation
[Termes IGN] toundraRésumé : (auteur) Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and regional declines in caribou. Little is known, however, about how biophysical controls mediate plant responses to climate warming, and available observational data are limited in temporal and spatial scope. In this study, we investigated the drivers of land cover change for the 9700 km2 extent of the park using satellite remote sensing and geostatistical modelling. Random forest classification was used to hindcast and simulate land cover change for four different land cover types from 1985 to 2019 with topographic and surface reflectance imagery (Landsat archive). The resulting land cover maps, in addition to topographic and biotic variables, were then used to predict where future shrub expansion is likely to occur using a binomial regression framework. Land cover hindcasts showed a 235% increase in shrub and a 105% increase in wet vegetation cover from 1985/89 to 2015/19. Shrub cover was highly persistent and displaced wet vegetation in southern, low-elevation areas, whereas wet vegetation expanded to formerly dry, mid-elevations. The predictive model identified both biotic (initial cover class, number of surrounding shrub neighbors), and topographic variables (elevation, latitude, and distance to the coast) as strong predictors of future shrub expansion. A further 51% increase in shrub cover is expected by 2039/43 relative to 2014 reference data. Establishing long-term monitoring plots within TMNP in areas where rapid vegetation change is predicted to occur will help to validate remote sensing observations and will improve our understanding of the consequences of change for biotic and abiotic components of the tundra ecosystem, including important cultural keystone species. Numéro de notice : A2021-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112085 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.3390/rs13112085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97832
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2085[article]Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkA novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)PermalinkA novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)PermalinkL'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)PermalinkDes pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)PermalinkShoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkSpectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkUsing a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide / Chaoyang Niu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkApport des images Landsat à l’étude de l’évolution de l’occupation du sol dans la plaine de Saïss au Maroc, pour la période 1987-2018 / Abdelkader El Garouani in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)Permalink3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)Permalink