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A 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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classe d'objets [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])  

Public [article]  inGeocarto international > vol 36 n° 4 [01/03/2021] . - pp 361 - 375 Titre : Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm Type de document : Article/Communication Auteurs : Siddhartho Shekhar Paul, Auteur ; Jianbing Li, Auteur ; Yubao Li, Auteur ; Lei Shen, Auteur Année de publication : 2021 Article en page(s) : pp 361 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] coupe rase (sylviculture) [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] érosion [Termes descripteurs IGN] modèle RUSLE [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] qualité des eaux [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Numéro de notice : A2021-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614099 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1614099 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97081 [article]

Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)  

Public [article]  inRemote sensing > Vol 13 n° 6 (March 2021) . - n° 1060 - 32 p. Titre : Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : AI4GEO / , MAESTRIA / Mallet, Clément Article en page(s) : n° 1060 - 32 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] Corine Land Cover [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] inférence [Termes descripteurs IGN] mise à jour automatique [Termes descripteurs IGN] mise à jour de base de données Résumé : (Auteur) CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC. Numéro de notice : A2021-244 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13061060 date de publication en ligne : 11/03/2021 En ligne : https://dx.doi.org/10.3390/rs13061060 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97311 [article]

Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 375 - 396 Titre : Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis Type de document : Article/Communication Auteurs : Marta Sapena, Auteur ; Luis Angel Ruiz, Auteur Année de publication : 2021 Article en page(s) : pp 375 - 396 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] carte d'utilisation du sol [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] distance euclidienne [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] pondération Résumé : (auteur) The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas. Numéro de notice : A2021-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817463 date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1817463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96752 [article]

A spatiotemporal structural graph for characterizing land cover changes / Bin Wu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 397 - 425 Titre : A spatiotemporal structural graph for characterizing land cover changes Type de document : Article/Communication Auteurs : Bin Wu, Auteur ; Ballang Yu, Auteur ; Song Shu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 397 - 425 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement temporel [Termes descripteurs IGN] graphe [Termes descripteurs IGN] New York (Etats-Unis ; état) [Termes descripteurs IGN] objet géographique [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena. Numéro de notice : A2021-041 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1778706 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1778706 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96753 [article]

Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

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Seeing the trees in the world’s forests: An extension of the forest transition concept / Jean-Daniel Bontemps (2021)  

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Innovative approaches, tools and visualization techniques for analysing land use structures and dynamics of cities and regions (Editorial) / Robert Hecht in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)  

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Détection du changement de l'étalement urbain au bas-Sahara algérien : apport de la télédétection spatiale et des SIG, cas de la ville de Biskra (Algérie) / Assoule Dechaicha in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)  

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Climate–growth relationships at the transition between Fagus sylvatica and Pinus mugo forest communities in a Mediterranean mountain / Chiara Calderano in Annals of Forest Science [en ligne], vol 77 n° 3 (September 2020)  

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Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

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Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis / Mathias Mayer in Forest ecology and management, Vol 466 (15 June 2020)  

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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)  

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A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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