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A combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])  

Public [article]  inGeocarto international > vol 36 n° 10 [01/06/2021] . - pp 1161-1177 Titre : A combined drought monitoring index based on multi-sensor remote sensing data and machine learning Type de document : Article/Communication Auteurs : Hongzhu Han, Auteur ; Jianjun Bai, Auteur ; Jianwu Yan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1161-1177 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] Chensi (Chine) [Termes IGN] classification par forêts aléatoires [Termes IGN] évapotranspiration [Termes IGN] humidité du sol [Termes IGN] image Terra-MODIS [Termes IGN] image TRMM-MI [Termes IGN] indice d'humidité [Termes IGN] indice de végétation [Termes IGN] précipitation [Termes IGN] sécheresse [Termes IGN] surveillance météorologique [Termes IGN] température au sol Résumé : (Auteur) The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors. Numéro de notice : A2021-369 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1633423 date de publication en ligne : 27/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1633423 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97734 [article]

Knowledge-guided consistent correlation analysis of multimode landslide monitoring data / Shuangxi Miao in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2255 - 2271 Titre : Knowledge-guided consistent correlation analysis of multimode landslide monitoring data Type de document : Article/Communication Auteurs : Shuangxi Miao, Auteur ; Qing Zhu, Auteur ; Bo Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 2255 - 2271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] base de connaissances [Termes IGN] Chensi (Chine) [Termes IGN] corrélation [Termes IGN] données multisources [Termes IGN] effondrement de terrain [Termes IGN] regroupement de données [Termes IGN] structure géologique [Termes IGN] surveillance géologique Résumé : (Auteur) A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method. Numéro de notice : A2017-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1356461 En ligne : https://doi.org/10.1080/13658816.2017.1356461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88081 [article]

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An automated approach for updating land cover maps based on integrated change detection and classification methods / X. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 71 (July 2012) . - pp 86 - 95 Titre : An automated approach for updating land cover maps based on integrated change detection and classification methods Type de document : Article/Communication Auteurs : X. Chen, Auteur ; J. Chen, Auteur ; Y. Shi, Auteur ; Yasushi Yamaguchi, Auteur Année de publication : 2012 Article en page(s) : pp 86 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] champ aléatoire de Markov [Termes IGN] Chensi (Chine) [Termes IGN] détection de changement [Termes IGN] image Landsat-ETM+ [Termes IGN] iterated training sample selecting [Termes IGN] mise à jour de base de données Résumé : (Auteur) Updating land cover maps from remotely sensed data in a timely manner is important for many areas of scientific research. Unfortunately, traditional classification procedures are very labor intensive and subjective because of the required human interaction. Based on the strategy of updating land cover data only for the changed area, we proposed an integrated, automated approach to update land cover maps without human interaction. The proposed method consists primarily of the following three parts: a change detection technique, a Markov Random Fields (MRFs) model, and an iterated training sample selecting procedure. In the proposed approach, remotely sensed data acquired in different seasons or from different remote sensors can be used. Meanwhile, the approach is completely unsupervised. Therefore, the methodology has a wide scope of application. A case study of Landsat data was conducted to test the performance of this method. The experimental results show that several sub-modules in this method work effectively and that reasonable classification accuracy can be achieved. Numéro de notice : A2012-350 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31796 [article]

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Evaluation of land use and cover changes in North Shaanxi, China / W. Wu in Photo interprétation, vol 39 n° 2 (Juin 2003)

Public [article]  inPhoto interprétation > vol 39 n° 2 (Juin 2003) . - pp 15 - 29 Titre : Evaluation of land use and cover changes in North Shaanxi, China Type de document : Article/Communication Auteurs : W. Wu, Auteur Année de publication : 2003 Article en page(s) : pp 15 - 29 Langues : Anglais (eng) Français (fre) Descripteur : [Vedettes matières IGN] Photo-interprétation [Termes IGN] analyse diachronique [Termes IGN] Chensi (Chine) [Termes IGN] classification [Termes IGN] désertification [Termes IGN] environnement [Termes IGN] géographie humaine [Termes IGN] image Landsat-TM [Termes IGN] image multitemporelle [Termes IGN] occupation du sol [Termes IGN] surface cultivée Résumé : (Auteur) As a transitional belt between the Mu Us Sandy Land and Loess Plateau, the north part of Shaanxi is one of the ecologically vulnerable and areas in northwest China, which are subject to the impacts of both natural factors and human activity. It is therefore of special importance to select such an area to carry out an intensive land use change study and establish a dynamic monitoring system thereof aiming at understanding the change mechanism, human-environmental relationships and producing useful references for the central and local governments in their sustainable land use planning. Multitemporal Landsat images were used for change detection by adopting a synthetic method including atmospheric correction, Tasseled Cap transformation, indicator differencing, thresholding, change type identification and county-level quantification. A classification on the recent image was conducted to quantify the present land use and cover pattern. A panel analysis calibrated by multivariate regression model was applied to reveal the driving forces of land cover changes and understand the interaction between the human activities and environment modifications by incorporating the results obtained from the change detection with the county-level socio-economic data. The regression modelling results suggest that the vegetation degradation- the predominant change type in the study area, is probably related to the soil erosion (R' = 0.994) and grassland degradation; the farmland extension, occurring principally in the valleys in the Yulin County, is associated with the foodproduct increase (R' = 0.987); the urban extension, spatially related to coal mining, is linked to the urban population growth (R' = 0. 881); and the surface decrease of waterbodies, having taken place in the sandy land, is negatively correlated to the cultivated land change (R' = 0. 562). Therefore, the factors which govern these land use and cover changes are various human socio-economic activities, and the root cause of land degradation is, however, some unreasonable land use policies. Numéro de notice : A2003-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26472 [article]

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