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Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan / Eunbeen Park in GIScience and remote sensing, vol 59 n° 1 (2022)
[article]
Titre : Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan Type de document : Article/Communication Auteurs : Eunbeen Park, Auteur ; Hyun-Woo Jo, Auteur ; Sujong Lee, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 36 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] changement temporel
[Termes IGN] image Terra-MODIS
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] indice de végétation
[Termes IGN] Kirghizistan
[Termes IGN] message d'alerte
[Termes IGN] modèle de simulation
[Termes IGN] plan de prévention des risques
[Termes IGN] prévision météorologique
[Termes IGN] sécheresseRésumé : (auteur) Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R2] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R2 = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage. Numéro de notice : A2022-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2021.2012370 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1080/15481603.2021.2012370 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99720
in GIScience and remote sensing > vol 59 n° 1 (2022) . - pp 36 - 53[article]Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 828 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Green Leaf Area Index
[Termes IGN] image Gaofen
[Termes IGN] image HJ-1A
[Termes IGN] image HJ-1B
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice foliaire
[Termes IGN] modèle de régression
[Termes IGN] rizièreRésumé : (auteur) Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas. Numéro de notice : A2022-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1745299 Date de publication en ligne : 03/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1745299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100530
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 828 - 840[article]Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)
[article]
Titre : Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods Type de document : Article/Communication Auteurs : Bin Zhang, Auteur ; Haijun Wang, Auteur Année de publication : 2022 Article en page(s) : pp 71 - 95 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] automate cellulaire
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] croissance urbaine
[Termes IGN] entropie maximale
[Termes IGN] modèle de simulation
[Termes IGN] paysage urbain
[Termes IGN] Pékin (Chine)
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] urbanisation
[Termes IGN] Wuhan (Chine)Résumé : (auteur) As a powerful predictive technique based on machine learning, the maximum entropy (MaxEnt) model has been widely used in geographic modeling. However, its performance in calibrating cellular automata (CA) for urban growth simulation has not been investigated. This study compares the MaxEnt model with logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) models to explore its advantages in simulating urban growth and interpreting driving mechanisms. With the land use data of 2000 and 2020 from GlobeLand30, the constructed LR-CA, ANN-CA, SVM-CA, and MaxEnt-CA models are applied to simulate the urban growth of Beijing, Tianjin, and Wuhan, respectively. Their performance has been evaluated from multiple aspects such as the accuracy of training, testing, and projecting, computational efficiency, simulation accuracy, and simulated urban landscape. The results indicate that the MaxEnt model is superior to the other models except for the computational efficiency, but the time required for the MaxEnt training and projecting is acceptable and far less than that of the SVM. Taking the LR-CA as the benchmark, the kappa coefficients (Kappa) of the MaxEnt-CA have been increased by 4.20%, 3.38%, and 5.87% in Beijing, Tianjin, and Wuhan, respectively; the increments of corresponding figure of merits (FoM) are 6.26%, 4.58%, and 8.49%. The driving mechanisms of urban growth such as the interactions, response curves, and importance of spatial variables, have also been revealed by the MaxEnt modeling. The driving mechanisms of urban growth in Tianjin are more complex than that in Beijing and Wuhan, because there are more variable interactions; the relationships between spatial factors and urban growth in the three study areas are all nonlinear; the topographic factors and city center of Beijing, the traffic factors and water bodies of Tianjin, and the traffic factors, city center and water bodies of Wuhan are significant factors affecting their urban growth. Numéro de notice : A2022-130 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/15481603.2021.2016240 Date de publication en ligne : 30/12/2021 En ligne : https://doi.org/10.1080/15481603.2021.2016240 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99715
in GIScience and remote sensing > vol 59 n° 1 (2022) . - pp 71 - 95[article]Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
[article]
Titre : Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network Type de document : Article/Communication Auteurs : Da He, Auteur ; Qian Shi, Auteur ; Xiaoping Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102667 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage profond
[Termes IGN] arbre hors forêt
[Termes IGN] arbre urbain
[Termes IGN] base de données localisées
[Termes IGN] Chine
[Termes IGN] image Sentinel-MSI
[Termes IGN] métropole
[Termes IGN] Pékin (Chine)
[Termes IGN] prise en compte du contexte
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Contrast to the global forest, few trees live in cities but contribute significantly to urban environment and human health. However, the classical satellite-derived land cover/forest cover products with limited resolution are not fine enough for the identification of urban tree, which is usually appeared in small size and intersected with infrastructure. To relieve the dilemma, this study developed an urban tree specific sub-pixel mapping (SPM) architecture with deep learning approach, which aimed to generate 2m fine-scale urban tree cover product from 10 m Sentinel-2 images for large-scale area of 34 metropolises in China. The proposed approach has remarkable reconstruction ability for delineating the contextual characteristic of the urban tree patterns, and reliable generalization ability to large-scale area. In addition, this study creates a large-volume urban tree cover dataset (UTCD) with 0.13 billion urban tree samples at 2 m resolution, which fills the deficiency of standard dataset in urban tree cover research field. Quantitative analysis of our products was conducted on two typical study sites of Beijing and Wuhan. The results show that our products recover averagely more than 58.72% of urban tree covers that have been underestimated in the existing land cover/forest cover products, and outperforms the state-of-the-art approach both visually and quantitatively, by averagely 11.31% improvement in overall accuracy. From our annual products during 2016–2020, we found an evolution characteristic of urban tree cover: it is more stable in developed cities like Beijing, while more fluctuated in developing cities like Wuhan, and the alteration are usually concentrated at the outer-ring of downtown, which may be caused by the municipal planning and the land development of real estate industry. Numéro de notice : A2022-073 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102667 En ligne : https://doi.org/10.1016/j.jag.2021.102667 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99438
in International journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102667[article]Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan / Katsuto Shimizu in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
[article]
Titre : Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan Type de document : Article/Communication Auteurs : Katsuto Shimizu, Auteur ; Tomohiro Nishizono, Auteur ; Fumiaki Kitahara, Auteur ; Keiko Fukumoto, Auteur ; Hideki Saito, Auteur Année de publication : 2022 Article en page(s) : n° 102658 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] Japon
[Termes IGN] Pinophyta
[Termes IGN] volume en boisRésumé : (auteur) The accurate estimation of tree attributes is essential for sustainable forest management. Terrestrial Laser Scanning (TLS) is a viable remote sensing technology suitable for estimating under canopy structure. However, TLS measurements generally underestimate tree height in taller trees, which leads to the underestimation of other tree attributes (e.g., stem volume). The integration of information derived from TLS and Unmanned Aerial Vehicle (UAV) photogrammetry could potentially improve tree height estimation. This study investigated the applicability of integrating TLS and UAV photogrammetry to estimate individual tree attributes in managed coniferous forests of Japan. Diameter at breast height (DBH), tree height, and stem volume were estimated by (1) TLS data only, (2) integrating TLS and UAV data with TLS tree locations, and (3) integrating TLS and UAV data with treetop detections of the tree canopy. The TLS data only approach achieved high accuracy for DBH estimations with a root mean squared error (RMSE) of 2.36 cm (RMSE% 5.6%); however, tree height was greatly underestimated, with an RMSE of 8.87 m (28.9%) and a bias of −8.39 m. Integrating TLS and UAV photogrammetric data improved tree height estimation accuracy for both the TLS tree location (RMSE of 1.89 m and a bias of −0.46 m) and the treetop detection (RMSE of 1.77 m and a bias of 0.36 m) approaches. Integrating TLS and UAV photogrammetric data also improved the accuracy of the stem volume estimations with RMSEs of 0.21 m3 (10.8%) and 0.21 m3 (10.5%) for the TLS tree location and treetop detection approaches, respectively. Although the tree height of suppressed trees tended to be overestimated by TLS and UAV photogrammetric data integration, a good performance was obtained for dominant trees. The results of this study indicate that the integration of TLS and UAV photogrammetry is beneficial for the accurate estimation of tree attributes in coniferous forests. Numéro de notice : A2022-071 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102658 En ligne : https://doi.org/10.1016/j.jag.2021.102658 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99423
in International journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102658[article]Novel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkQuantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data / Tianyu Hu in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkRecurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkSpatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)PermalinkAutomatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkCo-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkCombined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSemantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. 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