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Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)  

Public [article]  inGeo-spatial Information Science > vol 24 n° 4 (October 2021) . - pp 695 - 710 Titre : Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine Type de document : Article/Communication Auteurs : Daniel Marc G. dela Torre, Auteur ; Jay Gao, Auteur ; Cate Macinnis-Ng, Auteur ; Yan Shi, Auteur Année de publication : 2021 Article en page(s) : pp 695 - 710 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] Oryza (genre) [Termes IGN] phénologie [Termes IGN] rizière Résumé : (auteur) Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines. However, small farms are prevalent in the region, and current satellite-based mapping techniques do not distinguish between the two ecosystems at farm scales. This study developed an approach to rapidly map irrigated and rain-fed paddy rice in Iloilo, Philippines at 10 m resolutions using Google Earth Engine. This approach used an ensemble of classifiers based on time-series vegetation indices to produce dry and wet seasonal maps for the entire province. Results showed a predominance of rain-fed rice areas in both seasons, with irrigated rice making up only one-fourth of the total rice area. The overall accuracy was achieved at 68% for the dry season and 75% for the wet season based on ground-acquired points and very high-resolution imagery. The two types of paddies were classified at accuracies up to 87%. Furthermore, the land cover maps showed a strong agreement with the municipal statistics. The resultant maps complement current official statistics and demonstrate the prowess of phenology-based mapping to create paddy inventories in a timely manner to inform food security and agricultural policies. Numéro de notice : A2021-969 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10095020.2021.1984183 En ligne : https://doi.org/10.1080/10095020.2021.1984183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100385 [article]

A spatial anomaly points and regions detection method using multi-constrained graphs and local density / Yan Shi in Transactions in GIS, vol 21 n° 2 (April 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 2 (April 2017) . - pp 376 – 405 Titre : A spatial anomaly points and regions detection method using multi-constrained graphs and local density Type de document : Article/Communication Auteurs : Yan Shi, Auteur ; Min Deng, Auteur ; Xuexi Yang, Auteur ; Qiliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 376 – 405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de données [Termes IGN] analyse spatiale [Termes IGN] attribut sémantique [Termes IGN] cartographie statistique [Termes IGN] détection d'anomalie [Termes IGN] graphe [Termes IGN] interpolation spatiale [Termes IGN] programmation par contraintes [Termes IGN] triangulation de Delaunay Résumé : (auteur) Spatial anomalies may be single points or small regions whose non-spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi-constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non-spatial attributes of adjacent spatial entities, two levels of non-spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub-graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real-life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm. Numéro de notice : A2017-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12208 En ligne : http://dx.doi.org/10.1111/tgis.12208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84701 [article]