Centre de documentation
scientifique

Dépouillements

Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study Type de document : Article/Communication Auteurs : W.D.K.V. Nandasena, Auteur ; Lars Brabyn, Auteur ; Silvia Serrao-Neumanna, Auteur Année de publication : 2023 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] forêt [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] matrice de co-occurrence [Termes IGN] occupation du sol [Termes IGN] Sri Lanka Résumé : (auteur) Global land cover classifications may lead to the loss of important local and national nuances such as forest and agroforestry classes. These classes are important to local contexts because they contribute to sustainable land management systems. This paper demonstrates the application of Sentinel-2A satellite images, elevation data, and the Google Earth Engine platform to generate more detailed, specialist land cover classification for forestry classes important in Sri Lanka deriving ten spectral, 16 textural, and three topographical features from the input datasets. The random forest classification model discriminates vegetation types as forest, forest plantations, shrub, grassland, home garden, and cultivation with an overall accuracy of 94% and kappa value of 0.91. Results indicate the elevation feature contributes the most to discriminate forest and agroforestry classes, and red band (664.6 nm) textural metrics derived from grey-level co-occurrence matrix analysis are more useful for separating the home garden from other land cover classes. Numéro de notice : A2023-094 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.2022010 Date de publication en ligne : 29/12/2021 En ligne : https://doi.org/10.1080/10106049.2021.2022010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99617 [article]

Mapping active paddy rice area over monsoon asia using time-series Sentinel-2 images in Google earth engine : a case study over lower gangetic plain / Arabinda Maiti in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Mapping active paddy rice area over monsoon asia using time-series Sentinel-2 images in Google earth engine : a case study over lower gangetic plain Type de document : Article/Communication Auteurs : Arabinda Maiti, Auteur ; Prasenjit Acharya, Auteur ; Srikanta Sannigrahi, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] Gange (fleuve) [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] mousson [Termes IGN] plaine [Termes IGN] rizière Résumé : (auteur) We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β1) = 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security. Numéro de notice : A2022-924 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2022.2032396 En ligne : https://doi.org/10.1080/10106049.2022.2032396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99963 [article]

Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte topographique [Termes IGN] construction [Termes IGN] décomposition [Termes IGN] détection du bâti [Termes IGN] forme linéaire [Termes IGN] généralisation cartographique automatisée [Termes IGN] Ordnance Survey (UK) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260 [article]

Forest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] . - pp Titre : Forest road extraction from orthophoto images by convolutional neural networks Type de document : Article/Communication Auteurs : Erhan Çalişkan, Auteur ; Yusuf Sevim, Auteur Année de publication : 2023 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chemin forestier [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction automatique [Termes IGN] orthoimage [Termes IGN] segmentation sémantique Résumé : (auteur) Continuous monitoring of the forest road infrastructure and keeping track of the changes occurred are important for forestry practices, map updating, forest fire and forest transport decision support systems. In this context, the most of up to date data can be obtained by automatic forest road extraction from satellite images via machine learning (ML). Acquiring sufficient data is one of the most important factors which affect the success of ML and deep learning (DL). DL architectures yield more consistent results for complex data sets compared with ML algorithms. In the present study, three different deep learning (Resnet-18, MobileNet-V2 and Xception) architectures with semantic segmentation architecture were compared for extracting the forest road network from high-resolution orthophoto images and the results were analyzed. The architectures were evaluated through a multiclass statistical analysis based precision, recall, F1 score, intersection over union and overall accuracy (OA). The results present significant values obtained by the Resnet-18 architecture, with 99.72% of OA and 98.87% of precision and by the MobileNet-V2 architecture, with 97.76% of OA and 98.28% of precision. Also the results show that Resnet-18, MobileNet-V2 semantic segmentation architectures can be used efficiently for forest road extraction. Numéro de notice : A2022-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2060319 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2060319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100380 [article]

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis Type de document : Article/Communication Auteurs : Haifa Tamiminia, Auteur ; Bahram Salehi, Auteur ; Masoud Mahdianpari, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse aérienne [Termes IGN] boosting adapté [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification pixellaire [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Extreme Gradient Machine [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] réserve naturelle Résumé : (auteur) Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model. Numéro de notice : A2022-331 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2071475 Date de publication en ligne : 27/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2071475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100607 [article]

Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] Chine [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] mangrove [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356 [article]

Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier Type de document : Article/Communication Auteurs : Mahmoud Mohamed, Auteur ; Salem Morsy, Auteur ; Adel El-Shazly, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] zone urbaine Mots-clés libres : cylindrical neighbourhood = voisinage cylindrique Résumé : (auteur) 3D road mapping is essential for intelligent transportation system in smart cities. Road environment receives its data from mobile laser scanning (MLS) systems in the format of LiDAR point clouds, which are distinguished with their accuracy and high density. In this paper, a mobile LiDAR data classification method based on machine learning (ML) is presented. First, data subsampling and slicing are applied, followed by cylindrical neighbourhood selection method to determine the neighbourhood of each point. Second, a new LiDAR-based point feature namely Zmodis introduced, and used along with existing fifteen geometric features as input for a ML algorithm. Finally, Random Forest classifier is applied to a part of (Paris-Lille-3D) MLS point clouds belonging to NPM3D Benchmark. The dataset is about 1.5 km long road in Lille, France with more than 98 million points. The use of Zmod improved the accuracy from 90.26% to 95.23% and achieved sufficient results for classes with low points' portion in the dataset. In addition, the Zmod is the third important feature in the classification process among the sixteen features with about 14.63%. Moreover, using the first six important features achieved almost the maximum overall accuracy with about 60% reduction in the processing time. Numéro de notice : A2022-622 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102218 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101357 [article]