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The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests / Vahid Nasiri in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Fardin Moradi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] canopée
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] couvert forestier
[Termes IGN] forêt méditerranéenne
[Termes IGN] Google Earth Engine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Iran
[Termes IGN] placette d'échantillonnage
[Termes IGN] Quercus (genre)Résumé : (auteur) Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes. Numéro de notice : A2022-649 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080423 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.3390/ijgi11080423 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101465
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 423[article]Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 / Rong Zhang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)
[article]
Titre : Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 Type de document : Article/Communication Auteurs : Rong Zhang, Auteur ; Mingming Jia, Auteur ; Zongming Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102918 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme de Otsu
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Chine
[Termes IGN] dynamique de la végétation
[Termes IGN] image Sentinel-MSI
[Termes IGN] mangrove
[Termes IGN] réserve naturelleRésumé : (auteur) Mangrove National Nature Reserves (MNNRs) play an extraordinarily significant role in conserving mangrove forests and their habitats. In China, one-fourth of the total mangrove forests were located in MNNRs. Understanding annual spatial distributions and conversions of these mangrove forests are important for precision conservation and rehabilitation efforts. However, to date, annual land cover maps of China’s MNNRs are still unavailable. Here, we proposed a rapid and robust approach to produce annual maps of each MNNRs for the time period of 2016–2020 based on 10-m resolution Sentinel-2 imagery. The proposed approach was developed using object-based image analysis, Otsu and Random Forest algorithm. Results showed that 1) during 2016–2020, areal extents of mangrove forest in all the MNNRs continuously increased from 5912 ha to 6128 ha; 2) obvious increase were found in Zhanjiang Mangrove National Nature Reserve where mangrove forest increased by 127 ha, accounted for 59% of national total increases; 3) newly grown mangrove forests were mainly converted from tidal flats and aquaculture ponds. Our annual maps of China’s MNNRs could provide a basis for managing mangrove ecosystems and supporting the implementation of Sustainable Development Goals related to coastal development. Numéro de notice : A2022-583 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102918 En ligne : https://doi.org/10.1016/j.jag.2022.102918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101348
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102918[article]Transfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Transfer learning from citizen science photographs enables plant species identification in UAV imagery Type de document : Article/Communication Auteurs : Salim Soltani, Auteur ; Hannes Feilhauer, Auteur ; Robbert Duker, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données naturalistes
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] espèce végétale
[Termes IGN] filtrage de la végétation
[Termes IGN] identification de plantes
[Termes IGN] image captée par drone
[Termes IGN] orthoimage couleur
[Termes IGN] science citoyenne
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications. Numéro de notice : A2022-488 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100016 Date de publication en ligne : 23/05/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100956
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100016[article]A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])
[article]
Titre : A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway Type de document : Article/Communication Auteurs : Reza Sanayeia, Auteur ; Alireza Vafaeinejad, Auteur ; Jalal Karami, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4141 - 4157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] accident de la route
[Termes IGN] autocorrélation
[Termes IGN] autoroute
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] Téhéran
[Termes IGN] transformation en ondelettesRésumé : (auteur) The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82. Numéro de notice : A2022-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1871669 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1080/10106049.2021.1871669 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101472
in Geocarto international > vol 37 n° 14 [20/07/2022] . - pp 4141 - 4157[article]Segmentation and sampling method for complex polyline generalization based on a generative adversarial network / Jiawei Du in Geocarto international, vol 37 n° 14 ([20/07/2022])
[article]
Titre : Segmentation and sampling method for complex polyline generalization based on a generative adversarial network Type de document : Article/Communication Auteurs : Jiawei Du ; Fang Wu, Auteur ; Ruixing Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4158 - 4180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage de données
[Termes IGN] implémentation (informatique)
[Termes IGN] polyligne
[Termes IGN] rastérisation
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate. Numéro de notice : A2022-651 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1878288 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1080/10106049.2021.1878288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101473
in Geocarto international > vol 37 n° 14 [20/07/2022] . - pp 4158 - 4180[article]GNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)PermalinkCan machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkDiscriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition / Tiantian Yan in Pattern recognition, vol 127 (July 2022)PermalinkEstimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkGeographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)PermalinkGlobal forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)PermalinkImproving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)PermalinkInvestigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)PermalinkLidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)Permalink