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Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data / Zihao Huang in Remote sensing, vol 14 n° 7 (April-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 7 (April-1 2022) . - n° 1698 Titre : Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data Type de document : Article/Communication Auteurs : Zihao Huang, Auteur ; Xuejian Li, Auteur ; Qiang Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1698 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] automate cellulaire [Termes IGN] changement climatique [Termes IGN] changement d'utilisation du sol [Termes IGN] Chine [Termes IGN] écosystème forestier [Termes IGN] forêt tropicale [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] interaction homme-milieu [Termes IGN] modèle de simulation [Termes IGN] modèle dynamique [Termes IGN] modèle numérique de surface [Termes IGN] puits de carbone [Termes IGN] simulation spatiale Résumé : (auteur) Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province. Numéro de notice : A2022-281 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14071698 Date de publication en ligne : 31/03/2022 En ligne : https://doi.org/10.3390/rs14071698 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100297 [article]

Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 7 (April-1 2022) . - n° 1694 Titre : Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights Type de document : Article/Communication Auteurs : Marco Fiorucci, Auteur ; Wouter Baernd Verschoof-van der Vaart, Auteur ; Paolo Soleni, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification pixellaire [Termes IGN] détection d'objet [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] site archéologique Résumé : (auteur) Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools. Numéro de notice : A2022-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14071694 En ligne : https://doi.org/10.3390/rs14071694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100298 [article]

Assessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity / Stéphane Bertin in Remote sensing, vol 14 n° 7 (April-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 7 (April-1 2022) . - n° 1679 Titre : Assessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity Type de document : Article/Communication Auteurs : Stéphane Bertin, Auteur ; Pierre Stéphan, Auteur ; Jérôme Ammann, Auteur Année de publication : 2022 Article en page(s) : n° 1679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Bretagne [Termes IGN] centrale inertielle [Termes IGN] données GNSS [Termes IGN] géomorphologie locale [Termes IGN] géoréférencement [Termes IGN] image captée par drone [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] sédiment [Termes IGN] structure-from-motion [Termes IGN] surveillance du littoral Résumé : (auteur) Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale. Numéro de notice : A2022-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14071679 Date de publication en ligne : 31/03/2022 En ligne : https://doi.org/10.3390/rs14071679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100321 [article]