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Impacts of forest management on stand and landscape-level microclimate heterogeneity of European beech forests / Joscha H. Menge in Landscape ecology, vol 38 n° 4 (April 2023)
[article]
Titre : Impacts of forest management on stand and landscape-level microclimate heterogeneity of European beech forests Type de document : Article/Communication Auteurs : Joscha H. Menge, Auteur ; Paul Magdon, Auteur ; Stephan Wöllauer, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 903 - 917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] éclaircie (sylviculture)
[Termes IGN] écosystème forestier
[Termes IGN] Fagus (genre)
[Termes IGN] forêt équienne
[Termes IGN] forêt inéquienne
[Termes IGN] gestion forestière
[Termes IGN] hêtraie
[Termes IGN] microclimat
[Termes IGN] régression multiple
[Termes IGN] semis de points
[Termes IGN] température de l'air
[Termes IGN] ThuringeRésumé : (auteur) Context: Forest microclimate influences biodiversity and plays a crucial role in regulating forest ecosystem functions. It is modified by forest management as a result of changes in forest structure due to tree harvesting and thinning.
Objectives: Here, we investigate the impacts of even-aged and uneven-aged forest management on stand- and landscape-level heterogeneity of forest microclimates, in comparison with unmanaged, old-growth European beech forest.
Methods: We combined stand structural and topographical indices derived from airborne laser scanning with climate observations from 23 meteorological stations at permanent forest plots within the Hainich region, Germany. Based on a multiple linear regression model, we spatially interpolated the diurnal temperature range (DTR) as an indicator of forest microclimate across a 4338 ha section of the forest with 50 m spatial resolution. Microclimate heterogeneity was measured as α-, β-, and γ-diversity of thermal niches (i.e. DTR classes).
Results: Even-aged forests showed a higher γ-diversity of microclimates than uneven-aged and unmanaged forests. This was mainly due to a higher β-diversity resulting from the spatial coexistence of different forest developmental stages within the landscape. The greater structural complexity at the stand-level in uneven-aged stands did not increase α-diversity of microclimates. Predicted DTR was significantly lower and spatially more homogenous in unmanaged forest compared to both types of managed forest.
Conclusion: If forest management aims at creating a wide range of habitats with different microclimates within a landscape, spatially co-existing types of differently managed and unmanaged forests should be considered, instead of focusing on a specific type of management, or setting aside forest reserves only.Numéro de notice : A2023-224 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10980-023-01596-z Date de publication en ligne : 30/01/2023 En ligne : https://doi.org/10.1007/s10980-023-01596-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103175
in Landscape ecology > vol 38 n° 4 (April 2023) . - pp 903 - 917[article]Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks / Sina Mohammadi in ISPRS Journal of photogrammetry and remote sensing, vol 198 (April 2023)
[article]
Titre : Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks Type de document : Article/Communication Auteurs : Sina Mohammadi, Auteur ; Mariana Belgiu, Auteur ; Alfred Stein, Auteur Année de publication : 2023 Article en page(s) : pp 272 - 283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage profond
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] cultures
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by
scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves
scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision.Numéro de notice : A2023-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.03.007 Date de publication en ligne : 29/03/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103105
in ISPRS Journal of photogrammetry and remote sensing > vol 198 (April 2023) . - pp 272 - 283[article]Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery / Maryam Hosseini in Computers, Environment and Urban Systems, vol 101 (April 2023)
[article]
Titre : Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery Type de document : Article/Communication Auteurs : Maryam Hosseini, Auteur ; Andres Sevtsuk, Auteur ; Fabio Miranda, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'objet
[Termes IGN] Etats-Unis
[Termes IGN] image aérienne
[Termes IGN] navigation pédestre
[Termes IGN] segmentation sémantique
[Termes IGN] système d'information géographique
[Termes IGN] trottoir
[Termes IGN] vision par ordinateurRésumé : (auteur) While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. Numéro de notice : A2023-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101950 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102961
in Computers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101950[article]A network-constrained clustering method for bivariate origin-destination movement data / Wenkai Liu in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)
[article]
Titre : A network-constrained clustering method for bivariate origin-destination movement data Type de document : Article/Communication Auteurs : Wenkai Liu, Auteur ; Qiliang Liu, Auteur ; Jie Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 767 - 787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse bivariée
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] origine - destination
[Termes IGN] réseau routierRésumé : (auteur) For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services. Numéro de notice : A2023-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2137879 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2137879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103108
in International journal of geographical information science IJGIS > vol 37 n° 4 (April 2023) . - pp 767 - 787[article]Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)
[article]
Titre : Towards global scale segmentation with OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Munazza Usmani, Auteur ; Maurizio Napolitano, Auteur ; Francesca Bovolo, Auteur Année de publication : 2023 Article en page(s) : n° 100031 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données localisées des bénévoles
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du solRésumé : (auteur) Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications. Numéro de notice : A2023-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100031 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102807
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031[article]A tropospheric delay model to integrate ERA5 and GNSS reference network for mountainous areas: application to precise point positioning / Cuixian Lu in GPS solutions, vol 27 n° 2 (April 2023)PermalinkA unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments / Bo Xu in GPS solutions, vol 27 n° 2 (April 2023)PermalinkDesign and performance evaluation of a novel ranging signal based on an LEO satellite communication constellation / Jingfang Su in Geo-spatial Information Science, vol 26 n° 1 (March 2023)PermalinkDetermination of Helmert transformation parameters for continuous GNSS networks: a case study of the Géoazur GNSS network / Dinh Trong Tran in Geo-spatial Information Science, vol 26 n° 1 (March 2023)PermalinkDomain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)PermalinkA graph-based approach for representing addresses in geocoding / Chen Zhang in Computers, Environment and Urban Systems, vol 100 (March 2023)PermalinkIterative data assimilation approach for the refinement of marine geoid models using sea surface height and dynamic topography datasets / Sander Varbla in Journal of geodesy, vol 97 n° 3 (March 2023)PermalinkA new algebraic solution for transforming Cartesian to geodetic coordinates / Jia-Chun Guo in Survey review, vol 55 n° 389 (March 2023)PermalinkPoint cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkSeismic deformation in the Adriatic Sea region / B. Orecchio in Journal of geodynamics, vol 155 (March 2023)Permalink