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Variation in plant–soil interactions among temperate forest herbs / Jared J. Beck in Plant ecology, vol 222 n° 11 (November 2021)
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Titre : Variation in plant–soil interactions among temperate forest herbs Type de document : Article/Communication Auteurs : Jared J. Beck, Auteur Année de publication : 2021 Article en page(s) : pp 1225 - 1238 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] croissance végétale
[Termes IGN] forêt tempérée
[Termes IGN] herbe
[Termes IGN] phytoécologie
[Termes IGN] relations plante - sol
[Termes IGN] Wisconsin (Etats-Unis)
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Antagonistic interactions between plants and soil biota promote species diversity in many plant communities but little is known about how these plant–soil interactions influence herbaceous species in temperate forests. To assess the potential for soil biota to affect the growth of forest herbs, I conducted a greenhouse experiment in which seedlings of nine focal herb species common in Wisconsin (USA) forests were grown in soil derived from conspecific and heterospecific plants. This soil origin treatment was crossed with a subsequent treatment in which half of the soils were pasteurized to eliminate soil biota. The presence and origin of soil biota had variable effects on plant growth among the nine focal species. Thalictrum dioicum, Elymus hystrix, and Solidago flexicaulis growth were inhibited by the presence of soil biota in unpasteurized soils. Thalictrum dioicum seedlings grown in conspecific, unpasteurized soil accumulated 30% less biomass than seedlings grown in heterospecific, unpasteurized soil indicating that host-specific effects of microbial pathogens restrict seedling growth. Similarly, E. hystrix seedlings were 11% smaller in conspecific-trained soils. The remaining herb species showed no significant response to experimental treatments manipulating soil biota. These variable growth responses highlight the potential for differences in plant–soil interactions among plant species to influence local plant distributions and community dynamics. Janzen–Connell effects, like those observed in T. dioicum and E. hystrix, could promote coexistence among certain species and contribute to high local plant diversity in temperate forest understories. Numéro de notice : A2021-730 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s11258-021-01173-x Date de publication en ligne : 23/08/2021 En ligne : https://doi.org/10.1007/s11258-021-01173-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98674
in Plant ecology > vol 222 n° 11 (November 2021) . - pp 1225 - 1238[article]A vector-based method for drainage network analysis based on LiDAR data / Fangzheng Lyu in Computers & geosciences, vol 156 (November 2021)
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Titre : A vector-based method for drainage network analysis based on LiDAR data Type de document : Article/Communication Auteurs : Fangzheng Lyu, Auteur ; Xinlin Ma, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse vectorielle
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] interpolation spatiale
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau hydrographique
[Termes IGN] semis de pointsRésumé : (auteur) Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. Numéro de notice : A2021-755 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104892 Date de publication en ligne : 24/07/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98733
in Computers & geosciences > vol 156 (November 2021)[article]Comparison of digital elevation models through the analysis of geomorphic surface remnants in the Desatoya Mountains, Nevada / Bernadett Dobre in Transactions in GIS, vol 25 n° 5 (October 2021)
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Titre : Comparison of digital elevation models through the analysis of geomorphic surface remnants in the Desatoya Mountains, Nevada Type de document : Article/Communication Auteurs : Bernadett Dobre, Auteur ; Istvan P. Kovács, Auteur ; Titusz Bugya, Auteur Année de publication : 2021 Article en page(s) : pp 2262 - 2282 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] bassin hydrographique
[Termes IGN] carte géologique
[Termes IGN] données lidar
[Termes IGN] géomorphologie
[Termes IGN] GRASS
[Termes IGN] MNS ASTER
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] Nevada (Etats-Unis)
[Termes IGN] semis de points
[Termes IGN] sommet (relief)
[Termes IGN] zone semi-arideRésumé : (auteur) This article explores the advantages and limitations of open-source digital elevation models (DEMs) generated from various acquisition methods and at various spatial resolutions, through extracting geomorphic surface remnants in a semi-arid, mountainous topographic environment. Even if the tested models have well-known vertical accuracy and precision, their reliability for peak detection is still waiting to be studied. In this research, we investigate peaks as remnants of degraded geomorphic surfaces. Peaks of surface remnants can help to reconstruct geomorphic surfaces and evaluate DEM applicabilities, since they can enhance the identification of overall accuracy. Our methodology uses a well-known open-source GRASS GIS Geomorphons module (r.geomorphon) on several recently released and widely used DEMs covering the Desatoya Mountains study area. We conclude that, despite the characteristic differences in the accuracy of the analyzed DEMs, all of those examined proved to be appropriate to detect surface remnants. Numéro de notice : A2021-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/tgis.12819 Date de publication en ligne : 10/08/2021 En ligne : https://doi.org/10.1111/tgis.12819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99301
in Transactions in GIS > vol 25 n° 5 (October 2021) . - pp 2262 - 2282[article]Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)
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Titre : Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Ruiheng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] cartographie thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie
[Termes IGN] réflectance du sol
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. Numéro de notice : A2021-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112575 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98866
in Remote sensing of environment > vol 264 (October 2021) . - n° 112575[article]A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
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Titre : A deep multi-modal learning method and a new RGB-depth data set for building roof extraction Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2021 Article en page(s) : pp 759 - 766 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] détection du bâti
[Termes IGN] données multisources
[Termes IGN] effet de profondeur cinétique
[Termes IGN] empreinte
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image RVB
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] toitRésumé : (Auteur) This study focuses on tackling the challenge of building mapping in multi-modal remote sensing data by proposing a novel, deep superpixel-wise convolutional neural network called DeepQuantized-Net, plus a new red, green, blue (RGB)-depth data set named IND. DeepQuantized-Net incorporated two practical ideas in segmentation: first, improving the object pattern with the exploitation of superpixels instead of pixels, as the imaging unit in DeepQuantized-Net. Second, the reduction of computational cost. The generated data set includes 294 RGB-depth images (256 training images and 38 test images) from different locations in the state of Indiana in the U.S., with 1024 × 1024 pixels and a spatial resolution of 0.5 ftthat covers different cities. The experimental results using the IND data set demonstrates the mean F1 scores and the average Intersection over Union scores could increase by approximately 7.0% and 7.2% compared to other methods, respectively. Numéro de notice : A2021-677 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00007R2 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.14358/PERS.21-00007R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98878
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 759 - 766[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021101 SL Revue Centre de documentation Revues en salle Disponible Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques / Peter G. Chirico in Journal of maps, vol 17 n° 4 (October 2021)
PermalinkImpact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)
PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)
PermalinkRelative influence of stand and site factors on aboveground live-tree carbon sequestration and mortality in managed and unmanaged forests / Christel C. Kern in Forest ecology and management, vol 493 (August-1 2021)
PermalinkShore zone classification from ICESat-2 data over Saint Lawrence Island / Huan Xie in Marine geodesy, vol 44 n° 5 (September 2021)
PermalinkSpatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016 / Mbongowo J. Mbuh in Geocarto international, vol 36 n° 14 ([01/08/2021])
PermalinkExtracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)
PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)
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