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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)  

Public [article]  inPlant ecology > vol 222 n° 11 (November 2021) . - pp 1225 - 1238 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 forestière Résumé : (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 [article]

Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)  

Public [article]  inRemote sensing of environment > vol 258 (June 2021) . - n° 112382 Titre : Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Pinliang Dong, Auteur ; Changshan Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] arbre (flore) [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] peuplement mélangé [Termes IGN] semis de points [Termes IGN] surveillance forestière [Termes IGN] Wisconsin (Etats-Unis) Résumé : (auteur) Individual tree identification is a key step for forest surveying and monitoring. To identify individual trees with airborne LiDAR data, a local maximum (LM) filter technique is typically performed. With LM, the highest point in a filtering window is generally considered to represent the tree position. This assumption, however, has great limitations, especially for mixed forests. To address this problem, we developed a new approach, the cluster center of higher points (CCHP), for tree position detection with LiDAR data. CCHP assumes that a tree position is located at the clustering center of higher points within a spatial neighborhood, and the center can be detected by a location-based recursive algorithm. The developed CCHP method was applied to a simulated forest and then verified in two real urban forests. In comparison with the variable window-sized LM filter method and layer stacking method, CCHP successfully identified 97% of trees in the simulated forest, while only 78% and 81% of the trees were recognized by LM and layer stacking methods respectively. The average absolute and relative offsets of CCHP are 0.33 m and 6.59%, respectively, and over 80% of the detected trees have an offset of less than 10% of the tree crown radius. CCHP also correctly detected 93.80% and 88.74% of individual trees in the first and second real forests, respectively, but the detection rates from the variable window-sized LM approach and layer stacking were less than 80%. In addition, the tree positions located by CCHP are considerably more accurate than the other two methods. Therefore, CCHP is proven to be promising for detecting individual tree positions from airborne LiDAR data for both simulated and real forests. Numéro de notice : A2021-443 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112382 Date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97850 [article]

Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1155 - 1178 Titre : Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change Type de document : Article/Communication Auteurs : Jiawei Huang, Auteur ; Melissa S. Lucash, Auteur ; Robert M. Scheller, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1155 - 1178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] carte de la végétation [Termes IGN] changement climatique [Termes IGN] forêt [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] monde virtuel [Termes IGN] réalité virtuelle [Termes IGN] visualisation 3D [Termes IGN] Wisconsin (Etats-Unis) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public. Numéro de notice : A2021-384 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1830997 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1830997 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97641 [article]

Modeling land use change and forest carbon stock changes in temperate forests in the United States / Lucia Fitts in Carbon Balance and Management, vol 16 ([01/02/2021])  

Public [article]  inCarbon Balance and Management > vol 16 [01/02/2021] . - n° 20 (2021) Titre : Modeling land use change and forest carbon stock changes in temperate forests in the United States Type de document : Article/Communication Auteurs : Lucia Fitts, Auteur ; Matthew B. Russell, Auteur ; Grant M. Domke, Auteur ; Joseph F. Knight, Auteur Année de publication : 2021 Article en page(s) : n° 20 (2021) Langues : Anglais (eng) Descripteur : [Termes IGN] changement d'occupation du sol [Termes IGN] Colorado (Etats-Unis) [Termes IGN] forêt tempérée [Termes IGN] Géorgie (Etats-Unis) [Termes IGN] impact sur l'environnement [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modélisation spatio-temporelle [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] puits de carbone [Termes IGN] Texas (Etats-Unis) [Termes IGN] Wisconsin (Etats-Unis) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Background : Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine. Results : During the study period (2000–2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change. Conclusions : Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions. Numéro de notice : A2021-501 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : 10.1186/s13021-021-00183-6 Date de publication en ligne : 03/07/2021 En ligne : https://doi.org/10.1186/s13021-021-00183-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98099 [article]

Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 222 - 236 Titre : Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? Type de document : Article/Communication Auteurs : Istvan G. Lauko, Auteur ; Adam Honts, Auteur ; Jacob Beihoff, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] cartographie urbaine [Termes IGN] couleur (variable spectrale) [Termes IGN] densité de la végétation [Termes IGN] extraction de la végétation [Termes IGN] gestion urbaine [Termes IGN] image panoramique [Termes IGN] image Streetview [Termes IGN] indicateur environnemental [Termes IGN] indice de végétation [Termes IGN] Milwaukee [Termes IGN] paysage urbain [Termes IGN] rayonnement proche infrarouge Résumé : (auteur) Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems. These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy, but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover. Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View (GSV) images, made accessible by the Google Street View Image API. Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density, and it facilitates an analysis performed at the street-level. In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index. We deployed this image processing method and, using GSV images as a high-resolution GIS data source, we computed and mapped the green index of Milwaukee County, a 3,082 km2 urban/suburban county in Wisconsin. This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management, as well as for researchers investigating the correlation between environmental factors and human health outcomes. Numéro de notice : A2020-563 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805367 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805367 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95880 [article]

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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Incorporating crown shape information for identifying ash tree species / Haijian Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 8 (août 2018)  

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Utility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)  

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Mapping an invasive plant, Phragmites australis [roseau], in coastal wetlands using the EO-1 Hyperion hyperspectral sensor / B.W. Pengra in Remote sensing of environment, vol 108 n° 1 (15/05/2007)  

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Incorporating remote sensing information in modelling house values: a regression tree approach / D. Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 2 (February 2006)  

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PPGIS in community development planning: framing the organizational context / S. Elwood in Cartographica, vol 38 n° 3 - 4 (September 2001)  

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Conversion of automated geographic data to decision-making information / S.J. Ventura in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 4 (april 1990)  

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The use of intensity-hue-saturation transformations for merging Spot panchromatic and multispectral image data / W.J. Carper in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 4 (april 1990)  

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Assessing permit compliance in residential areas using color 35mm aerial photography / W.R. Niedzwiedz in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 2 (february 1990)  

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Synchronous fluorescence spectroscopy of dissolved organic matter in surface waters : application to airborne remote sensing / A. Vodacek in Remote sensing of environment, vol 30 n° 3 (01/12/1989)  

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