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scientifique

Keeping thinning-derived deadwood logs on forest floor improves soil organic carbon, microbial biomass, and enzyme activity in a temperate spruce forest / Meisam Nazari in European Journal of Forest Research, vol 142 n° 2 (April 2023)  

Public [article]  inEuropean Journal of Forest Research > vol 142 n° 2 (April 2023) . - pp 287 - 300 Titre : Keeping thinning-derived deadwood logs on forest floor improves soil organic carbon, microbial biomass, and enzyme activity in a temperate spruce forest Type de document : Article/Communication Auteurs : Meisam Nazari, Auteur ; Johanna Pausch, Auteur ; Samuel Bickel, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 287 - 300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne) [Termes IGN] biomasse forestière [Termes IGN] bois mort [Termes IGN] éclaircie (sylviculture) [Termes IGN] gestion forestière durable [Termes IGN] grume [Termes IGN] podzosol [Termes IGN] puits de carbone [Termes IGN] sol forestier [Vedettes matières IGN] Sylviculture Résumé : (auteur) Deadwood is a key component of forest ecosystems, but there is limited information on how it influences forest soils. Moreover, studies on the effect of thinning-derived deadwood logs on forest soil properties are lacking. This study aimed to investigate the impact of thinning-derived deadwood logs on the soil chemical and microbial properties of a managed spruce forest on a loamy sand Podzol in Bavaria, Germany, after about 15 years. Deadwood increased the soil organic carbon contents by 59% and 56% at 0–4 cm and 8–12 cm depths, respectively. Under deadwood, the soil dissolved organic carbon and carbon to nitrogen ratio increased by 66% and 15% at 0–4 cm depth and by 55% and 28% at 8–12 cm depth, respectively. Deadwood also induced 71% and 92% higher microbial biomass carbon, 106% and 125% higher microbial biomass nitrogen, and 136% and 44% higher β-glucosidase activity in the soil at 0–4 cm and 8–12 cm depths, respectively. Many of the measured variables significantly correlated with soil organic carbon suggesting that deadwood modified the soil biochemical processes by altering soil carbon storage. Our results indicate the potential of thinned spruce deadwood logs to sequester carbon and improve the fertility of Podzol soils. This could be associated with the slow decay rate of spruce deadwood logs and low biological activity of Podzols that promote the accumulation of soil carbon. We propose that leaving thinning-derived deadwood on the forest floor can support soil and forest sustainability as well as carbon sequestration. Numéro de notice : A2023-215 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-022-01522-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s10342-022-01522-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103144 [article]

A machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - n° 2163923 Titre : A machine learning method for Arctic lakes detection in the permafrost areas of Siberia Type de document : Article/Communication Auteurs : Piotr Janiec, Auteur ; Jakub Nowosad, Auteur ; Sbigniew Zwoliński, Auteur Année de publication : 2023 Article en page(s) : n° 2163923 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] Arctique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat-8 [Termes IGN] lac glaciaire [Termes IGN] MERIT [Termes IGN] modèle numérique de surface [Termes IGN] pergélisol [Termes IGN] Short Waves InfraRed [Termes IGN] Sibérie Résumé : (auteur) Thermokarst lakes are the main components of the vast Arctic and subarctic landscapes. These lakes can serve as geoindicators of permafrost degradation; therefore, proper lake distribution assessment methods are necessary. In this study, we compared four machine learning methods to improve existing lake detection systems. The northern part of Yakutia was selected as the study area owing to its complex environment. We used data from Landsat 8 and spectral indices to take into account the spectral characteristics of the lakes, and MERIT DEM data to take into account the topography. The lowest accuracy was found for the classification and regression trees (CART) method (overall accuracy = 81%). On the other hand, the random forests (RF) classification provided the best results (overall accuracy = 92%), and only this classification coped well in all problematic areas, such as shaded and humid areas, near steep slopes, burn scars, and rivers. The altitude and bands SWIR1 (Short wave infrared 1), SWIR2 (Short wave infrared 2), and Green were the most important. Spectral indices did not have significant impact on the classification results in the specific conditions of the thermokarst lakes environment. 17,700 lakes were identified with the total area of 271.43 km2. Numéro de notice : A2023-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2163923 Date de publication en ligne : 19/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2163923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103156 [article]

Impact of skidding operations on forest soils: a narrative review / Monica Cecilia Zurita Vintimilla in Revista Padurilor, vol 137 n° 4 (2022)  

Public [article]  inRevista Padurilor > vol 137 n° 4 (2022) Titre : Impact of skidding operations on forest soils: a narrative review Type de document : Article/Communication Auteurs : Monica Cecilia Zurita Vintimilla, Auteur Année de publication : 2022 Langues : Anglais (eng) Descripteur : [Termes IGN] débardage [Termes IGN] impact sur l'environnement [Termes IGN] sol forestier [Vedettes matières IGN] Foresterie Numéro de notice : A2022-584 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : sans Date de publication en ligne : 16/12/2022 En ligne : http://revistapadurilor.com/wp-content/uploads/2017/09/2.-IMPACT-OF-SKIDDING-OPE [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103252 [article]

Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)  

Public [article]  inGlobal ecology and conservation > vol 39 (November 2022) . - n° e02286 Titre : Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? Type de document : Article/Communication Auteurs : Arthur Sanguet, Auteur ; Nicolas Wyler, Auteur ; Blaise Petitpierre, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° e02286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte d'occupation du sol [Termes IGN] changement climatique [Termes IGN] distribution spatiale [Termes IGN] échantillonnage de données [Termes IGN] habitat (nature) [Termes IGN] modèle de simulation [Termes IGN] montagne [Termes IGN] pédologie locale [Termes IGN] Suisse [Termes IGN] télédétection [Termes IGN] topographie locale [Termes IGN] zone humide [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Species Distribution Models (SDM) represent a powerful tool to predict species’ habitat suitability on a landscape and fill the gap between truncated observation data and all possible locations. SDMs have been widely used in theoretical studies of species niches as well as in conservation applications. Here, we evaluated the impacts of predictors’ type on models’ performances and spatial predictions using 72 plant species belonging to six ecological groups at a regional scale in the area of Geneva (Switzerland). Twelve models were created using various combinations of high-resolution (25 m) explanatory variables including topography, pedology, climate, habitats and remote sensing data. Models integrating a combination of habitats and topopedo-climatic predictors had significantly higher performances, while remote sensing predictors showed low performances. Our results suggest that the number and the level of details of habitat predictors (broad or very precise) do not fundamentally affect prediction maps. However, selecting too few, overly simplified or exceedingly complex habitat predictors tend to lower models’ performances. The use of eight habitat categories complemented with eight topopedo-climatic predictors produced models with the highest performances. Ecological groups of species responded differently to models and while alpine and ruderal species have greater average performances due to a high affinity with topopedo-climatic predictors, wetlands’ species were less performant on average. These results underline the necessity of developing or having access to habitats distribution data especially in a conservation context. Numéro de notice : A2022-815 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.gecco.2022.e02286 Date de publication en ligne : 13/09/2022 En ligne : https://doi.org/10.1016/j.gecco.2022.e02286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101977 [article]

Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571 Titre : Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data Type de document : Article/Communication Auteurs : Yanan Zhou, Auteur ; Wei Wu, Auteur ; Hongbin Liu, Auteur Année de publication : 2022 Article en page(s) : n° 5571 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] composition des sols [Termes IGN] données multitemporelles [Termes IGN] Extreme Gradient Machine [Termes IGN] Fleuve bleu (Chine) [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] limon [Termes IGN] qualité du sol [Termes IGN] réflectance spectrale [Termes IGN] texture du sol Résumé : (auteur) Soil texture is a key soil property driving physical, chemical, biological, and hydrological processes in soils. The rapid development of remote sensing techniques shows great potential for mapping soil properties. This study highlights the effectiveness of multitemporal remote sensing data in identifying soil textural class by using retrieved vegetation properties as proxies of soil properties. The impacts of sensors, modeling resolutions, and modeling techniques on the accuracy of soil texture classification were explored. Multitemporal Landsat-8 and Sentinel-2 images were individually acquired at the same time periods. Three satellite-based experiments with different inputs, i.e., Landsat-8 data, Sentinel-2 data (excluding red-edge parameters), and Sentinel-2 data (including red-edge parameters) were conducted. Modeling was carried out at three spatial resolutions (10, 30, 60 m) using five machine-learning (ML) methods: random forest, support vector machine, gradient-boosting decision tree, categorical boosting, and super learner that combined the four former classifiers based on the stacking concept. In addition, a novel SHapley Addictive Explanation (SHAP) technique was introduced to explain the outputs of the ML model. The results showed that the sensors, modeling resolutions, and modeling techniques significantly affected the prediction accuracy. The models using Sentinel-2 data with red-edge parameters performed consistently best. The models usually gave better results at fine (10 m) and medium (30 m) modeling resolutions than at a coarse (60 m) resolution. The super learner provided higher accuracies than other modeling techniques and gave the highest values of overall accuracy (0.8429), kappa (0.7611), precision (0.8378), recall rate (0.8393), and F1-score (0.8398) at 30 m with Sentinel-2 data involving red-edge parameters. The SHAP technique quantified the contribution of each variable for different soil textural classes, revealing the critical roles of red-edge parameters in separating loamy soils. This study provides comprehensive insights into the effective modeling of soil properties on various scales using multitemporal optical images. Numéro de notice : A2022-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215571 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3390/rs14215571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102104 [article]

Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])  

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Experimental precipitation reduction slows down litter decomposition but exhibits weak to no effect on soil organic carbon and nitrogen stocks in three Mediterranean forests of Southern France / Mathieu Santonja in Forests, vol 13 n° 9 (september 2022)  

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Forest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)  

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Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry / Yufeng Hu in Journal of geodesy, vol 96 n° 8 (August 2022)  

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Optimal resolution of soil properties maps varies according to their geographical extent and location / Christian Piedallu in Geoderma, vol 412 (15 April 2022)  

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Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (March-1 2022)  

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Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)  

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Forest floor alteration by canopy trees and soil wetness drive regeneration of a spruce-beech forest / Pavel Daněk in Forest ecology and management, vol 504 (January-15 2022)  

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Fungal perspective of pine and oak colonization in Mediterranean degraded ecosystems / Irene Adamo in Forests, vol 13 n° 1 (January 2022)  

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Vegetation index and dynamics / Eusebio Cano Carmona (2022)  

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