Descripteur
Termes IGN > environnement > écologie > écosystème > biotope > milieu naturel > zone humide > tourbière
tourbière
Commentaire :
zone tourbeuse. zone humide, espace naturel. >> écologie des tourbières, marais, marécage, tourbe. Source(s) : Formations végétales et paysages ruraux / G. Plaisance, 1959. Equiv. LCSH : Peat bogs, Peatlands. Domaine(s) : 550; 580. Voir aussi |
Documents disponibles dans cette catégorie (12)



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The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)
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Titre : The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands Type de document : Article/Communication Auteurs : Aaron Judah, Auteur ; Baoxin Hu, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] image Sentinel-SAR
[Termes IGN] intégration de données
[Termes IGN] modèle numérique de terrain
[Termes IGN] précision de la classification
[Termes IGN] tourbière
[Termes IGN] utilisation du sol
[Termes IGN] zone humideRésumé : (auteur) Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality. Numéro de notice : A2022-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1967732 Date de publication en ligne : 13/11/2021 En ligne : https://doi.org/10.1080/07038992.2021.1967732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100614
in Canadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp 158 - 181[article]Age-dependence of stand biomass in managed boreal forests based on the Finnish National Forest Inventory data / Anna Repo in Forest ecology and management, vol 498 (15 October 2021)
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Titre : Age-dependence of stand biomass in managed boreal forests based on the Finnish National Forest Inventory data Type de document : Article/Communication Auteurs : Anna Repo, Auteur ; Tuomas Rajala, Auteur ; Helena M. Henttonen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] âge du peuplement forestier
[Termes IGN] bilan du carbone
[Termes IGN] biomasse
[Termes IGN] changement climatique
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] gestion forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modélisation de la forêt
[Termes IGN] puits de carbone
[Termes IGN] tourbière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Information on carbon stocks and the rate of carbon accumulation is needed to harness the climate change mitigation potential of boreal forests. While previous studies have revealed general patterns and mechanisms for age-dependence of stand biomass, simple stand-level models that address the age-biomass relationship on average in managed boreal forests in different environmental conditions are largely missing. We developed models for the relationship between stand age and biomass by forest types on peatlands and mineral soils across climate zones in managed forests in Finland based on National Forest Inventory measurements from 1996 to 2018. In addition, we analyzed at which rate biomass accumulates when managed forest ages in different growth conditions. In northern Finland the maximum biomass change rate was one third, and the maximum biomass stock less than half of the corresponding values in sub-xeric heath forests on minerals soils in southern Finland. On drained peatlands the maximum biomass growth rate was approximately half, and on undrained peatlands one third of the maximum growth rate on mineral soils. On most fertile sites on mineral soils the maximum biomasses were three times larger than on the poorest sites. Correspondingly, the maximum biomass stock change rates were almost eight times faster on most fertile sites. In the example cases presented, the highest annual biomass change rates were achieved in young forests on average at the stand ages of 7–32 years, whereas the 95% of the maximum stock were reached on average in stands of 63–147 years. At the age of highest biomass growth rate stands contained 27–59% of the maximum biomass stocks. The developed models can be used in practical applications such as accounting of biogenic carbon in life-cycle assessments, mapping carbon, or creating simple predictions of biomass stock development in regions, or estimating the mitigation potential of afforestation and reforestation or estimating the magnitude of carbon offsets projects. Numéro de notice : A2021-659 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119507 Date de publication en ligne : 30/07/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98398
in Forest ecology and management > vol 498 (15 October 2021) . - n° 119507[article]A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand / Narissara Nuthammachot in Geocarto international, vol 36 n° 2 ([01/02/2021])
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Titre : A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand Type de document : Article/Communication Auteurs : Narissara Nuthammachot, Auteur ; Dimitris Stratoulias, Auteur Année de publication : 2021 Article en page(s) : pp 212 - 225 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] cartographie des risques
[Termes IGN] climat
[Termes IGN] forêt marécageuse
[Termes IGN] historique des données
[Termes IGN] incendie de forêt
[Termes IGN] outil d'aide à la décision
[Termes IGN] prévention des risques
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] Thaïlande
[Termes IGN] tourbièreRésumé : (auteur) Forest fires are abrupt transformations of the natural ecosystem and management authorities are required to take preventive measures to tackle fire events. Geographic information system (GIS) is a powerful tool for providing information with a spatial context and analytical hierarchy process (AHP) is a well-established technique for multiple criteria decision making. In this study, GIS and AHP are combined to analyse seven fire-related factors related to climate, topography and human influence. Fire risk for a peat swamp forested area in Kuan Kreng, Nakorn Sri Thammarat province, Thailand is estimated in five categories. 705 historic fire events from 2006 to 2017 are used to validate our approach. 82% of the historic fire incidents occurred within the highest fire risk class categories while only a few omission errors were recorded. The combined approach of GIS and AHP techniques can yield useful fire risk maps, which can consequently be used for future planning and management of fire prone areas. Numéro de notice : A2021-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611946 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611946 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96832
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 212 - 225[article]Boreal peatland forests: ditch network maintenance effort and water protection in a forest rotation framework / Jenny Miettinen in Canadian Journal of Forest Research, vol 50 n° 10 (October 2020)
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Titre : Boreal peatland forests: ditch network maintenance effort and water protection in a forest rotation framework Type de document : Article/Communication Auteurs : Jenny Miettinen, Auteur ; Markku Ollikainen, Auteur ; Jukka Arovilita, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1025 – 1038 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Termes IGN] aménagement forestier
[Termes IGN] entretien du réseau
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] forêt équienne
[Termes IGN] nutriment végétal
[Termes IGN] protection des eaux
[Termes IGN] réseau de drainage
[Termes IGN] sédiment
[Termes IGN] tourbière
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Ditch network maintenance promotes forest growth in drained peatland forests but increases nutrient and sediment loads, which are detrimental to water quality. Society needs to balance the harvest revenue from improved forest growth against deteriorating water quality. We examine socially optimal even-aged forest management in drained peatlands when harvesting and ditch network maintenance cause nutrient and sediment loading. The means to reduce loading include establishing overland flow fields and abstaining from ditch network maintenance. We characterize this choice analytically in a rotation framework and examine, in a numerical model, the key factors affecting the choice of forest management and water protection measures. We choose a drained peatland forest site located in northeastern Finland in the vicinity of ecologically vulnerable forest headwater streams. On the given drained forest site, we find a set of parameters under which implementing ditch network maintenance is privately but not socially optimal. Numéro de notice : A2020-745 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1139/cjfr-2019-0339 Date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1139/cjfr-2019-0339 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96389
in Canadian Journal of Forest Research > vol 50 n° 10 (October 2020) . - pp 1025 – 1038[article]Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October-1 2020)
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Titre : Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 Type de document : Article/Communication Auteurs : Dirk Hoekman, Auteur ; Boris Kooij, Auteur ; Marcela J. Quiñones, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 32 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Amazonie
[Termes IGN] Bornéo, île de
[Termes IGN] déboisement
[Termes IGN] dégradation de l'environnement
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image radar
[Termes IGN] image Sentinel-SAR
[Termes IGN] image TerraSAR-X
[Termes IGN] modèle physique
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] tourbièreRésumé : (auteur) The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge. Numéro de notice : A2020-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12193263 Date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.3390/rs12193263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96056
in Remote sensing > vol 12 n° 19 (October-1 2020) . - 32 p.[article]Using spectral indices to estimate water content and GPP in sphagnum moss and other peatland vegetation / Kirsten J. Lees in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkProfitability of growing Scots pine on cutaway peatlands / Lasse Aro in Silva fennica, vol 54 n° 3 (June 2020)
PermalinkImproving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)
PermalinkIndividual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])
PermalinkDe l’origine des Pins de montagne européens / Renaud Cantegrel in Revue forestière française [en ligne], vol 71 n° 3 (2019)
PermalinkModels for diameter and height growth of Scots pine, Norway spruce and pubescent birch in drained peatland sites in Finland / Jaakko Repola in Silva fennica, vol 52 n° 5 (November 2018)
PermalinkInventaire faune, flore et habitats sur la zone humide de Petelin (Corbelin et Veyrins-Thuellin, Nord-Isère) / Alexandre Gauthier in Lo Parvi, n° 25 (2017)
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