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Pressures and threats to nature related to human activities in European urban and suburban forests / Ewa Referowska-Chodak in Forests, vol 10 n° 9 (September 2019)  

Public [article]  inForests > vol 10 n° 9 (September 2019) . - 26 p. Titre : Pressures and threats to nature related to human activities in European urban and suburban forests Type de document : Article/Communication Auteurs : Ewa Referowska-Chodak, Auteur Année de publication : 2019 Article en page(s) : 26 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] action anthropique [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] construction [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] impact sur l'environnement [Termes descripteurs IGN] périphérie urbaine [Termes descripteurs IGN] pollution atmosphérique [Termes descripteurs IGN] urbanisation [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) This review regards the pressures and threats linked with the human use of European urban and suburban forests. They can be divided into the following major categories: urban development, fragmentation, and isolation of forests; human pressures on soil and vegetation (e.g., changes in vegetation due to trampling, environmental and especially air pollution); human pressures on animals (e.g., wildlife losses due to collisions, frequent presence of dogs accompanying the visitors); and other threats and damages (e.g., littering and acts of vandalism). The directions of negative relations between people and forests shown in this review draw attention to the high complexity of the discussed issues. Awareness of this complexity (when planning and implementing forest management) can limit or counteract conflicts arising from the use of urban and suburban forests by people. This is of particular importance in the era of progressing urbanization and the evolution of human needs regarding the use of forests. Numéro de notice : A2019-336 Thématique : FORET/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f10090765 date de publication en ligne : 04/09/2019 En ligne : https://doi.org/10.3390/f10090765 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93362 [article]

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 189 - 201 Titre : Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images Type de document : Article/Communication Auteurs : Jie Wang, Auteur ; Xiangming Xiao, Auteur ; Rajen Bajgain, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 189 - 201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] paturage [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] régression multiple Résumé : (Auteur) Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI  2 m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management. Numéro de notice : A2019-269 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.007 date de publication en ligne : 21/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93086 [article]

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Occlusion probability in operational forest inventory field sampling with ForeStereo / Fernando Montes in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 7 (July 2019) . - pp 493 - 508 Titre : Occlusion probability in operational forest inventory field sampling with ForeStereo Type de document : Article/Communication Auteurs : Fernando Montes, Auteur ; Mariola Sánchez-González, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 493 - 508 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] capteur optique [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] image hémisphérique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] placette d'échantillonnage [Termes descripteurs IGN] Pyrénées (montagne) [Termes descripteurs IGN] volume en bois Résumé : (auteur) Field data in forest inventories are increasingly obtained using proximal sensing technologies, often under fixed-point sampling. Under fixed-point sampling some trees are not detected due to instrument bias and occlusions, hence involving an underestimation of the number of trees per hectare (N). The aim here is to evaluate various approaches to correct tree occlusions and instrument bias estimates calculated with data from ForeStereo (proximal sensor based on stereoscopic hemispherical images) under a fixed-point sampling strategy. Distance-sampling and the new hemispherical photogrammetric correction (HPC), which combines image segmentation-based correction for instrument bias with a novel approach for estimating the proportion of shadowed sampling area in stereoscopic hemispherical images, best estimated N and basal area (BA). Distance-sampling slightly overestimated N (11% bias, 0.60 Pearson coefficient with the reference measures) and BA (4%, 0.82). HPC provided less biased N estimates (-6%, 0.61) but underestimated BA (-8%, 0.83). HPC most accurately retrieved the diameter distribution. Numéro de notice : A2019-258 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.7.493 date de publication en ligne : 07/07/2019 En ligne : https://doi.org/10.14358/PERS.85.7.493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93060 [article]

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Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 78 (June 2019) . - pp 175 - 188 Titre : Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment Type de document : Article/Communication Auteurs : Eduarda M.O. Silveira, Auteur ; Sérgio Henrique G. Silva, Auteur ; Fausto Weimar Acerbi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 175 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] forêt équatoriale [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] Minas Gerais (Brésil) [Termes descripteurs IGN] montagne Résumé : (Auteur) The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R² (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment. Numéro de notice : A2019-230 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.02.004 date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1016/j.jag.2019.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92748 [article]

Tree and stand level estimations of Abies alba Mill. aboveground biomass / Andrzej M. Jagodzinski in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 76 n° 2 (June 2019) Titre : Tree and stand level estimations of Abies alba Mill. aboveground biomass Type de document : Article/Communication Auteurs : Andrzej M. Jagodzinski, Auteur ; Marcin K. Dyderski, Auteur ; et al., Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Abies alba [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] modélisation [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] puits de carbone [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Context : Among European forest-forming tree species with high economic and ecological significance, Abies alba Mill. is the least characterized in terms of biomass production. Aims : To provide a comprehensive set of tree- and stand-level models for A. alba biomass and carbon stock. We hypothesized that (among tree stand characteristics) volume will be the best predictor of tree stand biomass. Methods : We studied a chronosequence of 12 A. alba tree stands in southern Poland (8–115 years old). We measured tree stand structures, and we destructively sampled aboveground biomass of 96 sample trees (0.0–63.9 cm diameter at breast height). We provided tree-level models, biomass conversion and expansion factors (BCEFs) and biomass models based on forest stand characteristics. Results : We developed general and site-specific tree-level biomass models. For stand-level models, we found that the best predictor of biomass was stand volume, while the worst were stand basal area and density. Conclusion : Our models performed better than other published models, allowing for more reliable biomass predictions. Models based on volume are useful in biomass predictions and may be used in large-scale inventories. Numéro de notice : A2019-181 Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : doi.org/10.1007/s13595-019-0842-y date de publication en ligne : 15/05/2019 En ligne : https://doi.org/10.1007/s13595-019-0842-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92701 [article]

Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 (March 2019)  

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Quantifying spatiotemporal post‐disturbance recovery using field inventory, tree growth, and remote sensing / Shengli Huang in Earth and space science, vol 6 n° 3 (March 2019)  

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Estimating net biomass production and loss from repeated measurements of trees in forests and woodlands: Formulae, biases and recommendations / Takashi S. Kohyama in Forest ecology and management, vol 433 (15 February 2019)  

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Generation of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and lidar / Yang Lei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)  

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TanDEM-X digital surface models in boreal forest above-ground biomass change detection / Kirsi Karila in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)  

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Strategies for climate-smart forest management in Austria / Robert Jandl in Forests, vol 9 n° 10 (October 2018)  

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