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Ajouter le résultat dans votre panierStrategies for climate-smart forest management in Austria / Robert Jandl in Forests, vol 9 n° 10 (October 2018)
Titre : Strategies for climate-smart forest management in Austria Type de document : Article/Communication Auteurs : Robert Jandl, Auteur ; Thomas Ledermann, Auteur ; Georg Kindermann, Auteur ; Alexandra Freudenschuss, Auteur ; Thomas Gschwantner, Auteur ; Peter Weiss, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre caducifolié
[Termes IGN] Autriche
[Termes IGN] bioénergie
[Termes IGN] biomasse aérienne
[Termes IGN] changement climatique
[Termes IGN] gestion forestière durable
[Termes IGN] modèle de croissance végétale
[Termes IGN] Pinophyta
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (Auteur) We simulated Austrian forests under different sustainable management scenarios. A reference scenario was compared to scenarios focusing on the provision of bioenergy, enhancing the delivery of wood products, and reduced harvesting rates. The standing stock of the stem biomass, carbon in stems, and the soil carbon pool were calculated for the period 2010–2100. We used the forest growth model Câldis and the soil carbon model Yasso07. The wood demand of all scenarios could be satisfied within the simulation period. The reference scenario led to a small decrease of the stem biomass. Scenarios aiming at a supply of more timber decreased the standing stock to a greater extent. Emphasizing the production of bioenergy was successful for several decades but ultimately exhausted the available resources for fuel wood. Lower harvesting rates reduced the standing stock of coniferous and increased the standing stock of deciduous forests. The soil carbon pool was marginally changed by different management strategies. We conclude that the production of long-living wood products is the preferred implementation of climate-smart forestry. The accumulation of carbon in the standing biomass is risky in the case of disturbances. The production of bioenergy is suitable as a byproduct of high value forest products. Numéro de notice : A2018-474 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100592 Date de publication en ligne : 22/09/2018 En ligne : https://doi.org/10.3390/f9100592 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91173
in Forests > vol 9 n° 10 (October 2018)[article]
Titre : How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes Type de document : Article/Communication Auteurs : Niko Kulha, Auteur ; Leena Pasanen, Auteur ; Tuomas Aakala, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] biome
[Termes IGN] canopée
[Termes IGN] composition floristique
[Termes IGN] dendrochronologie
[Termes IGN] détection de changement
[Termes IGN] erreur systématique
[Termes IGN] étalonnage des données
[Termes IGN] forêt boréale
[Termes IGN] inférence statistique
[Termes IGN] photographie aérienneRésumé : (Auteur) Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstructions, and Bayesian inference to study changes in forests. Using stereopairs of aerial photographs from five boreal forest landscapes, we visually interpreted canopy cover in contiguous 0.1-ha cells at three time points during 1959–2011. We used tree-ring measurements to produce calibration data for the interpretation, and to quantify the bias and error associated with the interpretation. Then, we discerned credible canopy cover changes from the interpretation error noise using Bayesian inference. We underestimated canopy cover using the historical low-quality photographs, and overestimated it using the recent high-quality photographs. Further, due to differences in tree species composition and canopy cover in the cells, the interpretation bias varied between the landscapes. In addition, the random interpretation error varied between and within the landscapes. Due to the varying bias and error, the magnitude of credibly detectable canopy cover change in the 0.1-ha cells depended on the studied time interval and landscape, ranging from −10 to −18 percentage points (decrease), and from +10 to +19 percentage points (increase). Hence, changes occurring at stand scales were detectable, but smaller scale changes could not be separated from the error noise. Besides the abrupt changes, also slow continuous canopy cover changes could be detected with the proposed approach. Given the wide availability of historical aerial photographs, the proposed approach can be applied for forest change analysis in biomes where tree-rings form, while accounting for the bias and error in aerial photo interpretation. Numéro de notice : A2018-475 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100631 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.3390/f9100631 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91174
in Forests > vol 9 n° 10 (October 2018)[article]
Titre : Predicting tree diameter distributions from airborne laser scanning, SPOT 5 satellite, and field sample data in the perm region, Russia Type de document : Article/Communication Auteurs : Jussi Peuhkurinen, Auteur ; Timo Tokola, Auteur ; Kseniia Plevak, Auteur ; Sanna Sirparanta, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies sibirica
[Termes IGN] Alnus incana
[Termes IGN] Betula pendula
[Termes IGN] classification barycentrique
[Termes IGN] diamètre des arbres
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image SPOT 5
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Picea abies
[Termes IGN] Pinus sibirica
[Termes IGN] Pinus sylvestris
[Termes IGN] placette d'échantillonnage
[Termes IGN] Populus tremula
[Termes IGN] Russie
[Termes IGN] Salix caprea
[Termes IGN] Tilia cordata
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) A tree list is a list of trees in the area of interest containing, for example, the species, diameter, height, and stem volume of each tree. Tree lists can be used to derive various characteristics of the growing stock, and are therefore versatile and informative sources of data for several forest management purposes. Especially in heterogonous and unmanaged forest structures with multiple species, tree list estimates imputed from local reference field data can provide an alternative to mean value estimates of growing stock (e.g., basal area, total stem volume, mean tree diameter, mean tree height, and number of trees). In this study, reference field plots, airborne laser scanning (ALS) data, and SPOT 5 satellite (Satellite Pour l’Observation de la Terre) imagery were used for tree list imputation applying the k most similar neighbors (k-MSN) estimation method in the West Ural taiga region of the Russian Federation for diameter distribution estimation. In k-MSN, weighted average of k field reference plots with highest similarity between field reference plot and target (forest grid cell, or field plot) based on ALS and SPOT 5 features were used to predict the mean values of growing stock and tree lists for the target object simultaneously. Diameter distributions were then constructed from the predicted tree lists. The prediction of mean values and diameter distributions was tested in 18 independent validation plots of 0.25–0.5 ha in size, whose species specific diameter distributions were measured in the field and grouped into three functional groups (Pines, Spruce/Fir, Broadleaf Group), each containing several species. In terms of root mean squared error relative to mean of validation plots, the accuracy of estimation was 0.14 and 0.17 for basal area and total stem volume, respectively. Reynolds error index values and visual inspection showed encouraging results in evaluating the goodness-of-fit statistics of the estimated diameter distributions. Although estimation accuracy was worse for functional group mean values and diameter distributions, the results indicate that it is possible to predict diameter distributions in forests of the test area with the tested methodology and materials. Numéro de notice : A2018-476 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100639 Date de publication en ligne : 13/10/2018 En ligne : https://doi.org/10.3390/f9100639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91176
in Forests > vol 9 n° 10 (October 2018)[article]
Titre : Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest Type de document : Article/Communication Auteurs : Qingxia Zhao, Auteur ; Fei Wang, Auteur ; Jun Zhao, Auteur ; Jingjing Zhou, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'arbres
[Termes IGN] Enhanced vegetation index
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] loess
[Termes IGN] matrice de co-occurrence
[Termes IGN] plantation forestière
[Termes IGN] régression
[Termes IGN] Robinia pseudoacacia
[Termes IGN] Soil Adjusted Vegetation IndexRésumé : (Auteur) The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau. Numéro de notice : A2018-477 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100623 Date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.3390/f9100623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91178
in Forests > vol 9 n° 10 (October 2018)[article]
Titre : Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Chunying Ren, Auteur ; Bai Zhang, Auteur ; Zongming Wang, Auteur ; Yanbiao Xi, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre caducifolié
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle de simulation
[Termes IGN] montagne
[Termes IGN] régression géographiquement pondérée
[Termes IGN] surveillance forestière
[Termes IGN] texture d'image
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Numéro de notice : A2018-478 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100582 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.3390/f9100582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91180
in Forests > vol 9 n° 10 (October 2018)[article]
