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Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)
[article]
Titre : Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Hadi Beygi Heidarlou, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 165 - 182 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] allométrie
[Termes IGN] biomasse aérienne
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] forêt méditerranéenne
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Iran
[Termes IGN] Quercus brantii
[Termes IGN] taillisRésumé : (auteur) Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: MultiLayer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots. Numéro de notice : A2022-876 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2022.2390 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.15287/afr.2022.2390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102180
in Annals of forest research > vol 65 n° 1 (January - June 2022) . - pp 165 - 182[article]Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)
[article]
Titre : Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Ali Asghar Darvishsefat, Auteur ; Manizheh Rajab Pourrahmati, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Carpinus betulus
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] image Sentinel-MSI
[Termes IGN] Iran
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificielNuméro de notice : A2022-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13010104 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/f13010104 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99472
in Forests > vol 13 n° 1 (January 2022) . - n° 104[article]Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
[article]
Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)
Titre : Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon Type de document : Thèse/HDR Auteurs : Le Bienfaiteur Sagang Takougoum, Auteur ; Bonaventure Sonké, Directeur de thèse ; Nicolas Barbier, Directeur de thèse Editeur : Yaoundé : Université de Yaoundé Année de publication : 2022 Importance : 166 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Yaoundé 1, Spécialité Botanique-EcologieLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Cameroun
[Termes IGN] carte d'utilisation du sol
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] dynamique de la végétation
[Termes IGN] écotone
[Termes IGN] flore locale
[Termes IGN] forêt
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] incendie de forêt
[Termes IGN] modèle statistique
[Termes IGN] savane
[Termes IGN] surveillance forestièreIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Understanding the effects of global change (combining anthropic and climatic pressures) on biome distribution needs innovative approaches allowing to address the large spatial scales involved and the scarcity of available ground data. Characterizing vegetation dynamics at landscape to regional scale requires both a high level of spatial detail (resolution), generally obtained through precise field measurements, and a sufficient coverage of the land surface (extent) provided by satellite images. The difficulty usually lies between these two scales as both signal saturation from satellite data and ground sampling limitations contribute to inaccurate extrapolations. Airborne laser scanning (ALS) data has revolutionized the trade-off between spatial detail and landscape coverage as it gives accurate information of the vegetation’s structure over large areas which can be used to calibrate satellite data. Also recent satellite data of improved spectral and spatial resolutions (Sentinel 2) allow for detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. Another major obstacle comes from the lack of a temporal perspective on dynamics and disturbances. Growing satellite imagery archives over several decades (45 years; Landsat) and available computing facilities such as Google Earth Engine (GEE) provide new possibilities to track long term successional trajectories and detect significant disturbances (i.e. fire) at a fine spatial detail (30m) and relate them to the current structure and composition of the vegetation. With these game changing tools our objective was to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon with induced changes in the vegetatio structure and composition within two contrasted scenarios of anthropogenic pressures: 1) the Nachtigal area which is targeted for the dam construction and subject to intense agricultural activities and 2) the Mpem et Djim National Park (MDNP) which has no management plan. The maximum likelihood classification of the Spot 6/7 image aided with the information from the canopy height derived from ALS data discriminated the vegetation types within the Nachtigal area with good accuracy (96.5%). Using field plots data in upscaling aboveground biomass (AGB) form field plots estimates to the satellite estimates with model-based approaches lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS data (AGBALS) lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. However, these results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixel wise predictions, because of large relative RMSPE, especially above (200–250 Mg.ha−1). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. AGB and species diversity measured within 74 field inventory plots (distributed along a savanna to forest successional gradient) were higher for the vegetation located in the MDNP compared to their pairs in the Nachtigal area. The automated unsupervised long-term (45 years) land cover change monitoring from Landsat image archives based on GEE captured a consistent and regular pattern of forest progression into savanna at an average rate of 1% (ca. 6 km².year-1). No fire occurrence was captured for savanna that transited to forest within five years of monitoring. Distinct assemblages of spectral species are apparent in forest vegetation which is consistent with the age of transition. As forest gets older AGBALS recovers at a rate of 4.3 Mg.ha-1.year-1 in young forest stands ( Note de contenu : Chapter 1. Generalities
1.1 Introduction
1.2 Literature Review
Chapter 2. Material And Methods
2.1 Material
2.2 Methods
Chapter 3. Results And Discussion
3.1 Results
3.2 Discussion
Chapter 4. Conclusion And Perspectives
4.1 Conclusion
4.2 PerspectivesNuméro de notice : 26820 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de doctorat : Botanique-Ecologie : Yaoundé : 2022 Organisme de stage : Institut de Recherche pour le Développement IRD nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/04/2022 En ligne : https://hal.inrae.fr/tel-03528875/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100465 Climate warming-induced replacement of mesic beech by thermophilic oak forests will reduce the carbon storage potential in aboveground biomass and soil / Jan Kasper in Annals of Forest Science, vol 78 n° 4 (December 2021)
[article]
Titre : Climate warming-induced replacement of mesic beech by thermophilic oak forests will reduce the carbon storage potential in aboveground biomass and soil Type de document : Article/Communication Auteurs : Jan Kasper, Auteur ; Robert Weigel, Auteur ; Helge Walentowski, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 89 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie)
[Termes IGN] biomasse aérienne
[Termes IGN] changement climatique
[Termes IGN] dépérissement
[Termes IGN] écosystème forestier
[Termes IGN] écotone
[Termes IGN] Europe centrale
[Termes IGN] Fagus sylvatica
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] puits de carbone
[Termes IGN] Quercus sessiliflora
[Termes IGN] Roumanie
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Key message: Climate-warming related replacement of beech by oak forests in the course of natural forest succession or silvicultural decisions may considerably reduce ecosystem carbon storage of central European woodlands.
Context: Climate warming may change the carbon (C) storage in forest biomass and soil through future shifts in tree species composition. With a projected warming by 2–3 K over the twenty-first century, silvicultural adaptation measures and natural succession might lead to the replacement of European beech forests by thermophilic oak forests in drought- and heat-affected regions of central and south-eastern Europe, but the consequences for ecosystem C storage of this species shift are not clear.
Aims: To quantify the change in C storage in biomass and soil with a shift from beech (Fagus sylvatica) to oak forest (Quercus petraea, Q. frainetto, Q. cerris), we measured the aboveground biomass (AGC) and soil C pools (SOC).
Methods: AGC pools and SOC stocks to − 100 cm depth were calculated from forest inventory and volume-related SOC content data for beech, mixed beech-oak and oak forests in three transects in the natural beech-oak ecotone of western Romania, where beech occurs at its heat- and drought-induced distribution limit.
Results: From the cooler, more humid beech forests to the warmer, more xeric oak forests, which are 1–2 K warmer, AGC and SOC pools decreased by about 22% (40 Mg C ha−1) and 20% (17 Mg C ha−1), respectively. The likely main drivers are indirect temperature effects acting through tree species and management in the case of AGC, but direct temperature effects for SOC.
Conclusion : If drought- and heat-affected beech forests in Central Europe are replaced by thermophilic oak forests in future, this will lead to carbon losses of ~ 50–60 Mg ha−1, thus reducing ecosystem carbon storage substantially.Numéro de notice : A2021-766 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01081-0 Date de publication en ligne : 15/10/2021 En ligne : https://doi.org/10.1007/s13595-021-01081-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98812
in Annals of Forest Science > vol 78 n° 4 (December 2021) . - n° 89[article]Estimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkAbove-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)PermalinkMulti-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkThe effects of combining the variables in allometric biomass models on biomass estimates over large forest areas: A european beech case study / Erick O. Osewe in Forests, vol 12 n° 10 (October 2021)PermalinkMapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkTarget-based automated matching of multiple terrestrial laser scans for complex forest scenes / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkThe real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)Permalink