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Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland / Man Hu in Forest ecology and management, vol 529 (February-1 2023)  

Public [article]  inForest ecology and management > vol 529 (February-1 2023) . - n° 120702 Titre : Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland Type de document : Article/Communication Auteurs : Man Hu, Auteur ; Francesco Minunno, Auteur ; Mikko Peltoniemi, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120702 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] forêt inéquienne [Termes IGN] hauteur des arbres [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] mortalité [Termes IGN] peuplement mélangé [Termes IGN] photosynthèse [Termes IGN] Picea abies [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Foresterie Résumé : (auteur) The challenges of applying process-based models to uneven-aged forests are the difficulties in simulating the interactions between trees and resource allocation between size classes. In this study, we focused on a process-based forest growth model PREBAS which is a mean tree model with Reineke self-thinning mortality and was originally developed for even-aged forests. The primary aim was to test the application of PREBAS model to uneven-aged forests by introducing different diameter at breast height (DBH) size classes to better represent the forest structure. Additionally, we introduced a new mortality model to PREBAS which is developed for uneven-aged stands and compared with the current PREBAS version in which a modification Reineke rule is used. The tests were conducted in 26 old Norway spruce dominated stands in southern and central Finland with three consecutive measurements (on average a 25-year study period). To evaluate the model performance, we compared the estimations of stand averaged diameter at breast height (D), stand averaged tree height (H), stand averaged crown base height (), stand basal area (B) and density (N) with measurements. Moreover, biomass estimations of each tree component (foliage, branch and stem) were compared to estimations from empirical models. Results showed that introducing size distributions can represent better stand structure and improve the model predictions compared with data. Moreover, the new mortality model showed promise with qualitatively more realistic results especially among the largest tree size classes. However, model bias still existed in the simulation although the predictions were improved. It revealed that further calibration of the PREBAS model with size classes should be done to better extend the model applicability to uneven-aged forests. Numéro de notice : A2023-022 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120702 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120702 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102228 [article]

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis Type de document : Article/Communication Auteurs : Haifa Tamiminia, Auteur ; Bahram Salehi, Auteur ; Masoud Mahdianpari, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse aérienne [Termes IGN] boosting adapté [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification pixellaire [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Extreme Gradient Machine [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] réserve naturelle Résumé : (auteur) Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model. Numéro de notice : A2022-331 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2071475 Date de publication en ligne : 27/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2071475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100607 [article]

Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] Chine [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] mangrove [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356 [article]

Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography / Ihor Kozak in Urban Forestry & Urban Greening, vol 79 (January 2023)  

Public [article]  inUrban Forestry & Urban Greening > vol 79 (January 2023) . - n° 127793 Titre : Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography Type de document : Article/Communication Auteurs : Ihor Kozak, Auteur ; Mikhail Popov, Auteur ; Igor Semko, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 127793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] biomasse aérienne [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] forêt urbaine [Termes IGN] houppier [Termes IGN] image hémisphérique [Termes IGN] Leaf Area Index [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de régression [Termes IGN] modèle numérique de terrain [Termes IGN] photographie numérique [Termes IGN] Pinus sylvestris [Termes IGN] Pologne [Termes IGN] semis de points [Termes IGN] surface terrière Résumé : (auteur) The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction. Numéro de notice : A2023-023 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ufug.2022.127793 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.ufug.2022.127793 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102246 [article]

Management of birch spruce mixed stands with consideration of carbon stock in biomass and harvested wood products / Jānis Vuguls in Forests, vol 14 n° 1 (January 2023)  

Public [article]  inForests > vol 14 n° 1 (January 2023) . - n° 57 Titre : Management of birch spruce mixed stands with consideration of carbon stock in biomass and harvested wood products Type de document : Article/Communication Auteurs : Jānis Vuguls, Auteur ; Stefanija Dubra, Auteur ; Anete Garanca, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Betula (genre) [Termes IGN] bilan du carbone [Termes IGN] biomasse forestière [Termes IGN] changement climatique [Termes IGN] gestion forestière durable [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] plante ligneuse [Termes IGN] puits de carbone [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forests play an important role in climate change mitigation. Usage of harvested wood products (HWP) can extend the carbon cycle by retaining carbon as well as preventing new fossil emission via substitution. We compared carbon balance of different management strategies of birch spruce mixed stands over an eight-year period: unmanaged, representing a decision of prolonged rotation, and managed, representing a decision of final harvest of birch and retention of spruce for continuous forest cover and regeneration harvest. Management resulted in a higher contribution of mixed stands to climate change mitigation, if the carbon stock (CS) in biomass as well carbon balance (CB) of wood product is jointly considered in comparison to no management (prolonged rotation). Assortment structure plays an important role in CB of HWP, therefore a practice ensuring higher outcome of longer-lasting wood products are beneficial to climate change mitigation. Numéro de notice : A2023-041 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f14010057 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.3390/f14010057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102327 [article]

A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing / Yali Zhang in GIScience and remote sensing, vol 60 n° 1 (2023)  

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Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)  

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Wall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)  

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Development and long-term dynamics of old-growth beech-fir forests in the Pyrenees: Evidence from dendroecology and dynamic vegetation modelling / Dario Martín-Benito in Forest ecology and management, vol 524 (November-15 2022)  

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Estimating carbon stocks and biomass expansion factors of urban greening trees using terrestrial laser scanning / Linlin Wu in Forests, vol 13 n° 9 (september 2022)  

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Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])  

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Management or climate and which one has the greatest impact on forest soil’s protective value? A case study in Romanian mountains / Cosmin Cosofret in Forests, vol 13 n° 6 (June 2022)  

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Uncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations / Aitor Ameztegui in European Journal of Forest Research, vol 141 n° 3 (June 2022)  

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Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 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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