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FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach / Martin Schwartz in Earth System Science Data, vol 15 n° inconnu (2023)  

Public [article]  inEarth System Science Data > vol 15 n° inconnu (2023) Titre : FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Aurélien de Truchis, Auteur ; Jérôme Chave, Auteur ; Catherine Ottle, Auteur ; Cédric Vega , Auteur ; Jean-Pierre Wigneron, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] données allométriques [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle numérique de surface de la canopée [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote sensing measurements (NASA’s GEDI LiDAR mission and ESA’s Copernicus Sentinel 1 & 2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha-1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from Airborne Laser Scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 Mg ha-1 and 19.6 Mg.ha-1 respectively, providing greater performance than other AGBD products sampled over France. These results highlight the importance of coupling remote sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023). Numéro de notice : A2023-179 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-2023-196 En ligne : https://doi.org/10.5194/essd-2023-196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103341 [article]

Keeping thinning-derived deadwood logs on forest floor improves soil organic carbon, microbial biomass, and enzyme activity in a temperate spruce forest / Meisam Nazari in European Journal of Forest Research, vol 142 n° 2 (April 2023)  

Public [article]  inEuropean Journal of Forest Research > vol 142 n° 2 (April 2023) . - pp 287 - 300 Titre : Keeping thinning-derived deadwood logs on forest floor improves soil organic carbon, microbial biomass, and enzyme activity in a temperate spruce forest Type de document : Article/Communication Auteurs : Meisam Nazari, Auteur ; Johanna Pausch, Auteur ; Samuel Bickel, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 287 - 300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne) [Termes IGN] biomasse forestière [Termes IGN] bois mort [Termes IGN] éclaircie (sylviculture) [Termes IGN] gestion forestière durable [Termes IGN] grume [Termes IGN] podzosol [Termes IGN] puits de carbone [Termes IGN] sol forestier [Vedettes matières IGN] Sylviculture Résumé : (auteur) Deadwood is a key component of forest ecosystems, but there is limited information on how it influences forest soils. Moreover, studies on the effect of thinning-derived deadwood logs on forest soil properties are lacking. This study aimed to investigate the impact of thinning-derived deadwood logs on the soil chemical and microbial properties of a managed spruce forest on a loamy sand Podzol in Bavaria, Germany, after about 15 years. Deadwood increased the soil organic carbon contents by 59% and 56% at 0–4 cm and 8–12 cm depths, respectively. Under deadwood, the soil dissolved organic carbon and carbon to nitrogen ratio increased by 66% and 15% at 0–4 cm depth and by 55% and 28% at 8–12 cm depth, respectively. Deadwood also induced 71% and 92% higher microbial biomass carbon, 106% and 125% higher microbial biomass nitrogen, and 136% and 44% higher β-glucosidase activity in the soil at 0–4 cm and 8–12 cm depths, respectively. Many of the measured variables significantly correlated with soil organic carbon suggesting that deadwood modified the soil biochemical processes by altering soil carbon storage. Our results indicate the potential of thinned spruce deadwood logs to sequester carbon and improve the fertility of Podzol soils. This could be associated with the slow decay rate of spruce deadwood logs and low biological activity of Podzols that promote the accumulation of soil carbon. We propose that leaving thinning-derived deadwood on the forest floor can support soil and forest sustainability as well as carbon sequestration. Numéro de notice : A2023-215 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-022-01522-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s10342-022-01522-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103144 [article]

Forest structure and fine root biomass influence soil CO2 efflux in temperate forests under drought / Antonios Apostolakis in Forests, vol 14 n° 2 (February 2023)  

Public [article]  inForests > vol 14 n° 2 (February 2023) . - n° 411 Titre : Forest structure and fine root biomass influence soil CO2 efflux in temperate forests under drought Type de document : Article/Communication Auteurs : Antonios Apostolakis, Auteur ; Ingo Schöning, Auteur ; Beate Michalzik, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 411 Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne [Termes IGN] biomasse forestière [Termes IGN] forêt tempérée [Termes IGN] puits de carbone [Termes IGN] qualité du sol [Termes IGN] sécheresse [Termes IGN] structure d'un peuplement forestier [Termes IGN] température au sol [Termes IGN] teneur en carbone [Termes IGN] teneur en eau de la végétation [Vedettes matières IGN] Végétation et changement climatique Numéro de notice : A2023-165 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f14020411 Date de publication en ligne : 17/12/2023 En ligne : https://doi.org/10.3390/f14020411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102871 [article]

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])  

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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)  

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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)  

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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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A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band / Xinjie Liu in Remote sensing of environment, vol 284 (January 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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