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Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([15/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 8 [15/04/2021] . - pp 918 - 935 Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] matrice de co-occurrence [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] Pinus nigra [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] Turquie Résumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-308 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443 [article]

Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1832 - 1849 Titre : Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal Type de document : Article/Communication Auteurs : Santa Pandit, Auteur ; Satoshi Tsuyuki, Auteur ; Timothy Dube, Auteur Année de publication : 2020 Article en page(s) : pp 1832 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse multibande [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Népal Résumé : (auteur) The potential of the improved resolution Sentinel-2 MSI data was explored through texture metrics, vegetation indices (VIs) and pooled dataset using the Random Forest (RF) machine learning algorithm to estimate Above-ground Biomass (AGB) in a sub-tropical forest of Nepal. Texture metrics were derived based on different working window sizes (3 × 3, 5 × 5, 7 × 7 and 9 × 9), and the results were compared with those obtained, using raw traditional bands (Analysis set 1: 2, 3, 4, 8, 11 and 12), raw traditional and red edge bands (Analysis set 2: Set 1 + Band 5, 6, 7 and 8A), and red edge bands (Analysis set 3) only. Comparatively, the use of pooled data (texture and VIs) yielded higher biomass estimates. The results from pooled data based on the 7 × 7 window size resulted in models with better model fitting parameters. For instance, pooled data produced an R2 = 0.99 and a RMSE = 4.51 t ha−1 (relRMSE = 2.82). Further, the RF model selected dissimilarity, variance and mean from Band 2 and SAVI (Soil adjusted vegetation index) as the most important AGB predictor variables. The results demonstrated that like the red-edge bands, traditional bands were equally important in AGB estimation. Numéro de notice : A2020-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588390 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1588390 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96334 [article]

Improving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - n° 103 Titre : Improving aboveground biomass estimates by taking into account density variations between tree components Type de document : Article/Communication Auteurs : Antoine Billard, Auteur ; Rodolphe Bauer, Auteur ; Frédéric Mothe, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] allométrie [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] bois de chauffage [Termes descripteurs IGN] branche (arbre) [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] écorce [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] résineux [Termes descripteurs IGN] tomographie radar [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message: Strong density differences were observed between stem wood at 1.30 m and other tree components (stem wood, stem bark, knots, branch stumps and branches). The difference, up to 40% depending on the component, should be taken into account when estimating the biomass available for industrial uses, mainly fuelwood and wood for chemistry. Context: Basic density is a major variable in the calculation of tree biomass. However, it is usually measured on stem wood only and at breast height. Aims: The objectives of this study were to compare basic density of stem wood at 1.30 m with other tree components and assess the impact of differences on biomass. Methods: Three softwood species were studied: Abies alba Mill., Picea abies (L.) H. Karst., Pseudotsuga menziesii (Mirb.) Franco. X-Ray computed tomography was used to measure density. Results: Large differences were observed between components. Basic density of components was little influenced by tree size and stand density. Overall, bark, knot and branch biomasses were highly underestimated by using basic density measured at 1.30 m. Conclusion: Using available wood density databases mainly based on breast height measurements would lead to important biases (up to more than 40%) on biomass estimates for some tree components. Further work is necessary to complete available databases. Numéro de notice : A2020-714 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-020-00999-1 date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00999-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96282 [article]

Ground-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6844 - 6860 Titre : Ground-based remote sensing of forests exploiting GNSS signals Type de document : Article/Communication Auteurs : Leila Guerriero, Auteur ; Francisco Martin, Auteur ; Antonio Mollfulleda, Auteur Année de publication : 2020 Article en page(s) : pp 6844 - 6860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] atténuation du signal [Termes descripteurs IGN] bande L [Termes descripteurs IGN] bande P [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] canopée [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] Populus (genre) [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] signal GNSS Résumé : (auteur) The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results. Numéro de notice : A2020-585 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976899 date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95913 [article]

L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])  

Public [article]  inGeocarto international > vol 35 n° 12 [01/09/2020] . - pp 1327 - 1342 Titre : L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia Type de document : Article/Communication Auteurs : Bambang H Trisasongko, Auteur ; David J. Paull, Auteur Année de publication : 2020 Article en page(s) : pp 1327 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] arbre hors forêt [Termes descripteurs IGN] bande L [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carbone [Termes descripteurs IGN] données allométriques [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] Java (île de) Résumé : (auteur) This article discusses an experiment on the estimation of rubber tree biomass using L-band Synthetic Aperture Radar (SAR), to support recent efforts to include trees outside forest in global biomass and carbon accounting. We noted that date of acquisition is important, but certainly the selection of allometric equation serving as the reference data was paramount. Similarly, choosing a proper form of fully polarimetric data was instrumental, although this requires validation in different environmental settings. As expected, modern data mining approaches consistently delivered high accuracy. Extreme learning machine yielded the best estimate in terms of R2 (0.98) and RMSE (1.88 Mg/ha); nonetheless, it also delivered a slight negative estimation. In this case, we found that a variant of random forest produced an outcome without any negative estimation. This research suggests that estimated biomass or carbon information from rubber plantations would be an invaluable candidate for the improvement of global biomass data. Numéro de notice : A2020-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1573855 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1573855 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95630 [article]

Carbon stocks, partitioning, and wood composition in short-rotation forestry system under reduced planting spacing / Felipe Schwerz in Annals of Forest Science [en ligne], vol 77 n° 3 (September 2020)  

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Predicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)  

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Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass / Christoph Kleinn in Forest ecosystems, vol 7 (2020)  

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Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors / Svetlana Saarela in Forest ecosystems, vol 7 (2020)  

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Potential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)  

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Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])  

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Remote sensing technology applications in forestry and REDD+ / Kim Calders (2020)  

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Quantifying intra-annual dynamics of carbon sequestration in the forming wood: a novel histologic approach / Anjy Andrianantenaina in Annals of Forest Science [en ligne], Vol 76 n° 3 (September 2019)  

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