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Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])
[article]
Titre : Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery Type de document : Article/Communication Auteurs : Sikdar M. M. Rasel, Auteur ; Hsing-Chung Chang, Auteur ; Timothy J. Ralph, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1075-1099 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] biomasse
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] marais salé
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] variableRésumé : (Auteur) Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p = Numéro de notice : A2021-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624988 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624988 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97729
in Geocarto international > vol 36 n° 10 [01/06/2021] . - pp 1075-1099[article]Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)
[article]
Titre : Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia Type de document : Article/Communication Auteurs : Peter Kitin, Auteur ; Edgard Espinoza, Auteur ; Hans Beeckman, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] abattage (sylviculture)
[Termes IGN] Afzelia (genre)
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage non-dirigé
[Termes IGN] bois
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] identification de plantes
[Termes IGN] signature spectrale
[Termes IGN] spectrométrie
[Termes IGN] taxinomie
[Termes IGN] temps réelRésumé : (Auteur) Distinct chemical fingerprints of the wood of Afzelia pachyloba and A. bipindensis demonstrated an effective method for identifying these two commercially important species. Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) allowed high-throughput examination of chemotypes with vast potential in taxonomic, ecological, and forensic research of wood.
Context : Afzelia is a genus of valuable tropical timber trees. Accurate identification of wood is required for the prevention of illicit timber trade as well as for certification purposes in the forest and wood products industry. For many years, particular interest has been focused on attempts to distinguish the wood of A. bipindensis Harms from A. pachyloba Harms due to substantial differences in the commercial values of these two species.
Aims : We investigated if wood chemical signatures and microscopy could identify the wood of A. bipindensis and A. pachyloba.
Methods : We used two approaches, namely metabolome profiling by direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) and wood microstructure by light microscopy and SEM. In all, we analyzed samples from 89 trees of A. bipindensis, and A. pachyloba.
Results : The two species could not be separated by the IAWA standard microscopic wood features. SEM analysis showed considerable variation in the morphology of vestured pits; however, this variation was not species-specific. In contrast, DART-TOFMS followed by unsupervised statistics (Discriminant Analysis of Principal Components) showed distinct metabolome signatures of the two species.
Conclusion : DART-TOFMS provides a rapid method for wood identification that can be easily applied to small heartwood samples. Time- and cost-effective classification of wood chemotypes by DART-TOFMS can have potential applications in various research questions in forestry, wood science, tree-ecophysiology, and forensics.Numéro de notice : A2021-327 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01024-1 Date de publication en ligne : 31/03/2021 En ligne : https://doi.org/10.1007/s13595-020-01024-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97488
in Annals of Forest Science > vol 78 n° 2 (June 2021) . - Article 31[article]Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] Citrus (genre)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] feuille (végétation)
[Termes IGN] image hyperspectrale
[Termes IGN] photosynthèse
[Termes IGN] réflectance végétale
[Termes IGN] rendement agricole
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétationRésumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160[article]Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band Type de document : Article/Communication Auteurs : Xing Peng, Auteur ; Xinwu Li, Auteur ; Yanan Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] image 3D
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] itération
[Termes IGN] matrice de covariance
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de points
[Termes IGN] Suède
[Termes IGN] tomographie radarRésumé : (auteur) Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. Numéro de notice : A2021-441 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13112147 Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.3390/rs13112147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97828
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2147[article]Fractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Fractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method Type de document : Article/Communication Auteurs : Duanyang Liu, Auteur ; Kun Jia, Auteur ; Haiying jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2165 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert végétal
[Termes IGN] image Feng-Yun
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] réflectance végétale
[Termes IGN] régressionRésumé : (auteur) As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R2 = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy. Numéro de notice : A2021-439 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112165 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97824
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