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The process-based forest growth model 3-PG for use in forest management : A review / Rajit Gupta in Ecological modelling, vol 397 (1 April 2019)  

Public [article]  inEcological modelling > vol 397 (1 April 2019) . - pp 55 - 73 Titre : The process-based forest growth model 3-PG for use in forest management : A review Type de document : Article/Communication Auteurs : Rajit Gupta, Auteur ; Laxmi Kant Sharma, Auteur Année de publication : 2019 Article en page(s) : pp 55 - 73 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] productivité [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] teneur en eau de la végétation [Termes descripteurs IGN] variable biophysique (végétation) [Vedettes matières IGN] Végétation et changement climatique Mots-clés libres : 3-PG (Physiological Principles in Predicting Growth) Résumé : (Auteur) Forests are a critical resource, and need proper management in the face of dire climatic changes facing the world today. Advances in modelling system result in the formulation of numerous forest modelling approaches to provide an estimation of forests services. One such useful and straightforward forest modelling approach is process-based modelling, relying on physiological processes and biophysical parameters of forest ecosystems. It is based on parametric calculations and allometric equations, delivering crucial outputs for forest management. The dynamic 3-PG (Physiological Principles in Predicting Growth) is a process-based model (PBM) based on an ecosystem physiological process-based modelling approach. The various applications and flexible nature of the 3-PG model have resulted in its adoption and utilization over several regions of the world. The 3-PGS (Physiological Principles in Predicting Growth with Satellite) model is a modified and spatial version of the 3-PG model that took advantages of remote sensing & GIS (Geographical Information System) for estimation of biophysical variables like FAPAR (Fraction of absorbed photosynthetically active radiation), LAI (Leaf area index), and Canopy water content (CWC), which are tedious and laborious to calculate manually. The integration of remote sensing & GIS with PBMs offers insights to predict forest biomass and productivity at a regional level. Also, coupling of the 3-PG/3-PGS model with other modelling and statistical approaches in a GIS environment provides insights into the prediction of species distributions and potential disturbances due to climatic changes. The 3-PG model was originally designed for relatively homogenous forests; but with the recent development, the 3-PGmix has extended its use to mixed species forests. In this review, we have tried to emphasize the general overview, structure, applications, and efficacy of the process-based 3-PG model for forest management. In future, forests and their ecosystem services are expected to be rigorously influenced by climatic variations. Therefore, it is important to understand the role and effectiveness of the forest growth model 3-PG under the influence of climate change. The 3-PG model performs well for a diverse range of conditions for many forest types and species, and could be integrated with other models and approaches in order to widen its functions and applications. Areas such as Fertility Rating (FR), sensitivity and uncertainty of outputs to the model inputs in the 3-PG model requires attention to remove the weaker side, and to increase the effectiveness and accuracy of model outputs. In addition, the model performance can be improved by calculating its parameters from the population of interest, rather than using default values or values from extant literature. Furthermore, high-resolution remote sensing datasets and accurate input field data could increase the accuracy of the 3-PG/3-PGS model predictions at a broad regional level. In general, the simple forest growth model 3-PG delivers practical outputs, which are directly used in forest management. Additionally, the functions and applications of the 3-PG/3-PGS/3-PGmix model could be explored to deal with the impacts of climate change on forests and to ensure the sustainable management of forests. Numéro de notice : A2019-228 Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.ecolmodel.2019.01.007 date de publication en ligne : 12/02/2019 En ligne : https://doi.org/10.1016/j.ecolmodel.2019.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92743 [article]

Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)  

Public [article]  inForests > vol 9 n° 10 (October 2018) Titre : Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Chunying Ren, Auteur ; Bai Zhang, Auteur ; Zongming Wang, Auteur ; Yanbiao Xi, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Changbai, massif du [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] montagne [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Numéro de notice : A2018-478 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100582 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.3390/f9100582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91180 [article]

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 70 - 85 Titre : Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery Type de document : Article/Communication Auteurs : Jose Alan A. Castillo, Auteur ; Armando A. Apan, Auteur ; Tek N. Maraseni, Auteur ; Severino G. Salmo, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carte d'utilisation du sol [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] Philippines [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery. Numéro de notice : A2017-730 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88428 [article]

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Hyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 88 - 101 Titre : Hyperspectral dimensionality reduction for biophysical variable statistical retrieval Type de document : Article/Communication Auteurs : Juan Pablo Rivera-Caicedo, Auteur ; Jochem Verrelst, Auteur ; Jordi Munoz-Mari, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 88 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] image HYMAP [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] variable biophysique (végétation) [Termes descripteurs IGN] végétation Résumé : (Auteur) Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to retrieve leaf area index (LAI): (1) a simulated PROSAIL reflectance data (2101 bands), and (2) a field dataset from airborne HyMap data (125 bands). For the majority of MLRAs, applying first a DR method leads to superior retrieval accuracies and substantial gains in processing speed as opposed to using all bands into the regression algorithm. This was especially noticeable for the PROSAIL dataset: in the most extreme case, using the classical linear regression (LR), validation results (RMSECV) improved from 0.06 (12.23) without a DR method to 0.93 (0.53) when combining it with a best performing DR method (i.e., CCA or OPLS). However, these DR methods no longer excelled when applied to noisy or real sensor data such as HyMap. Then the combination of kernel CCA (KCCA) with LR, or a classical PCA and PLS with a MLRA showed more robust performances ( of 0.93). Gaussian processes regression (GPR) uncertainty estimates revealed that LAI maps as trained in combination with a DR method can lead to lower uncertainties, as opposed to using all HyMap bands. The obtained results demonstrated that, in general, biophysical variable retrieval from hyperspectral data can largely benefit from dimensionality reduction in both accuracy and computational efficiency. Numéro de notice : A2017-640 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86995 [article]

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Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval / Jochem Verrlest in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 86 (December 2013) . - pp 157 - 167 Titre : Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval Type de document : Article/Communication Auteurs : Jochem Verrlest, Auteur ; Juan Pablo Rivera, Auteur ; José Moreno, Auteur ; Gustavo Camps-Valls, Auteur Année de publication : 2013 Article en page(s) : pp 157 - 167 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de Gauss [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] teneur en chlorophylle des feuilles [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r2: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r2: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions. Numéro de notice : A2013-708 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32844 [article]

Active learning methods for biophysical parameter estimation / E. Pasolli in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 2 (October 2012)

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Urban surface biophysical descriptors and land surface temperature variations / D. Weng in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 11 (November 2006)

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Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)

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Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery / C. Wu in Remote sensing of environment, vol 93 n° 4 (15/12/2004)

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The consequences of urban transformation on net primary productivity in the United States / M.L. Imhoff in Remote sensing of environment, vol 89 n° 4 (29/02/2004)

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Réflectances foliaires et acclimatation à un déficit hydrique / P. Beaumont (1995)

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Documentation abrégée sur la télédétection appliquée à l'étude de l'environnement et des ressources naturelles / Secrétariat d'état à l'environnement (13/01/1988)

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