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Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data / Hemant Sahu in Geocarto international, vol 35 n° 14 ([15/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 14 [15/10/2020] . - pp 1627 - 1639 Titre : Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data Type de document : Article/Communication Auteurs : Hemant Sahu, Auteur ; Dipanwita Haldar, Auteur ; Abhishek Danodia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1627 - 1639 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle de rétrodiffusion [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variable biophysique (végétation) [Termes descripteurs IGN] verger Résumé : (auteur) Potential of Sentinel-1A SAR data was assessed for the time-series analysis of orchard biophysical parameters and crop system. The study revealed characteristics variations in the backscatter coefficient with respect to time and polarization for age in VH polarization than in VV and ratio of VV/VH polarization showing discrimination of young orchard particularly in VV polarization. The parameter of the orchard (age, DBH, canopy radius and visual height) shows a promising relationship with backscatter coefficient. Out of several regression models, VV channel responds with a fair regression coefficient of 0.54, 0.52, 0.48 and 0.44 for height with rmse of 0.5, 1.3, 0.7 and 0.6 for age, DBH, canopy radius and visual height, respectively. Multiple regression coefficient of 0.61 was observed for January 2018 in VV polarization as best date for study. These empirical relationships have potential for the inverse backscatter modelling. Numéro de notice : A2020-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583776 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96003 [article]

Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)  

Public [article]  inRemote sensing > vol 12 n° 1 (January 2020) . - 17 p. Titre : Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan Type de document : Article/Communication Auteurs : Emal Wali, Auteur ; Masahiro Tasumi, Auteur ; Masao Moriyama, Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Japon [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] rizière [Termes descripteurs IGN] surveillance agricole [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (auteur) This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage. Numéro de notice : A2020-223 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010189 date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010189 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94936 [article]

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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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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