Centre de documentation
scientifique

Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 138 - 151 Titre : Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data Type de document : Article/Communication Auteurs : Sugandh Chauhan, Auteur ; Roshanak Darvishzadeh, Auteur ; Mirco Boschetti, Auteur ; Andrew Nelson, Auteur Année de publication : 2020 Article en page(s) : pp 138 - 151 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agrégation de données [Termes IGN] analyse diachronique [Termes IGN] analyse discriminante [Termes IGN] blé (céréale) [Termes IGN] courbure [Termes IGN] gestion prévisionnelle [Termes IGN] image Radarsat [Termes IGN] image Sentinel-SAR [Termes IGN] Italie [Termes IGN] matrice de confusion [Termes IGN] méthode des moindres carrés [Termes IGN] rendement agricole [Termes IGN] surveillance agricole Résumé : (auteur) Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons. This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models. Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general. Numéro de notice : A2020-276 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.012 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.012 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95087 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020063	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020062	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123 Titre : Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits Type de document : Article/Communication Auteurs : Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bavière (Allemagne) [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] écosystème forestier [Termes IGN] hétérogénéité spatiale [Termes IGN] image Sentinel-MSI [Termes IGN] indice foliaire [Termes IGN] Leaf Mass per Area [Termes IGN] photosynthèse [Termes IGN] variation saisonnière Résumé : (Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. Numéro de notice : A2019-493 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.005 Date de publication en ligne : 11/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019113	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 58-70 Titre : Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model Type de document : Article/Communication Auteurs : Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; Haidi Abdullah, Auteur ; Elias Cherenet, Auteur Année de publication : 2019 Article en page(s) : pp 58-70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multibande [Termes IGN] bande rouge [Termes IGN] bande spectrale [Termes IGN] Bavière (Allemagne) [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] image RapidEye [Termes IGN] image Sentinel-MSI [Termes IGN] modèle d'inversion [Termes IGN] Picea abies [Termes IGN] réflectance végétale [Termes IGN] spectrophotométrie [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. Numéro de notice : A2019-460 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.03.003 Date de publication en ligne : 08/03/2019 En ligne : https://doi.org/10.1016/j.jag.2019.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93577 [article]

Retrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 390 - 401 Titre : Retrieval of leaf area index in different plant species using thermal hyperspectral data Type de document : Article/Communication Auteurs : Elnaz Neinavaz, Auteur ; Andrew K. Skidmore, Auteur ; Roshanak Darvishzadeh, Auteur ; Thomas A. Groen, Auteur Année de publication : 2016 Article en page(s) : pp 390 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Buxus sempervirens [Termes IGN] classification par réseau neuronal [Termes IGN] espèce végétale [Termes IGN] Euonymus japonicus [Termes IGN] image hyperspectrale [Termes IGN] indice foliaire [Termes IGN] Leaf Area Index [Termes IGN] méthode des moindres carrés [Termes IGN] photo-interprétation [Termes IGN] rayonnement infrarouge thermique [Termes IGN] régression [Termes IGN] Rhododendron (genre) Résumé : (Auteur) Leaf area index (LAI) is an important variable of terrestrial ecosystems because it is strongly correlated with many ecosystem processes (e.g., water balance and evapotranspiration) and directly related to the plant energy balance and gas exchanges. Although LAI has been accurately predicted using visible and short-wave infrared hyperspectral data (0.3–2.5 μm), LAI estimation using thermal infrared (TIR, 8–14 μm) measurements has not yet been addressed. The novel approach of this study is to evaluate the retrieval of LAI using TIR hyperspectral data. The leaf area indices were destructively acquired for four plant species: Azalea japonica, Buxus sempervirens, Euonymus japonicus, and Ficus benjamina. Canopy emissivity spectral measurements were obtained under controlled laboratory conditions using a MIDAC (M4401-F) spectrometer. The LAI retrieval was assessed using a partial least squares regression (PLSR), artificial neural networks (ANNs), and narrow band indices calculated from all possible combinations of waveband pairs for three vegetation indices including simple difference, simple ratio, and normalized difference. ANNs retrieved LAI more accurately than PLSR and vegetation indices (0.67 Numéro de notice : A2016-789 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82505 [article]

3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction / Xi Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 14 – 23 Titre : 3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction Type de document : Article/Communication Auteurs : Xi Zhu, Auteur ; Tiejun Wang, Auteur ; Roshanak Darvishzadeh, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 14 – 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] correction radiométrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] intensité lumineuse [Termes IGN] réflecteur [Termes IGN] télémétrie laser terrestre [Termes IGN] teneur en eau de la végétation Résumé : (auteur) Leaf water content (LWC) plays an important role in agriculture and forestry management. It can be used to assess drought conditions and wildfire susceptibility. Terrestrial laser scanner (TLS) data have been widely used in forested environments for retrieving geometrically-based biophysical parameters. Recent studies have also shown the potential of using radiometric information (backscatter intensity) for estimating LWC. However, the usefulness of backscatter intensity data has been limited by leaf surface characteristics, and incidence angle effects. To explore the idea of using LiDAR intensity data to assess LWC we normalized (for both angular effects and leaf surface properties) shortwave infrared TLS data (1550 nm). A reflectance model describing both diffuse and specular reflectance was applied to remove strong specular backscatter intensity at a perpendicular angle. Leaves with different surface properties were collected from eight broadleaf plant species for modeling the relationship between LWC and backscatter intensity. Reference reflectors (Spectralon from Labsphere, Inc.) were used to build a look-up table to compensate for incidence angle effects. Results showed that before removing the specular influences, there was no significant correlation (R2 = 0.01, P > 0.05) between the backscatter intensity at a perpendicular angle and LWC. After the removal of the specular influences, a significant correlation emerged (R2 = 0.74, P Numéro de notice : A2015-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79440 [article]