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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)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160 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 aléatoires [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 végétation Résumé : (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 [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)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2165 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 aléatoires [Termes IGN] couvert végétal [Termes IGN] image Feng-Yun [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] réflectance végétale [Termes IGN] régression Résumé : (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 [article]

Polarization of light reflected by grass: modeling using visible-sunlit areas / Bin Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 12 (December 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 12 (December 2020) . - pp 745 - 752 Titre : Polarization of light reflected by grass: modeling using visible-sunlit areas Type de document : Article/Communication Auteurs : Bin Yang, Auteur ; Lei Yan, Auteur ; Siyuan Liu, Auteur Année de publication : 2020 Article en page(s) : pp 745 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] aérosol [Termes IGN] canopée [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] ensoleillement [Termes IGN] image POLDER [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] polarisation [Termes IGN] réflectance de surface [Termes IGN] réflectance végétale Résumé : (Auteur) The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization. Numéro de notice : A2020-763 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.12.745 date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.14358/PERS.86.12.745 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96552 [article]

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Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition / Sheng Wang in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 58 - 71 Titre : Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition Type de document : Article/Communication Auteurs : Sheng Wang, Auteur ; Andreas Baum, Auteur ; Pablo J. Zarco-Tejada, Auteur ; Carsten Dam-Hansen, Auteur ; Anders Thorseth, Auteur ; Peter Bauer-Gottwein, Auteur ; Filippo Bandini, Auteur ; Monica Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 58 - 71 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] éclairement énergétique [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] nébulosité [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale [Termes IGN] tenseur Résumé : (Auteur) Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes. Numéro de notice : A2019-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.06.017 date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93336 [article]

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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

Public [article]  inGeocarto international > vol 34 n° 11 [15/08/2019] . - pp 1218 - 1236 Titre : Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data Type de document : Article/Communication Auteurs : Sitinor Atikah Nordin, Auteur ; Zulkiflee Abd Latif, Auteur ; Hamdan Omar, Auteur Année de publication : 2019 Article en page(s) : pp 1218 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] analyse multibande [Termes IGN] Asie du sud-est [Termes IGN] bande rouge [Termes IGN] canopée [Termes IGN] capteur hyperspectral [Termes IGN] carte forestière [Termes IGN] forêt tropicale [Termes IGN] image hyperspectrale [Termes IGN] image proche infrarouge [Termes IGN] image satellite [Termes IGN] niveau de gris (image) [Termes IGN] réflectance végétale [Termes IGN] segmentation d'image [Termes IGN] teneur en chlorophylle des feuilles [Termes IGN] tourbière Résumé : (Auteur) Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m2. Numéro de notice : A2019-463 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1475511 date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1475511 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93605 [article]

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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)  

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Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])  

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ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)  

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Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform / Mohd Shahrimie Mohd Asaari in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data / Xiaojun Xu in Annals of Forest Science [en ligne], vol 75 n° 1 (March 2018)  

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Estimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model / Xinyun Wang in Geocarto international, vol 33 n° 2 (February 2018)  

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Modeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Angular reflectance of leaves with a dual-wavelength terrestrial lidar and its implications for leaf-bark separation and leaf moisture estimation / Steven Hancock in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Change detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)  

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