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Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices / Zhe Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)
[article]
Titre : Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices Type de document : Article/Communication Auteurs : Zhe Li, Auteur ; Fei Zhang, Auteur ; Lihua Chen, Auteur ; Haiwei Zhang, Auteur ; Hsiang-Te Kung, Auteur Année de publication : 2018 Article en page(s) : pp 538 - 548 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] croissance végétale
[Termes IGN] feuille (végétation)
[Termes IGN] indice de végétation
[Termes IGN] plante halophile
[Termes IGN] Populus euphratica
[Termes IGN] signature spectrale
[Termes IGN] Sinkiang (Chine)
[Termes IGN] Tamarix (genre)
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) The vegetation water content (VWC) quantitative is useful for monitoring vegetation physiological growth. The relationship between VWC and vegetation water indices was analyzed. The optimal estimation model was established. The results show that: (1) Absorption bands primarily fell within 380 to 400 nm, 680 to 720 nm, 1420 to 1450 nm, 1900 to 1940 nm, and 2450 to 2500 nm; (2) comparing published vegetation water indices and developed vegetation indices, it showed that DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) had a better correlation with VWC than the published vegetation water; and (3) NDSI(2201,1870) and RSI(2259,1870) performed well in estimating vegetation water content, DVI(1712,1382) had a rough estimate of its water content. Moreover, the linear combination of DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) improved the estimation of VWC. The best vegetation indices for estimating VWC were found to be the linear combination of DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) in arid area of northwestern China. Numéro de notice : A2018-361 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.9.537 Date de publication en ligne : 01/09/2018 En ligne : https://doi.org/10.14358/PERS.84.9.537 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90672
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 9 (September 2018) . - pp 538 - 548[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018091 RAB Revue Centre de documentation En réserve L003 Disponible An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
[article]
Titre : An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data Type de document : Article/Communication Auteurs : Li Zhuo, Auteur ; Qingli Shi, Auteur ; Haiyan Tao, Auteur ; Jing Zheng, Auteur ; Qiuping Li, Auteur Année de publication : 2018 Article en page(s) : pp 64 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges temporels
[Termes IGN] détection de changement
[Termes IGN] Enhanced vegetation index
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] image DMSP-OLS
[Termes IGN] image Terra-MODIS
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surface imperméableRésumé : (Auteur) Impervious surface area (ISA) is an important indicator for monitoring the intensity of human activity and ecological environment changes. Developing effective methods for estimation of ISA at different scales has thus been pursued by many scientists. The temporal mixture analysis (TMA), which is a variant of spectral mixture analysis that makes full use of the phenological information of different land cover types, is suitable for estimating the ISA fraction at a large scale. The existing TMA-based ISA fraction estimation methods rely on the assumption that pure pixels exist for all the endmembers, which, however, is not true in the case of coarse-resolution datasets. Moreover, the existing method cannot effectively differentiate bare soil from ISA effectively, which may lead to overestimation of the ISA fraction. To address these problems, we propose a new ISA estimation method based on TMA in this study, using a Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) products, the GlobeLand30 product, and the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data. The proposed method contains four major steps. First, the MODIS NDVI time-series datasets and GlobeLand30 land cover product were used to create an NDVI temporal profile subset for the TMA model. Second, a preliminary ISA fraction map was derived on the basis of optimized endmember temporal profiles, which were generated by unmixing the selected NDVI temporal profile subset through an improved spatial-spectral preprocessing nonnegative matrix factorization algorithm (ISSPP-NMF). Then, the preliminary ISA fraction was further optimized by incorporating the EVI-adjusted night-time light index (EANTLI), which can mitigate both saturation problems and the blooming effect of the DMSP-OLS data. An effective threshold method was introduced in this step to reduce the impact of bare soil on the ISA estimation. Finally, the estimated fraction of ISA was evaluated through accuracy assessment. The proposed method was tested in two study areas, namely, Guangdong Province and the Yangtze River Delta (YRD) of China, to prove its applicability in different regions. Effectiveness of the proposed method was proven through the comparison between the proposed method with traditional TMA-based methods. The results from these analyses indicate that the proposed method outperforms the others in ISA estimation, with an overall root mean square error (RMSE) of 9.2% and a coefficient of determination (R2) of 0.8872 in Guangdong and a RMSE of 8.9% and R2 of 0.8923 in YRD. This study also proves that the ISSPP-NMF method can produce more appropriate endmembers regardless of the existence of pure pixels. The post-processing with the EANLTI procedure can effectively reduce the bare soil effect in TMA-based ISA estimation. Numéro de notice : A2018-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.016 Date de publication en ligne : 05/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90409
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 64 - 77[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
[article]
Titre : Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques Type de document : Article/Communication Auteurs : Chinsu Lin, Auteur ; Shih-Yu Chen, Auteur ; Chia-Chun Chen, Auteur ; Chia-Huei Tai, Auteur Année de publication : 2018 Article en page(s) : pp 174 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image orientée objet
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] drone
[Termes IGN] feuille (végétation)
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] Kappa de Cohen
[Termes IGN] Taïwan
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Phenological events of tree leaves from initiation to senescence is generally influenced by temperature and water availability. Detection of newly grown leaves (NGL) is useful in the diagnosis of growth of trees, tree stress and even climatic change. Utilizing very high resolution UAV images, this paper examines the feasibility of NGL detection using hyperspectral detection algorithms and anomaly detectors. The issues of pixel resolution and hard decision thresholding in deriving accurate NGL maps are also explored. Results showed that the blind-detection algorithms RXDs are not suitable for NGL detection due to the spectra similarity between NGL and both mature leaves and grass, while brighter pixels, such as those produced by soil and concrete materials, are more easily recognized as anomaly in contrast to forest. Matching filter (MF) based detectors are, however, able to accurately detect NGL over forest stands and are even more effective in the sense of achieving satisfactory true positives and true negatives while providing minimal false alarms. Of the tested partial knowledge MF algorithms, the covariance matched filter based distance (KMFD) detector performed very well with overall accuracy (OA) 0.97 and kappa coefficient () 0.60 on a natural resolution of 6.75 cm image. When a variety of mature-leaf nonobjective targets are included in the detection, the orthogonal subspace projector (OSP) tends to suppress NGL pixels as an unwanted signature and this leads to poor detection. Conversely, the target constrained interference minimized filter (TCIMF) detector is still able to effectively detect NGL with a satisfactory OA and through effective matching filter of the target signature as the hard-decision threshold is subject to a level of 5% or 1% probability of false alarms. From decimeter resolution satellite images, the KMFD and TCIMF detectors are capable of achieving an accuracy of OA = 0.94 and = 0.56 or OA = 0.87 and = 0.48 for images with a resolution of 33.75 cm or 67.50 cm respectively. This indicates that hyperspectral target detection techniques have great potential in NGL detection via high spatial resolution satellite multispectral images. Numéro de notice : A2018-294 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.022 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90412
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 174 - 189[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 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)
[article]
Titre : ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas Type de document : Article/Communication Auteurs : Karine R.M. Adeline, Auteur ; Xavier Briottet , Auteur ; X. Ceamanos, Auteur ; T. Dartigalongue, Auteur ; Jean-Philippe Gastellu-Etchegorry, Auteur Année de publication : 2018 Article en page(s) : pp 311 - 327 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre (flore)
[Termes IGN] correction atmosphérique
[Termes IGN] détection d'ombre
[Termes IGN] houppier
[Termes IGN] image à très haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] logiciel de traitement d'image
[Termes IGN] modèle de transfert radiatif
[Termes IGN] modélisation 3D
[Termes IGN] réflectance végétale
[Termes IGN] zone urbaineRésumé : (Auteur) Many applications dedicated to urban areas (e.g. land cover mapping and biophysical properties estimation) using high spatial resolution remote sensing images require the use of 3D atmospheric correction methods, able to model complex light interactions within urban topography such as buildings and trees. Currently, one major drawback of these methods is their lack in modeling the radiative signature of trees (e.g. the light transmitted through the tree crown), which leads to an over-estimation of ground reflectance at tree shadows. No study has been carried out to take into account both optical and structural properties of trees in the correction provided by these methods. The aim of this work is to improve an existing 3D atmospheric correction method, ICARE (Inversion Code for urban Areas Reflectance Extraction), to account for trees in its new version, ICARE-VEG (ICARE with VEGetation). After the execution of ICARE, the methodology of ICARE-VEG consists in tree crown delineation and tree shadow detection, and then the application of a physics-based correction factor in order to perform a tree-specific local correction for each pixel in tree shadow. A sensitivity analysis with a design of experiments performed with a 3D canopy radiative transfer code, DART (Discrete Anisotropic Radiative Transfer), results in fixing the two most critical variables contributing to the impact of an isolated tree crown on the radiative energy budget at tree shadow: the solar zenith angle and the tree leaf area index (LAI). Thus, the approach to determine the correction factor relies on an empirical statistical regression and the addition of a geometric scaling factor to account for the tree crown occultation from ground. ICARE-VEG and ICARE performance were compared and validated in the Visible-Near Infrared Region (V-NIR: 0.4–1.0 µm) with hyperspectral airborne data at 0.8 m resolution on three ground materials types, grass, asphalt and water. Results show that (i) ICARE-VEG improves the mean absolute error in retrieved reflectances compared to ICARE in tree shadows by a multiplicative factor ranging between 4.2 and 18.8, and (ii) reduces the spectral bias in reflectance from visible to NIR (due to light transmission through the tree crown) by a multiplicative factor between 1.0 and 1.4 in terms of spectral angle mapper performance. ICARE-VEG opens the way to a complete interpretation of remote sensing images (sunlit, shade cast by both buildings and trees) and the derivation of scientific value-added products over all the entire image without the preliminary step of shadow masking. Numéro de notice : A2018-296 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.015 Date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90415
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 311 - 327[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Intra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
[article]
Titre : Intra-annual phenology for detecting understory plant invasion in urban forests Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Yin-Hsuen Chen, Auteur ; Lindsey Smart, Auteur ; Josh Gray, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2018 Article en page(s) : pp 151 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité de la végétation
[Termes IGN] détection d'anomalie
[Termes IGN] espèce exotique envahissante
[Termes IGN] flore urbaine
[Termes IGN] forêt tempérée
[Termes IGN] image Landsat-TM
[Termes IGN] indice de végétation
[Termes IGN] Ligustrum sinense
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] protection de la biodiversité
[Termes IGN] surveillance forestièreRésumé : (Auteur) Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Numéro de notice : A2018-293 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.023 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90411
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 151 - 161[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkMapping rubber trees based on phenological analysis of Landsat time series data-sets / Janatul Aziera binti Abd Razak in Geocarto international, vol 33 n° 6 (June 2018)PermalinkRemote 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, vol 75 n° 1 (March 2018)PermalinkUnderstanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery / Pablo J. Zarco-Tejada in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkA comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere / Qing Chang in Geocarto international, vol 33 n° 1 (January 2018)PermalinkEstimation cohérente de l'indice de surface foliaire en utilisant des données terrestres et aéroportées / Ronghai Hu (2018)PermalinkA hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)PermalinkPermalinkPermalinkUn inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)Permalink