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Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
[article]
Titre : Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis Type de document : Article/Communication Auteurs : T. Poblete, Auteur ; C. Camino, Auteur ; P.S.A. Beck, Auteur Année de publication : 2020 Article en page(s) : pp 27 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce végétale
[Termes IGN] fluorescence
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] image thermique
[Termes IGN] Italie
[Termes IGN] maladie bactérienne
[Termes IGN] maladie phytosanitaire
[Termes IGN] Olea europaea
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétation
[Termes IGN] télédétection aérienne
[Termes IGN] traitement d'imageRésumé : (auteur) Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive. Numéro de notice : A2020-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.010 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94745
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 27 - 40[article]A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
[article]
Titre : A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions Type de document : Article/Communication Auteurs : Shahryar K. Ahmad, Auteur ; Faisal Hossain, Auteur ; Hisham Eldardiry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2471 - 2480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bangladesh
[Termes IGN] climat tropical
[Termes IGN] eau de surface
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-8
[Termes IGN] image PlanetScope
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] plan d'eau
[Termes IGN] radar à antenne synthétique
[Termes IGN] reconnaissance de surface
[Termes IGN] surveillance hydrologique
[Termes IGN] télédétection spatiale
[Termes IGN] zone humideRésumé : (auteur) Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both topographic layover and vegetation. Here, we compare and contrast inundation extent measurements from visible (Landsat-8 and Sentinel-2) and SAR (Sentinel-1) imagery. Each data type (wavelength) has complementary strengths and weaknesses which were gauged separately over selected water bodies in Bangladesh. High-resolution cloud-free PlanetScope imagery at 3-m resolution was used as a reference to check the accuracy of each technique and data type. Next, the optical and radar images were fused for a rule-based water area classification algorithm to derive the optimal decision for the water mask. Results indicate that the fusion approach can improve the overall accuracy by up to 3.8%, 18.2%, and 8.3% during the wet season over using the individual products of Landsat8, Sentinel-1, and Sentinel-2, respectively, at three sites, while providing increased observational frequency. The fusion-derived products resulted in overall accuracy ranging from 85.8% to 98.7% and Kappa coefficient varying from 0.61 to 0.83. The proposed SAR-visible fusion technique has potential for improving satellite-based surface water monitoring and storage changes, especially for smaller water bodies in humid tropical climate of South Asia. Numéro de notice : A2020-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950705 Date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950705 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94868
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2471 - 2480[article]Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
[article]
Titre : Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China Type de document : Article/Communication Auteurs : Mingyue Wang, Auteur ; Jun’e Fu, Auteur ; Zhitao Wu, Auteur ; et al., 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 IGN] changement climatique
[Termes IGN] coefficient de corrélation
[Termes IGN] données météorologiques
[Termes IGN] données spatiotemporelles
[Termes IGN] écosystème
[Termes IGN] Fleuve jaune (Chine)
[Termes IGN] image Aqua-MODIS
[Termes IGN] image SPOT
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] précipitation
[Termes IGN] série temporelle
[Termes IGN] température
[Termes IGN] variation saisonnièreRésumé : (auteur) Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area. Numéro de notice : A2020-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040282 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.3390/ijgi9040282 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95022
in ISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 17 p.[article]Temporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)
[article]
Titre : Temporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Ainong Li, Auteur ; Zhengjian Zhang, Auteur ; Guangbin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 225 - 233 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] plateau
[Termes IGN] prairie
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] TibetRésumé : (Auteur) Time series of leaf area index (LAI) products are now widely used, and the temporal validation is the prerequisite for their proper application. However, a systematical comparison between different products using both direct and indirect methods is still lacking. The objective of this paper is to assess and compare the temporal performances of four LAI products: Moderate Resolution Imaging Spectroradiometer (MODIS) LAI (MOD)15A2, MOD15A2h, Geoland2 Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). The study area, which is dominated by grasslands, is located in the northeastern Tibetan Plateau (TP), and temperature is the main stress factor affecting grass growth. Both a correlation analysis with temperature and a direct comparison with temporally continuous LAI reference maps were implemented in our temporal validation experiments. The results show that no single product can capture the rapid change and the seasonal trend in LAI simultaneously, and the compositing period used in each product determines the quality of the corresponding LAI time series. The MOD15A2 and MOD15A2h products, which have short compositing windows (eight days), are suitable for detecting rapid change. A grazing-induced biomass decrease that occurred around day of year 205 in 2014 in our study area was clearly revealed in these two products. For the GEOV1 and GLASS products, which have compositing windows of 30 days and 1 year, respectively, the grazing date was shifted (GEOV1) or even invisible (GLASS). However, products with prolonged compositing windows may be more robust to observation noise, and the resulting products may be suitable for capturing the seasonal trend. This study highlights that the concurrent use of data from various sensors onboard different satellites, and the introduction of new generations of satellites (e.g., Gaofen-6), are two promising ways to further improve existing LAI time series. Numéro de notice : A2020-129 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.225 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.14358/PERS.86.4.225 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94804
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 4 (April 2020) . - pp 225 - 233[article]Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data / Niraj Priyadarshi in Geocarto international, vol 35 n° 5 ([01/04/2020])
[article]
Titre : Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data Type de document : Article/Communication Auteurs : Niraj Priyadarshi, Auteur ; V.M. Chowdary, Auteur ; Iswar Chandra Das, Auteur ; Jeganathan Chockalingam, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 512 - 534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse spatio-temporelle
[Termes IGN] changement d'occupation du sol
[Termes IGN] Enhanced vegetation index
[Termes IGN] filtrage du bruit
[Termes IGN] série temporelle
[Termes IGN] transformation en ondelettesRésumé : (auteur) Land cover change analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series data for the period 2005–2014. MODIS EVI data coupled with Quality Assessment Science Data Sets (QASDS) was de-noised with Savitzky–Golay filter while enhancing quality and preserving the temporal profile of EVI. Wavelet transform (WT) based approach along with Sen slope’s method was used for land cover change and trend analysis. The WT based approach is useful for studying multiscale and non-stationary processes. Mann–Kendall test was performed to confirm the significance of the identified trends. Proposed approach identified 358 locations as change points, where 285 (79.6%) and 73 (20.4%) locations were detected as ‘Change’ and ‘False Change’ with respect to high resolution images. The proposed approach is useful for monitoring land cover changes that provide vital inputs for sustainable management of land resources. Numéro de notice : A2020-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520925 Date de publication en ligne : 24/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520925 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94769
in Geocarto international > vol 35 n° 5 [01/04/2020] . - pp 512 - 534[article]Réservation
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