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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)
[article]
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 d'arbres décisionnels
[Termes IGN] couvert végétal
[Termes IGN] image Feng-Yun
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] réflectance végétale
[Termes IGN] régressionRésumé : (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
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2165[article]Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)
[article]
Titre : Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing Type de document : Article/Communication Auteurs : Elliott White Jr, Auteur ; David Kaplan, Auteur Année de publication : 2021 Article en page(s) : n° 112385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] eau de mer
[Termes IGN] Enhanced vegetation index
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] littoral
[Termes IGN] Louisiane (Etats-Unis)
[Termes IGN] marais
[Termes IGN] Mexique (golfe du)
[Termes IGN] montée du niveau de la mer
[Termes IGN] salinité
[Termes IGN] série temporelleRésumé : (auteur) Coastal floodplain swamps (CFS) are an important part of the coastal wetland mosaic, however they are threatened due to accelerated rates of sea level rise and saltwater intrusion (SWI). While remote sensing-based detection of wholesale coastal ecosystem shifts (i.e., from forest to marsh) are relatively straightforward, assessments of chronic, low-level SWI into CFS using remote sensing have yet to be developed and can provide a critical early-warning signal of ecosystem deterioration. In this study, we developed nine ecologically-based hypotheses to test whether remote sensing data could be used to reliably detect the presence of CFS experiencing SWI. Hypotheses were motivated by field- and literature-based understanding of the phenological and vegetative dynamics of CFS experiencing SWI relative to unimpacted, control systems. Hypotheses were organized into two primary groups: those that analyzed differences in summary measures (e.g., median and distribution) between SWI-impacted and unimpacted control sites and those that examined timeseries trends (e.g., sign and magnitude of slope). The enhanced vegetation index (EVI) was used as a proxy for production/biomass and was generated using MODIS surface reflectance data spanning 2000 to 2018. Experimental sites (n = 8) were selected from an existing network of long-term monitoring sites and included 4 pairs of impacted/non-impacted CFS across the northern Gulf of Mexico from Texas to Florida. The four best-supported hypotheses (81% across all sties) all used summary statistics, indicating that there were significant differences in the EVI of CFS experiencing chronic, low-level SWI compared to controls. These hypotheses were tested using data across a large and diverse region, supporting their implementation by researchers and managers seeking to identify CFS undergoing the first phases of SWI. In contrast, hypotheses that assessed CFS change over time were poorly supported, likely due to the slow and variable pace of ecological change, relatively short remote sensing data record, and/or specific site histories. Overall, these results show that remote sensing data can be used to identify differences in CFS vegetation associated with long-term, low-level SWI, but further methodological advancements are needed to reliably detect the temporal transition process. Numéro de notice : A2021-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112385 Date de publication en ligne : 12/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112385 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97851
in Remote sensing of environment > vol 258 (June 2021) . - n° 112385[article]Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
[article]
Titre : Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine Type de document : Article/Communication Auteurs : Tongxi Hu, Auteur ; Elizabeth Myers Toman, Auteur ; Gang Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin hydrographique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification bayesienne
[Termes IGN] détection de changement
[Termes IGN] estimation bayesienne
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] précision infrapixellaire
[Termes IGN] série temporelleRésumé : (auteur) Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes. Numéro de notice : A2021-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.008 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97746
in ISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 250 - 261[article]Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
[article]
Titre : Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing Type de document : Article/Communication Auteurs : Michael L. Benson, Auteur ; Pierce Leland, Auteur ; Katleen Bergen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4635 - 4653 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] Canada
[Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat-TM
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] lasergrammétrie
[Termes IGN] Leaf Area Index
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] polarimétrie radar
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m 2 and 1.68 m respectively. Numéro de notice : A2021-423 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3018638 Date de publication en ligne : 09/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97778
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4635 - 4653[article]On the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])
[article]
Titre : On the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment Type de document : Article/Communication Auteurs : Salahuddin M. Jaber, Auteur Année de publication : 2021 Article en page(s) : pp 1117-1135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] coefficient de corrélation
[Termes IGN] image Terra-MODIS
[Termes IGN] Jordanie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] nuit
[Termes IGN] régression
[Termes IGN] température au sol
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] zone aride
[Termes IGN] zone semi-arideRésumé : (Auteur) This work focused on studying the relationships between Normalized Difference Vegetation Index (NDVI) and daytime and nighttime Land Surface Temperature (LST) in winter, spring, summer and fall and investigating the effects of land cover on these variables in Jordan, which represents a typical semi-arid to arid environment. Using MODIS-based data for the year 2017, multiple procedures were applied: one-way analysis of variance followed by comparison between means, Pearson correlation coefficient, global Moran’s index, simple linear regression, second-order polynomial regression, recursive-partitioning regression and geographically weighted regression. The results showed that land cover explained fair amount of the variability in NDVI but small amount of the variability in daytime and nighttime LST. In addition, an inverted surface urban heat island pattern was observed in daytime. Finally, applying different regression procedures produced different perspectives about the complex and variable relationships between daytime and nighttime LST and NDVI in different seasons. Numéro de notice : A2021-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1633421 Date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1633421 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97731
in Geocarto international > vol 36 n° 10 [01/06/2021] . - pp 1117-1135[article]Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkThe use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries / Nagihan Aslan in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkDetection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkMapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkSensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment / Maxime Soma in Remote sensing of environment, vol 257 (May 2021)PermalinkValidation and analysis of Terra and Aqua MODIS, and SNPP VIIRS vegetation indices under zero vegetation conditions: A case study using Railroad Valley Playa / Tomoaki Miura in Remote sensing of environment, vol 257 (May 2021)PermalinkAssessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkLeaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkThe delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkDes pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)PermalinkTemporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands / Emmanuelle Vaudour in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)PermalinkApport des images Landsat à l’étude de l’évolution de l’occupation du sol dans la plaine de Saïss au Maroc, pour la période 1987-2018 / Abdelkader El Garouani in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkDétection des zones de dégradation et de régénération de la couverture végétale dans le sud du Sénégal à travers l'analyse des tendances de séries temporelles MODIS NDVI et des changements d'occupation des sols à partir d'images LANDSAT / Boubacar Solly in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkEvaluation du potentiel des series d’images multi-temporelles optique et radar des satellites Sentinel 1 & 2 pour le suivi d’une zone côtière en contexte tropical: cas de l’estuaire du Cameroun pour la période 2015-2020 / Nourdi Njutapvoui in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkApplication of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkEarly detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS) / Langning Huo in Remote sensing of environment, Vol 255 (March 2021)PermalinkA soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar / Shoba Periasamy in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkChina’s high-resolution optical remote sensing satellites and their mapping applications / Deren Li in Geo-spatial Information Science, vol 24 n° 1 (March 2021)PermalinkCompressive Sensing appliqué au traitement de données InSAR pour le suivi de la déformation des zones urbaines / Matthieu Rebmeister in XYZ, n° 166 (mars 2021)PermalinkImpact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)Permalink