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A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)
Titre : A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images Type de document : Article/Communication Auteurs : Jing Zeng, Auteur ; Yonghua Sun, Auteur ; Peirun Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par arbre de décision
[Termes IGN] classification semi-dirigée
[Termes IGN] image Landsat-8
[Termes IGN] indice de végétation
[Termes IGN] Kiangsou (Chine)
[Termes IGN] marais salant
[Termes IGN] phénologie
[Termes IGN] réflectance de surfaceRésumé : (auteur) Coastal salt marshes, as a globally significant intertidal ecosystem, are highly productive but extremely fragile and unstable. Mapping coastal salt marshes accurately is the basis of assessing global climate change, biological invasion, and coastal erosion. Using Landsat 8 images, this paper integrated the advantages of pixel- and phenology-based algorithms and vegetation indices in vegetation classification. An enhanced phenology-based vegetation index classification (PVC) algorithm is proposed to obtain the spatial distribution and community composition of coastal salt marshes in Bohai Sea of China accurately and quickly. The results showed that (1) the coastal redness vegetation index (CRVI) can be used to extract Suaeda spp. effectively, and the phenology-based vegetation indices (PVIs) dataset can alleviate the spatial variability of phenology in coastal salt marshes; (2) the crucial phenological periods for identifying coastal salt marshes are May, October, and November, and the optimal PVIs are consistent with the phenological characteristics of salt marshes; (3) during the year 2018–2019, the overall accuracy (OA) of the PVC algorithm in Yancheng coast of Jiangsu Province and Bohai Sea coast reached 80.49 % and 90.8 % respectively. A total of 14,763.39 ha of salt marshes were found in the coastal area of Bohai Sea, and Shandong Province had the most abundant types of salt marshes and the largest area; (4) the classification model based on the PVC algorithm is stable and scalable in 2016–2017 and 2020–2021, with the OA of 89.19% and 86.67% respectively. These results demonstrate the value of the PVC algorithm in vegetation classification, and this study can provide a referable semi-automatic vegetation classification method for other coastal areas. Numéro de notice : A2022-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102776 Date de publication en ligne : 10/05/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101154
in International journal of applied Earth observation and geoinformation > vol 110 (June 2022) . - n° 102776[article]
Titre : Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network Type de document : Article/Communication Auteurs : Chen Chen, Auteur ; Yi Ma, Auteur ; Guangbo Ren, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112885 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte thématique
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral
[Termes IGN] marais salant
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Coastal wetlands are main components of the “blue carbon” ecosystems in coastal zones. Salt-marsh biomass is especially important regarding climate-change mitigation. Generating high precision biomass maps for evaluating the ecological functions of coastal wetlands is essential; however, conducting accurate biomass inversions with limited in situ observations from coastal wetlands is challenging. We propose a generative adversarial network with a constrained factor model (GAN-CF) for expanding limited in situ salt-marsh biomass observations. We used Sentinel-2 images and a deep belief network based on the conjugate gradient method (CG-DBN) for obtaining land-cover maps and the salt-marsh distribution (species: Phragmites australis, Suaeda glauca, Spartina alterniflora, and mixed species dominated by Tamarix chinensis) in the study area. This study bridges in situ hyperspectral and Sentinel-2 multispectral data by a satellite-band equivalent conversion model. The biomass and multispectral data derived from Sentinel-2 were used as input for the proposed GAN-CF model, which produced and constrained the generated samples based on the features (i.e., spectra, vegetation index, and biomass) of the in situ observations. Aboveground biomass (AGB) maps at 10-m spatial resolution were produced by constructing multiple linear regression models (MLRMs) based on the generated samples of each salt-marsh type using Sentinel-2 images. The quantity and richness of the generated samples improved the AGB estimations in the study area. The inversion accuracy of S. alterniflora was significantly improved (RMSE = 3.71 Mg/ha); the estimated AGB was strongly related to the in situ observations (R = 0.923). The estimated AGB was validated using in situ observations. The total amount of salt-marsh AGB in the study area in 2019 was estimated at 2.36 × 105 Mg, with 7.95 Mg/ha average. The salt-marsh biomass in decreasing order was as follows: P. australis (12.7 Mg/ha) > S. alterniflora (11.5 Mg/ha) > mixed species (8.97 Mg/ha) > S. glauca (2.18 Mg/ha). The salt-marsh area in decreasing order was as follows: S. glauca (10,410 ha) > P. australis (7320 ha) > mixed species (6740 ha) > S. alterniflora (5240 ha). By a feasibility analysis we estimated the biomass based on the Sentinel-2 data covering the Yellow River delta wetland in May, July, and September 2019 and the Jiaozhou Bay wetland in September 2019 by using the generated samples. The generated samples based on the 2013–2019 in situ observations constitute a salt-marsh biomass database, which can be useful for quantifying the regional carbon storage and ecological restoration monitoring. Numéro de notice : A2022-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112885 Date de publication en ligne : 07/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99710
in Remote sensing of environment > vol 270 (March 2022) . - n° 112885[article]
Titre : Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 Type de document : Article/Communication Auteurs : Anthony Campbell, Auteur ; Yeqiao Wang, Auteur Année de publication : 2020 Article en page(s) : pp 187 - 194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] Atlantique (océan)
[Termes IGN] biodiversité
[Termes IGN] cartographie thématique
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] lidar bathymétrique
[Termes IGN] littoral
[Termes IGN] marais salant
[Termes IGN] montée du niveau de la mer
[Termes IGN] service écosystémique
[Termes IGN] surveillance du littoralRésumé : (auteur) Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system. Numéro de notice : A2020-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.187 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94777
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 187 - 194[article]
Titre : Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données topographiques
[Termes IGN] Etats-Unis
[Termes IGN] inventaire
[Termes IGN] marais salant
[Termes IGN] modèle numérique de terrain
[Termes IGN] prédiction
[Termes IGN] surveillance écologique
[Termes IGN] Virgine OccidentaleRésumé : (Auteur) The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (RF) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (NWI) dataset and were tested using a separate random subset from the NWI and a database of wetlands not found in the NWI provided by the West Virginia Division of Natural Resources (WVDNR). The models produced area under the curve (AUC) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different AUC values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored DEM-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-NWI wetlands were mapped with an AUC value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the NWI . Numéro de notice : A2016-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.437 En ligne : http://dx.doi.org/10.14358/PERS.82.6.437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81348
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 437 - 447[article]
Titre : Challenges and lessons from a wetland LiDAR project: a case study of the Okefenokee Swamp, Georgia, USA Type de document : Article/Communication Auteurs : L. Shea Rose, Auteur ; Jeong C. Seong, Auteur ; Jared Ogle, Auteur ; Ed Beute, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 210 - 226 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Géorgie (Etats-Unis)
[Termes IGN] marais salant
[Termes IGN] modèle numérique de surface
[Termes IGN] pente
[Termes IGN] tourbe
[Termes IGN] zone humideRésumé : (Auteur) Airborne LiDAR (Light Detection and Ranging) provides opportunities to generate high-quality digital elevation models (DEMs) even in wetland environments. Our project, performed over the Okefenokee Swamp in Georgia during the spring of 2010, shows that several, distinctive factors must be considered to ensure successful wetland LiDAR projects. Some of the challenges include selecting optimal flight times in accordance with weather variability and water levels, having effective and quality control protocols, applying and developing filtering and interpolation algorithms, breaklines in swamps and accounting for data striping and noise. While some of these issues are faced in any airborne LiDAR acquisition, many of these require special consideration in a low-slope wetland environment with water saturated soils, widespread shallow water, and sediments and extensive vegetation. An examination of these issues and how they were handled will help in ensuring the success of future LiDAR acquisitions and, in particular, will advance knowledge of producing quality DEMs in wetland environments. Numéro de notice : A2013-396 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.681707 Date de publication en ligne : 14/05/2012 En ligne : https://doi.org/10.1080/10106049.2012.681707 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32534
in Geocarto international > vol 28 n° 3-4 (June - July 2013) . - pp 210 - 226[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2013021 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkLittoral 2004, 7th International Symposium, delivering sustainable coasts : Connecting science and policy, Aberdeen, 20th-22th September 2004, 2. Volume 2 / D.R. Green (2004)
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