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Mainstreaming remotely sensed ecosystem functioning in ecological niche models / Adrián Regos in Remote sensing in ecology and conservation, vol 8 n° 4 (August 2022)
[article]
Titre : Mainstreaming remotely sensed ecosystem functioning in ecological niche models Type de document : Article/Communication Auteurs : Adrián Regos, Auteur ; João Gonçalves, Auteur ; Salvador Arenas-Castro, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 431 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carbone
[Termes IGN] écologie forestière
[Termes IGN] écosystème forestier
[Termes IGN] habitat animal
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] niche écologiqueRésumé : (auteur) Biodiversity is declining globally at unprecedented rates. Ecological niche mod-els (ENMs) are one of the most widely used toolsets to appraise global changeimpacts on biodiversity. Here, we identify a variety of advantages of incorporat-ing remotely sensed ecosystem functioning attributes (EFAs) into ENMs. Thedevelopment of ENMs that explicitly incorporate ecosystem functioning willallow a more holistic and integrative perspective of the habitat dynamics. Thesynergies between the increasingly available open-access satellite images andcloud-based platforms for planetary-scale geospatial analysis offer an unprece-dented opportunity to incorporate ecosystem processes and disturbances (suchas fires, insect outbreaks or droughts) that have been so far largely neglected inecological niche characterization and modelling. The most paradigmatic exam-ple of EFAs is the application of time series of spectral vegetation indicesrelated to primary productivity and carbon cycle. EFAs related to surface energybalance and water cycles derived from remote sensing products such as landsurface temperature or soil moisture enable a fine-scale characterization of thespecies’ niche—eventually improving the predictive performance of ENMs. Allthese advantages confirm that a new generation of ENMs based on such EFAswould offer great perspectives to increase our ability to monitor habitat suit-ability trends and population dynamics. However, despite the technicaladvances and increasing effort of remote sensing community to develop inte-grative EFAs, ENMs have yet to make full profit of the most recent develop-ments by integrating them in ENMs. A coordinated agenda for remote sensingexperts and ecological modellers will be essential over the coming years tobridge the gap between remote sensing and ecology disciplines and to take full(and timely) advantage of the fast-growing body of Earth observation data andremote sensing technologies—with special emphasis on the development andtesting of new variables related to key processes driving ecosystem functioning. Numéro de notice : A2022-715 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : 10.1002/rse2.255 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1002/rse2.255 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101614
in Remote sensing in ecology and conservation > vol 8 n° 4 (August 2022) . - pp 431 - 447[article]Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)
[article]
Titre : Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series Type de document : Article/Communication Auteurs : Maximilian Lange, Auteur ; Hannes Feilhauer, Auteur ; Ingolf Kühn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échantillonnage de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] prairie
[Termes IGN] série temporelleRésumé : (auteur) Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands. Numéro de notice : A2022-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112888 Date de publication en ligne : 13/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100805
in Remote sensing of environment > vol 277 (August 2022) . - n° 112888[article]Remote sensing and phytoecological methods for mapping and assessing potential ecosystem services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria / Amal Louail in Forests, Vol 13 n° 8 (August 2022)
[article]
Titre : Remote sensing and phytoecological methods for mapping and assessing potential ecosystem services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria Type de document : Article/Communication Auteurs : Amal Louail, Auteur ; François Messner, Auteur ; Yamna Djellouli, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Algérie
[Termes IGN] analyse multicritère
[Termes IGN] carte thématique
[Termes IGN] entropie de Shannon
[Termes IGN] forêt méditerranéenne
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] protection de la biodiversité
[Termes IGN] relevé phytoécologique
[Termes IGN] service écosystémiqueRésumé : (auteur) Regardless of their biogeographic origins or degree of artificialization, the world’s forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult. Our work presents an introductory approach, allowing us to set up methodological and scientific milestones based on open-access remote sensing data and already tested geospatial processing associated with phytoecological surveys to assess the ES provided by forests in an Algerian study area. Specifically, several indicators used to assess (both qualitatively and quantitatively) the potential ES of the Ouled Hannèche forest, a forest located in the Hodna Mountains, are derived from LANDSAT 8 OLI images from 2017 and an ALOS AW3D30 DSM. The qualitative ES typology is jointly based on an SVM classification of topographically corrected LANDSAT images and a geomorphic-type classification using the geomorphon method. NDVI is a quantitative estimator of many plant ecosystem functions related to ES. It highlights the variations in the provision of ES according to the types of vegetation formations present. It serves as a support for estimating spectral heterogeneity through Rao’s quadratic entropy, which is considered a relative indicator of biodiversity at the landscape scale. The two previous variables (the multitemporal NDVI and Rao’s Q), completed by the Shannon entropy method applied to the geomorphon classes as a proxy for topo-morphological heterogeneity, constitute the input variables of a quantitative map of the potential supply of ES in the forest determined by Spatial Multicriteria Analysis (SMCA). Ultimately, our results serve as a useful basis for land-use planning and biodiversity conservation. Numéro de notice : A2022-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13081159 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.3390/f13081159 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101502
in Forests > Vol 13 n° 8 (August 2022) . - n° 1159[article]The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests / Vahid Nasiri in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Fardin Moradi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] canopée
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] couvert forestier
[Termes IGN] forêt méditerranéenne
[Termes IGN] Google Earth Engine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Iran
[Termes IGN] placette d'échantillonnage
[Termes IGN] Quercus (genre)Résumé : (auteur) Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes. Numéro de notice : A2022-649 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080423 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.3390/ijgi11080423 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101465
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 423[article]Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 / Rong Zhang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)
[article]
Titre : Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 Type de document : Article/Communication Auteurs : Rong Zhang, Auteur ; Mingming Jia, Auteur ; Zongming Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102918 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme de Otsu
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Chine
[Termes IGN] dynamique de la végétation
[Termes IGN] image Sentinel-MSI
[Termes IGN] mangrove
[Termes IGN] réserve naturelleRésumé : (auteur) Mangrove National Nature Reserves (MNNRs) play an extraordinarily significant role in conserving mangrove forests and their habitats. In China, one-fourth of the total mangrove forests were located in MNNRs. Understanding annual spatial distributions and conversions of these mangrove forests are important for precision conservation and rehabilitation efforts. However, to date, annual land cover maps of China’s MNNRs are still unavailable. Here, we proposed a rapid and robust approach to produce annual maps of each MNNRs for the time period of 2016–2020 based on 10-m resolution Sentinel-2 imagery. The proposed approach was developed using object-based image analysis, Otsu and Random Forest algorithm. Results showed that 1) during 2016–2020, areal extents of mangrove forest in all the MNNRs continuously increased from 5912 ha to 6128 ha; 2) obvious increase were found in Zhanjiang Mangrove National Nature Reserve where mangrove forest increased by 127 ha, accounted for 59% of national total increases; 3) newly grown mangrove forests were mainly converted from tidal flats and aquaculture ponds. Our annual maps of China’s MNNRs could provide a basis for managing mangrove ecosystems and supporting the implementation of Sustainable Development Goals related to coastal development. Numéro de notice : A2022-583 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102918 En ligne : https://doi.org/10.1016/j.jag.2022.102918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101348
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102918[article]Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkHeat wave-induced augmentation of surface urban heat islands strongly regulated by rural background / Shiqi Miao in Sustainable Cities and Society, vol 82 (July 2022)PermalinkLittoraux sous double surveillance / Laurent Polidori in Géomètre, n° 2204 (juillet-août 2022)PermalinkQuantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkHow large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (June-15 2022)PermalinkHow can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)PermalinkA 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)PermalinkThe interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkThe promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)Permalink