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Extensification and afforestation of cultivated mineral soil for climate change mitigation in Finland / Boris Tupek in Forest ecology and management, vol 501 (1 December 2021)  

Public [article]  inForest ecology and management > vol 501 (1 December 2021) . - n° 119672 Titre : Extensification and afforestation of cultivated mineral soil for climate change mitigation in Finland Type de document : Article/Communication Auteurs : Boris Tupek, Auteur ; Aleski Lehtonen, Auteur ; Raisa Mäkipää, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119672 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] afforestation [Termes IGN] Betula (genre) [Termes IGN] boisement artificiel [Termes IGN] changement d'occupation du sol [Termes IGN] dioxyde de carbone [Termes IGN] écologie forestière [Termes IGN] Finlande [Termes IGN] modèle de croissance végétale [Termes IGN] Picea abies [Termes IGN] puits de carbone [Termes IGN] reboisement [Termes IGN] surface cultivée [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Offsetting nation-wide CO2 emissions by carbon sinks from land use change (LUC), e.g. agricultural fields extensification and afforestation, is considered as a major climate change mitigation option. We evaluated the LUC potential for reducing emissions and creating annual soil and ecosystem carbon sinks in Finland. We used agricultural statistics, the forest growth model MOTTI, the soil carbon model Yasso07, and the RCP8.5 climate scenario. The soil carbon stock (SOC) of extensified grasslands showed on average less carbon loss than cropland, thus reducing future carbon emissions by LUC between 0.17 Mg ha−1 y-1, initially, and 0.08 Mg ha−1 y-1 after 50 years. The annual rate of such carbon gain was in comparison to SOC between 1.4‰ and 0.7‰ which is lower than proposed by the Paris 4‰ initiative for offsetting global anthropogenic CO2 emissions. Furthermore, after afforestation, estimated SOC is expected to increase above pre-LUC levels with 30 years lag. Estimated SOC sink from afforestation when compared to continuous cultivation varied depending on dominant tree species and soil fertility from between 0.19 Mg ha−1 y-1 (1.7‰ for spruce in medium fertile soil) to 0.46 Mg ha−1 y-1 (3.7‰ for silver birch in highly fertile soil). Future total soil and biomass carbon sink attributed to afforestation ranged between 1.65 and 2.44 Mg ha−1 y-1. Combined carbon sinks created by the present LUC could with 30 years lag offset annually between 0.01 and 4% of the present national net CO2 emissions in Finland. The long delay and a small scale of potential future carbon emission reduction by the LUC highlighted the importance of employing additional tools to reach the national neutrality targets due in next 15 or 30 years. Numéro de notice : A2021-744 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119672 date de publication en ligne : 22/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98685 [article]

Geoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)

Public [article]  inGéomètre > n° 2194 (septembre 2021) . - pp 17 - 17 Titre : Geoglam, l'agriculture par satellite Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] blé (céréale) [Termes IGN] carte d'occupation du sol [Termes IGN] Glycine max [Termes IGN] image à haute résolution [Termes IGN] Leaf Area Index [Termes IGN] maïs (céréale) [Termes IGN] modèle dynamique [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] observation de la Terre [Termes IGN] Oryza (genre) [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (Auteur) Des satellites pour la sécurité alimentaire et la transparence du marché agricole Numéro de notice : A2021-579 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 13/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98400 [article]

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Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7308 - 7321 Titre : Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops Type de document : Article/Communication Auteurs : Davide Palmisano, Auteur ; Francesco Mattia, Auteur ; Anna Balenzano, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7308 - 7321 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse de sensibilité [Termes IGN] angle d'incidence [Termes IGN] bande C [Termes IGN] carte agricole [Termes IGN] Castille-et-Leon (Espagne) [Termes IGN] corrélation temporelle [Termes IGN] cultures [Termes IGN] humidité du sol [Termes IGN] image Sentinel-SAR [Termes IGN] Pouilles (Italie) [Termes IGN] réseau hydrographique [Termes IGN] rétrodiffusion [Termes IGN] transfert radiatif Résumé : (auteur) Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m 3 /m 3 , at low incidence, to ~0.071 m 3 /m 3 , at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution. Numéro de notice : A2021-646 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033887 date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98351 [article]

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 1 - 17 Titre : A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; Diogo Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] carte agricole [Termes IGN] Citrus sinensis [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comptage [Termes IGN] cultures [Termes IGN] détection d'objet [Termes IGN] extraction de la végétation [Termes IGN] gestion durable [Termes IGN] image captée par drone [Termes IGN] maïs (céréale) [Termes IGN] rendement agricole Résumé : (auteur) Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems. Numéro de notice : A2021-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.024 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97171 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021043	DEP-RECP	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Temporal 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 96 (April 2021) . - n° 102277 Titre : Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands Type de document : Article/Communication Auteurs : Emmanuelle Vaudour, Auteur ; Cécile Gomez, Auteur ; Philippe Lagacherie, Auteur Année de publication : 2021 Article en page(s) : n° 102277 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] humidité du sol [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] mosaïquage d'images [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] puits de carbone [Termes IGN] réflectance spectrale [Termes IGN] série temporelle [Termes IGN] sol nu [Termes IGN] surface cultivée [Termes IGN] teneur en carbone [Termes IGN] terre arable [Termes IGN] Yvelines (78) Résumé : (auteur) The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 to predict SOC content over temperate agroecosystems characterized with annual crops. However, because spectral models are only applicable on bare soils, the mapping of SOC is often obtained on limited areas. A possible improvement for increasing the number of pixels on which SOC can be retrieved by inverting bare soil reflectance spectra, consists of using optical images acquired at several dates. This study compares different approaches of Sentinel–2 images temporal mosaicking to produce a composite multi-date bare soil image for predicting SOC content over agricultural topsoils. A first approach for temporal mosaicking was based on a per-pixel selection and was driven by soil surface characteristics: bare soil or dry bare soil with/without removing dry vegetation. A second approach for creating composite images was based on a per-date selection and driven either by the models performance from single-date, or by average soil surface indicators of bare soil or dry bare soil. To characterize soil surface, Sentinel-1 (S1)-derived soil moisture and/or spectral indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio 2 (NBR2), bare soil index (BSI) and a soil surface moisture index (S2WI) were used either separately or in combination. This study highlighted the following results: i) none of the temporal mosaic images improved model performance for SOC prediction compared to the best single-date image; ii) of the per-pixel approaches, temporal mosaics driven by the S1-derived moisture content, and to a lesser extent, by NBR2 index, outperformed the mosaic driven by the BSI index but they did not increase the bare soil area predicted; iii) of the per-date approaches, the best trade-off between predicted area and model performance was achieved from the temporal mosaic driven by the S1-derived moisture content (R2 ~ 0.5, RPD ~ 1.4, RMSE ~ 3.7 g.kg-1) which enabled to more than double (*2.44) the predicted area. This study suggests that a number of bare soil mosaics based on several indicators (moisture, bare soil, roughness…), preferably in combination, might maintain acceptable accuracies for SOC prediction whilst extending over larger areas than single-date images. Numéro de notice : A2021-238 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102277 date de publication en ligne : 14/12/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102277 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97258 [article]

Détection 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, Vol 223 (mars 2021)  

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Suivi de la dynamique de l’occupation du sol en République de Guinée par imagerie satellitaire Spot : transfert technologique pour le développement d’outils performants d’aide à la décision / Gabriel Jaffrain in Revue Française de Photogrammétrie et de Télédétection, Vol 223 (mars 2021)  

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Application 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])  

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Agricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm / Mehrdad Bijandi in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)  

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Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Apport de la télédétection pour la simulation spatialisée des composantes du bilan carbone des cultures et des effets d'atténuation biogéochimiques et biogéophysiques des cultures intermédiaires / Gaétan Pique (2021)  

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Spatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia / Meron Awoke Eshetae in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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Télédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)  

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The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January 2021)  

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