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Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)  

Public [article]  inGIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2163070 Titre : Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data Type de document : Article/Communication Auteurs : Thuong V. Tran, Auteur ; David Bruce, Auteur ; Cho-Ying Huang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2163070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spectrale [Termes IGN] changement d'occupation du sol [Termes IGN] image Terra-MODIS [Termes IGN] indice d'humidité [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] parcelle agricole [Termes IGN] sécheresse [Termes IGN] série temporelle [Termes IGN] surveillance agricole [Termes IGN] variation temporelle [Termes IGN] Viet Nam Résumé : (auteur) Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p  Numéro de notice : A2023-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2022.2163070 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/15481603.2022.2163070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102329 [article]

Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)  

Public [article]  inRemote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100859 Titre : Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image Type de document : Article/Communication Auteurs : Taposh Mollick, Auteur ; MD Golam Azam, Auteur ; Sabrina Karim, Auteur Année de publication : 2023 Article en page(s) : n° 100859 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage automatique [Termes IGN] Bangladesh [Termes IGN] classification non dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par nuées dynamiques [Termes IGN] classification pixellaire [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] occupation du sol [Termes IGN] rendement agricole [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (auteur) Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective. Numéro de notice : A2023-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100859 Date de publication en ligne : 22/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100859 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102224 [article]

GIS-based land-use suitability analysis for urban agriculture development based on pollution distributions / Fatemeh Kazemi in Land use policy, vol 123 (December 2022)  

Public [article]  inLand use policy > vol 123 (December 2022) . - n° 106426 Titre : GIS-based land-use suitability analysis for urban agriculture development based on pollution distributions Type de document : Article/Communication Auteurs : Fatemeh Kazemi, Auteur ; Nazanin Hosseinpour, Auteur Année de publication : 2022 Article en page(s) : n° 106426 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] agriculture [Termes IGN] distribution spatiale [Termes IGN] espace vert [Termes IGN] Iran [Termes IGN] milieu urbain [Termes IGN] paysage urbain [Termes IGN] pollution atmosphérique [Termes IGN] pollution des sols [Termes IGN] processus de hiérarchisation analytique [Termes IGN] utilisation du sol Résumé : (auteur) With recent increases in urban population and development, agricultural lands have been changed to residential areas and industrial settlements. Such urban population growth increases necessitate emerging relatively new strategies and forms in urban parks and landscape designs to meet today's economic, social, and ecological needs toward sustainable development. Urban agriculture is one of the strategies for achieving sustainable development and creating multi-purpose landscapes. Such an approach appears to partially solve the problem of agricultural land reduction and degradation. However, urban pollutants seem to hinder the development of such a sustainable concept in the cities. This research aimed to identify suitable locations for urban agriculture in urban green spaces according to various contamination criteria and the uniform distribution of the green spaces in Mashhad, Iran. In this research, using an Analytical Hierarchy Process (AHP) and a Geographic Information System (GIS) facilitated the decision-making process. It also determined the importance of the land use criteria for urban agricultural development. The results showed that the most suitable areas for urban agricultural development with the least impact of pollutants in Mashhad were the suburbs, especially in the northeast and southeast parts. These research results have practical implications for planning urban agricultural development at the city level across the world. Numéro de notice : A2022-895 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.landusepol.2022.106426 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.1016/j.landusepol.2022.106426 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102244 [article]

Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 22 (November-2 2022) . - n° 5894 Titre : Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging Type de document : Article/Communication Auteurs : Igor Petrovic, Auteur ; Matej Sečnik, Auteur ; Marko Hočevar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5894 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse comparative [Termes IGN] couvert végétal [Termes IGN] défoliation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage de données [Termes IGN] épandage [Termes IGN] lasergrammétrie [Termes IGN] pas d'échantillonnage au sol [Termes IGN] photogrammétrie aérienne [Termes IGN] Slovénie [Termes IGN] viticulture Résumé : (auteur) For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies. Numéro de notice : A2022-881 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14225894 Date de publication en ligne : 21/11/2022 En ligne : https://doi.org/10.3390/rs14225894 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102203 [article]

Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France / Christophe Sauser in Biological Conservation, vol 274 (octobre 2022)  

Public [article]  inBiological Conservation > vol 274 (octobre 2022) . - n° 109730 Titre : Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France Type de document : Article/Communication Auteurs : Christophe Sauser, Auteur ; Loïc Commagnac , Auteur ; Cyril Eraud, Auteur ; et al., Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 109730 Note générale : bibliographie Addendum : "The authors add: This study was partly funded and forms part of OFB's contribution to the European Commission contract ENV.D.3/SER /2019/0021 “Development of a population model and adaptive harvest mechanism for Turtle Dove (Streptopelia turtur)”. The authors would like to apologise for any inconvenience caused." Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agronomie [Termes IGN] analyse diachronique [Termes IGN] Aves [Termes IGN] habitat animal [Termes IGN] haie [Termes IGN] impact sur l'environnement [Termes IGN] jachère [Termes IGN] lisière [Termes IGN] modèle numérique [Termes IGN] politique de conservation (biodiversité) [Termes IGN] R (langage) Mots-clés libres : tourterelle des bois  Streptopelia turtur Résumé : (auteur) Agricultural changes in recent decades have led to a widespread loss of biodiversity, with habitat loss considered as the main factor in the decline. The European turtle dove is one of the farmland birds that has declined markedly in Europe, leading the IUCN to downgrade its status in 2015 from “Near Threatened” to “Vulnerable”. Knowledge of how habitat factors and agricultural practices influence the turtle dove population is crucial for the conservation of this species through the implementation of targeted measures. Here we investigate how foraging and nesting habitats influence the abundance of turtle doves at national and regional scales, using a 23-year dataset from point counts carried out throughout France, a stronghold country for this species during the breeding season. We found that turtle dove abondance was positively affected by fallow lands, both at national and regional scales. Turtle dove abundance was also negatively affected by fodder crop area at national scale, but the effect was detected in only four of the 13 French regions. We also showed that an increase in hedgerows length had a positive effect on turtle dove abundance. On the other hand, forest edges length showed a bell-shaped trend, suggesting that an increase in forest edges length may have a favourable effect on turtle dove abundance only up to a given threshold. We suggest that targeted conservation measures combining an increase in fallow lands and hedgerows length could allow the stabilisation or even an increase in turtle dove abundance in France, but also in European countries with similar landscapes. Numéro de notice : A2022-687 Affiliation des auteurs : IGN+Ext (2020- ) Autre URL associée : Addendum Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.biocon.2022.109730 Date de publication en ligne : 09/09/2022 En ligne : https://doi.org/10.1016/j.biocon.2022.109730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101612 [article]

Comparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)  

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Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2 / Akiko Elders in Remote Sensing Applications: Society and Environment, RSASE, Vol 27 (August 2022)  

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Analysis of the land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process / Ahmad Al-Hanbali in Geo-spatial Information Science, vol 25 n° 2 ([01/06/2022])  

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Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)  

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Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 / Wenquan Xie in Geocarto international, vol 37 n° 9 ([15/05/2022])  

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Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)  

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Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])  

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Assessment of land suitability potentials for winter wheat cultivation by using a multi criteria decision Support-Geographic information system (MCDS-GIS) approach in Al-Yarmouk Basin (Syria) / Safwan Mohammed in Geocarto international, vol 37 n° 6 ([01/04/2022])  

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Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

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Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany / Chenyao Yang in Remote sensing, vol 14 n° 6 (March-2 2022)  

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