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Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method / Felicia Akinyemi in Geocarto international, vol 36 n° 16 ([01/09/2021])
[article]
Titre : Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method Type de document : Article/Communication Auteurs : Felicia Akinyemi, Auteur ; Laura T. Tlhalerwa, Auteur ; Peter N. Eze, Auteur Année de publication : 2021 Article en page(s) : pp 1838 - 1854 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] Botswana
[Termes IGN] carte de la végétation
[Termes IGN] dégradation de l'environnement
[Termes IGN] dégradation des sols
[Termes IGN] données de terrain
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone arideRésumé : (auteur) Increasing environmental and socioeconomic transformations in African drylands are driving land degradation. Using the Composite Land Degradation Index, this study assessed physical, chemical and biological degradation by determining their extent and severity. Palapye, an agro-pastoral region in eastern Botswana was used as a case study. Land degradation maps (status and indicators) were created with data from the field, soil chemical properties and image interpretation. Areas in the vicinity of settlements with Luvisols at elevations between 773 and 893 m were most degraded, implying impacts from human activities. This study developed a comprehensive list of of land degradation indicators for Botswana and created additional symbols for mapping indicators. Creation of these reference data for 2015 will facilitate the monitoring of land degradation in Palapye. The integrative and spatially explicit procedure utilized in this study can be adapted for assessing and validating local-level land degradation baseline and estimates towards operationalizing Land Degradation Neutrality in all countries. Numéro de notice : A2021-582 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678673 Date de publication en ligne : 25/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98194
in Geocarto international > vol 36 n° 16 [01/09/2021] . - pp 1838 - 1854[article]Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
[article]
Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Brésil
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps / Izabela Karsznia in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)
[article]
Titre : Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps Type de document : Article/Communication Auteurs : Izabela Karsznia, Auteur ; Izabela Golebiowska, Auteur ; Jolanta Korycka-Skorupa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] analyse spatiale
[Termes IGN] carte choroplèthe
[Termes IGN] carte thématique
[Termes IGN] échelle cartographique
[Termes IGN] enquête
[Termes IGN] généralisation
[Termes IGN] lecture de carte
[Termes IGN] reconnaissance de formes
[Termes IGN] répartition géographique
[Termes IGN] représentation cartographique
[Termes IGN] utilisateur
[Termes IGN] visualisation cartographiqueRésumé : (auteur) Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations. Numéro de notice : A2021-686 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10090576 Date de publication en ligne : 25/08/2021 En ligne : https://doi.org/10.3390/ijgi10090576 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98412
in ISPRS International journal of geo-information > vol 10 n° 9 (September 2021) . - n° 576[article]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)
[article]
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 radiatifRésumé : (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
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7308 - 7321[article]Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America Type de document : Article/Communication Auteurs : Bin Chen, Auteur ; Ying Tu, Auteur ; Yimeng Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 203 - 218 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] carte d'utilisation du sol
[Termes IGN] données massives
[Termes IGN] données multisources
[Termes IGN] Etats-Unis
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] métropole
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] zone urbaineRésumé : (auteur) Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial “big data”. With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories. Numéro de notice : A2021-564 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.010 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98129
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 203 - 218[article]Réservation
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