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Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation / Lauren Lyn Williams in Transactions in GIS, vol 26 n° 7 (November 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 7 (November 2022) . - pp 3074 - 3089 Titre : Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation Type de document : Article/Communication Auteurs : Lauren Lyn Williams, Auteur ; Melanie Lück-Vogel, Auteur Année de publication : 2022 Article en page(s) : pp 3074 - 3089 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] Afrique du sud (état) [Termes IGN] ArcGIS [Termes IGN] données lidar [Termes IGN] milieu urbain [Termes IGN] modèle numérique de surface [Termes IGN] semis de points [Termes IGN] submersion marine [Termes IGN] système d'information géographique Résumé : (auteur) concerning digital surface models (DSMs) to determine: (a) the highest appropriate resolution achievable from available LiDAR data and consider variations between derived sub-meter DSMs; (b) optimal DSM horizontal resolution for coastal inundation modeling based on “out-the-box” solutions; and (c) mechanisms to address the challenge presented by DSMs regarding overhanging structures for a study site in False Bay, South Africa. Results showed that while sub-meter DSMs are achievable, low point cloud densities may result in the misrepresentation of structures, which affects the inundation extents. High horizontal resolution DSMs are required for inundation modeling in an urban setting to account for narrow thoroughfares. Challenges posed by first return LiDAR depicting bridges as solid structures could be circumvented by modifying the input water source for the eBTM processing. Numéro de notice : A2022-888 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1111/tgis.12995 Date de publication en ligne : 18/10/2022 En ligne : https://doi.org/10.1111/tgis.12995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102232 [article]

Drawing attention via diversity in thematic map design, as demonstrated by student maps of Northern South Africa / G. Schaab in International journal of cartography, vol 8 n° 2 (July 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 2 (July 2022) . - pp 243 - 264 Titre : Drawing attention via diversity in thematic map design, as demonstrated by student maps of Northern South Africa Type de document : Article/Communication Auteurs : G. Schaab, Auteur ; Sybil Adams, Auteur ; Serena Coetzee, Auteur Année de publication : 2022 Article en page(s) : pp 243 - 264 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] Afrique du sud (état) [Termes IGN] carte analytique [Termes IGN] carte thématique [Termes IGN] conception cartographique [Termes IGN] données socio-économiques [Termes IGN] formation [Termes IGN] infographie [Termes IGN] pertinence [Termes IGN] représentation cartographique [Termes IGN] visualisation de données Résumé : (auteur) In today’s information age, thematic maps increasingly appear in all kinds of media and publications and many users control the map design process themselves. Due to wider prevalence of data, teaching the principles of thematic cartography is gaining interest. Students need to understand the power of thematic maps to reveal geographic patterns and relations, and should learn how to create convincing maps. In this paper, student maps featuring socio-economic themes for Northern South Africa show how attention can be drawn to information hidden in data. Seven students each prepared a black-and-white traditional thematic map and a coloured infographics-style map, which were later enhanced by a well-trained cartographer. Through these maps, we demonstrate that the power of thematic maps depends on the chosen cartographic representation and that diversity of visualization options matters when telling a story with a map. Discussion of the maps illustrates the relevance and challenge of thematic maps for society, the need to develop map literacy, and the possibility to accommodate new visualization trends, like narrative data visualization, in thematic cartography teaching. The emphasis should be on using multivariate data and allowing infographics characteristics, thus fostering creativity and preparing students for a role in interdisciplinary data journalism teams. Numéro de notice : A2022-922 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1839207 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1080/23729333.2020.1839207 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102462 [article]

An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 267 - 274 Titre : An informal road detection neural network for societal impact in developing countries Type de document : Article/Communication Auteurs : Inger Fabris-Rotelli, Auteur ; Abraham Wannenburg, Auteur ; Gao Maribe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 267 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Afrique du sud (état) [Termes IGN] apprentissage profond [Termes IGN] données étiquetées d'entrainement [Termes IGN] extraction du réseau routier [Termes IGN] image satellite [Termes IGN] impact social [Termes IGN] pays en développement [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data. Numéro de notice : A2022-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-267-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100729 [article]

Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])  

Public [article]  inGeocarto international > vol 37 n° 6 [01/04/2022] . - pp 1574 - 1586 Titre : Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Shenelle Lottering, Auteur ; Paramu Mafongoyab, Auteur ; Romano Lottering, Auteur Année de publication : 2022 Article en page(s) : pp 1574 - 1586 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique du sud (état) [Termes IGN] cartographie thématique [Termes IGN] données météorologiques [Termes IGN] données multitemporelles [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-TM [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] sécheresse [Termes IGN] stress hydrique [Termes IGN] température au sol Résumé : (auteur) Drought has become a more frequent phenomenon under changing climatic conditions, particularly in Sub Saharan Africa. This study tested the utility of a newly proposed Temperature-Vegetation Water Stress Index (T-VWSI) in detecting drought severity using Landsat data for the years 2008, 2012, 2016 and 2018. This index was created using both NDVI and LST to detect drought severity within the region. The results show that the year 2016 experienced the most severe levels of drought, with the northern areas of the uMsinga region being most severely affected. SPI was used to corroborate the findings of the T-VWSI index and also established that the year 2016 was the year of severe drought in uMsinga. The results of this study have illustrated the potential of the T-VWSI index in effectively mapping and detecting drought over large spatial areas. Numéro de notice : A2022-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1783580 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1783580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100820 [article]

Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 2 [15/01/2022] . - pp 526 - 542 Titre : Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa Type de document : Article/Communication Auteurs : Mangana Rampheri, Auteur ; Timothy Dube, Auteur ; Inos Dhau, Auteur Année de publication : 2022 Article en page(s) : pp 526 - 542 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique du sud (état) [Termes IGN] arbre (flore) [Termes IGN] bande spectrale [Termes IGN] biodiversité végétale [Termes IGN] conservation de la flore [Termes IGN] détection de changement [Termes IGN] espèce végétale [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] indice de végétation [Termes IGN] régression [Termes IGN] réserve naturelle Résumé : (auteur) We use remotely sensed data to estimate species diversity in Blouberg Nature Reserve (BNR) in the Limpopo province, South Africa to understand the state of biodiversity since communities’ involvement in conservation initiatives. To achieve this objective, Landsat series data collected before and after community involvement in biodiversity conservation were used in conjunction with selected diversity indices i.e., Shannon-Wiener Index (H’) and Simpson Index (D). Thirty 15 m × 15 m field plots were selected and all trees within each plot were identified, with the help of Botanists. Further, we applied regression analysis to determine the relationship between satellite derived tree species diversity and field observations. The results of the study demonstrated a significant (p  Numéro de notice : A2022-052 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1723717 Date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1723717 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99443 [article]

Classification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)  

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Agroforestry / Gopal Shukla (2021)  

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Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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Modeling the risk of robbery in the city of Tshwane, South Africa / Nicolas Kemp in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)  

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Strategic and Acupunctural GIS Implementation within Community-Oriented Organizations: Evidence-Based Insights from a South African Participatory Action Research for Informal Settlement Upgrading / Jennifer Barella in Cartographica, vol 55 n° 4 (Winter 2020)  

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Emplaced distances / Giovanni Spissu in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

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Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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2014 Hartebeesthoek co-location survey reprocessing report / Jean-Michaël Muller (2020) 

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Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 ([15/07/2019])  

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