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Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 28 - 51 Titre : Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Type de document : Article/Communication Auteurs : Riantini Virtriana, Auteur ; Agung Budi Harto, Auteur ; Fiza Wira Atmaja, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 28 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] base de données d'images [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] dommage matériel [Termes IGN] données Copernicus [Termes IGN] image Sentinel-MSI [Termes IGN] image Worldview [Termes IGN] Indonésie [Termes IGN] modèle numérique de surface [Termes IGN] segmentation d'image [Termes IGN] tsunami Résumé : (auteur) In Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. Numéro de notice : A2023-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2147455 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1080/19475705.2022.2147455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102307 [article]

Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 27 [20/12/2022] . - pp 15161 - 15186 Titre : Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows Type de document : Article/Communication Auteurs : Pramaditya Wicaksono, Auteur ; Amanda Maishella, Auteur ; Wahyu Lazuardi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 15161 - 15186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] carte thématique [Termes IGN] classification par arbre de décision [Termes IGN] classification pixellaire [Termes IGN] correction d'image [Termes IGN] filtrage du bruit [Termes IGN] herbier marin [Termes IGN] image PlanetScope [Termes IGN] Indonésie Résumé : (auteur) Seagrass percent cover is a crucial and influential component of the biophysical characteristics of seagrass beds and is a key parameter for monitoring seagrass conditions. Therefore, the availability of seagrass percent cover maps greatly assists in sustainable coastal ecosystem management. This research aimed to assess the consistency of PlanetScope imagery for seagrass percent cover mapping using two study areas, namely Parang Island and Labuan Bajo, Indonesia. Assessing the consistency of the PlanetScope imagery performance in seagrass percent cover mapping helps understand the effects of variations in the image quality on its performance in monitoring changes in seagrass cover. Percent cover maps were derived using object-based image analysis (image segmentation and random forest) and pixel-based random forest algorithm. Accuracy assessment and consistency analysis were conducted on the basis of the following three approaches: overall accuracy consistency, agreement percentage and consistent pixel locations. Results show that PlanetScope images can fairly consistently map seagrass percent cover for a specific area across different dates. However, these images produced different levels of accuracy when used for mapping in seagrass meadows with various characteristics and benthic cover complexities. The mapping accuracy (OA–overall accuracy) and consistency (AP–agreement percentage) in patchy seagrass meadows (Parang Island, mean OA 18.4%–38.6%, AP 44.1%–70.3%) are different from those in continuous seagrass meadows (Labuan Bajo, OA 43.0%–56.2%, and AP 41.8%–55.8%). Moreover, PlanetScope images are consistent when used for mapping seagrasses with low and high percent covers but strive to obtain good consistency for medium percent cover due to the combination of seagrass and non-seagrass in a pixel. Furthermore, images with relatively similar image acquisition conditions (i.e., winds, aerosol optical depth, signal-to-noise ratio, and sunglint intensity) produce better consistency. The OA is related to the image acquisition conditions, whilst the AP is related to variation in these conditions. Nevertheless, PlanetScope is still the best high spatial resolution image that provides daily acquisition and is highly beneficial for various applications in tropical areas with persistent cloud coverage. Numéro de notice : A2022-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2096122 Date de publication en ligne : 06/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2096122 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102668 [article]

Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia / Medria Shekar Rani in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 12 (December 2022) . - pp 2549 - 2562 Titre : Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia Type de document : Article/Communication Auteurs : Medria Shekar Rani, Auteur ; Ross Cameron, Auteur ; Olaf Schrott, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2549 - 2562 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] bassin hydrographique [Termes IGN] carte thématique [Termes IGN] changement d'occupation du sol [Termes IGN] Java (île de) [Termes IGN] mise à jour [Termes IGN] modèle de Markov [Termes IGN] modélisation spatiale [Termes IGN] Perceptron multicouche Résumé : (auteur) In developing countries, data gaps are common and lead to uncertainties in land cover change analysis. This study demonstrates how to mitigate uncertainties in modeling land change in the Ci Kapundung upper water catchment area by comparing the outcomes of two simulation phases. A conventional cellular automata (CA)–Markov model was complemented with a multilayer perceptron (MLP) to project future land cover maps in the study area. The CA–Markov–MLP model results in high uncertainties in forested sites where a data gap is apparent in the input data (land cover maps and driver variables) and parameters. The results show that the model accuracy is improved from 47.90% in the first phase to 81.36% in the second phase. Both first and second phases integrate six demographic–economic and environmental drivers in the modeling, but the second phase also incorporates an updating and backdating analysis to revise the base-maps. This study suggests that uncertainties can be mitigated by linking such base-map revision process with the updating and backdating analyses using remote sensing datasets retrieved at different times. Numéro de notice : A2022-845 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2103820 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2103820 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102076 [article]

Design and construction of a colourblind-friendly Surabaya city angkot route map prototype / Arzakhy Indhira Pramesti in Cartographica, vol 57 n° 3 (September 2022)  

Public [article]  inCartographica > vol 57 n° 3 (September 2022) . - pp 195 - 212 Titre : Design and construction of a colourblind-friendly Surabaya city angkot route map prototype Type de document : Article/Communication Auteurs : Arzakhy Indhira Pramesti, Auteur ; Noorhadi Rahardjo, Auteur Année de publication : 2022 Article en page(s) : pp 195 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] carte routière [Termes IGN] chromatopsie [Termes IGN] conception cartographique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] daltonisme [Termes IGN] Indonésie [Termes IGN] itinéraire [Termes IGN] lisibilité perceptive [Termes IGN] prototype [Termes IGN] transport collectif Résumé : (auteur) Angkot is the most often found public transportation in Surabaya City. However, there is no angkot routes map, and the officially published route information is textual, thus hard to get the transit information quickly. Meanwhile, people with colour vision impairment have a different perception of colour compared to people with normal vision. It can affect them in making decisions when reading a map. The purpose of this study is to design a colourblind-friendly Surabaya City angkot route map prototype and to conduct a cartographic evaluation of the map by considering the colour vision impairment factor. The map was created using ArcGIS and CorelDRAW then checked by using several software packages to ensure that the colours are colourblind-friendly then tested on people with normal vision and people with colour vision impairment. Fifteen out of 15 respondents with normal vision and 11 out of 11 respondents with colour vision impairment could distinguish the colours of the route. All respondents mentioned that symbols and some texts were too small. It shows that the colours on the map can accommodate both groups, but they have difficulty reading the route map because the size of the symbols and the text is too small. Numéro de notice : A2022-850 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2021-0005 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3138/cart-2021-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102088 [article]

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Volunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)  

Public [article]  inComputers & geosciences > vol 161 (April 2022) . - n° 105073 Titre : Volunteered geographic information mobile application for participatory landslide inventory mapping Type de document : Article/Communication Auteurs : Raden Muhammad Anshori, Auteur ; Guruh Samodra, Auteur ; Djati Mardiatno, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105073 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] base de données [Termes IGN] cartographie thématique [Termes IGN] données localisées des bénévoles [Termes IGN] effondrement de terrain [Termes IGN] géopositionnement [Termes IGN] inventaire [Termes IGN] Java (île de) [Termes IGN] téléphonie mobile Résumé : (auteur) Participatory landslide inventory mapping using the Volunteered Geographic Information (VGI) mobile app is a promising method to produce a landslide inventory map. The aim of this research is to describe the development and implementation of the VGI mobile app for participatory landslide inventory mapping. The architecture VGI mobile app is developed on the basis of Free Open-source Software for Geospatial Application server-client software to ensure reproducibility and flexibility, and to reduce cost. Anyone can reproduce, modify, and share the code, which suggests improvement in the collective ability to use, prepare, and landslide inventory update. Landslide inventory using VGI mobile app shows that the tool and method successfully map landslides in the landslide prone area (Magelang Regency, Central Java Province, Indonesia) with fairly high levels of effectiveness and convenience. Magelang Regency, one of the landslide prone areas in Java, is located in the intermountain basin surrounded by Menoreh Mountain, Merapi, Merbabu, Suropati-Telomoyo Complex, and Sumbing Volcano. In this study, landslide inventory mapping using VGI mobile app was applied in Magelang Regency by 17 volunteers from BPBD (Regional Agency for Disaster Management) Magelang Regency for three days. Landslides area occurred from 2017 to 2019 were properly identified and mapped by the volunteers. The sizes of landslides varied from 5.2 m2 to 4,632.5 m2, and the average was 208.2 m2. A team of volunteer was able to map 7-10 landslides per day. Participatory mapping using VGI mobile app reduces the time in transferring field data to a GIS database, in contrast to conventional participatory landslide inventory mapping. VGI mobile app allows users to provide new geographical landslide data, share landslide data rapidly, ensure consistency of landslide data, and improve accessibility of landslide data. The use of the VGI mobile app for participatory landslide inventory mapping provides new opportunities to improve risk assessment, preparedness, and early action and warning to landslide hazard. Numéro de notice : A2022-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.cageo.2022.105073 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105073 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99918 [article]

Co-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)  

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Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])  

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Artificial intelligence methods applied to urban remote sensing and GIS / Chang-Wook Lee (2021)  

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Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Soil erosion / António Vieira (2021)  

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Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])  

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What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 / Christopher M. Wade in Forests, vol 11 n° 5 (May 2020)  

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Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)  

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Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)  

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