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Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([01/05/2022])
Titre : Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data Type de document : Article/Communication Auteurs : Santanu Malik, Auteur ; Tridip Bhowmik, Auteur ; Umesh Mishra, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2198 - 2214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] estimation bayesienne
[Termes IGN] géostatistique
[Termes IGN] gestion durable
[Termes IGN] Inde
[Termes IGN] krigeage
[Termes IGN] modèle de simulation
[Termes IGN] puits de carbone
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sol arableRésumé : (auteur) Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management. Numéro de notice : A2022-505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1815864 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1815864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101026
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2198 - 2214[article]
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 agricoleRésumé : (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
in ISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 1 - 17[article]Réservation
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Titre : Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam Type de document : Article/Communication Auteurs : Sevim Sezi Karayazi, Auteur ; Gamze Dane, Auteur ; Bauke de Vries, Auteur Année de publication : 2021 Article en page(s) : n° 198 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Amsterdam (Pays-Bas)
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatiale
[Termes IGN] attractivité (aménagement)
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] gestion durable
[Termes IGN] image Flickr
[Termes IGN] musée
[Termes IGN] patrimoine
[Termes IGN] point d'intérêt
[Termes IGN] régression géographiquement pondérée
[Termes IGN] tourismeRésumé : (auteur) Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. Numéro de notice : A2021-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040198 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.3390/ijgi10040198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97424
in ISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 198[article]Climate variability and climate change impacts on land surface, hydrological processes and water management / Yongqiang Zhang (2019)
Titre : Climate variability and climate change impacts on land surface, hydrological processes and water management Type de document : Monographie Auteurs : Yongqiang Zhang, Éditeur scientifique ; Hongxia Li, Éditeur scientifique ; Paolo Reggiani, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 460 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03921-508-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] données GRACE
[Termes IGN] dynamique de la végétation
[Termes IGN] gestion de l'eau
[Termes IGN] gestion durable
[Termes IGN] hydrologie
[Termes IGN] image Landsat
[Termes IGN] inondation
[Termes IGN] modèle de régression
[Termes IGN] modèle hydrographique
[Termes IGN] occupation du sol
[Termes IGN] sécheresse
[Termes IGN] utilisation du solRésumé : (éditeur) During the last several decades, Earth´s climate has undergone significant changes due to anthropogenic global warming, and feedbacks to the water cycle. Therefore, persistent efforts are required to improve our understanding of hydrological processes and to engage in efficient water management strategies that explicitly consider changing environmental conditions. The twenty-four contributions in this book have broadly addressed topics across four major research areas: (1) Climate and land-use change impacts on hydrological processes, (2) hydrological trends and causality analysis faced in hydrology, (3) hydrological model simulations and predictions, and (4) reviews on water prices and climate extremes. The broad spectrum of international contributions to the Special Issue indicates that climate change impacts on water resources analysis attracts global attention. We hope that the collection of articles presented here can provide scientists, policymakers and stakeholders alike with insights that support sustainable decision-making in the face of climate change and increasingly scarce environmental resources. Note de contenu : 1- Climate variability and climate change Impacts on Land Surface, Hydrological Processes and Water Management
2- Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin
3- Quantifying the impacts of climate change, coal mining and soil and water conservation on streamflow in a Coal mining concentrated watershed on the Loess Plateau, China
4- A statistical–distributed model of average annual runoff for water resources assessment in DPR Korea
5- Climate change and intense irrigation growth in Western Bahia, Brazil: The urgent need for hydroclimatic monitoring
6- Influence of power operations of cascade hydropower stations under climate change and human activities and revised optimal operation strategies: A case study in the Upper Han River, China
7- Comparative study of two state-of-the-art semi-distributed hydrological models
8- The use of large-scale climate indices in monthly reservoir inflow forecasting and its application on time series and artificial intelligence models
9- Land use and climate change effects on surface runoff variations in the upper Heihe
River Basin
10- Meteorological factors affecting pan evaporation in the Haihe River Basin, China
11- Effects of the three gorges project on runoff and related benefits of the key regions along main branches of the Yangtze River
12- Analysis of the recent trends of two climate parameters over two eco-regions of Ethiopia
13- Evaluating future flood scenarios using CMIP5 climate projections
14- Analyzing the impacts of climate variability and land surface changes on the annual
water–energy balance in the Weihe River Basin of China
15- Quantifying the impact of climate change and human activities on streamflow in a semi-arid watershed with the Budyko Equation incorporating dynamic vegetation information
16- Spatiotemporal variation of snowfall to precipitation ratio and its implication on water resources by a regional climate model over Xinjiang, China
17- Observed trends of climate and river discharge in Mongolia’s Selenga Sub-Basin of the Lake Baikal basin
18- Multiple linear regression models for predicting nonpoint-source pollutant discharge from a highland agricultural region
19- Analysis of natural streamflow variation and its influential factors on the Yellow River from 1957 to 2010
20- The effects of litter layer and topsoil on surface runoff during simulated rainfall in Guizhou Province, China: A plot scale case study
21- Integrating field experiments with modeling to evaluate the freshwater availability at ungauged sites: A case study of Pingtan Island (China)
22- Assessing the influence of the three gorges dam on hydrological drought using GRACE data
23- Explaining water pricing through a water security lens
24- Compound extremes in hydroclimatology: A reviewNuméro de notice : 28538 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03921-508-9 En ligne : https://doi.org/10.3390/books978-3-03921-508-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97368
Titre : GIS in sustainable urban planning and management : A global perspective Type de document : Monographie Auteurs : Martin van Maarseveen, Éditeur scientifique ; Javier Martinez, Éditeur scientifique ; Johannes Flacke, Éditeur scientifique Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2019 Importance : 352 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-1-138-50555-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] changement climatique
[Termes IGN] gestion durable
[Termes IGN] organisation spatiale
[Termes IGN] planification urbaine
[Termes IGN] risque naturel
[Termes IGN] système d'information urbainRésumé : (éditeur) GIS is used today to better understand and solve urban problems. GIS in Sustainable Urban Planning and Management: A Global Perspective, explores and illustrates the capacity that geo-information and GIS have to inform practitioners and other participants in the processes of the planning and management of urban regions. The first part of the book addresses the concept of sustainable urban development, its different frameworks, the many ways of measuring sustainability, and its value in the urban policy arena. The second part discusses how urban planning can shape our cities, examines various spatial configurations of cities, the spread of activities, and the demands placed on different functions to achieve strategic objective. It further focuses on the recognition that urban dwellers are increasingly under threat from natural hazards and climate change.
Written by authors with expertise on the applications of geo-information in urban management, this book showcases the importance of GIS in better understanding current urban challenges and provides new insights on how to apply GIS in urban planning. It illustrates through real world cases the use of GIS in analyzing and evaluating the position of disadvantaged groups and areas in cities and provides clear examples of applied GIS in urban sustainability and urban resilience. The idea of sustainable development is still very much central in the new development agenda of the United Nations, and in that sense, it is of particular importance for students from both the Global South and Global North. Professionals, researchers, and students alike will find this book to be an invaluable resource for understanding and solving problems relating to sustainable urban planning and management.Note de contenu : Part I- The "Sustainable City" and the "Inclusive City"
Part II- The "Compact-Competitive City" and the "Resilient City"Numéro de notice : 25851 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Monographie DOI : sans En ligne : https://www.taylorfrancis.com/books/e/9781315146638 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95441 Rapport d'activité 2018 de l'Institut National de l'Information Géographique et Forestière IGN, 1. Les missions et activités de l'IGN / Institut national de l'information géographique et forestière (2012 -) (2019)
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