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Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)
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Titre : Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data Type de document : Article/Communication Auteurs : Alexander Cass, Auteur ; George P. Petropoulos, Auteur ; Konstantinos P. Ferentinos, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 277 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie thématique
[Termes IGN] classification orientée objet
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Landsat-TM
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] Pays de Galles
[Termes IGN] surface cultivéeRésumé : (Auteur) Earth Observation (EO) provides a unique means of obtaining information on land use/cover and of its changes, which is of key importance in many scientific and practical applications. EO data is already widely used, for example, in environmental practices or decision-making related to food availability and security. As such, it is imperative to examine the suitability of different EO datasets, including their synergies, in respect to their ability to create products and tools for such practices and to guide effectively such decisions. This work aims at exploring the added value of the synergistic use of optical and radar data (from the Landsat TM and Advanced Synthetic Aperture Radar (ASAR) sensors respectively). Such information can help towards improving the accuracy of land cover classifications from EO datasets. As a case study, the region of Wales in the UK has been used. Two classifications—one based on optical data alone and another one developed from the synergy of optical and RADAR datasets acquired nearly, concurrently were developed for the studied region. Evaluation of the derived land/use cover maps was performed on the basis of the confusion matrix using validation points derived from a Phase 1 habitat map of Wales. The results showed 15% increase in overall accuracy (84% from 69%) and kappa coefficient (0.81 from 0.65) using the synergistic approach over the scenario where only optical data were used in the classification. In addition, McNemar’s test was used to assess the statistical significance of the obtained results. Results of this test provided further confirmed that the use of optical data synergistically with the radar data provides more accurate land use/cover maps in comparison with the use of optical data alone. Numéro de notice : A2019-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00258-7 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1007/s12518-019-00258-7 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93601
in Applied geomatics > vol 11 n° 3 (September 2019) . - pp 277 - 288[article]Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
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Titre : Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Yongwan Chun, Auteur Année de publication : 2019 Article en page(s) : pp 1703 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approximation
[Termes IGN] autocorrélation spatiale
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-TM
[Termes IGN] régression linéaire
[Termes IGN] segmentation d'image
[Termes IGN] tessellation
[Termes IGN] vecteur propreMots-clés libres : Moran eigenvector spatial filtering Résumé : (auteur) Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. Numéro de notice : A2019-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1080/13658816.2019.1593421 Date de publication en ligne : 02/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1593421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93479
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1703 - 1717[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)
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Titre : Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment Type de document : Article/Communication Auteurs : Eduarda M.O. Silveira, Auteur ; Sérgio Henrique G. Silva, Auteur ; Fausto Weimar Acerbi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 175 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution spatiale
[Termes IGN] forêt équatoriale
[Termes IGN] image Landsat-TM
[Termes IGN] Minas Gerais (Brésil)
[Termes IGN] montagneRésumé : (Auteur) The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R² (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment. Numéro de notice : A2019-230 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.02.004 Date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1016/j.jag.2019.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92748
in International journal of applied Earth observation and geoinformation > vol 78 (June 2019) . - pp 175 - 188[article]Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change / Hao Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)
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Titre : Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Zhen Li, Auteur ; Keith C. Clarke, Auteur ; Wenzhong Shi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1040 - 1061 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] changement d'occupation du sol
[Termes IGN] changement d'utilisation du sol
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de Markov
[Termes IGN] simulationRésumé : (Auteur) Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size. Numéro de notice : A2019-443 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1568441 Date de publication en ligne : 18/02/2019 En ligne : https://doi.org/10.1080/13658816.2019.1568441 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92777
in International journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1040 - 1061[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019052 RAB Revue Centre de documentation En réserve L003 Disponible Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])
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Titre : Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images Type de document : Article/Communication Auteurs : Linda Gulbe, Auteur ; Aleksandrs Kozlovs, Auteur ; Janis Donis, Auteur ; Agris Tradkovs, Auteur Année de publication : 2019 Article en page(s) : pp 113 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] classification floue
[Termes IGN] estimation statistique
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] LettonieRésumé : (auteur) Countrywide up-to-date tree cover maps provide valuable information for planning and management purposes to investigate location of the resources and to identify afforestation and deforestation patterns. Landsat programme offers freely available satellite data with time span more than three decades and it can serve as bases for tree cover map calculation using satellite image classification; however, practical use of classification methods is limited due to lack of user-friendly solutions and complex interpretation of the results. The objective of this study is to evaluate user-friendly hybrid classification scheme for tree cover mapping in Latvia and to explore the nature of the spectral classes and consistency of the results when methodology is applied to images of different dates. Tree cover in this context means the area covered by crown of the tree, which may or may not be considered as forest according to local provisions. Tree cover is estimated using unsupervised fuzzy c-means methods with the stability check to ensure the presence of the same spectral classes in independent tests. Spectral classes are classified into two categories: tree cover and other by employing k-nearest neighbours. Such approach does not require high quality sample data and does not include user defined internal parameters of the algorithms (however, they can be specified if needed). The best overall accuracy achieved for year 2014 was 94.2% with producer's accuracy 98.7% (tree cover), 90.5% (other land cover), user's accuracy 90.0% (tree cover), 98.8% (other land cover) and kappa 0.89. Consistency studies showed high impact (within 10% of overall accuracy) of unique conditions during the image acquisition. Some of the spectral classes represent borderline case between relatively dense tree cover and other land cover types like sparse young stands. Those cases are the main threat to the consistency between the results of different dates and seasons. Numéro de notice : A2019-375 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : sans En ligne : https://balticforestry.lammc.lt/bf/PDF_Articles/2019-25%5B1%5D/Baltic%20Forestry [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93417
in Baltic forestry > vol 25 n° 1 [01/02/2019] . - pp 113 - 123[article]Intra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkUncertainties in tree cover maps of Sub-Saharan Africa and their implications for measuring progress towards CBD Aichi Targets / Dorit Gross in Remote sensing in ecology and conservation, vol 4 n° 2 (June 2018)
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PermalinkExploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area / Saygin Abdikan in Geocarto international, vol 33 n° 1 (January 2018)
PermalinkUn inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)
PermalinkExtraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)
PermalinkReconstruction of time-varying tidal flat topography using optical remote sensing imageries / Kuo-Hsin Tseng in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkSpatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)
PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)
PermalinkTM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)
PermalinkEvaluation of multisource data for glacier terrain mapping : a neural net approach / Aparna Shukla in Geocarto international, vol 32 n° 5 (May 2017)
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