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Optimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - pp Titre : Optimal segmentation of high spatial resolution images for the classification of buildings using random forests Type de document : Article/Communication Auteurs : James Bialas, Auteur ; Thomas Oommen, Auteur ; Timothy C. Havens, Auteur Année de publication : 2019 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bâtiment [Termes descripteurs IGN] Christchurch [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] dommage matériel [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] qualité du processus [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] séisme [Termes descripteurs IGN] zone urbaine Résumé : (auteur) In the application of machine learning to geographic object based image analysis, several parameters influence overall classifier performance. One of the first parameters is segmentation size—for example, how many pixels should be grouped together to form an image object. Often, trial and error methods are used to obtain segmentation parameters that best delineate the borders of real world objects. Several attempts at automated methods have produced promising results, but manual intervention is still necessary. Meanwhile, numerous measures of segmentation quality have been defined, but their relationship to classifier performance is not then directly shown. For example, as measures of segmentation quality improve, do classification results improve as well? Our work considers the problem of building classification in high resolution aerial imagery of urban areas. Based on user defined training polygons generated with or without a reference segmentation, we have found several measures of segmentation quality and feature performance that can help users narrow the range of appropriate segmentations. Furthermore, our work finds that given this range, performance of machine learning algorithms remains relatively constant for any given segmentation as long as features used for classification are chosen correctly. We find that the range of scale parameters capable of producing an accurate classification is much broader than typically assumed and trial and error methods for finding this parameter may be an acceptable approach. Numéro de notice : A2019-472 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.06.005 date de publication en ligne : 08/06/2019 En ligne : https://doi.org/https://doi.org/10.1016/j.jag.2019.06.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93632 [article]

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1960 - 1983 Titre : Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur Année de publication : 2019 Article en page(s) : pp 1960 - 1983 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] Auckland [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalement urbain [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] optimisation (mathématiques) Résumé : (auteur) Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. Numéro de notice : A2019-428 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1600701 date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1600701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93561 [article]

Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October 2019)  

Public [article]  inRemote sensing > vol 11 n° 19 (October 2019) . - 18 p. Titre : Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments Type de document : Article/Communication Auteurs : Leigh Tait, Auteur ; Jochen Bind, Auteur ; Hannah Charan-Dixon, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] algue [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] estran [Termes descripteurs IGN] habitat (nature) [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] Nouvelle-Zélande [Termes descripteurs IGN] orthophotoplan numérique [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Developments in the capabilities and affordability of unmanned aerial vehicles (UAVs) have led to an explosion in their use for a range of ecological and agricultural remote sensing applications. However, the ubiquity of visible light cameras aboard readily available UAVs may be limiting the application of these devices for fine-scale, high taxonomic resolution monitoring. Here we compare the use of RGB and multispectral cameras deployed aboard UAVs for assessing intertidal and shallow subtidal marine macroalgae to a high taxonomic resolution. Our results show that the diverse spectral profiles of marine macroalgae naturally lend themselves to remote sensing and habitat classification. Furthermore, we show that biodiversity assessments, particularly in shallow subtidal habitats, are enhanced using six-band discrete wavelength multispectral sensors (81% accuracy, Cohen’s Kappa) compared to three-band broad channel RGB sensors (79% accuracy, Cohen’s Kappa) for 10 habitat classes. Combining broad band RGB signals and narrow band multispectral sensing further improved the accuracy of classification with a combined accuracy of 90% (Cohen’s Kappa). Despite notable improvements in accuracy with multispectral imaging, RGB sensors were highly capable of broad habitat classification and rivaled multispectral sensors for classifying intertidal habitats. High spatial scale monitoring of turbid exposed rocky reefs presents a unique set of challenges, but the limitations of more traditional methods can be overcome by targeting ideal conditions with UAVs. Numéro de notice : A2019-553 Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11192332 date de publication en ligne : 08/10/2019 En ligne : https://doi.org/10.3390/rs11192332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94206 [article]

Thinking outside the square: Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics / Thomas S. H. Paul in Methods in ecology and evolution, vol 10 n° 3 (March 2019)  

Public [article]  inMethods in ecology and evolution > vol 10 n° 3 (March 2019) . - pp 381 - 388 Titre : Thinking outside the square: Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics Type de document : Article/Communication Auteurs : Thomas S. H. Paul, Auteur ; Mark O. Kimberley, Auteur ; Peter N. Beets, Auteur Année de publication : 2019 Article en page(s) : pp 381 - 388 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] cercle (géométrie) [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Nouvelle-Zélande [Termes descripteurs IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Plot‐based data collection is an important component of quantitative ecological research and is widely used. Some of the most extensive plot‐networks can be found in country‐wide forest inventories, which provide critical information about the state of forest ecosystems. While sampling designs for forest inventories have been well studied, plot design and installation has received less attention. The New Zealand National Forest Inventory of natural forest uses a nested plot design with a 0.126 ha circular plot superimposed concentrically on a 0.04 ha square plot. Stems ≥ 60 cm diameter at breast height (DBH) are measured in the circular plot while stems ≥ 2.5 DBH are measured in the square plot. Stem density of ≥60 cm DBH stems measured in the circular plots were compared with those from square plots. Stem densities estimated from square plot measurements were 23.7% higher than those estimated from circular plot measurements in the 2002–2007 inventory, and 18.4% higher in the 2009–2014 inventory. The main cause of this discrepancy appears to be due to the placement of plot boundaries during establishment of square plots. This effect may have resulted from a subconscious tendency of field teams to include large trees inside plots when laying out these boundaries. It is concluded that estimates from the circular plots are unlikely to be biased while those from the square plots are positively biased. This study highlights the critical importance of plot design and plot placement in forest inventories to ensure that estimates of stand attributes are unbiased. Especially on undulating or uneven terrain, methods of determining whether trees are inside or outside plot boundaries of circular plots are likely to be more accurate than those typically used for square or rectangular plots. Numéro de notice : A2019-372 Thématique : FORET Nature : Article DOI : 10.1111/2041-210X.13113 date de publication en ligne : 29/10/2018 En ligne : https://doi.org/10.1111/2041-210X.13113 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93409 [article]

Comparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 257 - 267 Titre : Comparison of high-density LiDAR and satellite photogrammetry for forest inventory Type de document : Article/Communication Auteurs : Grant D. Pearse, Auteur ; Jonathan P. Dash, Auteur ; Henrik J. Persson, Auteur ; Michael S. Watt, Auteur Année de publication : 2018 Article en page(s) : pp 257 - 267 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] Nouvelle-Zélande [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Pinus radiata [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] surface terrière [Termes descripteurs IGN] sylviculture [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Point cloud data derived from stereo satellite imagery has the potential to provide large-scale forest inventory assessment but these methods are known to include higher error than airborne laser scanning (ALS). This study compares the accuracy of forest inventory attributes estimated from high-density ALS (21.1 pulses m−2) point cloud data (PCD) and PCD derived from photogrammetric methods applied to stereo satellite imagery obtained over a Pinus radiata D. Don plantation forest in New Zealand. The statistical and textural properties of the canopy height models (CHMs) derived from each point cloud were included alongside standard PCD metrics as a means of improving the accuracy of predictions for key forest inventory attributes. For mean top height (a measure of dominant height in a stand), ALS data produced better estimates (R2 = 0.88; RMSE = 1.7 m) than those obtained from satellite data (R2 = 0.81; RMSE = 2.1 m). This was attributable to a general over-estimation of canopy heights in the satellite PCD. ALS models produced poor estimates of stand density (R2 = 0.48; RMSE = 112.1 stems ha−1), as did the satellite PCD models (R2 = 0.42; RMSE = 118.4 stems ha−1). ALS models produced accurate estimates of basal area (R2 = 0.58; RMSE = 12 m2 ha−1), total stem volume (R2 = 0.72; RMSE = 107.5 m3 ha−1), and total recoverable volume (R2 = 0.74; RMSE = 92.9 m3 ha−1). These values differed little from the estimates of basal area (R2 = 0.57; RMSE = 12.2 m2 ha−1), total stem volume (R2 = 0.70; RMSE = 112.6 m3 ha−1), and total recoverable volume (R2 = 0.73; RMSE = 96 m3 ha−1) obtained from satellite PCD models. The statistical and textural metrics computed from the CHMs were important variables in all of the models derived from both satellite and ALS PCD, nearly always outranking the standard PCD metrics in measures of importance. For the satellite PCD models, the CHM-derived metrics were nearly exclusively identified as important variables. These results clearly show that point cloud data obtained from stereo satellite imagery are useful for prediction of forest inventory attributes in intensively managed forests on steeper terrain. Furthermore, these data offer forest managers the benefit of obtaining both inventory data and high-resolution multispectral imagery from a single product. Numéro de notice : A2018-295 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.006 date de publication en ligne : 22/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90413 [article]

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