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Unsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 225 - 236 Titre : Unsupervised object-based differencing for land-cover change detection Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Yanjun Su, Auteur ; Qinghua Guo, Auteur ; Thomas C. Harmon, Auteur Année de publication : 2017 Article en page(s) : pp 225 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme espérance-maximisation [Termes IGN] altération [Termes IGN] autocorrélation [Termes IGN] changement d'occupation du sol [Termes IGN] Chine [Termes IGN] classification non dirigée [Termes IGN] classification orientée objet [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image SPOT-HRV [Termes IGN] occupation du sol [Termes IGN] traitement d'image Résumé : (Auteur) One main problem of the spectral decomposition-based change detection method is the lack of efficient automatic techniques for developing the difference image. Traditional techniques generally assume that gray-level values in a difference image are independent and multitemporal images are co-registered/rectified perfectly without error. However, such assumptions are often violated because of the inevitable image misregistration and the interference of correlations between spectral bands. This study proposes an automated method based on the object-based multivariate alteration detection/maximum autocorrelation factor approach and the Gaussian mixture model-expectation maximization algorithm to obtain unsupervised difference images. This procedure is applied to bi-temporal (2005 and 2006) SPOT-HRV images at Panyu District Ponds, China. Results show that the proposed method successfully excludes the correlations of spectral bands and the influence of misregistration, as evidenced by a higher accuracy (up to 93.6 percent). These unique technical characteristics make this analytical framework suitable for detecting changes. Numéro de notice : A2017-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.225 En ligne : https://doi.org/10.14358/PERS.83.3.225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84424 [article]

Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas / Xiaoqian Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 79 – 91 Titre : Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas Type de document : Article/Communication Auteurs : Xiaoqian Zhao, Auteur ; Qinghua Guo, Auteur ; Yanjun Su, Auteur ; Baolin Xue, Auteur Année de publication : 2016 Article en page(s) : pp 79 – 91 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage numérique d'image [Termes IGN] forêt [Termes IGN] semence [Termes IGN] test de performance [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%. Numéro de notice : A2016-582 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81723 [article]

Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories / Shengli Tao in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 66 – 76 Titre : Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Fangfang Wu, Auteur ; Qinghua Guo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 66 – 76 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] écologie forestière [Termes IGN] segmentation d'image [Termes IGN] télémétrie laser terrestre Résumé : (auteur) The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data. Numéro de notice : A2015-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79444 [article]

A geometric method for wood-leaf separation using terrestrial and simulated Lidar data / Shengli Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 10 (October 2015) . - pp 767 - 776 Titre : A geometric method for wood-leaf separation using terrestrial and simulated Lidar data Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Qinghua Guo, Auteur ; Shiwu Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 767 - 776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] feuille (végétation) [Termes IGN] géométrie [Termes IGN] semis de points [Termes IGN] système de coordonnées [Termes IGN] traitement de données localisées [Termes IGN] tronc Résumé : (auteur) Terrestrial light detection and ranging (lidar) can be used to record the three-dimensional structures of trees. Wood-leaf separation, which aims to classify lidar points into wood and leaf components, is an essential prerequisite for deriving individual tree characteristics. Previous research has tended to use intensity (including a multi-wavelength approach) and waveform information for wood-leaf separation, but use of the most fundamental information from a lidar point cloud, i.e., the x-, y-, and z- coordinates of each point, for this purpose has been poorly explored. In this study, we introduce a geometric method for wood-leaf separation using the x-, y-, and zcoordinates of each point. The separation results indicate that first-, second-, and third-order branches can be extracted from the raw point cloud by this new method, suggesting that it might provide a promising solution for wood-leaf separation. Numéro de notice : A2015-987 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.81.10.767 En ligne : https://doi.org/10.14358/PERS.81.10.767 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80268 [article]

Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass / Le Li in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 198 - 208 Titre : Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass Type de document : Article/Communication Auteurs : Le Li, Auteur ; Qinghua Guo, Auteur ; Shengli Tao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 198 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse forestière [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] régression linéaire Résumé : (auteur) Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250–1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53–74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. Numéro de notice : A2015-694 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78328 [article]

Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)  

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