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Auteur Jason R. Parent
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A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)
Titre : A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape Type de document : Article/Communication Auteurs : Jason R. Parent, Auteur ; John C. Volin, Auteur ; Daniel L. Civco, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 29 Note générale : bibiographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes descripteurs IGN] carte d'occupation du sol
[Termes descripteurs IGN] classification automatique
[Termes descripteurs IGN] classification pixellaire
[Termes descripteurs IGN] Connecticut (Etats-Unis)
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] feuillu
[Termes descripteurs IGN] forêt ripicole
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] pinophyta
Résumé : (auteur) Information on land cover is essential for guiding land management decisions and supporting landscape-level ecological research. In recent years, airborne light detection and ranging (LiDAR) and high resolution aerial imagery have become more readily available in many areas. These data have great potential to enable the generation of land cover at a fine scale and across large areas by leveraging 3-dimensional structure and multispectral information. LiDAR and other high resolution datasets must be processed in relatively small subsets due to their large volumes; however, conventional classification techniques cannot be fully automated and thus are unlikely to be feasible options when processing large high-resolution datasets. In this paper, we propose a fully automated rule-based algorithm to develop a 1 m resolution land cover classification from LiDAR data and multispectral imagery.
The algorithm we propose uses a series of pixel- and object-based rules to identify eight vegetated and non-vegetated land cover features (deciduous and coniferous tall vegetation, medium vegetation, low vegetation, water, riparian wetlands, buildings, low impervious cover). The rules leverage both structural and spectral properties including height, LiDAR return characteristics, brightness in visible and near-infrared wavelengths, and normalized difference vegetation index (NDVI). Pixel-based properties were used initially to classify each land cover class while minimizing omission error; a series of object-based tests were then used to remove errors of commission. These tests used conservative thresholds, based on diverse test areas, to help avoid over-fitting the algorithm to the test areas.
The accuracy assessment of the classification results included a stratified random sample of 3198 validation points distributed across 30 1 × 1 km tiles in eastern Connecticut, USA. The sample tiles were selected in a stratified random manner from locations representing the full range of rural to urban landscapes in eastern Connecticut. The overall land cover accuracy was 93% with accuracies exceeding 90% for deciduous trees, low vegetation, water, buildings, and low impervious cover. Slight confusion occurred between coniferous and deciduous trees; major confusion occurred between water and riparian wetlands; and moderate confusion occurred between medium vegetation and other vegetation classes. The algorithm was robust for the forested suburban landscape of eastern Connecticut, which is typical for much of the northeastern U.S., and the algorithm shows promise for applications in similar landscapes with similar datasets. Further research is needed to test the applicability of the algorithm to more diverse landscapes as well as with different LiDAR and multispectral datasets.
Numéro de notice : A2015-698 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://www.sciencedirect.com/science/article/pii/S092427161500057X Format de la ressource électronique : URL article Permalink :
in ISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 18 - 29[article]Assessing the potential for leaf-off LiDAR data to model canopy closure in temperate deciduous forests / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)
Titre : Assessing the potential for leaf-off LiDAR data to model canopy closure in temperate deciduous forests Type de document : Article/Communication Auteurs : Jason R. Parent, Auteur ; John C. Volin, Auteur Année de publication : 2014 Article en page(s) : pp 134 – 145 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] canopée
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] feuillu
[Termes descripteurs IGN] forêt
[Termes descripteurs IGN] objectif grand angulaire
[Termes descripteurs IGN] photographie aérienne
[Termes descripteurs IGN] télémétrie laser aéroporté
Résumé : (Auteur) Estimates of canopy closure have many important uses in forest management and ecological research. Field measurements, however, are typically not practical to acquire over expansive areas or for large numbers of locations. This problem has been addressed, in recent years, through the use of airborne light detection and ranging (LiDAR) technology which has proven effective in modeling canopy closure remotely. The techniques developed to use LiDAR for this purpose have been designed and evaluated for datasets acquired during leaf-on conditions. However, a large number of LiDAR datasets are acquired during leaf-off conditions since their primary purpose is to generate bare-earth Digital Elevation Models. In this paper, we develop and evaluate techniques for leveraging small-footprint leaf-off LiDAR data to model leaf-on canopy closure in temperate deciduous forests.
We evaluate three techniques for modeling canopy closure: (1) the canopy-to-total-return-ratio (CTRR), (2) the canopy-to-total-pixel-ratio (CTPR), and (3) the hemispherical-viewshed (HV). The first technique has been used widely, in various forms, and has been shown to be effective with leaf-on LiDAR datasets. The CTRR technique that we tested uses the first-return LiDAR data only. The latter two techniques are new contributions that we develop and present in this paper. These techniques use Canopy Height Models (CHM) to detect significant gaps in the forest canopy which are of primary importance in estimating closure.
The techniques we tested each showed good promise for predicting canopy closure using leaf-off LiDAR data with the CTPR and HV models having particularly high correlations with closure estimates from hemispherical photographs. The CTRR model had performance on par with results from previous studies that used leaf-on LiDAR, although, with leaf-off data the model tended to be negatively biased with respect to species having simple and compound leaf types and positively biased for coniferous species. The CTPR and HV models also showed some slight negative biases for compound-leaf species. The biases for the CTPR and HV models were mitigated when the CHM data were smoothed to fill in small gaps. The CHM-based models were robust to changes in the CHM model resolution which suggests that these methods may be applicable to a variety of small-footprint LiDAR datasets. In this research, the new CTPR and HV methods showed a strong ability to predict canopy closure using leaf-off data, however, future work will be needed to test the applicability of the models to variations in LiDAR datasets, forest types, and topography.
Numéro de notice : A2014-477 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink :
in ISPRS Journal of photogrammetry and remote sensing > vol 95 (September 2014) . - pp 134 – 145[article]
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