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Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape / M.L. Clarke in Remote sensing of environment, vol 91 n° 1 (15/05/2004)
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Titre : Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape Type de document : Article/Communication Auteurs : M.L. Clarke, Auteur ; D. Clark, Auteur ; D.A. Roberts, Auteur Année de publication : 2004 Article en page(s) : pp 68 - 89 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] corrélation linéaire
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] forêt tropicale
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] sous-bois
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.
A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 in tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r 2 = 0,97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.Numéro de notice : A2004-237 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.02.008 En ligne : https://doi.org/10.1016/j.rse.2004.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26764
in Remote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 68 - 89[article]Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis / R. Lawrence in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
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Titre : Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis Type de document : Article/Communication Auteurs : R. Lawrence, Auteur ; A. Bunn, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 331 - 336 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-ETM+
[Termes IGN] image PROBE
[Termes IGN] précision de la classification
[Termes IGN] sylvicultureRésumé : (Auteur) Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the training data and (2) combining the multiple trees iteratively developed to classify the data. We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City mining district of Montana, and a series of Landsat ETM+ images from the Greater Yellowstone Ecosystem (GYE). SGB improved the overall accuracy of the IKONOS classification from 84% to 95% and the Probe-1 classification from 83% to 93%. The worst performing classes using CTA exhibited the largest increases in class accuracy using SGB. A slight decrease in overall classification accuracy resulted from the SGB analysis of the Landsat data. Numéro de notice : A2004-200 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.007 En ligne : https://doi.org/10.1016/j.rse.2004.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26727
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 331 - 336[article]Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions / Matti Maltamo in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
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Titre : Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions Type de document : Article/Communication Auteurs : Matti Maltamo, Auteur ; Kalle Eerikäinen, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 319 - 330 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] couvert forestier
[Termes IGN] Finlande
[Termes IGN] forêt
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] mesure de précision
[Termes IGN] Pinophyta
[Termes IGN] sylviculture
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] troncRésumé : (Auteur) Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the twoparameter left-truncated Weibull distribution is fitted to the detected tree height distribution.
The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25% when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75%. Predictions for both characteristics are also highly biased and the underestimates are 24% and 62%, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0% and 49.2%, respectively. The bias for the estimates is also decreased to 6.3% for timber volume and 8.2% for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5% and 72.7%, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories.Numéro de notice : A2004-199 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.006 En ligne : https://doi.org/10.1016/j.rse.2004.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26726
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 319 - 330[article]Hyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests / Prasad S. Thenkabail in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
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Titre : Hyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests Type de document : Article/Communication Auteurs : Prasad S. Thenkabail, Auteur ; E.A. Enclona, Auteur ; M.S. Ashton, Auteur ; C. Legg, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 23 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] Cameroun
[Termes IGN] carbone
[Termes IGN] carte de la végétation
[Termes IGN] classification
[Termes IGN] Congo (bassin)
[Termes IGN] forêt équatoriale
[Termes IGN] image EO1-ALI
[Termes IGN] image EO1-Hyperion
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-ETM+
[Termes IGN] indice de végétation
[Termes IGN] masse végétale
[Termes IGN] occupation du solRésumé : (Auteur) The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and anced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semideciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes. The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors. When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed. Numéro de notice : A2004-127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.11.018 En ligne : https://doi.org/10.1016/j.rse.2003.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26654
in Remote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 23 - 43[article]The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery / J.R. Dymond in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
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Titre : The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery Type de document : Article/Communication Auteurs : J.R. Dymond, Auteur ; J.D. Shepherd, Auteur Année de publication : 2004 Article en page(s) : pp 116 - 125 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biodiversité
[Termes IGN] carte de la végétation
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] éclairement énergétique
[Termes IGN] Fagus (genre)
[Termes IGN] feuillu
[Termes IGN] forêt
[Termes IGN] image Landsat-ETM+
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Nouvelle-Zélande
[Termes IGN] Pinophyta
[Termes IGN] réflectance végétaleRésumé : (Auteur) In order to improve biodiversity management in the Wellington region of New Zealand, it is necessary to make an inventory of the indigenous forest-where is it, and what type is it? The single greatest impediment to making a spatially (i.e., 1:50,000 scale) and thematically detailed inventory from satellite imagery has been the topography of the three mountainous ranges in the Wellington region. The effective irradiance of incoming light varies with slope orientation, as does the proportion of light that is reflected towards the satellite (the bidirectional reflectance). In this paper, we show how satellite imagery may be processed to standardised spectral reflectance, which is a property of the vegetation alone, independent of sun position, slope, and view direction. Because of this, the use of automatic methods to map vegetation and provide spatially and thematically detailed maps is greatly simplified. Using this method, we produce a land-cover map of the Wellington region, with eight classes, to a classification accuracy of approximately 95%. We also show how the proportions of conifer, broadleaved, and beech trees may be determined for indigenous forest to provide a framework for forest-type inventory. Numéro de notice : A2004-131 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.11.013 En ligne : https://doi.org/10.1016/j.rse.2003.11.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26658
in Remote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 116 - 125[article]Dynamique de la déforestation et agriculture pionnière dans le sud-ouest de Madagascar : exploitation diachronique de l'imagerie satellitale haute résolution / F. Lasry in Photo interprétation, vol 40 n° 1 (Mars 2004)
PermalinkDynamiques et représentations spatiales de la déforestation en Côte d'Ivoire : l'exemple de la foret classée du Haut-Sassandra (1986-2001) / J. Oszwald in Photo interprétation, vol 40 n° 1 (Mars 2004)
PermalinkPermalinkImproving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing / C. Tottrup in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)
PermalinkProspects for quantifying structure, floristic composition and species richness of tropical forests / T.W. Gillespie in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)
PermalinkPredicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features / Onisimo Mutanga in Remote sensing of environment, vol 89 n° 3 (15/02/2004)
PermalinkRemote sensing in BOREAS [BOReal Ecosystem Atmosphere Study]: Lessons learned / John A. Gamon in Remote sensing of environment, vol 89 n° 2 (30/01/2004)
PermalinkPermalinkMise en œuvre d'une solution de cartographie en ligne appliquée au document de gestion de l'espace agricole et forestier de la Savoie / Hélène Buissart (2004)
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