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Predicting stem total and assortment volumes in an industrial pinus taeda L. forest plantation using airborne laser scanning data and random forest / Carlos Alberto Silva in Forests, vol 8 n° 7 (July 2017)  

Public [article]  inForests > vol 8 n° 7 (July 2017) Titre : Predicting stem total and assortment volumes in an industrial pinus taeda L. forest plantation using airborne laser scanning data and random forest Type de document : Article/Communication Auteurs : Carlos Alberto Silva, Auteur ; Carine Klauberg, Auteur ; Andrew Thomas Hudak, Auteur ; Lee Alexander Vierling, Auteur ; Wan Shafrina Wan Mohd Jaafar, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Brésil [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modèle de simulation [Termes IGN] Pinus taeda [Termes IGN] plantation forestière [Termes IGN] volume en bois Résumé : (Auteur) Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume. Numéro de notice : A2017-875 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f8070254 Date de publication en ligne : 17/07/2017 En ligne : https://doi.org/10.3390/f8070254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91214 [article]

Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology / Jan U.H. Eitel in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 97 (November 2014) . - pp 229 – 240 Titre : Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology Type de document : Article/Communication Auteurs : Jan U.H. Eitel, Auteur ; Troy Magney, Auteur ; Lee Alexander Vierling, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 229 – 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] agriculture de précision [Termes IGN] biochimie [Termes IGN] distribution binomiale [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] données lidar Résumé : (Auteur) Advanced technologies for improved nitrogen (N) fertilizer management are paramount for sustainably meeting future food demands. Green laser systems that measure pulse return intensity can provide more reliable information about foliar N than can traditional passive remote sensing devices during the critical early crop growth stages (e.g., before canopy closure when vegetation and soil signals are spectrally mixed) when further decisions regarding N management can be made. However, current green laser systems are not designed for agricultural applications and only employ a single green laser wavelength, which may limit applications because many factors that require normalization techniques can affect pulse return intensity. Here, we describe the design of a tractor-mountable, green (532 nm)- and red (658 nm) dual wavelength laser system and evaluate the potential of an additional red reference wavelength to improve laser based estimates of foliar N by calculating laser spectral indices based on ratio combinations of green laser return intensity (GLRI) and red laser return intensity (RLRI). We hypothesized that such laser spectral indices aid in accounting for factors that confound laser based foliar N estimates including variations in leaf angle, measurement distance, soil returns, and mixed edge returns. Leaf level measurements in winter wheat (Triticum aestivum) revealed that the two laser spectral indices improved the relationship with foliar N (r2 > 0.71, RMSE Numéro de notice : A2014-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74144 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
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A simple and effective radiometric correction method to improve landscape change detection across sensors and across time / X. Chen in Remote sensing of environment, vol 98 n° 1 (30/09/2005)  

Public [article]  inRemote sensing of environment > vol 98 n° 1 (30/09/2005) . - pp 63 - 79 Titre : A simple and effective radiometric correction method to improve landscape change detection across sensors and across time Type de document : Article/Communication Auteurs : X. Chen, Auteur ; Lee Alexander Vierling, Auteur ; D. Deering, Auteur Année de publication : 2005 Article en page(s) : pp 63 - 79 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] correction radiométrique [Termes IGN] couvert végétal [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] Enhanced vegetation index [Termes IGN] forêt boréale [Termes IGN] groupe [Termes IGN] image Landsat [Termes IGN] image multitemporelle [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] Sibérie Résumé : (Auteur) Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multitemporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forest near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where FVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phonological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes. Numéro de notice : A2005-403 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2005.05.021 En ligne : https://doi.org/10.1016/j.rse.2005.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27539 [article]

Using Lidar and effective LAI data to evaluate Ikonos and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest / X. Chen in Remote sensing of environment, vol 91 n° 1 (15/05/2004)  

Public [article]  inRemote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 14 - 26 Titre : Using Lidar and effective LAI data to evaluate Ikonos and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest Type de document : Article/Communication Auteurs : X. Chen, Auteur ; Lee Alexander Vierling, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 14 - 26 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse comparative [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse multicritère [Termes IGN] couvert forestier [Termes IGN] Dakota du Sud (Etats-Unis) [Termes IGN] données lidar [Termes IGN] forêt [Termes IGN] image Ikonos [Termes IGN] image Landsat-ETM+ [Termes IGN] indice de végétation [Termes IGN] Leaf Area Index [Termes IGN] Pinus ponderosa Résumé : (Auteur) Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAIe) (r2 = 0.55, p Numéro de notice : A2004-235 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.11.003 En ligne : https://doi.org/10.1016/j.rse.2003.11.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26762 [article]