Figure 0.1. Schéma du fonctionnement d’un lidar aéroporté illustrant son interaction avec la végétation. A gauche, un lidar embarqué sur un avion émet un pulse en direction du sol. A droite, l’énergie rétrodiffusée par la végétation et le sol permet de caractériser la structure de la végétation. ... 10 Figure 0.2. Carte des hauteurs d’arbres réalisée à partir de données ICESat (lidar).
Crédit: Lefsky (2010). ... 10 Figure 0.3. Exemple d’une placette de la forêt landaise vue par trois systèmes lidars
différents. A gauche, un lidar terrestre avec une très grande densité de points de 4 points/cm2 à 100 m (soit environ 70 000 000 points pour une placette de 15 m de rayon). Au centre, un lidar aéroporté avec 10 points/m2 (soit près de 7 000 points pour une placette de 15 m de rayon). A droite, un profil de végétation du lidar satellitaire ICESat (avec une empreinte au sol de 35 m de rayon). ... 12 Figure I.1. Approche à l’arbre. Résultats d’une segmentation des houppiers d’une placette
de pins dans les Landes, vue de côté (à gauche) et du dessus (à droite). ... 16 Figure I.2. Approche à la placette. Principales étapes pour le développement d'un modèle
d'estimation d'un attribut forestier et la réalisation de carte de cet attribut à partir de données lidar. ... 17 Figure I.3. Localisation des trois sites d'étude (en bleus). ... 18 Figure I.4. LAD (Leaf Area Density) profile (dashed line) and normalized LAD profile
(solid line). The LAD profile was corrected from occlusion effects to produce a normalized LAD profile... 33 Figure I.5. Observed values of (a,b,c) wood volume, (d,e,f) stem volume, (g,h,i) AGB, and
(j,k,l) BA versus their estimates for the three study sites (coniferous, deciduous and mountainous sites). ... 40 Figure I.6. Observed values of wood volume versus their estimates for the mixed
mountainous forest, which was stratified into three forest types: (a) coniferous, (b) mixed and (c) deciduous stands. ... 41 Figure II.1. Location of the two study sites in the Landes region, in southwestern France. ... 60 Figure II.2. A lidar point cloud acquired at four different pulse densities (0.5, 1, 2, and 4
pulses/m2) at Site 1. ... 64 Figure II.3. Rate of change of RMSE, expressed as a percentage of the RMSE obtained
with the whole field plot data set, i.e. 19.15 Mg/ha with 100 plots, for AGB models calibrated and validated using different subsets of field plots from 100 to 20 at Site 1. Subsets were randomly selected from among all the field plots
the median, with the box representing the 25th and 75th percentiles, the whiskers the 5th and 95th percentiles, and outliers are represented by dots. 70 Figure II.4. Rate of change of RMSE, expressed as a percentage of the RMSE obtained
with the maximum plot radius, i.e. 10.73 Mg/ha with a 15 m radius, for AGB models calibrated and validated using different field plot radius from 15 to 6 m at Site 2. Only the 31 plots collected in Site 2 with radius of 15 m were used. ... 70 Figure II.5. Rate of change in RMSE, expressed as a percentage of the RMSE obtained
with DBHmin used in the field, i.e. 10.73 Mg/ha with DBHmin = 7.5 cm, for AGB models calibrated and validated using different minimum DBH thresholds (DBHmin) from 7.5 to 17.5 cm with regular steps of 0.5 cm. Only the 31 plots collected in Site 2 with radius of 15 m were used. ... 71 Figure II.6. Rate of change of RMSE, expressed as a percentage of the RMSE obtained
with actual GPS precision obtained using both a DGPS and a total station, i.e. 10.73 Mg/ha for plot center position accuracy below 10 m, for AGB models calibrated and validated using field plots shifted by two random error terms (x,y) at Site 2. Error terms were generated with standard deviation ranging from 0 to 10 m with regular steps of 0.5 m. Dark horizontal lines represent the median, with the box representing the 25th and 75th percentiles, the whiskers the 5th and 95th percentiles, and outliers represented by dots. . 72 Figure II.7. Rate of change of RMSE, expressed as a percentage of the RMSE obtained
with actual H measurement values, i.e. 10.73 Mg/ha, for AGB models calibrated and validated using noisy H measurements at Site 2. Error terms were generated with standard deviation ranging from 0 and 0.1 with regular steps of 0.01, corresponding to an error value ranging from 0% to 10%. Dark horizontal lines represent the median, with the box representing the 25th and 75th percentiles, the whiskers the 5th and 95th percentiles, and outliers represented by dots. ... 72 Figure II.8. Rate of change of RMSE, expressed as a percentage of the RMSE obtained
with real DBH measurement values, i.e. 10.73 Mg/ha, for AGB models calibrated and validated using noisy DBH measurements at Site 2. Error terms were generated with standard deviation ranging from 0 to 5 cm with regular steps of 0.5 cm. Dark horizontal lines represent the median, with the box representing the 25th and 75th percentiles, the whiskers the 5th and 95th percentiles, and outliers represented by dots. ... 73 Figure II.9. Histograms of RMSE for the 300,000 Monte Carlo combinations obtained by
varying H, DBH and field plot position measurement errors according to the defined distribution laws, and by varying the allometric equation used to predict AGB at Site 2. Four protocols have been investigated: (a) 7.5 cm minimum DBH threshold (DBHmin) and 15 m field plot radius; (b) 17.5 cm DBHmin and 15 m field plot radius; (c) 7.5 cm DBHmin and 11.28 m field plot radius; and (d) 17.5 cm DBHmin and 11.28 m field plot radius. ... 75
errors, and allometric equations used to predict AGB at Site 2. Four protocols have been investigated: (a) 7.5 cm minimum DBH threshold (DBHmin) and 15 m field plot radius; (b) 17.5 cm DBHmin and 15 m field plot radius; (c) 7.5 cm DBHmin and 11.28 m field plot radius; and (d) 17.5 cm DBHmin and 11.28 m field plot radius. ... 76
Figure III.1. Localisation des deux sites d’études. ... 86 Figure III.2. ΔDIC for floristic models depending on abundance and richness indicators in
the Lowland and Mountain sites. Dark horizontal lines represent the median; boxes represent the 25th and 75th percentiles; whiskers the 5th and 95th percentiles; outliers are represented by dots. The lower the ΔDIC, the more the model is improved by the lidar variable... 102 Figure III.3. ΔDIC for abundance and richness models depending on lidar variables in the
Lowland and Mountain sites. Dark horizontal lines represent the median; boxes represent the 25th and 75th percentiles; whiskers the 5th and 95th percentiles; outliers are represented by dots. The lower the ΔDIC, the more the model is improved by the lidar variable... 103 Figure III.4. Number of lidar variables which were significantly negative or positive non- negligible when used in floristic models. Abundance and richness models were considered in the Lowland and Mountain sites. The lidar variables were extracted from circular plots within the same radius as the field plots (9 m at the Lowland site and 15 m at the Mountain site), and also with radiuses of 50 m, 100 m and 200 m. ... 105 Annexe 1. Carte de volume de bois total du site de conifères (en haut) et du site de feuillus
(en bas). ... 147 Annexe 2. Carte de volume de bois total du site de montagne. ... 148 Annexe 3. Carte de volume de bois marchand du site de conifères (en haut) et du site de
feuillus (en bas). ... 149 Annexe 4. Carte de volume de bois marchand du site de montagne... 150 Annexe 5. Carte de biomasse aérienne du site de conifères (en haut) et du site de feuillus
(en bas). ... 151 Annexe 6. Carte de biomasse aérienne du site de montagne. ... 152 Annexe 7. Carte de surface terrière du site de conifères (en haut) et du site de feuillus (en
bas). ... 153 Annexe 8. Carte de surface terrière du site de montagne. ... 154 Annexe 11. Village de Villiers-le-Sec dans la forêt de feuillus (acquisition en feuilles). .. 159
Bibliographie
Al Bassatneh, M.C., Fady, B., Simon-Teissier, S., Tatoni, T., 2007. Biodiversité floristique et gestion sylvicole. Rev. Int. D’écologie Méditerranéenne Mediterr. J. Ecol. 33, 29.
ALCIMED-PIPAME, 2012. Prospective sur le marché actuel des nouveaux produits issus du bois et des évolutions à échéance 2020.
Allen, R.G., 1997. Self-calibrating method for estimating solar radiation from air temperature. J. Hydrol. Eng. 2, 56–67.
Allouis, T., Durrieu, S., Véga, C., Couteron, P., 2013. Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: Contribution of full- waveform signals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6, 924–934. doi:10.1109/JSTARS.2012.2211863
Alperson-Afil, N., Goren-Inbar, N., 2010. The Acheulian site of Gesher Benot Ya’aqov volume II: Ancient flames and controlled use of fire. Springer.
Andersen, H.-E., Reutebuch, S.E., McGaughey, R.J., 2006. A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Can. J. Remote Sens. 32, 355–366.
Ascough Ii, J.C., Maier, H.R., Ravalico, J.K., Strudley, M.W., 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecol. Model. 219, 383–399.
Asner, G.P., 2009. Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environ. Res. Lett. 4, 034009. doi:10.1088/1748- 9326/4/3/034009
Asner, G.P., Mascaro, J., Muller-Landau, H.C., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J.S., van Breugel, M., 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168, 1147–1160.
Austin, M.P., Van Niel, K.P., 2011. Improving species distribution models for climate change studies: variable selection and scale. J. Biogeogr. 38, 1–8.
Avery, T.E., Burkhart, H.E., 2001. Forest measurements 5th ed. Boston, MA: McGraw- Hill.
Axelsson, P., 2000. DEM generation from laser scanner data using adaptive TIN models. Int. Arch. Photogramm. Remote Sens. 33, 111–118.
Baldini, S., Berti, S., Cutini, A., Mannucci, M., Mercurio, R., Spinelli, R., 1989. Prove sperimentali di primo diradamento in un soprassuolo di pino marittimo (Pinus pinaster Ait.) originato da incendio: aspetti silvicolturali, di utilizzazione e caratteristiche della biomassa. Ann. Ist. Sper. Selvic. 20, 385–436.
essences forestières sur la diversité floristique des forêts tempérées. Université d’Orléans.
Barbier, S., Chevalier, R., Loussot, P., Bergès, L., Gosselin, F., 2009. Improving biodiversity indicators of sustainable forest management: Tree genus abundance rather than tree genus richness and dominance for understory vegetation in French lowland oak hornbeam forests. For. Ecol. Manag. 258, S176–S186.
Barbier, S., Gosselin, F., Balandier, P., 2008. Influence of tree species on understory vegetation diversity and mechanisms involved—a critical review for temperate and boreal forests. For. Ecol. Manag. 254, 1–15.
Baskerville, G.L., 1972. Use of logarithmic regression in the estimation of plant biomass. Can. J. For. Res. 2, 49–53.
Battles, J.J., Shlisky, A.J., Barrett, R.H., Heald, R.C., Allen-Diaz, B.H., 2001. The effects of forest management on plant species diversity in a Sierran conifer forest. For. Ecol. Manag. 146, 211–222.
Bauwens, S., Bartholomeus, H.M., Piboule, A., Calders, K., Lejeune, P., 2014. Improving the efficiency of forest inventory with Terrestrial LiDAR: what about Hand-held Mobile LiDAR?, in: Book of Abstracts ForestSAT2014: A Bridge between Forest Sciences, Remote Sensing and Geo-Spatial Applications.
Bellier, J., Monnet, J.-M., Dupire, S., Cordonnier, T., 2014. Evaluation of the effect of accessibility on forest stand structure with airborne laser scanning data and GIS- based models.
Bolstad, P., Jenks, A., Berkin, J., Horne, K., Reading, W.H., 2005. A Comparison of Autonomous, WAAS, Real-Time, and Post-Processed Global Positioning Systems (GPS) Accuracies in Northern Forests. North. J. Appl. For. 22, 5–11.
Bontemps, J.-D., Bouriaud, O., 2014. Predictive approaches to forest site productivity: recent trends, challenges and future perspectives. Forestry 87, 109–128.
Bouget, C., 2005. Short-term effect of windstorm disturbance on saproxylic beetles in broadleaved temperate forests: Part II. Effects of gap size and gap isolation. For. Ecol. Manag. 216, 15–27.
Boulier, J., Simon, L., 2010. Les forêts au secours de la planète: quel potentiel de stockage du carbone? L’Espace Géographique 309–324.
Bouvier, M., Durrieu, S., Fournier, R.A., Renaud, J.-P., 2015. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sens. Environ. 156, 322–334. doi:10.1016/j.rse.2014.10.004 Bouvier, M., Durrieu, S., Fournier, R.A., Saint-Geours, N., Guyon, D., Grau, E.,
Submitted. Influence of sampling design parameters on biomass predictions from airborne lidar data. Forestry.
Bradbury, R.B., Hill, R.A., Mason, D.C., Hinsley, S.A., Wilson, J.D., Balzter, H., Anderson, G.Q., Whittingham, M.J., Davenport, I.J., Bellamy, P.E., 2005. Modelling relationships between birds and vegetation structure using airborne
environments. Ibis 147, 443–452.
Brandtberg, T., 2007. Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS J. Photogramm. Remote Sens. 61, 325–340. Braun-Blanquet, J., 1964. Pflanzensoziologie: grundzüge der vegetationskunde.
Bréda, N.., 2003. Ground‐based measurements of leaf area index: a review of methods, instruments and current controversies. J. Exp. Bot. 54, 2403–2417.
Brown, S., Lugo, A.E., 1984. Biomass of tropical forests: A new estimate based on forest volumes. Science, New Series 223, 1290–1293. doi:10.2307/1692790
Brunet, J., Falkengren-Grerup, U., Tyler, G., 1996. Herb layer vegetation of south Swedish beech and oak forests—effects of management and soil acidity during one decade. For. Ecol. Manag. 88, 259–272.
Campbell, G.S., Norman, J.M., 1989. The description and measurement of plant canopy structure, in: Plant Canopies: Their Growth, Form and Function. pp. 1–19.
Camp, R.J., Seavy, N.E., Gorresen, P.M., Reynolds, M.H., 2008. A statistical test to show negligible trend: comment. Ecology 89, 1469–1472.
Cao, L., Coops, N.C., Hermosilla, T., Innes, J., Dai, J., She, G., 2014. Using small- footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests. Remote Sens. 6, 7110– 7135.
Carey, A.B., Hardt, M.M., Horton, S.P., Biswell, B.L., 1991. Spring bird communities in the Oregon Coast Range. USDA For. Serv. Gen. Tech. Rep. PNW-GTR-Pac. Northwest Res. Stn. USA.
Cariboni, J., Gatelli, D., Liska, R., Saltelli, A., 2007. The role of sensitivity analysis in ecological modelling. Ecol. Model. 203, 167–182.
Celeux, G., Forbes, F., Robert, C.P., Titterington, D.M., 2006. Deviance information criteria for missing data models. Bayesian Anal. 1, 651–673.
Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B.W., Ogawa, H., Puig, H., Riéra, B., Yamakura, T., 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99. doi:10.1007/s00442-005-0100-x
Chave, J., Condit, R., Aguilar, S., Hernandez, A., Lao, S., Perez, R., 2004. Error propagation and scaling for tropical forest biomass estimates. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 359, 409–420.
Chen, Q., 2013. Lidar remote sensing of vegetation biomass. Remote Sens. Nat. Resour. CRC Press Taylor Francis Group 399–420.
Chen, Q., 2010. Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry. Remote Sens. Environ. 114, 1610–1627. doi:10.1016/j.rse.2010.02.016
volume for individual trees from lidar data. Photogramm. Eng. Remote Sens. 73, 1355.
Clark, J.S., 2005. Why environmental scientists are becoming Bayesians. Ecol. Lett. 8, 2– 14.
Cohen, W.B., Spies, T.A., 1992. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery. Remote Sens. Environ. 41, 1–17. doi:10.1016/0034-4257(92)90056-P
CREM, 2009. Guidance on the development evaluation and application of environmental models (Technical Report). US Environmental Protection Agency.
Crutzen, P.J., Stoermer, E.F., 2000. The Anthropocene IGBP Newsletter, 41. R. Swed. Acad. Sci. Stockh. Swed.
Dale, M.R., 1999. Spatial pattern analysis in plant ecology. Camb. U. K.
Dalponte, M., Bruzzone, L., Gianelle, D., 2008. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. Geosci. Remote Sens. IEEE Trans. On 46, 1416–1427.
Dassot, M., Colin, A., Santenoise, P., Fournier, M., Constant, T., 2012. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput. Electron. Agric. 89, 86–93. Dassot, M., Constant, T., Fournier, M., 2011. The use of terrestrial LiDAR technology in
forest science: application fields, benefits and challenges. Ann. For. Sci. 68, 959– 974.
Deleuze, C., Senga Kiessé, T., Renaud, J.-P., Morneau, F., Rivoire, M., Santenoise, P., Longuetaud, F., Mothe, F., Hervé, J.-C., Bouvet, A., 2013. Rapport final EMERGE sur les modèles de volumes. Projet ANR- 08-BIOE-003, Programme BIOE 2008. Dik, E.J., 1984. Estimating the wood volumes of standing trees in forestry practice.
Uitvoerig Versl. DorschkampNetherlands 19, 1–114.
Dixon, P.M., Pechmann, J.H., 2005. A statistical test to show negligible trend. Ecology 86, 1751–1756.
Dubayah, R., Knox, R., Hofton, M., Blair, J.B., Drake, J., 2000. Land surface characterization using lidar remote sensing, in: Spatial Information for Land Use Management. International Publishers Direct, Singapore, pp. 25–38.
Duguid, M.C., Ashton, M.S., 2013. A meta-analysis of the effect of forest management for timber on understory plant species diversity in temperate forests. For. Ecol. Manag. 303, 81–90.
Duncanson, L.I., Cook, B.D., Hurtt, G.C., Dubayah, R.O., 2014. An efficient, multi- layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sens. Environ.
Duplat, P., Perrotte, G., France, O.N. des F., 1981. Inventaire et estimation de l’accroissement des peuplements forestiers. Office national des forêts.
structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis. Int. J. Appl. Earth Obs. Geoinformation 25, 76–86. doi:10.1016/j.jag.2013.04.001
Durrieu, S., Véga, C., Bouvier, M., Gosselin, F., Renaud, J.-P., Saint-André, L., in press. Optical remote sensing of tree and stand heights, in: Remote Sensing Handbook, Vol. 2, Land Resources Monitoring, Modeling, and Mapping. Taylor & Francis Group.
Eid, T., Gobakken, T., Næsset, E., 2004. Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses. Scand. J. For. Res. 19, 512–523.
Ellenberg, H., Weber, H.E., Düll, R., Wirth, V., Werner, W., Paulißen, D., 1992. Zeigerwerte von Pflanzen in Mitteleuropa. Verl. Goltze Gött.
Ellison, A.M., 1996. An introduction to Bayesian inference for ecological research and environmental decision-making. Ecol. Appl. 1036–1046.
European Commission, 2009. Impact Assessment Guidelines. SEC.
Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., Crookston, N.L., 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Can. J. Remote Sens. 32, 153–161.
Fang, J.Y., Liu, G.H., Xu, S.L., 1996. Biomass and net production of forest vegetation in China. Acta Ecol. Sin. 16, 497–508.
FAO, 2011. State of the World’s Forests 2011. Food and Agriculture Organization of the United Nations (FAO) Rome, Italy.
FAO, 2010. Global Forest Resources Assessment 2010 Main Report. FAO.
Ferster, C.J., Coops, N.C., Trofymow, J.A., 2009. Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar. Can. J. Remote Sens. 35, 270–275.
Finney, D.J., 1941. On the distribution of a variate whose logarithm is normally distributed. Suppl. J. R. Stat. Soc. 155–161.
Fisher, R.A., 1935. The design of experiments., Oliver & Boyd, Edinburgh. ed.
Franklin, J., 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Prog. Phys. Geogr. 19, 474–499. Franklin, J.F., Spies, T.A., Pelt, R.V., Carey, A.B., Thornburgh, D.A., Berg, D.R.,
Lindenmayer, D.B., Harmon, M.E., Keeton, W.S., Shaw, D.C., 2002. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For. Ecol. Manag. 155, 399– 423.
Franklin, S.E., 2001. Remote sensing for sustainable forest management. CRC Press, Boca Raton.
fonction de l’exportation de biomasse. Optimiser Gest. Peuplements Pin Marit. Pour Un Essor Ind. Durable.
Frazer, G.W., Magnussen, S., Wulder, M.A., Niemann, K.O., 2011. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sens. Environ. 115, 636–649.
García, M., Riaño, D., Chuvieco, E., Danson, F.M., 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens. Environ. 114, 816–830.
García-Quijano, M.J., Jensen, J.R., Hodgson, M.E., Hadley, B.C., Gladden, J.B., Lapine, L.A., 2008. Significance of altitude and posting density on LiDAR-derived elevation accuracy on hazardous waste sites. Photogramm. Eng. Remote Sens. 74, 1137–1146.
Gégout, J.-C., Coudun, C., Bailly, G., Jabiol, B., 2005. EcoPlant: a forest site database linking floristic data with soil and climate variables. J. Veg. Sci. 16, 257–260. Gelman, A., Rubin, D.B., 1992. Inference from iterative simulation using multiple
sequences. Stat. Sci. 457–472.
Genet, A., Wernsdörfer, H., Jonard, M., Pretzsch, H., Rauch, M., Ponette, Q., Nys, C., Legout, A., Ranger, J., Vallet, P., 2011. Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe. For. Ecol. Manag. 261, 1188–1202.
Georges-Leroy, M., Bock, J., Dambrine, E., Dupouey, J.-L., Laffite, J.-D., 2014. Parcellaires et habitat antiques des forêts du plateau de Haye en Lorraine: bilan et perspectives, in: Les Parcellaires Conservés Sous Forêt. Paris, France.
Germaine, S.S., Vessey, S.H., Capen, D.E., 1997. Effects of small forest openings on the breeding bird community in a Vermont hardwood forest. Condor 708–718.
Getzin, S., Wiegand, K., Schöning, I., 2012. Assessing biodiversity in forests using very high‐resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 3, 397– 404.
Gilliam, F.S., Roberts, M.R., 1995. Impacts of forest management on plant diversity. Ecol. Appl. 5, 911–912.
Ginet, C., Carette, T., 2014. Cartomob - Outil de simulation et d’aide à la décision pour la gestion et l’exploitation forestière.
Gleason, C.J., Im, J., 2011. A review of remote sensing of forest biomass and biofuel: options for small-area applications. GIScience Remote Sens. 48, 141–170.
Gobakken, T., Næsset, E., 2009. Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Can. J. For. Res. 39, 1036–1052.
intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can. J. For. Res. 38, 1095–1109.
Goerndt, M.E., Monleon, V.J., Temesgen, H., 2010. Relating forest attributes with area- and tree-based light detection and ranging metrics for western Oregon. West. J. Appl. For. 25, 105–111.
Goetz, S., Dubayah, R., 2011. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Manag. 2, 231– 244.
Goodwin, N.R., Coops, N.C., Culvenor, D.S., 2006. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens. Environ. 103, 140–152.
Greaves, H.E., Vierling, L.A., Eitel, J.U., Boelman, N.T., Magney, T.S., Prager, C.M., Griffin, K.L., 2015. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sens. Environ. 164, 26–35.
Gregoire, T.G., Valentine, H.T., 2007. Sampling strategies for natural resources and the environment. CRC Press.
Hall, F.G., Bergen, K., Blair, J.B., Dubayah, R., Houghton, R., Hurtt, G., Kellndorfer, J., Lefsky, M., Ranson, J., Saatchi, S., 2011. Characterizing 3D vegetation structure from space: Mission requirements. Remote Sens. Environ. 115, 2753–2775.
Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R., Stoker, J.M., 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. For. Ecol. Manag. 208, 189–209.
Harrell, F.E., 2001. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer.
Herpigny, B., Gosselin, F., 2015. Analyzing plant cover class data quantitatively: Customized zero-inflated cumulative beta distributions show promising results. Ecol. Inform. 26, Part 3, 18–26. doi:10.1016/j.ecoinf.2014.12.002