PE&RS July 2019 - page 507

Gómez, C. and D. Green. 2017. Small unmanned airborne systems to
support oil and gas pipeline monitoring and mapping.
Arabian
Journal of Geoscience
10:202.
Hafner, J., H. S. Sawhney, W. Equitz, M. Flickner and W. Niblack.
1995. Efficient color histogram indexing for Quadratic Form
Distance functions.
IEEE Transactions on Pattern Analysis and
Machine Intelligence
17:729–736.
Hauglin, M., T. Gobakken, R. Astrup, L. Ene and E. Næsset. 2014.
Estimating single-tree crown biomass of Norway spruce by
airborne laser scanning: A comparison of methods with and
without the use of terrestrial laser scanning to obtain the ground
reference data.
Forests
5:384–403.
Herrera, P. J., G. Pajares, M. Guijarro, J. J. Ruz and J. M. Cruz. 2011. A
stereovision matching strategy for images captured with fish-eye
lenses in forest environments. Sensors 11:1756–1783.
Herrera, P. J., G. Pajares, M. Guijarro, J. J. Ruz, J. M. Cruz and F.
Montes. 2009. A featured-based strategy for stereovision
matching in sensors with fish-eye lenses for forest environments.
Sensors 9:9468–9492.
Hilker, T., M. A. Wulder and N. C. Coops. 2008. Update of forest
inventory data with lidar and high spatial resolution satellite
imagery.
Canadian Journal of Remote Sensing
34:5–12.
Holmgren J., Å. Persson and U. Söderman. 2008. Species
identification of individual trees by combining high resolution
LiDAR
data with multi-spectral images.
International Journal of
Remote Sensing
29:1537–1552.
Korpela, I., T. Tuomola and E. Välimäki. 2007. Mapping forest plots:
An efficient method combining photogrammetry and field
triangulation.
Silva Fennica
41:457–469.
Leberl, F., A. Irschara, T. Pock, P. Meixner, M. Gruber, S. Scholz and
A. Wiechert. 2010. Point clouds:
LiDAR
versus three dimensional
vision.
Photogrammetric Engineering and Remote Sensing
76:1123–1134.
Liang, X., V. Kankare, J. Hyypä, Y. Wang, A. Kukko, H. Haggrén, X.
Yu, H. Kaartinen, A. Jaakkola, F. Guan, M. Holopainen and M.
Vastaranta. 2016. Terrestrial laser scanning in forest inventories.
ISPRS Journal of Photogrammetry and Remote Sensing
115:63–
77. doi:
.
Lindberg, E., J. Holmgren, K. Olofsson and H. Olsson. 2012.
Estimation of stem attributes using a combination of terrestrial
and airborne laser scanning.
European Jour
Research
131 (6):1917–1931.
Lovell, J. L., D. L. B. Jupp, D. S. Culvenor and N.
Using airborne and ground-based ranging li
canopy structure in Australian forests.
Canadian Journal of
Remote Sensing
29:607–622. doi: 10.5589/m03-026.
Lovell, J., D. B. L. Jupp, G. Newnham and D. Culvenor. 2011.
Measuring tree stem diameters using intensity profiles from
ground-based scanning Lidar from a fixed viewpoint.
ISPRS
Journal of Photogrammetry and Remote Sensing
66:46–55. doi:
10.1016/j.isprsjprs.2010.08.006.
Magnussen, S., E. Næsset and T. Gobakken. 2013. Prediction of tree
size distributions and inventory variables from cumulants of
canopy height distributions.
Forestry
86:583–595.
Maltamo, M., H. O. Ørka, O. M. Bollandsås, T. Gobakken and E.
Næsset. 2015. Using pre-classification to improve the accuracy
of species-specific forest attribute estimates from airborne laser
scanner data and aerial images.
Scandinavian Journal of Forest
Research
30:336–345.
Manfreda, S., M. F. McCabe, P. E. Miller, R. Lucas, V. Pajuelo-
Madrigal, G. Mallinis, E. Ben-Dor, D. Helman, L. Estes, G.
Ciraolo, J. Müllerová, F. Tauro, M. I. de Lima, J. L. M. P. de Lima,
A. Maltese, F. Frances, K. Cylor, M. Kohv, M. Perks, G. Ruiz-
Pérez, Z. Su, G. Vico and B. Toth. 2018. On the use of unmanned
aerial systems for environmental monitoring.
Remote Sensing
10:641.
Mandallaz, D. and R. Ye. 1999. Forest inventory with optimal two-
phase two-stage sampling schemes based on the anticipated
variance.
Canadian Journal of Forest Research
29:1691–1708.
Marino, E., F. Montes, J. L. Tomé, J. A. Navarro and C. Hernando.
2018. Vertical forest structure analysis for wild re prevention:
Comparing airborne laser scanning data and stereoscopic
hemispherical images.
International Journal of Applied Earth
Observation and Geoinformation
73:438–449.
Marques, F. F. C. and S. T. Buckland. 2003. Incorporating covariates
into standard line transect analyses.
Biometrics
59:924–935. doi:
10.1111/j.0006-341X.2003.00107.x.
Matasci, G., T. Hermosilla, M. A. Wulder, J. C. White, N. C. Coops, J.
W. Hobart, D. Bolton, P. Tompalski and C. W. Bater. 2018. Three
decades of forest structural dynamics over Canada’s forested
ecosystems using Landsat time-series and Lidar plots.
Remote
Sensing of Environment
216:697–714.
Miller, D. L. and L. Thomas. 2015. Mixture models for distance
detection functions.
PLoS ONE
10:e0118726. doi: 10.1371/
journal.pone0118726.
Montes, F., A. Ledo, A. Rubio, P. Pita and I. Cañellas. 2009. Use of
stereoscopic hemispherical images for forest inventories. In
90th
anniversary of the Forestry Faculty in Prague, Forest Wildlife
and Wood Sciences for Society Development
, held in Prague,
Czech Republic, 16–18 April 2009. Edited by R.,Z., E. and M.
Prague, Czech Republic: Czech University of Life Sciences.
ISBN: 978-80-213-2019-2.
Morsdorf, F., B. Kotz, E. Meier, K. I. Itten and B. Allgower. 2006.
Estimation of LAI and fractional cover from small footprint
airborne laser scanning data based on gap fraction.
Remote
Sensing of Environment
104: 50–61.
Næsset, E. 2002. Predicting forest stand characteristics with airborne
scanning laser using a practical two-stage procedure and field
data.
Remote Sensing of Environment
80:88–99.
Næsset, E. and T. Gobakken. 2008. Estimation of above- and
belowground biomass across regions of the boreal forest zone
using airborne laser.
Remote Sensing of Environment
112:3079–
3090. doi:10.1016/j.rse.2008.03.004.
Newnham, G. J., J. D. Armston, K. Calders, M. I. Disney, J. L. Lovell,
C. B. Schaaf, A. H. Strahler and F. M. Danson. 2015. Terrestrial
laser scanning for plot-scale forest measurement.
Current
Forestry Reports
1:239–251. doi:10.1007/s40725-015-0025-5.
Pimont, F., J-L. Dupuy, E. Rigolot, V. Prat and A. Piboule. 2015.
Estimating leaf bulk density distribution in a tree canopy using
Li
DAR
and a straightforward calibration procedure.
ing
7:7995–8018.
uddenbaum and J. Hill. 2012. An efficient approach
ing the processing of hemispherical images for the
estimation of forest structural attributes.
Agricultural and Forest
Meteorology
160:1–13.
Puttonen, E., J. Suomalainen, T. Hakala, E. Räikkönen, H. Kaartinen,
S. Kaasalainen and P. Litkey. 2010. Tree species classification
from fused active hyperspectral reflectance and LIDAR
measurements.
Forest Ecology and Management
10:1843–1852.
Qi, W., Lee, S. Hancock, S. Luthcke, H. Tang, J. Armston and R.
Dubayah. 2019. Improved forest height estimation by fusion of
simulated GEDI Lidar data and TanDEM-X InSAR data.
Remote
Sensing of Environment
221:621–634.
Raumonen, P., M. Kaasalainen, M. Åkerblom, S. Kaasalainen,
H. Kaartinen, M. Vastaranta, M. Holopainen, M. Disney and
P. Lewis. 2013. Fast automatic precision tree models from
terrestrial laser scanner data.
Remote Sensing
5:491–520.
Ringdahl, O., P. Hohnloser, T. Hellström, J. Holmgren and O.
Lindroos. 2013. Enhanced algorithms for estimating tree trunk
diameter using 2D laser scanner.
Remote Sensing
5:4839–4856.
doi: 10.3390/rs5104839.
Rodríguez-García, C., F. Montes, F. Ruiz, I. Cañellas and P. Pita.
2014. Stem mapping and estimating standing volume from
stereoscopic hemispherical images.
European Journal of Forest
Research
133:895–904.
Rubio-Cuadrado, A., J. J. Camarero, M. del Río, M. Sánchez-González,
R. Ruiz-Peinado, A. Bravo-Oviedo, L. Gil and F. Montes.
2018a. Drought modifies tree competitiveness in an oak-beech
temperate forest.
Forest Ecology and Management
429:7–17. doi:
10.1016/j.foreco.2018.06.035.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2019
507
463...,497,498,499,500,501,502,503,504,505,506 508,509,510,511,512,513,514,515,516,517,...530
Powered by FlippingBook