Publications
Feb 29 2024
Ilkka Korpela
Articles in international refereed scientific journals (44) – up-to-date
Anna Shcherbacheva et al.
2024. A study of annual tree-wise LiDAR intensity
patterns of boreal species observed using a hyper-temporal laser scanning time
series. Remote Sensing of Environment.
Polvivaara et al. 2024. Detecting tree mortality using
waveform features of airborne LiDAR. Remote Sensing of Environment.
Zhang et al. 2024 Comparison of Semi-physical and
Empirical Models in the Estimation of Boreal Forest Leaf Area Index and
Clumping with Airborne Laser Scanning Data. IEEE TGRS. (to
appear)
Korpela I, Polvivaara A, Hovi A, Junttila S,
Holopainen M 2023. Influence of phenology on
waveform features in deciduous and coniferous trees in airborne LiDAR. Remote Sensing of Environment.
Korpela I, Polvivaara A, Papunen
S, Jaakkola L, Tienaho N, Uotila J, Puputti T, Flyktman A 2023. Airborne dual-wavelength
waveform LiDAR improves species classification accuracy of boreal broadleaved
and coniferous trees. Silva Fennica 56(4). https://doi.org/10.14214/sf.22007
Ribas Costa, Vicent ; Durand, Maxime; Robson, T
Matthew; Porcar-Castell, Albert; Korpela, Ilkka;
Atherton, Jon 2022. UAS spherical
photography for the vertical characterization of canopy structural traits. New Phytologist pp-pp.
Eelis Halme, Olli Ihalainen, Ilkka Korpela and
Matti Mottus 2022. Assessing spatial variability and estimating mean
crown diameter in boreal forests using variograms and amplitude spectra of
very-high-resolution remote sensing data. International Journal of Remote Sensing pp-pp.
https://doi.org/10.1080/01431161.2021.2018148
Alekseychik, P., Korrensalo, A., Mammarella,
I., Launiainen, S., Tuittila, E.-S., Korpela,
I., Vesala, T. 2021. Carbon balance of a Finnish bog: temporal variability
and limiting factors based on 6 years of eddy-covariance data. Biogeosciences 18(16):4681-4704. https://bg.copernicus.org/articles/18/4681/2021/
Alekseychik, P., Katul, G., Korpela, I.,
Launiainen, S. 2021. Eddies
in motion: visualizing boundary-layer turbulence above an open boreal peatland
using UAS thermal videos. Atmos. Meas. Tech. AMT 14(5): 3501-3521. https://amt.copernicus.org/articles/14/3501/2021/
Korpela, I., Haapanen, R.,
Korrensalo, A., Tuittila, E.-S., Vesala, T. 2020. Fine-resolution
mapping of microforms on a boreal bog using aerial images and
waveform-recording LiDAR. Mires and Peat. DOI: 10.19189/MaP.2018.OMB.388
Blomley, R., Hovi, A., Weinmann, M., Hinz, S., Korpela, I., Jutzi, B.
2017. Tree species classification using within
crown localization of waveform LiDAR attributes. ISPRS JPRS.
Majasalmi, T., Korhonen, L., Korpela, I.,
Vauhkonen, J. 2017. Application of 3D
triangulations of airborne laser scanning data to estimate boreal forest leaf
area index. International
Journal of Applied Earth Observation and Geoinformation
Korpela, I.
2017..Acquisition and
evaluation of radiometrically comparable multi-footprint airborne LiDAR data
for forest remote sensing. Remote Sensing of Environment
Matthieu Molinier *, Carlos
A. López-Sánchez, Timo Toivanen, Ilkka Korpela, José J.
Corral-Rivas, Renne Tergujeff, Tuomas Häme. 2016. Mobile and
Participative In-Situ Forest Biomass Measurements Supporting Satellite Image
Mapping. Remote Sensing.
Hovi, A., Korhonen, L., Vauhkonen, J., Korpela, I. 2015.LiDAR waveform features for tree species classification
and their sensitivity to tree- and acquisition related parameters.Remote Sensing of Environment.
Korpela, I., Mehtätalo, L., Markelin,
L., Seppänen, A. &Kangas, A. 2014.Tree
species identification in aerial image data using directional reflectance
signatures. Silva Fennica 48(3) ID 1087. http://dx.doi.org/10.14214/sf.1087.
Pant, P., Heikkinen, V., Korpela, I. , Hauta-Kasari, M.&
Tokola, T. 2014. Logistic Regression Based Spectral Band Selection for
Tree Species Classification – Effects of Spatial Scale and Balance in Training
Samples. IEEE Geoscience and
Remote Sensing Letters 11(9).
http://dx.doi.org/10.1109/LGRS.2014.2301864.
Hovi A.& Korpela I. 2013. Real and simulated waveform-recording LiDAR data in
juvenile boreal forest vegetation. Remote Sensing of Environment 140, 665-678.
http://dx.doi.org/10.1016/j.rse.2013.10.003,
Pant, P., Heikkinen, V., Hovi, A., Korpela, I.,
Hauta-Kasari, M. & Tokola, T. 2013. Evaluation of simulated bands in airborne optical
sensors for tree species identification.
Remote Sensing of Environment 138, p. 27-37.
http://dx.doi.org/10.1016/j.rse.2013.07.016.
Korhonen, L., Heiskanen, J. & Korpela, I. 2013. Modelling lidar-derived boreal forest canopy cover
with SPOT 4 HRVIR data. International
Journal of Remote Sensing. 34, 22, p. 8172-8181. http://dx.doi.org/10.1080/01431161.2013.833361
Korpela, I., Hovi,. A. &Korhonen, L.
2013. Backscattering of individual LiDAR
pulses from forest canopies explained by photogrammetrically derived vegetation
structure. ISPRS Journal of Photogrammetry and
Remote Sensing 83, p. 81-93. http://dx.doi.org/10.1016/j.isprsjprs.2013.06.002
Korhonen, L., Vauhkonen, J., Virolainen, A., Hovi, A.& Korpela, I.
2013. Estimation of tree crown volume from airborne
lidar data using computational geometry. International Journal Remote Sensing 34(20), 7236-7248. http://dx.doi.org/10.1080/01431161.2013.817715
Markelin, L., Honkavaara, E., Schläpfer,
D., Bovet, S.& Korpela, I. 2012. Assessment of radiometric correction methods for ADS40
imagery. Photogrammetrie, Ferkundung, Geoinformation
(PFG) 2012/3, 251-266. http://dx.doi.org/10.1127/1432-8364/2012/0115
Korpela, I., Hovi, A. &Morsdorf, F.
2012. Understory trees in
airborne LiDAR data - Selective mapping due to transmission losses and
echo-triggering mechanisms. Remote Sensing of
Environment 119, 92-104. http://dx.doi.org/10.1016/j.rse.2011.12.011
Vastaranta, M., Korpela, I., Hovi, A., Uotila, A. &Holopainen,
M. 2011. Mapping of
snow-damaged trees based on bitemporal airborne LiDAR data. European Journal of Forest Reserach 131(4) 1217-1228.
http://dx.doi.org/10.1007/s10342-011-0593-2
Korpela, I.,
Heikkinen, V., Honkavaara, E., Rohrbach
F. &Tokola, T. 2011. Variation and anisotropy
of reflectance of forest trees in radiometrically calibrated airborne line
sensor images – implications for species classification in digital aerial
images. Remote Sensing of Environment
115(8), http://dx.doi.org/10.1016/j.rse.2011.04.008.
Heikkinen, V., Korpela I., Honkavaara E.,
Parkkinen, J.&Tokola, T. 2011.Classification of tree species using support vector
machines and radiometrically corrected multiangular
airborne image data. IEEE Transactions on
Geoscience and Remote Sensing48(3), 1355-1364. http://dx.doi.org/10.1109/TGRS.2009.2032239
Korhonen L., Korpela, I., Heiskanen, J.&Maltamo,
M. 2010. Airborne discrete-return LiDAR data in the
estimation of vertical canopy cover, angular canopy closure and leaf area index.
Remote Sensing of Environment 115(4), 1065-1080.
http://dx.doi.org/10.1016/j.rse.2010.12.011
Korpela I, Ørka H.O., Heikkinen V, Tokola T,
&Hyyppä J. 2010. Range-
and AGC normalization of LIDAR intensity data for vegetation classification.
ISPRS Journal of Photogrammetry and Remote Sensing
65(4), 369-379. http://dx.doi.org/10.1016/j.isprsjprs.2010.04.003
Korpela, I.,
Ørka, H.O., Maltamo, M.,
Tokola, T. & Hyyppä, J. 2010. Tree species classification using airborne LiDAR –
effects of stand and tree parameters, downsizing of training set, intensity
normalization and sensor type. Silva
Fennica 44(2). http://m.metla.eu/silvafennica/full/sf44/sf442319.pdf
Vauhkonen J., Korpela, I., Maltamo,
M. &Tokola, T. 2010. Imputation
of single-tree attributes using airborne laser scanning-based height,
intensity, and alpha shape.Remote Sensing of Environment
114(6), 1265-1276.
http://dx.doi.org/10.1016/j.rse.2010.01.016
Heikkinen, V., Tokola, T., Parkkinen, J., Korpela,I. &Jääskeläinen T. 2009 Simulated Multispectral Imagery for Tree Species
Classification Using Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing48(3),
1355-1364. http://dx.doi.org/10.1109/TGRS.2009.2032239.
Korpela, I., Koskinen, M., Vasander, H., Holopainen, M. &Minkkinen, K., 2009.
Airborne small-footprint discrete-return
LiDAR data in the assessment of boreal mire surface patterns, vegetation and habitats.
Forest Ecology and Management 258 (7):1549-1566.http://dx.doi.org/10.1016/j.foreco.2009.07.007
Korpela I., Tuomola T., Tokola T. & Dahlin, B. 2008. Appraisal of seedling stand vegetation with airborne
imagery and discrete-return LiDAR – an exploratory analysis.Silva
Fennica 42(5):
753-772.http://www.metla.fi/silvafennica/full/sf42/sf425753.pdf
Korpela, I. 2008. Mapping of understory
lichens with airborne discrete-return LiDAR data. Remote Sensing of Environment. 112(10): 3891-3897.
http://dx.doi.org/10.1016/j.rse.2008.06.007
Korpela I., Tuomola, T. &Välimäki, E. 2007. Mapping forest plots: An efficient method combining
photogrammetry and field triangulation. Silva Fennica 41(3): 457-469.
http://www.metla.fi/silvafennica/full/sf41/sf413457.pdf
Korpela I. 2007. 3D
treetop positioning by multiple image matching of aerial images in a 3D search
volume bounded by lidar surface models. Photogrammetrie, Ferkundung, Geoinformation (PFG) 1/2007: 35-44. ---
Mäkinen, A., Korpela, I., Tokola,
T. & Kangas, A. 2006. Effects
of imaging conditions on crown diameter measurements from high resolution
aerial images. Canadian Journal of
Forest Research 36: 1206-1217. http://dx.doi.org/10.1139/X06-011
Korpela I. 2006. Geometrically accurate time
series of archived aerial images and airborne lidar data in a forest
environment. Silva Fennica 40(1): 109-126.http://www.metla.fi/silvafennica/full/sf40/sf401109.pdf.
Korpela I.,
Anttila, P. & Pitkänen J. 2006. A local
maxima method in detecting individual trees in color-infrared aerial
photographs. International Journal of
Remote Sensing. 27/6: 1159-1175. http://dx.doi.org/10.1080/01431160500354070
Korpela, I. &Tokola T. 2006.Potential
of aerial image-based monoscopic and multiview single-tree forest inventory - a simulation
approach. Forest Science 52(2):136-147.---
Korpela, I. &
Anttila, P. 2004.Appraisal of the mean height of
trees by means of image matching of digitised aerial
photographs. The photogrammetric Journal of
Finland 19/1: 23-36.---
Korpela I. 2004. Individual tree measurements by means of digital aerial
photogrammetry. Silva Fennica
monographs 3. 93 p.
http://www.metla.fi/silvafennica/full/smf/smf003.pdf
Articles in international conference proceedings with a peer review (4)
Vastaranta, M., Korpela, I., Uotila, M., Hovi, A. and Holopainen, M. Area-based snow damage
classification of forest canopies using bi-temporal lidar data. In Lichti, D. & Habib,
A.. 2011. ISPRS LaserScanning 2011 proceedings.
Markelin L., Honkavaara,
E., Beisl, U., Korpela, I. 2010. Validation of the radiometric correction chain of the
airborne photogrammetric ADS40 imaging system. ISPRS
2010 conference in Vienna.International Archives of
Photogrammetry, Remote Sensing and Spatial Information Sciences 38: 7A. 145-150
Korpela, I., B. Dahlin, H. Schäfer,
E. Bruun, F. Haapaniemi, J. Honkasalo, S. Ilvesniemi, V. Kuutti, M. Linkosalmi,
J. Mustonen, M. Salo, O. Suomi, and H. Virtanen. 2007. Single-tree forest inventory using LiDAR and aerial
images for 3D treetop positioning, species recognition, height and crown width
estimation. IAPRS Volume XXXVI, Part
3 / W52, 2007. pp. 227-233
Korpela I. 2006. 3D treetop positioning by
multiple image matching of aerial images in a 3D search volume bounded by lidar
surface models. In: International archives
of the photogrammetry, remote sensing
and spatial information sciences. Volume XXXVI, Part 3. Symposium of ISPRS
Commission III. Photogrammetric Computer Vision, PCV '06. Bonn, 20-22 September
2006. Editors: Wolfgang Förstner, Richard Steffen. pp. 191-196. ISSN 1682-1750.
Articles in refereed Finnish scientific journals
Fish, S., Haakana, M., Korpela, I. &Melkas, T. 1998. Spatial
relationships in a forest stand. In: Spatial
Statistics in GIS Applications. (Eds. Artimo, K. and Haggrén, H.) Cartography and Geoinformatics Publications 4.
Helsinki University of Technology. pp. 7-39.
Korpela, I. &Välimäki, E.
2007. Talousmetsän maanpintamallinnus
arkistoilmakuvilta ja laserkeilauksella. [Elevation modeling in forests using archived aerial images and modern LiDAR].Maanmittaus 2/2007.
Scientific monographs (3)
Korpela I. 2003.Individual
tree measurements by means of digital aerial photogrammetry PhD thesis. University of Helsinki. Department of Forest
Resource Management. 132 p. Oct 1, 2003.
Korpela I. 2000. 3-D
Matching of tree tops using digitized panchromatic
aerial photographs. Licentiate thesis.
University of Helsinki. Department of Forest Resource Management. 109 p. http://www.honeybee.helsinki.fi/users/korpela/l_thesis/
Korpela I. 1993.Neste Oy:n
Porvoon tuotantolaitosten vaikutus lähimetsien kasvuun. MSc thesis. University of
Helsinki. Department of Forest Resource Management. 92 p. (English and Swedish
Summary)
Articles in international conference proceedings
without a review (10)
Korpela, I.,
Hovi, A., Korhonen, L. 2013. Backscattering of
individual LiDAR pulses from forest canopies explained by photogrammetrcally
derived vegetation structure. International
Archives of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, Volume XL-1/W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013,
Hannover, Germany
Korpela, I.,
Hovi. A., Morsdorf, F. 2011.Mapping
of understory trees in discrete-return LiDAR data.ISPRS Hannover workshop, June 2011. CD-rom.
E. Honkavaara, R. Arbiol, L. Markelin, L. Martinez, S. Bovet,
M. Bredif, L. Chandelier,
V. Heikkinen, I. Korpela, L. Lelegard, F.
Pérez, D. Schläpfer, T. Tokola, 2011. EuroSDR project "Radiometric Aspects of Digital
Photogrammetric Images" - Results of Empirical Phase. ISPRS Hannover workshop 2011.
Korpela I.,
Rohrbach F. 2010. Variation and anisotropy of
forest trees in radiometrically calibrated airborne line-sensor images -
implications for species classification.ISPRS Commission VII Symposium. Vienna 2010. International
Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38:
7A, #-#.
Korpela, I.,
Tokola, T., Ørka, H.O., Koskinen, M. 2009. Small footprint discrete-return LiDAR in tree species
recognition. Proceedings of the ISPRS
Hannover Workshop 2009, Hannover, Germany, June 2-5, 2009.
Honkavaara, E., Arbiol, R., Markelin,
L., Martinez L., Cramer, M., Korpela, I., Bovet, S., Thom, C.,
Chandelier, L., Ilves, R., Klonus, S., Reulke, R., Marshall, P., Tabor, M., Schläpfer,
D., and N. Veje, 2009. Status report of the EuroSDR project Radiometric aspects of digital
photogrammetric airborne images. Proceedings
of the ISPRS Hannover Workshop 2009, Hannover, Germany, June 2-5, 2009.
Korpela, I.
2007.Incorporation of allometry in single-tree
remote sensing with LiDAR and multiple images.ISPRS Hannover workshop 2007. High Resolution Earth Imaging
for Geospatial Information. Hannover, Germany, May 29-June 1, 2007.Editors: C. Heipke, K. Jacobsen, M. Gerke. ISPRS XXXVI Part I/W51, ISSN
No. 1682-1777. CD-Rom.
Anttila, P., Korpela, I. and Pitkänen, J. 2005.
The performance of a local maxima method in
detecting individual trees in aerial photographs. Proc 1st Göttingen GGRS days. Remote Sensing and
Geographical Information Systems for Environmental Studies. Applications in Forestry. Schriften aus der
Forstlichen Fakultät der Universität Göttingen und der Niedersächsishen
Forstlichen Versuchanstalt,
Band 138. pp. 15-24. ISBN 3-7939-5138-3.
Korpela, I.,
Rasinmäki,
J. and Tokola, T. 2005. Estimation Chains for Single Tree Based Forest
Inventory Using using GIS and Digitised
Aerial Photography. Proc
1st Göttingen GGRS days. Remote Sensing and Geographical Information Systems
for Environmental Studies. Applications in Forestry. Schriften aus der Forstlichen
Fakultät der Universität Göttingen und der Niedersächsishen Forstlichen Versuchanstalt, Band
138. pp. 63-73. ISBN
3-7939-5138-3.
Korpela, I. and Tokola, T. 2004. Comparison of Alternatives Estimation Chains for
Single Tree Based Forest Inventory Using Digitised
Aerial Photography - a Simulation Approach. Proc
2nd International Conference on Forest Measurements and Quantitative Methods
and Management & the 2004 Southern Mensurationists
Meeting. Hot Springs,
Arkansas. June 15-18, 2004.
Other publications
(not updatred)
Kaikki irti laserkeilauksesta. Maanmittaustieteiden päivät 2011 (Moniulotteinen maanmittaus, toim.
Kirsikka Niukkanen). 1.-2.12.2011 Tieteiden talo. Maanmittaustieteiden Seuran
julkaisu n:o 48. p. 61-71.
Tokola, T., Korpela, I., Andersson, K. 2007. Automaattinen puuston kartoitus ja mittaus ilmasta käsin
- Kolmiulotteinen kuvatulkinta lentotiedustelun lennokkikuvilla. MATINE
Tutkimusraportti 679.
Korpela I., Kallonen, J., Välimäki, E. 2006.Aika- ja paikkamatkailua Hyytiälän
metsissa. Time-travelling
in Hyytiälä 1946-2006. Web-site.
Published August 31, 2006.
http://www.helsinki.fi/~korpela/Hyytiala/Sisallys.html
Soiden luokitus. (Classification of mires) In: Metsälehden
Metsäkoulu. Metsälehti Kustannus. 1998. pp. 20-21.
Helsingin yliopisto on tutkinut metsien
kasvua Skölvikin öljynjalostamon ympäristössä. Helsingfors Universitet har utfört en undersökning om
barrskogarnas tillväxt kring Sköldvik. Press
release 15.5.1992. 7 p.
Poster presentations & Talks in conferences/meetings (with
abstracts) (not updated)
Kaikki irti laserkeilauksesta. Invited talk at Maanmittaustieteiden päivät (Geodesy & Surveying days). Helsinki. Dec 2,
2011.
Mapping mires from the air – What can we
do with timestamped and free photons? Talk at UMB
Norway. Use of Airborne Laser Scanner Data for Ecological Applications
–seminar. June 23, 2011.
Mapping understory
trees in discrete-return data. Talk at ISPRS
Hannover workshop, June 14, 2011
Airborne optical remote sensing of the
forests and mires of Hyytiälä 1997 - 2011. Finnish Geodetic Institute's Research seminar, March 22, 2011.
Variation and directional anisotropy of
reflectance at the crown scale – implications for tree species classification
in digital aerial images. Nordic
Remote Sensing Days, VTT
Espoo, Nov 2010.
Puulajitulkinta digi-ilmakuvilta- mitä
LEICA ADS40-kuvat paljastavat?
Aaltomuotolaser metsässä – pelkkää kovalevyn täytettäkö?
Näkeekö laser metsän puilta seminaari. Evo / Hamk, Nov 9, 2010.
Laserkokeiluja Hyytiälän
metsissä ja soilla 2004-2010.
Maanmittauslaitoksen laserkeilaus- ja korkeusmalliseminaari 2010. Oct 8, 2010.
Laserkeilausinformaation hyödyntäminen
puunkorjuun suunnittelussa (Utilizing airborne LiDAR in wood procurement planning - peatlands) . Metla/Metsähallitus research excursion. Hyytiälä Sept 14, 2010.
Turvemaiden kartoitus ilmasta käsin –
Riittääkö auringonvalo vai pannaanko laseria peliin? Mapping peatlands from the air – is daylight enough or
do we need to bring our own lasers? Invited talk at
Metsähallitus seminar for experts in forestry and
environmental monitoring. Feb 4, 2010.
Yksittäisten puiden inventointi
laser- ja ilmakuva-aineistosta. Single-tree remote
sensing methods based on airborne LiDAR and images.
Invited talk at Evo Forest School / HAMK – Remote sensing seminar for
foresters, Nov 25, 2008.
Mapping of Understory Lichens with
Airborne Discrete-Return LiDAR Data. (Academic) Physense seminar at UH. June 3, 2008.
Laserkeilaus ilmakuvaperustaisessa puiden
kartoituksessa ja mittauksessa. Combining
lidar surface models with aerial image -based 3D positioning of trees. An invited talk at the meeting of Taksaattoriklubi.
Helsinki, March 30, 2006.
Metsäalueen historian rekonstruointi ilmakuva-aikasarjoista. Reconstructing the silvicultural
history using a time-series of archived aerial photographs. An invited talk at the annual surveying expo "Paikkatietomarkkinat".
Helsinki, Sept 27, 2005.
Puiden tunnistus rakennetun tai
rakennettavan ympäristön visualisointitarpeisiin, menetelmäkuvaus. Mapping trees for visualization of built non-built
areas, method description. Rakennetun ympäristön 3D
visualisointimallien luominen –seminaari. Seminar of
TEKES MASI program. VTT
Espoo. May 9, 2005.
Puiden kartoitus ja mittaus ilmakuvilta. Mapping and
measuring tree in aerial images. An invited talk at the
annual meeting of the Finnish Society of Forest Science. Helsinki, April 29,
2004.
Pystymittaus digitaalisen fotogrammerian menetelmin. Stand
cruising by means of digital aerial photogrammetry.
An invited talk at the meeting of the Finnish society of forest inventory and
management planning experts, Taksaattoriklubi.
Helsinki, March 30, 2004.
Tree detection from digital aerial
photographs. Presentation at the Nordic workshop in image analysis
and spatial statistics in forestry. Frederiksberg, Denmark. Nov 2, 1999.
A forest growth study in the vicinity of a petrochemical
complex. Abstract and presentation. 4th International
Conference on Statistical Methods for the Environmental Sciences. Conference on
Environmetrics in Espoo, Finland. August 17-21, 1992.
COMPUTER PROGRAMS
Research and teaching
KUVAMITT.
1998-2023. A digital photogrammetric workstation written in Visual Basic &
C/C++ and Matlab dlls.
* Aerial triangulation by means of hybrid bundle block
adjustment,
* Anaglyphic stereo, i.e. computation and visualization of stereo-normalized
imagery
* Area-based image matching, semiautomatic (for polygons) and manual (for
points), stereo and triplet images.
* Feature-based treetop positioning in multi-view imagery (up to 9 images, free
scale)
* Manual image-matching of multiple images, using epipolar
constraints (forward intersection)
* Support for frame and line-sensor imagery: monochromatic, RGB, RGBN, ADS40;
raw, epipolar and ortho
* Support for raster (BIP, raw binary) and TIN (ascii) surfaces models, DEM or
DSM
* Integrated analyses of LiDAR data and images, e.g. monoplotting, and
WLS-modeling of surfaces in LiDAR points
Used at several UH and UEF (NOVA) courses and by several researchers.
RESECTION.
2006-2014. Program that performs network adjustment for planimetric positioning
of points using angle and distance observations (combined triangulation and
trilateration),
* Using redundant interpoint azimuth and distance
observations.
* Includes a simulator version for teaching.
* Used at UH courses MARV1, MARV4/2, FOR110B, ME-013C and in field measurement
campaigns to improve
the efficiency of accurate, dm-level tree positiong
under canopy.
DTM-FILTER.
2006-2014. An iterative gradient-based DEM creation by filtering of TIN-models
generated from LiDAR data, or image-matching (KUVAMITT) point clouds.
AT-MONTECARLO.
2006-2008. Monte-Carlo simulator for hybrid aerial triangulation (weighted
non-linear regression). Allows the study of the effects different observation errors
(XYZ-, XY-, and Z control points, XYZ-, XY-, and Z distances, image
observations, direct sensor orientation observations) image overlaps and scale,
and interior orientation parameters on the accuracy of reconstructed points and
exterior orientation parameters.
AT
2000-2014. Aerial (image) triangulation (block adjustment), * Uses
Weighted-Least-Squares,
* All XYZ points are weighted, tie points can have Z or XY constraints,
* Image orientation parameters are all given a priori weights.
Teaching
Excel macro programs and simulators
(2001-2023). Stem
bucking using taper curves, kNN-area based LiDAR
remote sensing tool, inventory simulator (random, systematic, cluster, line,
adaptive) for area estimates, inventory simulator for mapped forest stands
(stand variable computations, autocorrelation analyses), etc. Used on UH
courses MEK100b, ME-011B, ME-017, ME-233, FOR-254
MARV4.
1999-2000. Interface between forest compartment data in landscape ecological
planning and MELA simulator/optimization program. The program formulated the
MELA tasks from the ecological classifications and data.
MARV3.
1997-2000. Calculations of basic plot level, static and increment-related stand
variables from measurements on permanent forest plots.
MARV1.
1995-1998 (-2009). A selection of routines: Computation of single tree volumes
and stem bucking based on polynomial taper curve functions. Calculations of
stratified forest inventory results. Calculation of permanent forest plot
results. The source code from 1995-96 is still in use on the MARV1 course.
Teaching material
Links to past course material can be found at http://www.helsinki.fi/~korpela/work_teaching.html