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iForest - Biogeosciences and Forestry

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Estimating crown defoliation of Scots pine (Pinus sylvestris L.) trees using small format digital aerial images

G Mozgeris (1)   , A Augustaitis (2)

iForest - Biogeosciences and Forestry, Volume 6, Issue 1, Pages 15-22 (2013)
doi: https://doi.org/10.3832/ifor0705-006
Published: Jan 14, 2013 - Copyright © 2013 SISEF

Research Articles

Collection/Special Issue: IUFRO 7.01.00 - COST Action FP0903, Kaunas (Lithuania - 2012)
Biological Reactions of Forest to Climate Change and Air Pollution
Guest Editors: Elena Paoletti, Andrzej Bytnerowicz, Algirdas Augustaitis


This study focuses on the possibilities of using small format digital aerial images for the estimation of tree crown condition. The test area was located in the eastern part of Lithuania where Scots pine (Pinus sylvestris L.) trees prevail and was photographed using a Canon EOS-1DsMark II digital camera installed on-board a SkyArrow ultra-light aircraft. The camera lenses were adopted to capture images corresponding to conventional color-infrared photography. In addition, the test area was photographed using a large format digital frame aerial camera (Vexcel UltraCam D) installed on board a Rockwell Turbo Commander 690A high performance commuter aircraft. The ground sampling density of the images taken was around 9-10 cm. Crown defoliation was assessed in the field for more than 500 Scots pine trees located in 46 sample plots representing stands of trees that were either 65 years old or 170 years old. Spearman’s correlations coefficients were used to check for relationships between tree crown defoliation and image characteristics. The defoliation was also predicted using the non-parametric k-Nearest Neighbor method applied on data available from aerial images alone. The results were validated using the “Leave One Out” technique by comparing the obtained data with data from the field assessed defoliation rates. The prediction root mean square errors were calculated using data from the small format aerial images as being 11.5% for the younger trees, whereas those calculated using conventional aerial images were between 9.5 and 9.9%. The differences in predicted root mean square errors disappeared in the older stands and both methods produced errors of between 8.1 and 8.5%. Defoliation class was correctly predicted for approximately 84-88% of the older tree crowns and correctly for 75-85% of the younger tree crowns. These results showed that small format aerial images had the potential to predict defoliation in tree crowns and were comparable with results obtained using conventional aerial images. Their main advantage is that small format images are much cheaper to obtain than conventional images when the areas targeted are thousands of hectares in size.

  Keywords


k-Nearest Neighbor, Small Format Aerial Images, Tree Crown Defoliation, Ultra-light Aircraft

Authors’ address

(1)
G Mozgeris
Institute of Forest Management and Wood Science, Aleksandras Stulginskis University, Studentu 11, LT-53361 Akademija, Kaunas distr. (Lithuania)
(2)
A Augustaitis
Laboratory of Forest Monitoring, Institute of Forest Management and Wood Science, Aleksandras Stulginskis University, Studentu 11, LT-53361 Akademija, Kaunas distr. (Lithuania)

Corresponding author

Citation

Mozgeris G, Augustaitis A (2013). Estimating crown defoliation of Scots pine (Pinus sylvestris L.) trees using small format digital aerial images. iForest 6: 15-22. - doi: 10.3832/ifor0705-006

Academic Editor

Elena Paoletti

Paper history

Received: Jul 26, 2012
Accepted: Nov 19, 2012

First online: Jan 14, 2013
Publication Date: Feb 05, 2013
Publication Time: 1.87 months

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List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Aber JS, Aber SW, Pavri F (2002)
Unmanned small-format aerial photography from kites for acquiring large-scale, high-resolution, multiview-angle imagery. Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings.
Online | Gscholar
(2)
Ambrosini I, Gherardi L, Viti ML, Maresi G, Turchetti T (1997)
Monitoring diseases of chestnut stands by small format aerial photography. Geocarto International 12(3): 41-46.
CrossRef | Gscholar
(3)
Anderson WH, Walner FX (1978)
Small format aerial photography: a selected bibliography. U.S. geological survey, Sioux Falls, South Dakota, USA, pp. 6.
Online | Gscholar
(4)
Ardö J (1998)
Remote sensing of forest decline in the Czech Republic. Department of Physical Geography, Lund University, Sweden, pp. 47.
Gscholar
(5)
Atzberger C, Werner W (1998)
Needle reflectance of healthy and diseased spruce stands. In: “1st EARSeL Workshop on Imaging Spectroscopy” (Schaepman M, Schläpfer D, Itten KI eds). Remote Sensing Laboratories, University of Zurich (Switzerland) 6-8 October 1998. Impression Dumas, Saint-Etienne, France, pp. 271-283.
Gscholar
(6)
Auclair AND, Worrest RC, Lachance D, Martin HC (1992)
Climatic perturbation as a general mechanism of forest dieback. In: “Forest decline concepts” (Manion PD, Lachance D eds). St. Paul, Minnesota, USA, pp. 38-58.
Gscholar
(7)
Augustaitis A, Mozgeris G (2003)
Cartographical modeling of tree crown defoliation. Silviculture, transactions of Lithuanian forest institute and Lithuanian University of agriculture 1(53): 75-87. [in Lithuanian]
Gscholar
(8)
Augustaitis A, Bytnerowicz A (2008)
Contribution of ambient ozone to Scots pine defoliation and reduced growth in the Central European forests: A Lithuanian case study. Environment Pollution 155: 436-445.
CrossRef | Gscholar
(9)
Augustaitis A, Mozgeris G, Eigirdas M, Sajonas M (2009)
Color infrared aerial images to evaluate tree crown defoliation. In: Proceedings of the “4th International Scientific Conference on Rural Development”. Akademija, Kaunas r. (Lithuania), 15-17 October 2009. Vol. 4, Book 2, pp. 213-216.
Gscholar
(10)
Barry KM, Stone C, Mohammed CL (2008)
Crown-scale evaluation of spectral indices for defoliated and discoloured eucalypts. International Journal of Remote Sensing 29 (1): 47-69.
CrossRef | Gscholar
(11)
Bater CW, Wulder MA, White JC, Coops NC (2010)
Integration of LiDAR and digital aerial imagery for detailed estimates of Lodgepole Pine (Pinus contorta) volume killed by Mountain Pine Beetle (Dendroctonus ponderosae). Journal of Forestry 108 (3): 111-119.
Gscholar
(12)
Bikuviene I, Mozgeris G (2010)
Testing the simultaneous use of laser scanning and aerial image data for estimation of tree crown density. In: Proceedings of the 16th annual conference “Research for Rural Development”. University of Agriculture, Jelgava, Latvia, Vol. 1, pp. 201-207.
Gscholar
(13)
Ciesla WM (2000)
Remote sensing in forest health protection. FHTET Report No. 00-03, Forest Health Technology Enterprise Team, USDA Forest Service. Remote Sensing Applications Center, pp. 276.
Gscholar
(14)
Ciesla WM, Dull CW, Acciavatti RE (1989)
Interpretation of SPOT-1 color composites for mapping of defoliation of hardwood forests by gypsy moth. Photogrammetric Engineering and Remote Sensing 55 (10): 1465-1470.
Gscholar
(15)
Crookston NL, Moeur M, Renner D (2002)
Users guide to the most similar neighbor imputation program Version 2. Gen. Tech. Rep. RMRS-GTR-96, Rocky Mountain Research Station, USDA Forest Service, Ogden, Utah, USA, pp. 35.
Gscholar
(16)
Daniulis J (1998)
Aerial photography. Enciklopedija, Vilnius, Lithuania, pp. 248. [in Lithuanian]
Gscholar
(17)
Daniulis J, Mozgeris G (1993)
Investigations of interpretation criteria of defoliated pine stands. Transactions of Lithuanian University of Agriculture 42: 21-23. [in Lithuanian].
Gscholar
(18)
De Vries W, Klap J, Erisman JW (2000)
Effects of environmental stress on forest crown condition in Europe. Part I: Hypotheses and approach to the study. Water, Air, and Soil Pollution 119: 317-333.
CrossRef | Gscholar
(19)
Entcheva Campbell PK, Rock BN, Martin ME, Neefus CD, Irons JR, Middleton EM, Albrechtova J (2004)
Detection of initial damage in Norway spruce canopies using hyperspectral airborne data. International Journal of Remote Sensing 25 (24): 5557-5583.
CrossRef | Gscholar
(20)
Ferretti M (1998)
Potential and limitation of visual indices of tree condition. Chemosphere 4-5: 1031-1036.
CrossRef | Gscholar
(21)
Gates DM, Keegan HJ, Schleter JC, Weidner VR (1965)
Spectral properties of plants. Applied Optics 4 (1): 11-20.
CrossRef | Gscholar
(22)
Gausman HW, Escobar DE, Knipling EB (1977)
Relation of Peperomia obtusifolia’s anomalous leaf reflectance to its leaf anatomy. Photogrammetric Engineering and Remote Sensing 43(9): 1183-1185.
Gscholar
(23)
Haara A, Nevalainen S (2002)
Detection of dead or defoliated spruces using digital aerial data. Forest Ecology and Management 160: 97-107.
CrossRef | Gscholar
(24)
Hagner O (1990)
Computer aided forest stand delineation and inventory based on satellite remote sensing. In: Proceedings of the SNS/IUFRO workshop “The usability of remote sensing for forest inventory and planning”. Umeå (Sweden) 26-28 February 1990, pp. 94-105.
Gscholar
(25)
Heikkilä J, Nevelainen S, Tokola T (2002)
Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. Forest Ecology and Management 158: 9-23.
CrossRef | Gscholar
(26)
Hildebrandt G (1993)
Central European contribution to remote sensing and photogrammetry in forestry. In: Proceedings of the IUFRO centennial meeting “Forest resource inventory and monitoring and remote sensing technology”. Berlin (Germany) 31 August - 4 September 1992. Japan Society for Forest Planning Press, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Saiwaicho, Fucku, Tokyo, Japan, pp. 196-212.
Gscholar
(27)
Innes JL (1995)
Influence of air pollution on the foliar nutrition of conifers in Great Britain. Environmental Pollution 88: 183-192.
CrossRef | Gscholar
(28)
Johnson J, Jacob M (2010)
Monitoring the effects of air pollution on forest condition in Europe: is crown defoliation an adequate indicator? iForest 3: 86-88.
CrossRef | Gscholar
(29)
Juknys R, Vensloviene J, Stravinskiene V, Augustaitis A, Bartkevicius E (2003)
Scots pine (Pinus sylvestris L.) growth and condition in a polluted environment: From decline to recovery. Environmental Pollution 125: 205-212.
CrossRef | Gscholar
(30)
Klap JM, Oude Voshaar JH, De Vries W, Erisman JW (2000)
Effects of environmental stress on forest crown condition in Europe. Part IV: Statistical analyses of relationships. Water, Air and Soil Pollution 119: 387-420.
CrossRef | Gscholar
(31)
Kuhl WE (1989)
A method to detect forest decline in Germany - results of a color-infra-red airphoto interpretation. Forestry 62: 51-61.
CrossRef | Gscholar
(32)
Kuliešis A (2008)
Forest Inventory. In: “Forest use and logistic: Textbook.” (Mažeika JA ed). Lithuanian University of Agriculture, Akademija, Kaunas r, Lithuania, pp. 227-287. [in Lithuanian].
Gscholar
(33)
Leckie DG, Teillet PM, Fedosejevs G, Ostaff DP (1988)
Reflectance characteristics of cumulative defoliation of balsam fir. Canadian Journal of Forest Research 18:(8) 1008-1016.
CrossRef | Gscholar
(34)
Lillesand TM, Kiefer RW, Chipman JW (2008)
Remote sensing and image interpretation (6 edn). John Wiley & Sons, Inc., USA, pp. 756.
Gscholar
(35)
Lyytikäinen-Saarenmaa P, Holopainen M, Ilvesniemi S, Haapanen R (2008)
Detecting pine sawfly defoliation by means of remote sensing and GIS. Forstschutz Aktuell 44: 14-15.
Online | Gscholar
(36)
Martins LM, Lufinha MI, Marques CP, Abreu CG (2001)
Small format aerial photography to assess chestnut ink disease. Forest, Snow and Landscape Research 76 (3): 357-360.
Online | Gscholar
(37)
Mattioli W, Quatrini V, Di Paolo S, Di Santo D, Giuliarelli D, Angelini A, Portoghesi P, Corona P (2012)
Experimenting the design-based k-NN approach for mapping and estimation under forest management planning. iForest 5: 26-30.
CrossRef | Gscholar
(38)
Moeur M, Stage AR (1995)
Most similar neighbor: an improved sampling inference procedure for natural resource planning. Forest Science 41 (2): 337-359.
Gscholar
(39)
Moskal LM, Franklin SE (2004)
Relationship between airborne multispectral image texture and aspen defoliation. International Journal of Remote Sensing 25(14): 2701-2711.
CrossRef | Gscholar
(40)
Mozgeris G (1996)
Dynamic stratification for estimating point-wise forest characteristics. Silva Fennica 30 (1): 61-72.
CrossRef | Gscholar
(41)
Mozgeris G, Augustaitis A (1999)
Using GIS techniques to obtain a continuous surface of tree crown defoliation. Baltic forestry 5(1): 69-74.
Online | Gscholar
(42)
Mozgeris G, Jonikavičius D (2007)
The use of k-NN method for estimating forest characteristics - the role of integrated information available from spatial images and conventional stand-wise forest inventory. Vagos 77 (30): 34-44. [in Lithuanian with English abstract]
Gscholar
(43)
Mozgeris G (2008)
Estimation and use of continuous surfaces of forest parameters: options for Lithuanian forest inventory. Baltic Forestry 14 (2): 176-184.
Online | Gscholar
(44)
Mozgeris G, Galaune A, Jonikavičius D (2009)
Research on geometrical accuracy of orthophoto maps developed on the base of ultra-light aircraft imaging. Vagos 82 (35): 113-119. [in Lithuanian]
Gscholar
(45)
Mozgeris G, Masaitis G (2010)
Aerial photography of Lithuanian forests: challenges and prospects for tomorrow. In: Proceedings of the Conference “Surveying Engineering and GIS”. Department of Geodesy, Faculty of Landscape management, Kaunas College, Mastaičiai, Lituania, pp. 49-54. [in Lithuanian with English summary]
Gscholar
(46)
Mozgeris G, Augustaitis A, Gečionis A (2011)
Small format aerial images to estimate the pine crown defoliation. In: Proceedings of the “5 International Scientific Conference on Rural Development. Akademija, Aleksandras Stulginskis University (Lithuania) 24-25 November 2011, vol. 5, book 2, pp. 452-458.
Gscholar
(47)
Nilsson M (1997)
Estimation of forest variables using satellite image data and airborne LiDAR. PhD thesis, Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Acta Universitatis Agriculturae Sueciae. Silvestrias, pp. 17.
Gscholar
(48)
Pontius J, Martin M, Plourde L, Hallet R (2008)
Ash decline assessment in Emerald Ash Borer-infested regions: a test of tree-level, hyperspectral technologies. Remote Sensing of Environment 112: 2665-2676.
CrossRef | Gscholar
(49)
Poso S, Paananen R, Simila M (1987)
Forest inventory by compartments using satellite imagery. Silva Fennica 21 (1): 69-94.
CrossRef | Gscholar
(50)
Sarkeala J (2008)

Online | Gscholar
(51)
Solberg S, Næsset E, Lange H, Bollandsås OM (2004)
Remote sensing of forest health. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34, 8/W2, pp. 161-166.
Gscholar
(52)
Solberg S, Næsset E, Hanssen KH, Christiansen E (2006)
Mapping defoliation during a severe insect attack on Scots Pine using airborne laser scanning. Remote Sensing of Environment 102: 364-376.
CrossRef | Gscholar
(53)
Tomppo E (1993)
Multi-source national forest inventory of Finland. In: Proceedings of the “IUFRO S4.02 Ilvessalo Symposium on National Forest Inventories”. Finnish Forest Research Institute, University of Helsinki (Helsinki), pp. 52-60.
Gscholar
(54)
Tomppo E (2005)
The Finnish multisource national forest inventory - small area estimation and map production. Chapter 12. In: “Forest inventory: methodology and applications” (Kangas A, Maltamo M eds). Springer, Berlin, Germany, pp. 191-220.
Gscholar
(55)
UNE-CE (1993)
Manual for integrated monitoring programme. Phase 1993-1996. Environmental Report 5. Environmental Data Centre, National Board of Waters and the Environment, Helsinki, Finland.
Gscholar
(56)
UN-ECE (1994)
Manual on methods and criteria for harmonised sampling, assessment, monitoring and analysis of the effects of air pollution on forests. ICP, pp. 178.
Gscholar
(57)
Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006)
Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. Forest Ecology and Management 221: 27-41.
CrossRef | Gscholar
(58)
Zawila-Niedzwiecki T (1996)
The use of GIS and remote sensing for forest monitoring in Poland. In: “Remote Sensing and Computer Technology for Natural Resource Assessment” (Saramaki J, Koch B and Lund G, eds). Proceedings of the Subject Group S4.02-00 “Forest Resource Inventory and Monitoring” and Subject Group S4.12-00 “Remote Sensing Technology”, Volume II. IUFRO XX World Congress, Tampere (Finland) 6-12 August 1995. The University of Joensuu, Faculty of Forestry, Research Notes 48: 29-42.
Gscholar
 

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