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Sensitivity analysis of RapidEye spectral bands and derived vegetation indices for insect defoliation detection in pure Scots pine stands

Alexander Marx (1)   , Birgit Kleinschmit (2)

iForest - Biogeosciences and Forestry, Volume 10, Issue 4, Pages 659-668 (2017)
doi: https://doi.org/10.3832/ifor1727-010
Published: Jul 11, 2017 - Copyright © 2017 SISEF

Research Articles


This study investigated the statistical relationship between defoliation in pine forests infested by nun moths (Lymantria monacha) and the spectral bands of the RapidEye sensor, including the derived normalized difference vegetation index (NDVI) and the normalized difference red-edge index (NDRE). The strength of the relationship between the spectral variables and the ground reference samples of percent remaining foliage (PRF) was assessed over three test years by the Spearman’s ρ correlation coefficient, revealing the following ranking order (from high to low ρ): NDRE, NDVI, red, NIR, green, blue, and red-edge. A special focus was directed at the vegetation indices. In both discriminant analyses and decision tree classification, the NDRE yielded higher classification accuracy in the defoliation classes containing none to moderate levels of defoliation, whereas the NDVI yielded higher classification accuracy in the defoliation classes representing severe or complete defoliation. We concluded that the NDRE and the NDVI respond very similarly to changes in the amount of foliage, but exhibit particular strengths at different defoliation levels. Combining the NDRE and the NDVI in one discriminant function, the average gain of overall accuracy amounted to 7.8 percentage points compared to the NDRE only, and 7.4 percentage points compared to the NDVI only. Using both vegetation indices in a machine-learning-based decision tree classifier, the overall accuracy further improved and reached 81% for the test year 2012, 71% for 2013, and 79% for the test year 2014.

  Keywords


Forest Health, Discriminant Analysis, Pine Defoliation, Normalized Difference Red-edge Index, Decision Tree Classification

Authors’ address

(1)
Alexander Marx
Planet Labs Germany GmbH, Kurfürstendamm 22, D-10719 Berlin (Germany)
(2)
Birgit Kleinschmit
Technische Universität Berlin, Geoinformation in Environmental Planning Lab, Straße des 17.Juni 145, D-10623 Berlin (Germany)

Corresponding author

 
Alexander Marx
alexander.marx@planet.com

Citation

Marx A, Kleinschmit B (2017). Sensitivity analysis of RapidEye spectral bands and derived vegetation indices for insect defoliation detection in pure Scots pine stands. iForest 10: 659-668. - doi: 10.3832/ifor1727-010

Academic Editor

Alessandro Montaghi

Paper history

Received: Jun 02, 2015
Accepted: May 04, 2017

First online: Jul 11, 2017
Publication Date: Aug 31, 2017
Publication Time: 2.27 months

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

 
(1)
Adelabu S, Mutanga O, Adam E (2014)
Evaluating the impact of red-edge band from RapidEye image for classifying insect defoliation levels. ISPRS Journal of Photogrammetry and Remote Sensing 95: 34-41.
CrossRef | Gscholar
(2)
Backhaus K, Erichson B, Plinke W, Weiber R (2003)
Multivariate analyse methoden [Multivariate analysis methods]. Springer-Verlag, Berlin-Heidelberg-New York, pp. 818. [in German]
Gscholar
(3)
Barclay HJ, Goodman D (2000)
Conversion of total to projected leaf area index in conifers. Canadian Journal of Botany 78: 447-454.
CrossRef | Gscholar
(4)
Bauer A, Dammann I, Gawehn P, Schröck HW, Wendland J, Ziegler C (2007)
Waldbäume: Bilderserien zur Einschatzung von Kronenverlichtungen bei Waldbäumen [Forest trees: Image series for the estimation of defoliation]. Arbeitsgemeinschaft Kronenzustand, Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz (BMELV), Kassel, Germany, pp. 130. [in German]
Gscholar
(5)
Planet (2017)
RapidEye imagery product specifications (version 6.1). Planet Inc., San Francisco, CA, USA, pp. 50.
Online | Gscholar
(6)
Bréda NJJ (2003)
Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany 54 (392): 2403-2417.
CrossRef | Gscholar
(7)
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984)
Classification and regression trees. Chapman & Hall/CRC, New York, USA, pp. 384.
Gscholar
(8)
Chavez PS (1996)
Image-based atmospheric corrections - revisited and improved. Photogrammetric Engineering and Remote Sensing 62: 1025-1036.
Online | Gscholar
(9)
Chávez R, Clevers J (2012)
Object-based analysis of 8-bands WorldView2 imagery for assessing health condition of desert trees. CGI Report 2012-001, Centre for Geo-Information, Wageningen University, Wageningen, Netherlands, pp. 17.
Online | Gscholar
(10)
Chen JM, Black TA (1992)
Defining leaf area index for non-flat leaves. Plant, Cell and Environment 15: 421-429.
CrossRef | Gscholar
(11)
Congalton R, Green K (1957)
Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Taylor & Francis Group, NY, USA, pp. 183.
CrossRef | Gscholar
(12)
De Beurs KM, Townsend PA (2008)
Estimating the effect of gypsy moth defoliation using MODIS. Remote Sensing of Environment 112: 3983-3990.
CrossRef | Gscholar
(13)
Eickenscheid N, Wellbrock N (2014)
Consistency of defoliation data of the national training courses for the forest condition survey in Germany from 1992 to 2012. Environmental Monitoring Assessment 186: 257-275.
CrossRef | Gscholar
(14)
Eitel UH, Keefe RF, Long DS, Davis AS, Vierling LA (2010)
Active ground optical remote sensing for improved monitoring of seedling stress in nurseries. Sensors 10: 2843-2850.
CrossRef | Gscholar
(15)
Falkenström H, Ekstrand S (2002)
Evaluation of IRS-1c LISS-3 satellite data for defoliation assessment on Norway spruce and Scots pine. Remote Sensing of Environment 82: 208-223.
CrossRef | Gscholar
(16)
FAO (2000)
Global forest resource assessment 2000: main report. Forestry paper 140, FAO, Rome, Italy, pp. 479.
Gscholar
(17)
FAO (2001)
Protecting plantations from pests and diseases. Forest Plantation Thematic Papers, Working Paper 10, Forest Resources Development Service, Forest Resources Division, FAO, Rome, Italy, pp. 19.
Online | Gscholar
(18)
FAO (2010)
Global forest resource assessment 2010: main report. Forestry paper no. 163, FAO, Rome, Italy, pp. 340.
Online | Gscholar
(19)
German Forest Protection Society (2017)
Die Waldkiefer (Pinus sylvestris L.) [The Scots pine (Pinus sylvestris L.)]. Schutzgemeinschaft Deutscher Wald, Bundesverband e. V. (SDW), Bonn, Germany, pp. 3. [in German]
Online | Gscholar
(20)
Gitelson AA, Gritz Y, Merzylak MN (2003)
Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160: 271-282.
CrossRef | Gscholar
(21)
Green AA, Berman M, Switzer P, Craig MD (1988)
A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing 26 (1): 65-74.
CrossRef | Gscholar
(22)
Hall RJ, Crown PH, Titus SJ, Volney WJA (1995)
Evaluation of Landsat Thematic Mapper data for mapping top kill caused by Jack Pine budworm defoliation. Canadian Journal of Remote Sensing 21 (4): 388-399.
CrossRef | Gscholar
(23)
Hall RJ, Fernandes EH, Brandt JP, Butson C, Case BS, Leblanc SG (2003)
Relating aspen defoliation to changes in leaf area derived from field and satellite remote sensing data. Canadian Journal of Remote Sensing 29 (3): 299-313.
CrossRef | Gscholar
(24)
Heikkilä J, Nevalainen 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
(25)
Hofmann G (1997)
Mitteleuropäische Wald- und Forstökosysteme in Wort und Bild [Middle European forests and plantation ecosystems in text and image]. AFZ Der Wald, Stuttgart, Germany, pp. 91. [in German]
Gscholar
(26)
Homolová L, Malenovsky Z, Hanuš J, Tomášková I, Dvoráková M, Pokorny R (2007)
Comparison of different ground techniques to map leaf area index of Norway spruce forest canopy. In: Proceedings of the ISPRS Working Group VII/1 Workshop ISPMSRS ’07 “Physical Measurements and Signatures in Remote Sensing”. Davos (Switzerland) 12-14 Mar 2007, pp. 6.
Online | Gscholar
(27)
ICP Forests (2016)
Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Part IV: Visual Assessment of Crown Condition and Damaging Agents. ICP Forests Expert Panel on Crown Condition and Damage Causes. PCC of ICP Forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, pp. 54.
Online | Gscholar
(28)
IBM (2012)
IBM SPSS Decision Trees 21: users manual. IBM Corporation, Armonk, NY, USA, pp. 106.
Online | Gscholar
(29)
Jensen JR (2000)
Remote sensing of the environment - an earth resource perspective. Pearson Prentice Hall, MN, USA, pp. 544.
Gscholar
(30)
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004)
Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121 (1-2): 19-35.
CrossRef | Gscholar
(31)
Lecki DG, Teillet PM, Ostaff DP, Fedosejevs G (1988)
Sensor band selection for detecting current defoliation caused by spruce budworm. Remote Sensing of the Environment 26: 31-50.
CrossRef | Gscholar
(32)
Lein JK (2012)
Environmental sensing: analytical techniques for Earth observation. Springer, New York, USA, pp. 334.
Gscholar
(33)
Majunke C, Möller K, Funke M (2004)
Die Nonne (Lymantria monacha) [The Nun-moth]. Waldschutz-Merkblatt 52, Landesforstanstalt Eberswalde, Hendrik Bäßler Verlag, Berlin, Germany, pp. 25. [in German]
Online | Gscholar
(34)
Maritz JS (1981)
Distribution-free statistical methods. Chapman and Hall, London, UK, pp. 276.
Gscholar
(35)
Müller E, Stierlin HR (1990)
Sanasilva tree crown photos. Eidgenössische Anstalt für das forstliche Versuchswesen, Birmensdorf, Switzerland, pp. 129.
Gscholar
(36)
Muukkonen P, Lindgren M, Nevalainen S (2014)
Accuracy of visual tree defoliation assessment: a case study in Finland. Working Paper 307, Finnish Forest Research Institute, METLA, Vantaa, Finland, pp. 22.
Online | Gscholar
(37)
Patil N, Lathi R, Chitre V (2012)
Comparison of C5.0 and CART classification algorithms using pruning technique. International Journal of Engineering Research and Technology 1 (4): 1-5.
Online | Gscholar
(38)
Rabinowitch E, Govindjee X (1969)
Photosynthesis. Wiley and Sons Inc., New York, USA, pp.273.
Online | Gscholar
(39)
Radeloff CV, Mladenoff DJ, Boyce MS (1999)
Detecting jack pine budworm defoliation using spectral mixture analysis: separating effects from determinants. Remote Sensing of the Environment 69: 159-169.
CrossRef | Gscholar
(40)
Ritchie GL, Bednarz CW (2005)
Estimating defoliation of two distinct cotton types using reflectance data. The Journal of Cotton Science 9: 182-188.
Gscholar
(41)
Roloff A (2001)
Baumkronen - Verständnis und praktische Bedeutung eines komplexen Naturphänomens [Tree crowns - comprehension and practical meaning of a complex phenomenon]. Eugen Ulmer GmbH, Stuttgart, Germany, pp.164. [in German]
Gscholar
(42)
Rullan-Silva CD, Olthoff AE, Delgado De La Mata JA, Pajares-Alonso JA (2013)
Remote sensing of forest insect defoliation - a review. Forest Systems 22 (3): 377-391.
CrossRef | Gscholar
(43)
Running SW, Peterson DL, Spanner MA, Teuber KB (1986)
Remote sensing of coniferous forest leaf area. Ecology 67 (1): 273-276.
CrossRef | Gscholar
(44)
Sangüesa-Barreda G, Camarero JJ, Garcia-Martin A, Hernández R, De La Riva J (2014)
Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecology and Management 320: 171-181.
CrossRef | Gscholar
(45)
Schiffman B, Basson G, Lue E, Ottman D, Hawk A, Ghosh M, Melton F, Schmidt C, Skiles JW (2008)
Estimation of Leaf Area Index (LAI) through the acquisition of ground truth data in Yosemite national park. In: Proceedings of the “ASPRS 2008 Annual Conference”. Portland (Oregon, USA) 28 Apr - 2 May 2008, pp. 11.
Online | Gscholar
(46)
Sims NC, Stone C, Coops NC, Ryan P (2007)
Assessing the health of Pinus radiata plantations using remote sensing data and decision tree analysis. New Zealand Journal of Forestry Science 37: 57-80.
Online | Gscholar
(47)
Steele M, Gitelson AA, Rundquist D (2008)
Nondestructive estimation of leaf chlorophyll content in grapes. American Journal of Ecology 59 (3): 299-305.
Online | Gscholar
(48)
Stenberg P, Rautiainen M, Manninen T, Voipio P, Mottus M (2008)
Boreal forest leaf area index from optical satellite images: model simulations and empirical analyses using data from central Finland. Boreal Environment Research 13: 433-443.
Online | Gscholar
(49)
Tallent-Halsell NG (1994)
Forest health monitoring 1994 field methods guide. EPA/620/R-94/027, Office of Research and Development, US Environmental Protection Agency, Washington, DC, USA, pp. 266.
Gscholar
(50)
Thayn JB (2013)
Using a remotely sensed optimized Disturbance Index to detect insect defoliation in the Apostle Islands, Wisconsin, USA. Remote Sensing of Environment 136: 210-217.
CrossRef | Gscholar
(51)
Timofeev R (2004)
Classification and regression trees (CART) theory and applications. Master Thesis, CASE - Center of Applied Statistics and Economics Humboldt University, Berlin, Germany, pp. 39.
Online | Gscholar
(52)
Tso B, Mather PM (2009)
Classification methods for remotely sensed data. Taylor and Francis Group, New York, USA, pp. 372.
Online | Gscholar
(53)
Tufféry S (2011)
Data mining and statistics for decision making. John Wiley and Sons Ltd, London, UK, pp. 716.
Online | Gscholar
(54)
Viña A, Gitelson AA, Nguy-Robertson AL, Yi P (2011)
Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment 115 (12): 3468-3478.
CrossRef | Gscholar
(55)
Wenk M, Apel KH (2007)
Die Regenerationsfähigkeit von durch Fraß des Kiefernspinners (Dendrolimus pini L.) und der Nonne (Lymantria monacha L.) geschädigten Kiefernbeständen in Brandenburg. [Regeneration potential of Scots pine stands in Brandenburg after defoliation by the Pine Tree Lappet and the Nun-moth]. In: “Die Kiefer im nordostdeutschen Tiefland - Ökologie und Bewirtschaftung” [Scots pine in the plains of Northeast Germany - Ecology and management]. Eberswalder Forstliche Schriftenreihe Band 32: 280-287. [in German]
Online | Gscholar
(56)
Wenk M, Möller K (2013)
Prognose Bestandesgefährdung - Bedeutet Kahlfraß das Todesurteil für Kiefernbestände? [Prognosis stand exposure - Does complete defoliation imply the death sentence for Pine stands?]. Eberswalder Forstliche Schriftenreihe 51: 9-14. [in German]
Gscholar
(57)
Zeng G, Moskal LM (2009)
Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors 2009 (9): 2719-2745.
CrossRef | Gscholar
 

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