Spatio-temporal modelling of forest monitoring data: modelling German tree defoliation data collected between 1989 and 2015 for trend estimation and survey grid examination using GAMMs

doi:10.3832/ifor2932-012

Spatio-temporal modelling of forest monitoring data: modelling German tree defoliation data collected between 1989 and 2015 for trend estimation and survey grid examination using GAMMs

Nadine Eickenscheidt^(1-2) , Nicole H Augustin⁽³⁾, Nicole Wellbrock⁽¹⁾

iForest - Biogeosciences and Forestry, Volume 12, Issue 4, Pages 338-348 (2019)
doi: https://doi.org/10.3832/ifor2932-012
Published: Jul 05, 2019 - Copyright © 2019 SISEF

Research Articles

PDF Version

Full Text

Citation

Spatio-temporal modelling of tree defoliation data from the German forest condition survey is statistically challenging, particularly due to irregular grids. In the present study, generalized additive mixed models (GAMMs) were used to estimate the spatio-temporal trends in defoliation of the main tree species spruce, pine, beech and oak from 1989 to 2015 and to examine the suitability of different monitoring grid resolutions (standard 16 × 16 km grid and denser grids). Although data has been collected since 1989, this is the first time spatio-temporal modelling for all of Germany has been carried out. GAMMs proved to be a statistically sound and highly flexible choice for spatio-temporal modelling of defoliation data. In addition to the space-time component, stand age showed a significant effect on defoliation. The mean age and the species-specific relation between defoliation and age determined the general level of defoliation. However, further investigations are necessary in order to understand what is behind the age effect. Adjustment for stand age was carried out for identifying hotspots of high defoliation that are not merely the result of the age effect. Fluctuations in defoliation were most likely related to weather conditions. South-western Germany has emerged as the region with the highest defoliation since the drought year 2003. This region was characterized by the strongest water deficits in 2003 compared to the long-term reference period (1961-1990). Furthermore, the spatio-temporal model was used to carry out a simulation study to compare different survey grid resolutions in terms of prediction error. The model-based approach for grid analysis turned out to be appropriate for the given data and sample design. The grid analysis indicated that an 8 × 8 km grid instead of the standard 16 × 16 km grid is necessary for spatio-temporal trend estimation and for detecting hotspots in defoliation in space and time, especially regarding oaks.

Keywords

Age Effect, Drought Stress, Forest Condition Survey, Generalized Additive Mixed Models, Grid Examination, Spatio-temporal Model, Survey Design, Tensor Product Smooth

Authors’ address

(1)

0000-0003-1162-3978

Thünen Institute of Forest Ecosystems, Alfred-Möller-Strasse 1, 16225 Eberswalde (Germany)

(2)

0000-0003-1162-3978
State Agency for Nature, Environment and Consumer Protection of North Rhine-Westphalia, Leibnizstrasse 10, 45659 Recklinghausen (Germany)

(3)

0000-0001-6644-3742
University of Bath, Claverton Down, Bath BA2 7AY (United Kingdom)

Corresponding author

Nadine Eickenscheidt
nadine.eickenscheidt@lanuv.nrw.de

Citation

Eickenscheidt N, Augustin NH, Wellbrock N (2019). Spatio-temporal modelling of forest monitoring data: modelling German tree defoliation data collected between 1989 and 2015 for trend estimation and survey grid examination using GAMMs. iForest 12: 338-348. - doi: 10.3832/ifor2932-012

Academic Editor

Matteo Garbarino

Paper history

Received: Jul 25, 2018
Accepted: Apr 10, 2019

First online: Jul 05, 2019
Publication Date: Aug 31, 2019
Publication Time: 2.87 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 38493
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 32566
Abstract Page Views: 2924
PDF Downloads: 2429
Citation/Reference Downloads: 17
XML Downloads: 557

Web Metrics
Days since publication: 1966
Overall contacts: 38493
Avg. contacts per week: 137.06

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2019): 6
Average cites per year: 1.20

(no Plumx Metrics available for this paper)

Publication Metrics

by Dimensions ^©

Articles citing this article

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

(1)

Anders S, Beck W, Lux W, Müller J, Fischer R, König A, Küppers J-G, Thoroe C, Kätzel R, Löffler S, Heydeck P, Möller K (2004)
Auswirkung der Trockenheit 2003 auf Waldzustand und Waldbau [Effect of the drought 2003 on crown condition and forestry]. BFH, Eberswalde, Germany, pp. 109. [in German]
Gscholar

(2)

Augustin NH, Lang S, Musio M, Von Wilpert K (2007)
A spatial model for the needle losses of pine-trees in the forests of Baden-Wurttemberg: an application of Bayesian structured additive regression. Journal of the Royal Statistical Society Series C-Applied Statistics 56: 29-50.
CrossRef | Gscholar

(3)

Augustin NH, Musio M, Von Wilpert K, Kublin E, Wood SN, Schumacher M (2009)
Modeling spatiotemporal forest health monitoring data. Journal of the American Statistical Association 104 (487): 899-911.
CrossRef | Gscholar

(4)

BMEL (2017)
Ergebnisse der Waldzustandserhebung 2016 [Results of the forest condition survey 2016]. BMEL, Bonn, Germany, pp. 50. [in German]
Gscholar

(5)

Brus DJ, De Gruijter JJ (1997)
Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion). Geoderma 80 (1): 1-44.
CrossRef | Gscholar

(6)

De Vries W, Dobbertin MH, Solberg S, Van Dobben HF, Schaub M (2014)
Impacts of acid deposition, ozone exposure and weather conditions on forest ecosystems in Europe: an overview. Plant and Soil 380 (1): 1-45.
CrossRef | Gscholar

(7)

Eichhorn J, Icke R, Isenberg A, Paar U, Schönfelder E (2005)
Temporal development of crown condition of beech and oak as a response variable for integrated evaluations. European Journal of Forest Research 124 (4): 335-347.
CrossRef | Gscholar

(8)

Eichhorn J, Roskams P, Potočić N, Timmermann V, Ferretti M, Mues V, Szepesi A, Durrant D, Seletković I, Schröck H-W, Nevalainen S, Bussotti F, Garcia P, Wulff S (2016)
Part IV: visual assessment of crown condition and damaging agents. In: “Manual on Methods and Criteria For Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests” (UNECE ICP Forests Programme Co-ordinating Centre ed). Thünen Institute of Forest Ecosystems, Eberswalde, Germany, pp. 54.
Online | Gscholar

(9)

Eickenscheidt 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 and Assessment 186: 257-275.
CrossRef | Gscholar

(10)

Eickenscheidt N, Puhlmann H, Riek W, Schmidt-Walter P, Augustin NH, Wellbrock N (2019)
Chapter 11 - Spatial response patterns in biotic reactions of forest trees and their associations with environmental variables in Germany. In: “Status and dynamics of forest soils in an ecosystem context” (Wellbrock N, Bolte A eds). Ecological Studies. [in press]
Gscholar

(11)

Fahrmeir L, Lang S (2001)
Bayesian inference for generalized additive mixed models based on Markov random field priors. Journal of the Royal Statistical Society: Series C (Applied Statistics) 50 (2): 201-220.
CrossRef | Gscholar

(12)

Ferretti M, Fischer R, Mues V, Granke O, Lorenz M (2010)
Basic design principles for the ICP forests monitoring networks. Manual Part II. In: “Manual on Methods and Criteria For Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests” (UNECE ICP Forests Programme Co-ordinating Centre ed). vTI - Institute for World Forestry, Hamburg, Germany, pp. 22.
Gscholar

(13)

Ferretti M, Nicolas M, Bacaro G, Brunialti G, Calderisi M, Croisé L, Frati L, Lanier M, Maccherini S, Santi E, Ulrich E (2014)
Plot-scale modelling to detect size, extent, and correlates of changes in tree defoliation in French high forests. Forest Ecology and Management 311: 56-69.
CrossRef | Gscholar

(14)

Kammann EE, Wand MP (2003)
Geoadditive models. Journal of the Royal Statistical Society Series C-Applied Statistics 52: 1-18.
CrossRef | Gscholar

(15)

Klap JM, Oude Voshaar JH, De Vries W, Erisman JW (2000)
Effects of environmental stress on forest crown condition in Europe. Part IV: Statistical Analysis of Relationships. Water Air and Soil Pollution 119: 387-420.
CrossRef | Gscholar

(16)

Kneib T, Fahrmeir L (2011)
A space-time study on forest health. In: “Statistical methods for trend detection and analysis in the environmental sciences” (Chandler RE, Scott EM eds). Wiley, Chichester, UK, pp. 25.
CrossRef | Gscholar

(17)

Köhl M, Innes JL, Kaufmann E (1994)
Reliability of differing densities of sample grids used for the monitoring of forest condition in Europe. Environmental Monitoring and Assessment 29 (3): 201-220.
CrossRef | Gscholar

(18)

Lark RM, Cullis BR (2004)
Model-based analysis using REML for inference from systematically sampled data on soil. European Journal of Soil Science 55 (4): 799-813.
CrossRef | Gscholar

(19)

Lin XH, Zhang DW (1999)
Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society Series B-Statistical Methodology 61: 381-400.
CrossRef | Gscholar

(20)

Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, Lexer MJ, Marchetti M (2010)
Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management 259 (4): 698-709.
CrossRef | Gscholar

(21)

MULEWF (2015)
Waldzustandsbericht 2015 [Forest condition report 2015]. Ministerium für Umwelt, Landwirtschaft, Ernährung, Weinbau und Forsten, Mainz, Germany, pp. 106. [in German]
Gscholar

(22)

Musio M, Von Wilpert K, Augustin NH (2007)
Crown condition as a function of soil, site and tree characteristics. European Journal of Forest Research 126 (1): 91-100.
CrossRef | Gscholar

(23)

Pinheiro JC, Bates DM (2000)
Mixed-effects models in S and S-PLUS. Springer, New York, USA, pp. 548.
Gscholar

(24)

Pinheiro JC, Bates DM, DebRoy S, Sarkar D, The R Core Team (2015)
nlme: R Package Version 3:1-127.
Online | Gscholar

(25)

Pretzsch H, Del Río M, Ammer C, Avdagic A, Barbeito I, Bielak K, Brazaitis G, Coll L, Dirnberger G, Drössler L, Fabrika M, Forrester DI, Godvod K, Heym M, Hurt V, Kurylyak V, Löf M, Lombardi F, Matovic B, Mohren F, Motta R, Den ouden J, Pach M, Ponette Q, Schütze G, Schweig J, Skrzyszewski J, Sramek V, Sterba H, Stojanovic Svoboda M, Vanhellemont M, Verheyen K, Wellhausen K, Zlatanov T, Bravo-Oviedo A (2015)
Growth and yield of mixed versus pure stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) analysed along a productivity gradient through Europe. European Journal of Forest Research 134 (5): 927-947.
CrossRef | Gscholar

(26)

R Development Core Team (2015)
R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar

(27)

Ribeiro JPJ, Diggle PJ (2015)
geoR: R Package version 1:7-5.1. Web site.
Online | Gscholar

(28)

Riek W, Wolff B (1997)
Repräsentanz des europäischen 10 × 16 km-Erhebungsnetzes für Aussagen zum deutschen Waldbodenzustand [Representativity of the European 10 × 16 km grid regarding conclusions about the German forest soil condition]. BFH, Hamburg, Germany, pp. 57. [in German]
Gscholar

(29)

Riek W, Wolff B (1999)
Integrierende Auswertung bundesweiter Waldzustandsdaten [Integrative evaluation of the German forest condition data]. BFH, Eberswalde, Germany, pp. 141. [in German]
Gscholar

(30)

Saborowski J, Dahm S, Ackermann J (1998)
Stichprobentheoretische Analyse der Waldschadenserhebung in Niedersachsen [Sampling theory-based analysis of the forest condition survey of Lower Saxony]. AFJZ 169 (3): 47-52. [in German]
Gscholar

(31)

Schütt P, Blaschke H, Hoque E, Koch W, Lang KJ, Schuck HJ (1983)
Erste Ergebnisse einer botanischen Inventur des “Fichtensterbens” [First results of a botanical inventory of the “spruce dieback”]. Forstwissenschaftliches Centralblatt 102 (1): 158-166. [in German]
CrossRef | Gscholar

(32)

Schwarz G (1978)
Estimating the dimension of a model. The Annals of Statistics 6 (2): 461-464.
CrossRef | Gscholar

(33)

Seidling W (2001)
Integrative studies on forest ecosystem conditions. UNECE, EC and Flemish Community, Geneva, Switzerland, Brussels and Gent, Belgium, pp. 88.
Gscholar

(34)

Seidling W (2004)
Crown condition within integrated evaluations of Level II monitoring data at the German level. European Journal of Forest Research 123 (1): 63-74.
CrossRef | Gscholar

(35)

Seidling W, Mues V (2005)
Statistical and geostatistical modelling of preliminarily adjusted defoliation on an European scale. Environmental Monitoring and Assessment 101 (1): 233-247.
CrossRef | Gscholar

(36)

Seidling W (2007)
Signals of summer drought in crown condition data from the German Level I network. European Journal of Forest Research 126 (4): 529-544.
CrossRef | Gscholar

(37)

Silverman BW (1985)
Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society. Series B (Methodological) 47 (1): 1-52.
CrossRef | Gscholar

(38)

Ulrich B (1984)
Effects of air pollution on forest ecosystems and waters the principles demonstrated at a case study in Central Europe. Atmospheric Environment (1967) 18 (3): 621-628.
CrossRef | Gscholar

(39)

Wellbrock N, Eickenscheidt N, Hilbrig L, Dühnelt P, Holzhausen M, Bauer A, Dammann I, Strich S, Engels F, Wauer A (2018)
Leitfaden und Dokumentation zur Waldzustandserhebung in Deutschland [Guideline and documentation for the forest condition survey in Germany]. Thünen Working Paper 84, Johann Heinrich von Thünen-Institut, Braunschweig, Germany, pp. 97. [in German]
Gscholar

(40)

Wood SN (2006a)
Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62 (4): 1025-1036.
CrossRef | Gscholar

(41)

Wood SN (2006b)
On confidence intervals for generalized additive models based on penalized regression splines. Australian and New Zealand Journal of Statistics 48 (4): 445-464.
CrossRef | Gscholar

(42)

Wood SN (2015)
mgcv: R Package Version 1:8-10. Web site.
Online | Gscholar

(43)

Wood SN (2017)
Generalized additive models. An introduction with R (2nd edn). Chapman and Hall/CRC Press, Boca Raton, FL, USA, pp. 476.
CrossRef | Gscholar

(44)

Zierl B (2004)
A simulation study to analyse the relations between crown condition and drought in Switzerland. Forest Ecology and Management 188 (1-3): 25-38.
CrossRef | Gscholar

iForest - Biogeosciences and Forestry

Contents

Search

Journal Info

Journal Subjects and Fields

For Authors

For Reviewers

For Readers

SISEF Publishing

Search iForest Contents