14 . Kongress der Internationalen Gesellschaft fur Photogramme trie Hamburg 1980 , Kornrnission VII , Arbeitsgruppe 2 A SAMPLING TECHNIQUE TO ASSESS SITE , STAND , AND DAMAGE CHARACTERISTICS OF PINE FORESTS ON CIR AERIAL PHOTOGRAPHS H. U. Scherrer , H. Fltihler, F . Mahrer , and o. u. Braker Federal Institute of Forestry Research , CH - 8903 Birrnensdorf Switzerland Abstract A test area of the severely damaged pine forests in the lower Swiss Rhone Valley was sampled systematically . Nine site, stand , and damage characteristics were rated and encoded for each sample plot using medium scale color infrared (CIR) aerial photographs . The objective of this study is to quantify the interdependences between these variables . Using a multiple linear regression in connection with a principal component analysis , the damage variable " pine mortality " .is expressed in terms of site and stand characteristics . Introduction This study is part of a forest damage investigation carried out in the lower Rhone Valley , Switzerland (SCHERRER et al ., in prep . ) . Some of its pine forests are severely damaged . This study was undertaken with the following question in mind : To what extent is it possible to explain " pine mortality " with those site and stand characteristi cs which can be clearly recognized on medium scale erR - photographs? In this sense , it i s an analysis of how the interdependent variables con tribute to "pi ne mortality ". The surveyed area is 426 sqkrn with 130 sqkrn of forest land . The industrialized val l ey is more than 100 krn long and rela tively narrow , encompassed by alpine mountains rising to an elevation of some 3000 - 4000 rn above the valley bottom . The frequent , low and stab l e i nversion layers enclose an atmos pher i c volume of a limi ted di l ution capacity for air pollutants. Not only air pollution but also periodic drought and other natural stress factors might be possible causes of the observed forest damage . Methods and results The 781 sample plots , arranged in a systemat i c network covering a test area of 196 ha , were examined on CIR - aerial photographs (23x23 ern , f=l53 rnrn , scale 1 : 13000) using an autograph Wild B8 . Each plot of 0 . 25 ha was rated according to the nine criteria given in Tab . 1. BDL.:&:. Tab. 1 Rat in g criteria a) site charact er istics b) stand characteristics 1 elevation above sea level 50 m intervals (1 = 451 -500 m ... 15 = 1151·-1200 m) 4 stand type 6 classes: composition of pine, dec iduous and other coniferous species 2 exposure 9 classes: 8 az imuth segments and horizontal plots without exposure 5 canopy coverage 4 classes: 0.1 -0 .3, 0.3-0.6, 0.6-0.8, >0.8 6 age of stand 4 classes : young, intermediate, old, heterogeneous 3 topography 5 classes : trough, horizonta l, gentle slope, steep slope, ridge top 7 site index 3 classes: poor, intermediate, good c) damage characteristics 9 age of the dead pine trees 5 classes: no dead pine trees, young, intermediate, o ld, heterogeneous 8 pine mortality 4 classes: expressed in% of all pine trees per plot The frequency distributions of the 781 observations per criteri o n prov ide a first quantitative informat i on (SCHERRER et al . 1979). They are illustrated in Fig. 1 for the two criteria "topography " and "exposure ". 83 % of all sampled plo ts are located on slopes , 8 % on poor ridge top sites and on ly 9 % on fairly pr oduct ive sites in d epr ess ions (troughs , Fig . la). The predominant exposure is NW (63 %, 4 89 plots) which is perpendicular to the valley axis (Fig . lb) . Frequen t and strong winds , mainly fr om the west , aggravate the effects of drought periods on W- exposed sites (11 %) • On N-facing slopes the water regime is more favorable . The Fig. 2 Sampling plots with and without dead pine trees in different altitudes Fig. 1 Absolute freq uencies of stand characteristics of the sampled plots % equals number of D all100 plots without dead pi ne trees (N 302) lliiiiJ m = 1100 1000 = E Q) I I 900 > 2 300 ni number of sampling plots I "'> IIIIII l 0 "'c: 700 0 ·;::; "'> b) Exposure "' 600 I a; ~ I I 1111111111111111 I 500 (51 II I "'~ BOO .0 ~ I 100% equals number of all plots wit h dead pme t rees (N = 479) I L 400 (%) (61 aos. 15 10 5 0 5 10 15 (%) variable "exposure" contains information about several cli matic factors such as wind, precipitation and ~alation. Stratification of the sample plots according to one variable , i.e. "damaged" vs . "undamaged" stands, provides insight into mutual interdependences of the correlated variables . Fig . 2 shows the relative occurence of damaged and undamaged stands at different elevations . The maxima and minima of the two distributions are complements of eachother. The most severely damaged stands are located at the average elevation of typi cal pine sites . A systematic multifactorial sampling network yields a mosaic type map for each variable which is a useful tool for forest management . This information about the spatial distribution of the nine characteristics is obtained in a way as by - product of the analysis (Fig . 3) . Fig. 3 M~saic-type map of pine mortality + + + + + t 0 0 @ + + 0 @ 00~ + + + + 0 mortality 0% 0 @mortality <20% 00 @)mortality >20% to <40% Charrat-Vison ~mortality + + + + WRLDSCHRDENKRRTIERUNG CHRRRRT-SRX0N (VSJ, M0RTRLITRET DER F0EHREN 806. ;;;.40% + 500 m + In the context of this study , " pine mortality " is of prime concern . It might be considered as the dependent variable Yi expr essed as a funct i on of the other eight variables which were included in th i s study . In order to apply a multiple linear regression model (MLR) two conditions must be met . First , the eight "independent " variables should be uncorrelated and second , the average "pine mortality " per criteria class of the individual vari ables should exhibit an either i ncreasing or decreasing trend. Whenever it can be logically justified the code classes should be r earranged to satisfy this requirement . For certain cr i teria such as stand type , topography and exposure one cannot objec tively def i ne the sequence and espec i al l y the ranking codes because they were arbitrarily chosen and encoded . Four criteria were partially re - classified . Since for a given pine mortality the relative frequencies per observation class are unevenly distributed the following procedure was applied : The observed frequencies were expressed as percentages per code class of the re - examined variable . Doing this , the un equa lly represented frequencies are we i ghted . As shown in Tab . 2a and 2b , and i n F i g . 4a and 4b the original code classes Tab. 2a Absolute and relat ive frequencies of different exposures per pine tree mortality class Tab . 2b Absolute and relative f requencies of different forest stand types per p ine mortality class stand type exposure w SN+NW S+O SE+N * * E+NE pine mo rtal ity pi ne mortal ity * * ** 0 '<!" 0 Cl \1 \/ 8 8 * '*' O?f?.o OO?f?. o ?f2, o VI/\ V /\ u II\ I /'t. ~om ~mo 0. "0 u 0."0 u ~o<D ?ft.?!?. 00 '<!"Cl vv ...... ?f?.?f?. 0 0 oo?f?. ~<Do *mo 0 \,/ +-" o*-*... oo 0 ~ ~ ~. <0 " '<!" * *~ 0 Cl /\. !'\ II · ~ · ~ 0. "0 u 0. "0 u o.::~ o.c~ 1 (0%) 17 (20) 186 (37) 30 (39) 60 (62) 9 (60) 1 (0%) 137 (88) (72) 21 (36) 22 (17) 23 (12) 3 (3) 2 (< 20%) 16 (19) 11 6 (23) 14 (18) 12 ( 12) 3 (20) 2 (< 20%) 8 (5) 6 (4) 22 (36) 29 (22) 65 (32) 31 (30) ( ~ · 20% 3 19 (23) 103 (20) 15 ( 19) 15 ( 16) 0 (0) 3 5 (3) 7 (5) 8 ( 14) 37 (29) 57 (29) 38 (36) 4 (:;;;,40%) 31 (38) 103 (20) 19 (24) 10 ( 10) 3 (20) 4 (:;;;,40%) 6 (4) 25 (19) 8 ( 14) 41 (32) 53 (27) 33 (3 1) total 83 ( 100) 508 (100) 78 (100) 97 (100) 15 (100) total 156 (100) 134 (100) 59 (100) 129 (100) 198 (100) 105 ( 100) orig inal code 8 7+9 6+ 1 5+2 4+3 orig ina l code 5 1 6 2 3 4 rearranged code 1 2 3 4 5 rearranged code 1 2 3 4 5 6 to < 40%) (percentages in parentheses) ( ;.. 20% to < 40%) 96 (percentages in pa rentheses) 807. p = p ine trees d = deciduous trees c = other co ni ferous trees were rearranged in a way that the relative frequencies either increase or decrease within each pine mortality class . The average "pine mortality" of the nine exposure classes exhibits a max i mum on W- facing , a minimum on E-facing, and an intermediate value on horizontal sites . Note, that only the code sequence but not the numerical value of individual observations was adjusted. Pi 1%] =number of sample plots per 100 total number of topography class (s. Tab . 1). Pi [%] =number of sample plots per total number of elevation class (s. Tab. 1 ). 100 I pine mortality class 1 (no mortality) 80 ----- 60 - I pine mortality class 4 (mortality > 40%) I o.c. original code I r.c. rearranged code pine mortality class 1 (mortality O' ;) 80 - - pine mortality class 4 (mortality : 40 ~,) o.c. original code 60 (= 6 height from 725 m) 40 40 I ,- _, 20 I I '' ' 1 20 \ \ \ 0 ____ ... ' \. .......... , ... I o~~-;---+--,_--r--+---r--+-~r--+--4---+-_,--'~ -~ o.c. r.c. Fig. 4a 1 6 475 2 3 5 4 525 575 4 5 6 3 2 1 625 675 725 7 8 9 10 11 12 13 14 15 2 3 4 5 6 7 8 9 10 775 825 875 925 975 1025 1075 1125 11 75 m a.s.l. o.c. 5 r.c. 1 1 2 2 3 rn .r: c: "'c.0 u; g"' ::> 2 '§ .r: ~ ~ ~ "' pine mortality J:lasses. Most of the nine variables, each with 781 observations, are highly correlated (Tab. 3) . Correlation coefficients higher than 0 . 09 or 0 .1 2 are significant at a 95 % or 99 % level, respectively . Hence these variables are not "independent" as required for a regression analysis . Correlation coefficiants between the rated variables "' ~ ..... Q) Q) Q) Ol > .<=: E c: -~ ~ ..... "'> Q) "'"'c. Q; Q) 0 X > ..r:::: c. E Ol 0 c. ..... 0 Q) c. .....> "t:J c: "' t> Q) "t:J 0 -= > u > c. 0 c: "'u c: ..... "'"' 0 Q) "' Cii X Q) "t:J pine mortality age of dead pine trees BOB. t 0 c: E ..... c: c. Q) ·;;; 1.00 0.07 - 0.10 -0.26 0.03 -0.02 0.28 exposure 1.00 0.06 -0.20 0.13-0.03 0.17 topography 1.00 0.17 -0.18 -0.05 -0.40 1.00 -0.17 0.08 -0.50 stand type 1.00 0.00 0.4 7 canopy coverage 1.00 0.07 age of stand 1.00 site index elevation 4 5 "' c. 0 u; c. 0 u; c. ~ "' ill, "0 ·;::: Fi~. 4b Relative frequencies of different topagraphy classes for two Relative frequencies of different elevations for two pine mortality classes. Tab. 3 3 4 Q) c: c. "t:J "' Q) "t:J 0 Q) Ol "' 0.37-0.32 0.19 0.21 0.25 0.21 0.54 0.63 0.11 -0.09 0.22 0.33 0.42 -0.38 1.00 0.83 1.00 In order to obtain truly "independent" variables the observations Xij (variable j, plot i) were normalized , Xij=(Xij-x)/sx· r and then transformed by means of a principal component (PC) J analysis which yields the orthogonal , uncorrelated variables *Xij !/. The multiple linear regression program for "pine mortality" (yi), run with the *Xij's returns the regression coefficients for both , the orthogonal and the original coordinates , *Xij and Xij r respectively . This technique was used by FRITTS et al . (1971) for a s i milar statistical problem i n the field of tree ring analysis . The response functions which are the regression coefficients computed with the *Xij's and projected onto the coordinates of the Xij ' s reflect the relative importance of the individual variables with respect to pine mortality (yi) • k Yi = '1 + Sy l: j=l a . x .. J l J In a first run (model A) of the PC-MLR- program all variables (k=8) were included. This model explains 69 . 9 % of the observed variance in Yi (Fig . Sa) . Those variables wi th coefficients significantly different from zero were included i n a second run (model B, k=4) whi ch still accounts for 69 . 9 % of the variance (Fig . Sb) . With only the two most significant vari ables (k=2) of run A and B ( " age of dead pine trees " and " elevation " ) 68 . 8 % of the variance i s exp l ained (model c, Fig. Sc) . Almost no in formation is lost due to the drastically reduced number of variables . A simple linear regression versus the most significant variable ( " age of dead p i ne trees " ) accounts for 67 . 7 % of the variance . The role of the remaining site and stand var i ables i s obviously covered up by the dominant "age of dead pine trees" variable . Without this var i able but including all the others , model D (Fig . Sd , k=7) leads to the interesting conclusion that all remaining site and stand variables significantly contr i bute to the pred i ctor of Yi · However, i n this last run only 42 % of the var i ance is explained . !/ Program package RESPONS Version 1971 , H. C . FRITTS, Tree Ring Laboratory, Univ. of Arizona , Tucson . 809. rT 0.8 a) c) M =9 M =2 R2 = 0.688 R2 = 0.699 d) M =7 R2 = 0.420 0.6 0.4 0.2 0.0 ~ ill Q) ~ ~ Q) Q) c "'E "'>0 Cl -0.2 c 0 -~ > "' -.; E iii0 Q. X "' > .r::: Q. E Cl "' Q. ~ 0 1J B t! Q. c: u > Q. 0 c "' u c ·c. 1J 1J c t; "' 0 Q) Cl "' Fig . 5a ·- d "'"' -5"' ..... 1J 1J X "' .!': 1J ~ ·;;; 0 "' "' Cl ·c. 1J "'"' 1J c 0 ·;:; > "' "' -.; > .c Q. E Cl 0 Q. B "'"' 1J X "' 1J .!': ~ ·;;; -5"' .....0 "' "' Cl c: 0 ·;:; > "' "' -.; -5"' ..... 0 "' Cl "' "'E "'>0 Cl c 0 ·;:; "'> "' -.; :;"' > .r::: Q. "' Q. E ~ <':l Cl 0 1J X B "' Q. "' Q. c: t; u 1J c "' t; X ..... 1J 0 0 "' .!': "'u "' ~ ·;;; > Q. c: Cl "' Regression coefficients of damage prediction models (M =normalized variables) Conclusions This statistical approach overcomes the difficu lty that part of the i nformation of a s i ngle variable is contained in others , t oo . It must be recognized that this analysis is no proof of cause and effect but it is a strong indication of possible synergisms between different facto rs . In this sense we might hypothesize that natural or premature aging is e ither the cause itself or a prerequis it e for the observed severe forest damage . Under the influence of adverse site conditions such as drought and/or shallow soils on steep slopes or eroded ridge tops this trend appears to be more pronounced . 8:1.0. References FRI TTS , H. C ., T . J . BLASING , B. P . HAYDEN , J . E . KUTZBACH (1971) . Multivariate Techniques for Specifying Tree - Growth and Climate Relationships and for Reconstructing Anomalies in Paleoclimate . Appl . Meteorology 10(5) : 845 - 864 . SCHERRER , rating on Proc . 7th the Plant H. U., H. FLUEHLER , B. OESTER (1979) . Pine damage medium and large scale infrared aerial photographs . Biennal Workshop on Color Aerial Photography in Sciences , Davis CA . P . 151- 160 . SCHERRER , H. U., H. FLUEHLER , F . MAHRER , O. U. BRAEKER (1980) . Walliser Waldschaden . I . Alternati ve Verfahren flir d i e Inter pretation von Fohrenschaden (P . silvestris L . ) auf mittelmass stabl i chen I nfrarot- Farbaufnahmen . (in prep . ) . 811.