Download THE APPLICATION OF QUANTIFICATION THEORY I IN SITE

c 2009 Institute for Scientific
°
Computing and Information
INTERNATIONAL JOURNAL OF
INFORMATION AND SYSTEMS SCIENCES
Volume 5, Number 3-4, Pages 528–531
THE APPLICATION OF QUANTIFICATION THEORY I IN SITE
QUALITY EVALUATION OF JAPANESE LARCH-TREE
(LARIX KAEMFERI)
SHURONG HUI, ZHENZHEN LI, FANG YANG, QIANG LIU, AND LIFENG LI
Abstract. We chose 6 vertical factors and utilized the method of quantification theory I in the comprehensive analysis of the influence of Larch-tree(Larix
kaempferi) growth environment factors. This paper constructed the model
of eastern part of Liaoning Province , and the result of examination indicated that the model met the precision requirements. Using this model to
appraise the eastern part of Liaoning Province area Japanese Larch-tree(Larix
kaempferi) vertical quality , it provided the theory basis for eastern part of
Liaoning province Japanese Larch-tree(Larix kaempferi)management of cultivation production.
Key Words. Japanese larch-tree(larix kaempferi), Quantification theory I,
Site quality evaluation.
1. Introduction
Forest site is a collectivity of environments that have signality for forest in some
space, including physiognomy, climate, soil, ect. Site quality evaluation is a basic
work in foresty industry[1]. The effect of forest management depends upon the
quality of woodland and reasonable using and exploiting directly[2]. Site quality
evaluation evaluates suitability and competitive strength on some specific sit plot for
choosing proper tree species, taking reasonable management measure, and bringing
into play the industry potential of site[3][5]. This research investigated and analysed
168 sampled stands in the east part of Liaoning province, and evaluated site quality
of Japanese Larch-tree in the eastern part of Liaoning province scientific. The paper
got a method of site quality evaluation for Japanese Larch-tree in the east part of
Liaoning province. That provided a scientific and dependable theory for practice[4].
2. Site quality evaluation
2.1. Choose sampled stands and factor of environment. All sampled stands
were growning normally pure Japanese Larch-tree. We chose 168 Japanese Larchtree sampled stands that has canopy density above 0.2, area 0.60-1.00ha, and more
than 100 trees in each sampled stands. Some environment factors should be measured: aspect, slope position, soil type, slope degree, altitude, soil depth. Then
each site factor is standarded. There are 19 categories in total(table 1).
Received by the editors May 14, 2008 and, in revised form, October 1, 2008.
2000 Mathematics Subject Classification. 35R35, 49J40, 60G40.
This research was supported by liaoning university scientific research plan projects(2008626).
528
THE APPLICATION OF QUANTIFICATION
529
Table 1. Standard division of item and category for each site factor
Item
Variable Category 1
Aspect
x1
Adret
Slope Position
x2
Ridgetop
Soil Type
x3
Sandy Loam
Slope Degree
x4
> 25◦
Altitude
x5
500m
Soil Depth
x6
> 60cm
Category 2 Category 3
Category 4
Ubac
Middle
Bottom
Light Loam Medium Loam Weight Loam
5◦ 25◦
< 5◦
200m-500m
< 200m
30cm-60cm
< 30cm
2.2. Quantification theory I approach. Every site factors are items, and each
levels are actegories. We sign different variable according to principle of Quantification theory I.
δ(j,k) = {
1 j th item is chosed
1 not
The mean dominant height of sample plot is dependent variable, and environment
factors and age are independent variable. We can get the model of Quantification
theory I.
(1)
yi =
ri
m X
X
bj,k δ(j,k) + blgA + εi
j=1 k=1
with:
yi is independent variable, δj,k is the mean dominant height of i th sample plot.
bik is the score of j th item and k th category. b is the score of the age of stand.
A is the age of stand.εi is random error. i =1,2,. . . ,n is the numbers of sample
plot;j =1,2,. . . ,m is the number of item.i =1,2,. . . ,ri is the number of category. (Table 2)
Undetermined coefficient can get from the method above. The standard age is 20
years. We can get the regression equation.
y
= −0.89867δ(1,1) + 0.13501δ(2,1) + 0.70058δ(2,2) + 0.26208δ(3,1,) + 1.13315δ(3,2)
(2)
+0.90594δ(3,3) + 2.66827δ(4,1) + 2.01263δ(4,2) + 3.72503δ(5,1) + 4.84854δ(5,2)
+0.64677δ(6,1) − 0.53705δ(6,2) + 10.379641
2.3. Model Validation. The model was verified the reliability with t test of
multiple correlation coefficient and partial correlation coefficient.
r
(3)
t=r
n−m−1
,
1 − r2
with:
n is the numbers of sample plot;m is the number of item;r is multiple(partial)
correlation coefficient. The result is in table 2. t0 = 32.62618 > t0.05 = 1.64485That
mean average dominant height have close relation with the six environment factors.
530
S. HUI, Z. LI, F. YANG, Q. LIU, AND L.LI
Table 2. Quantified score of each site fator
Item
Aspect
Slioe Position
Soil Type
Slope Degree
Attitude
Soil Depth
Category
Adret
Ubac
Ridgetop
Middle
Bottom
Sandy Loam
Light Loam
Medium Loam
Weight Loam
> 25◦
5◦ − 25◦
< 5◦
> 500m
200m − 500m
< 200m
> 60cmm
30cm − 60cm
< 30cm
Age
Score
-0.89867
Score range
0.89867
C.P.C.
0. 1997704
t Value
2.578901
0. 13501
0.70058
0
0. 26208
1.13315
0.90594
0
2. 66827
2.01263
0
3. 72503
4. 84854
0
0. 64677
-0.537050
0
23.00977
0. 70058
0. 1510598
1.932953
1. 13315
0. 1421746
1.816838
2. 66827
0. 1503214
1.923286
4. 84854
0. 5817446
9.046975
1. 18382
0. 2120812
2.745084
0. 8912763
24.86181
The model fit data well, and the result is accurately. It can be used put into
practice.
2.4. Site Quality Evaluation. Levels of different site quality evaluation base on
score of every sampled stand according to under prerequisites. Results all sampled
stands are divided into three categories as good, average and bad.
if y ≥ ȳ + ∆s,valuation is good;
if ȳ + ∆s > y ≥ ȳ − ∆s,valuation is average;
if y < ȳ − ∆s,valuation is bad.
with:
y is the score of sampled stand; ȳ is average value of all samplde stands;∆ s is
standard deviation.The result is in table 3.
Table 3. Levels of different site quality evaluation
Level
Good ≥ 18.4907
Average(14.2483 − 18.4907)
Bad(< 14.2483)
Number Of Sampled Stand
5, 58, 73, 74,. . . ,164, 167, 168
3, 4, 6, 7,. . . ,165, 166, 169
2, 9, 23, 100,. . . ,145, 146, 147
Number
26
109
33
3. Results and Discussion
The research constructed a method of site quality evaluation for Japanese Larchtree(Larix kaemferi) in eastern part of Liaoning province, basiced on analysing
comprehensively the influence of Japanese Larch-tree(Larix kaempferi) growth environment factors, chosed six environment factors such as:aspect, slope position, soil
type, slope degree, altitude, soil depth, and used the method of quantification theory I. The Japanese Larch-tree(Larix kaemferi) site evaluation modle fit the original
data well. It can be used for scientific management of Lach-tree(Larix kaemferi)
in eastern part of Liaoning province. That can help to improve the production
THE APPLICATION OF QUANTIFICATION
531
potential of forest land, and enhance the ability of forestation and management.
According to the result of the model, every factor has some influence for Japanese
Larch-tree(Larix kaemferi). The order is altitude, soil depth, aspect, slope position,
slope degree,
There are 26 score of plots is above 18.4907, 109 score of plots are above14.2483
and under 18.4907. They are comfortable for Japanese Larch-tree(Larix kaemferi).
There are 33 score of plots are under 14.2483. They are not comfortable for Japanese
Larch-tree(Larix kaemferi). Other tree species should be consider for better effect
of management.
References
[1] You Yin,Meng Wang, Research on Forest Site Classification and Evaluation , Journal of
Anhui Agri.Sci., china, 2007.
[2] Weiji Yang,Xiuru Wang, Analysis on the Study of Site Quality Evaluation in China, Research
of Soil and Water Conservation, 2004.
[3] Xiaohong Fan,Dong Xu, Forest Site Classification and Evaluation,Journal of Sichuan Forestry
Science and Technology,1995.
[4] Zhiyuan Ni,Guiqiao Lu,S.C. Zhang,X.Li, Study on Site Quality Evaluation of Walnut in the
Mountainous Area of Hebei Province, Journal of Forestry and Orchard Research,2006.
[5] Harrington, Constance A. , A Method of Site Quality Evaluation for Red Alder, U.S. Department of Agrculture, 1986.
college of science, shenyang agricultural university, liaoning, 100161, china
E-mail: [email protected]
No. 77, Shenyang Agriculture University, Shenyang, Liaoning, China
E-mail: [email protected].
No. 77, Shenyang Agriculture University, Shenyang, Liaoning, China
E-mail: [email protected]
College of Information and Electrical Engineering, Shenyang Agriculture University, Shenyang,
Liaoning, China
E-mail: [email protected]
College of Science, Shenyang Agricultural University, Liaoning, 100161, China