In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
409 
Gray level co-occurrence matrix (GLCM) based texture 
LH™CM£)= y iP u 
_ 
F 
2. HaiKaQm*M = / i ■■■ ' 
¿U l+O-/) 1 
I 
3, « GMtnut CC©3 = y i P 4i i;i-i> z 
4» Sl^Tsdsfiil intfiwcCSnfl = vT"* 
when VA == y 4 (i - M&3 5 
*■* 
LifemmilsfifjCSl) = y i ji—j| 
ar-ji 
& Emlrmpy&N} = y 4 (-1*5 Fj) 
¿4* 
L4SM3 = y i 
Ltevn« Difference C/D) = 
.v-i 
9. GLDT J^iacr 5#iKmd Mswien* IS ASM) — X ly 
lb*6 
JKF-S 
10, t*aropy i££N} = y V*. <-&* l\) 
lb«P 
P (i, j) is the normalized co-occurrence matrix such that 
SUM (i, j-0, N-l) (P (i,j)) = l. 
V(k) is the normalized grey level difference vectorV(k) 
: SUM (i, j=0, N-l and [i-j ] = k) P (i, j) 
Sum & difference histogram (SADH) based texture 
parameter 
r jr 
1, Mean <j4 = —=—- 
I 
|2 -;! 5 
, , £* K ~*M 
2»Mean dtvuitiim (Jit?) = 
n 
¡E~0^ -jy 3 
3kM#sf5 Euclidean dist&n-cm CM ED} = j 
X (*a -p) 3 
*y 
A.Variance (ir) = 
n, -1 
y 
i.sk**-nssHSk)= 
X t* w - j*) 4 
IKurtosis (Eu) = —-———— 
(« - l>r* 
Y, .'V*. 
8. fn#r#y<r) = *"*** « 
* 
Z x 
X*.* i / m 
4* = pixel value of pixel (*■*.§) in kernel, ' * = the number 
of pixels that is summed, X c - the kernel’s center pixel 
value, P* = the normalized pixel value. 
Table 1. Formulae of texture measurements used in this study 
9. Entropy Qi) = — y p u i»£p JL wrer* ja 4 
4. RESULTS AND ANALYSIS 
The field biomass data from the 50 field plots ranged from 
52t/ha to 530t/ha. In all modeling processes, the 50 field plots 
were used as the dependent variable and parameters (AVNIR-2 
and/or SPOT-5) derived from different processing steps were 
used as independent variables. 
The best estimates of biomass using simple spectral bands of 
AVNIR-2 and SPOT-5 as well as different combinations of 
band ratios and PCA produced only ca. 60% useable accuracy 
due to (i) the complexity of forest structure and terrain in the 
study areas, (ii) The very high field biomass in this study area 
(52t/ha to 530t/ha), and (iii) strong multicollinearity effects 
among the 8 bands and band ratios from the two sensors used. 
A notable improvement was observed for both sensors using 
texture parameters (Table 2). For single band texture, the 
highest (ANVIR F= 0.742 and SPOT-5 r 2 = 0.769) and lowest 
(ANVIR r 2 =0.309 and SPOT-5 1^=0.326) accuracies were 
obtained from the texture parameters of NIR and Red bands 
respectively. The pattern of accuracy was similar to that 
obtained using raw spectral bands although the performance 
was much higher for texture measurement. Moreover, as with 
raw data, the second highest accuracies (ANVIR r 2 =0.547 and 
SPOT-5 1^=0.615) were also obtained from green and SWIR 
bands using AVNIR-2 and SPOT-5 data respectively. These 
patterns of improvement were consistent for both sensors and 
very much in agreement with the general behavior of interaction 
between different wavelengths and vegetation. Thus we found 
that texture measurement enhanced biomass estimation across 
all bands but greater improvement was observed from the bands 
where reflectance from vegetation is higher. 
However, unlike raw spectral bands and simple ratios of raw 
spectral bands, texture parameters from all bands together 
(either all bands of AVNIR-2 or SPOT-5) were found to be very 
useful, and obtained accuracies of 0.786 (r 2 for AVNIR-2) 
(model 1 in Table 2) and 0.854 (r 2 for SPOT-5) (model 2 in 
Table 2) Apart from the improved accuracies the developed 
models (using all texture parameters of an individual sensor 
together) were significant and no multicollinearity effects were 
evident. 
When texture parameters from both sensors were combined 
together in the model (model 3 in Table 2), as well as all texture 
parameters of PCA of both sensors together (model 4 in Table 
2), and all texture parameters from averaging of both sensors 
together (model 5 in Table 2), very significant improvements 
were obtained although PCA was not found to be very effective. 
The highest (r 2 =0.91) and the second highest (r^O.90) 
accuracies were obtained from the texture parameters from the 
averaging of both sensors, and texture parameters of both 
sensors in the model respectively. These differences were 
attributed to the fact that averaging is a type of data fusion, and