In the recent decades in some where, forests are destroyed by human activities
so that they should be again reforested by suitable and compatible hardwood
or softwood species. Management and make decision of these reforested
areas needs to have accurate different maps such as stand or types extension.
In other hand, the reforested types or stands are timely changing due
to growing up and substituting of domestic or unfavorable species between
reforested species. Therefore, managing of these changeable reforested
areas needs to have up to dated maps.
Forest or reforested extent mapping or up to dating of those maps trough
current fielding ways is time consuming and cost-intensive. Using satellite
Imagery and its potentials are new tools in order to managing and mapping
forest/reforested covered area. Land cover and forest/reforested areas
have been one of the first and main products from remote sensing data.
Its dynamics are strongly influenced by socio-economic factors and political
decisions, generating a need for adequate mapping and monitoring tools.
Consequently, robust and sophisticated analysis methods are required for
accurate information extraction and fast and efficient data analysis adapted
to the rapid advances in image and sensor technologies. Furthermore, multi-temporal
and multi-sensor approaches are becoming more and more important not only
for change detection but also for more detailed classification approaches.
Forest or reforested extent mapping has been reported feasible with good
certainty using satellite data in the northern mountainous forests of
Iran (Darvishsefat and Shataee, 1997; Rafieian, 2002). The next step for
forest managers was a feasibility study to apply satellite data to classify
northern forest types.
Previous results have shown that the discrimination of forest types that are
composed only of one species, as pure types is very successful by using satellite
data (Metzler and Sader, 2005; Mayer and Fox, 1981). When a forest type is composed
of two or many species such as in the study area, separating the types will
be difficult (Shataee and Darvishsefat, 2004). In the reforested area when natural
and endemic species (i.e., hardwoods) based on succession of natural hardwood
forest are growing up between conifer replanted species the classification of
reforested types would be difficult using single satellite data imagery.
The previous results showed that using only single date images couldnt
accurately classified the main reforested types in the study area (Najjarlou,
2005). Overlapping of spectral response of fruits gardens and wheat plants
with natural hardwoods as well as low quality of radiometric conditions
of images, have reported as causes of low accuracy of classification results.
She suggested that using multi-dates images with different radiometric
correction and from different seasons may be improved the results.
Sometimes, radiometric correction needs many parameters that accurately
computing and determining of a clear condition would be difficult or impossible.
Even applying proper radiometric correction of images, the spectral responses
of objects wouldnt be true. Therefore, using multi-dates images,
which have different radiometric conditions, might better help to classify
Hobbs (1990) expressed those ecological aspects and differentiated between
seasonal vegetation responses, inter-annual variability can be affected
on the classification of images. Aldrich (1975) found that late spring
and summer data considerably enhanced the discrimination of forest cover
vegetation in Georgia. Jano and Pala (1984) stated that while the mapping
of forest cut-overs in pure or predominantly coniferous stands was optimal
with early spring imagery, summer data did better for cut-overs in deciduous
stand. Multi temporal remote sensing data are widely acknowledged as having
significant advantages over single date imagery (Townshend et al.,
How the multi-dates images can be used for classification of reforested
types to improve the accuracy the results of classification as compared
to single date images? Since, many techniques have applied to use the
multi dates images. One of the common and current techniques is linear
Two linear data transformation techniques are frequently applied to multi-date
imagery that has been stacked in 2n-dimensional space (where n is the
number of input bands per images): Principal Component Analysis (PCA)
and tasseled cap (Coppin and Bauer, 1996). The PCA as a linear data transformation
technique can be used to concentrate correlated information of multi-date
imagery. The PCA analysis applied by Richards (1984) to two-date MSS imagery
to monitor brushfire damage and vegetation re-growing over extensive areas
in Australia. The first three components of Principal Component Analysis
contain more information contrary to each band individually.
One of the other techniques that can be used to reduce atmosphere effect
on the imagery and decreasing of variations in DNs of multi date imagery
recorded at the different radiometric conditions is mathematical transformation
or called as ratio transformation. The Ratio transformations are often
used in image processing to reduce radiometric effects of slope, illumination
angle or seasonal variability (Ivits and Koch, 2002).
With these mentioned causes, the goals of this research were reforested
type classification and up to date mapping of reforested area by means
of multi dates ETM+ data using techniques which apply the multi date imagery
in classification process.
MATERIALS AND METHODS
Study area: The study area is located at the southern reforested
area of Kordkooy region in the Golestan Province, Iran (Fig.
1). The study areas are relatively flat (slop <10%) and altitude
ranges between -20 to 100 m with about 600 ha.
Before plantation, the study area had been deforested and shifted from
hardwood forests to farm lands by rural peoples to extend their farmlands.
In 1985, in order to prevent of deforestation and recovering of farm lands
to natural forest lands, the area was planted with conifers species of
pine (Pinus teda, Pinus eldarica, Pinus broucia),
cypresses (Cupressus horizantalis, Cupressus sempervirens)
and hardwood species of eucalyptus (Eucalyptus camaldulensis)
and walnut (Juglans nigra).
||Location of the study area in the south of Kordkooy,
Golestan province, north of Iran
After 20 years, the some endemic and domestically hardwood species were
growing between plantations so that in the somewhere they were dominated
on conifer species.
Data: In this study, small windows on 163-34 scene were selected
from two successive available dates of spring (19th April 2000, 2nd April
2001) and summer data from 16th July 2001. In addition, a digital aerial
ortho photo-mosaic with scale of 1:40000 was used to generate ground truth
map. To geo-referencing of images and selecting of GCPs, two 1:2500 scale
digital maps of study were also used.
Pre-processing of images: Geometric correction of images was done
in two steps: first the image of 19th April 2000 was geo-referenced by
GCPs gathered from 1:25000 scale digital maps with total RMSe less than
0.5 pixel. The two other dates images were then rectified to geo-referenced
images by image to image registration method with RMSe less than 0.45
pixel. Using of nearest neighbour technique to preserve initial radiometric
conditions did Resampling of the DNs of all geometric corrected images.
Multi-temporal analyses: In addition to apply single date imagery
to classify reforested classes individually, two methods were applied
to multi-date imagery to reduce impact of radiometric condition and seasonal
differentials: Temporal Principal Component Analysis (TPCA) and Temporal
Averaging Transformation (TAT). The results of these methods were used
to classify reforested type and comparison with other data from single
Temporal Principal Component Analysis (TPCA): PCA can be used
to reduce the information included in the raw data into two or three bands
without losing significant information (Monger, 2002). In spit of currently
PCA analysis that compresses the correlated bands of a source, it is used
to merge similar bands of multi-date imagery. Because of high correlated
similar bands from multi-date imagery, they can be transformed to many
components. The first component would be containing more information from
used three date bands. Therefore, the six first components were extracted
from six similar bands from three dates.
Where TPCAi is the first component of PCA on the three date similar bands
Temporal Averaging Transformation (TAT): Atmosphere or other radiometric
distortion parameters often may effect on spectral reflectance of vegetation
at different dates so that the similar bands will have different digital
numbers for an equal object as noises. To ignore and reducing of seasonal
radiometric effects on bands and improving classification results of the
reforested types, the simple mathematical operation as multi data averaging
was applied to the three temporal similar bands. This operation was, respectively
applied to all six bands from three date bands so that results of operation
were six images with reducing of variance of certain pixels.
Where TATij is the average of three date similar bands (i)
Classification: Classification of imagery was accomplished with
different combinations (Table 1). In order to test effectiveness
of new data set produced from multi temporal principal analysis (C5) and
temporal averaged bands (C4) to improve reforested type classification
results, classification of reforested types accomplished with the new
data sets i.e., TPCA and TAT. In addition, the single date data sets (C1,
C2 and C3) were individually classified to compare the best date for forest
Also classification of reforested types was done with two seasonal data
sets (spring and summer data). In other hand, the 12 combined bands from
22nd April 2000 and 17th July 2001 (C6) and also the 12 combined bands
from 6th April 2001 and 17th July 2001 (C7) were individually classified
in order to using the multi-seasonal data set.
Following many studies, where the maximum likelihood classifier was reported
as a suitable classifier (Hopkins et al., 1988; Williams, 1992;
Darvishsefat, 1994; Shataee and Dravishsefat, 2004) and this classifier
was applied to separate reforested types.
Ground truth generation and accuracy assessment: The accuracy
of classification results was assessed with a ground truth map. The ground
truth map including reforested area map (four classes of hardwoods, softwoods,
mixed plantations and non-forested area) have generated by visual interpretation
of digital orthophotomosaic and field check (Fig. 2).
||Image band combinations used in classification
||Ground truth map of reforested types at the study area
The digital orthophotomosaic was produced using with eleven 1:40000 scale
aerial photos from autumn of 2001. The camera parameters, fiducial marks,
ground control points and digital elevation model have been used for ortho
rectification of aerial photos.
RESULTS AND DISCUSSION
As a result of a field study and visual interpretation of aerial photography
(1: 40000 scale) of the study area, four major reforested types or classes
were delineated and fields were selected to collect representative pixels
for the classes to be used in classification processes. Thus, a ground
truth image containing a total of all reforested areas was generated for
Temporal comparisons of images to classify reforested types need to have
equal geometric condition for all images. It should be evident that accurate
spatial registration of the multi-date imagery is essential to compare
results. The results of geometric corrections and rectification showed
that images have almost rectified with suitable geometric conditions (RMSe
under 1 pixel and the study area have no relief displacement due to be
Signature separability analysis using both Bhattacharya distance and
transformed divergence indices showed that the mixed and broadleaf classes
have lower separability than other classes.
The accuracy assessment of the classification results showed that the
imagery on 22 April 2000 among single date imagery could better classified
reforested area (Table 2). This result showed that the
best date to classify forest and reforested types is middle of spring
when the forest and reforested area have reached their maturity in growth.
Although, having the uncommon radiometric responses of images on different
dates may be effected on the quality of results.
||Accuracy assessment of classification results with different
With over viewing on the error matrices of accuracy assessments, it was
seen a considerable error reducing for mixed type on 22 April 2000 imagery
compared with two other dates. This may be referring to silviculture treatments
on 2000. The non-forest class contains agriculture and bare lands could
classify on summer date imagery (17 July) but the conifer class was poorly
classified compared with other classes.
In order to reducing of radiometric effects on the single date imagery
and improvement of classification results, Temporal Averaging Transformation
(TAT) was done on the three dates in compliance with equation
(1). Accuracy assessment of results showed a higher overall accuracy
(83%) and kappa coefficient (0.762) compared with using single date imagery
This results exposed that single date imagery even on the best date due
to atmospheric condition on the acquisition time or different phonological
condition for classes can not well classify forest types and it should
be used together other dates. Using temporal rationing on similar bands,
we found that the broadleaf class could better classified rather than
other last methods.
Regarding to three dates imagery were almost from 1 year anniversary,
so that except for non-forested area all classes almost didnt change
throughout 1 year, similar bands contain almost information with low variances
and repeated information. With this assumption, the principal component
analysis on similar bands may be integrated to create a first component
that would have more variances. After temporal principal component analysis
based on above assumptions, it was found a more improvement in accuracy
(83.7% overall accuracy or 0.773 kappa coefficient) compared with other
last methods and certified our theory basses (Fig. 3).
In this study, we also applied the two-date imagery directly (c6 and
c7 combination) to classify the reforested types. Using c6 combination
(22 April 2000 and 17 July 2001) with about 1.5 year temporal difference
could little improved results of classification in comparison with TPCA.
Although, using more channels in classification process may improve the
results, but it leads to some problem such as redundancy and costly, especially,
when multi temporal imagery with more than two dates are used for classification.
||The classification map of reforested types using multi-dates
Therefore, this method can not be present as best method due to restricted
Using c7 combination to classify could not improved the results (81.22%
overall accuracy), but in comparison with single date imagery, those could
better classified reforested types. These results show that using beginning
of spring data (6 April) corporate with summer data cannot be produce
acceptable results and this refers to immaturity of vegetation and forest
on that time.
This study exposed the advantages of using multi dates imagery to classify
reforested types in a small area. This research showed that using only
single date imagery can not be classify interested classes due to atmospheric,
phonological condition of classes together with different cultivation
at the agriculture.
At this study we investigated the some methods to use multi-date imagery
to classify four reforested types and non-forest class contained from
different agriculture productions, orchards and non-cultivated lands.
These lands and their productions on some date had reflectance overlaps
with hardwood and mixed classes and this problem can not be solved only
with multi seasonal or multi-date imagery.
At this research, the multi-dates images were selected based on at least
changes for reforested types and natural forests in study area so that
the temporal differences between three images was maximum one year (22
April 2000 to 17 July 2001).
The results showed that the overall accuracy of imagery on summer date
was lower than other dates. Although, the July images (beginning of summer)
provided phonological stability (seasonal maturity) for hardwood and softwood
trees, but up growing of hardwood trees and shrubs between softwood trees
causes mixed classes, which have reflectance overlap with both classes.
This study certified other researches (Knight et al., 2004) that
spring imagery is suitable to classify reforested types rather than summer
However, among investigated methods, the TPCA method can be introduced
as a best method for classification to use multi dates imagery which,
were acquired over a short periodic time and with no considerable change
or difference on types (maximum 2 years). This method can be used to up
to date mapping of other same reforested area like the study area and
in particular, when the multi seasonal data were used to classify reforested
or forests types.
Investigating on other multi-temporal and multi-seasonal classification
methods or using other spectral and non spectral data could be challenged
in future to develop results of this research.