Using Remote Sensing
technology for dynamic monitoring forest resources
Kou Wenzheng, Xu
Maosong
Sun Xiangran, Zhang Reixi
Academy of Forest Inventory,
Planning and
Designing, Ministry of Forestry, PRC
Abstract
The RGC method has been developed for
dynamic monitoring of forest resources which combines Remote Sensing
technique, Geographic Information System (GIS) and traditional Continuous
Forest Inventory (CFI) technique. The technical designing of RGC method,
the functions and contributions of each techniques to RGC method, the
advantages of RGC method of and an example of using RGC method for
monitoring resources in Jilin province of China are fully described in
this paper.
Introduction
Forest is an important
biological resources. It has the characteristics of wide distributions and
long growing period. Changes often take place under the actions of
man-made elements and natural elements. Promptly and accurately monitoring
dynamic changes of forest resources, mastering the changing regularity of
forest resources, have an important social, economic and ecological
significance. The methods of monitoring forest resources have caused
common interests of international specialists in forestry. In China,
dynamic monitoring of forest resources mainly depends on CFI system. All
over the country, more than 250,000 permanent sample plots have been
established, and checked every 5 years, In this paper, we introduce RGC
method which combines remote sensing technique, GIS technique and
traditional CFI system to enrich and improve the methods of dynamic
monitoring of forest resources.
RGC Method
RGC method is
based on remote sensing technique, GIS technique & CFI technique, to
assimilate the advantages of each technique, to monitor dynamic changes of
forest resources. Remote sensing information is used to extract the areas
of each land classes and the distributions of forest types; GIS is used to
determine the geographic location to ensure repeatedly examining same
sample plot; CFI system is used in necessary surveys. It is the central
point of RGC method to completely and accurately obtain continuous and
comparable dynamic estimations of forest resources.
Compared with
traditional CFI technology, RGC method has following advantages:
- Increasing the precision of forest resources allocation maps and the
precision of corresponding statistics;
- Effectively decreasing unnecessary field works. In low forest
coverage areas, the effect is ore evident;
- Increasing the area estimation precision of each classes; solving
the area estimation of small proportions classes under the circumstance
of not increasing field works;
- To some extent, decreasing the influences of error estimations in
CFI system.
Designing of RGC method
The designing
& implementation of RGC method can be summarized as follows:
- The Landsat Image Map Making.
The landsat image which is
geometrically corrected and registered with geographic information is
called image map: it is the base map for monitoring forest resources,
the carrier for sample layout and the fundamentals of interpretation and
classification:
When it is used to monitor an area larger than a
county, the image map should be made at the scale of 1:50,000 using TM
data. The indispensable elements on the image map are administrative
boundaries, inventory boundaries, longitude lines and latitude liens,
kilometer nets. The density and intervals of boundary lines are
determined by inventory aim and range.
When an image map is made
as the base map for monitoring forest resources, the features of forest
vegetation and the identifications of other land covers must be taken
into consideration in selection of TM bands, ranges of contrast stretch.
Gauss-Kruger projection is the standard coordinate system for image
geometric correction and geographic information input. The errors of
geometric correction and registration with GIS should be less than 0.5
pixel.
On land types interpretation, it is necessary to pay more
attention to mosaic precision of different scences of TM data and
difference between different seasons, because usually the forest
investigation covers very large area.
- Determining the Number of Sample Plots
The RGC method is
required to determine the number of sample plots. The RGC has modified
CFI system which is based on the principal of systematic two-phase
sampling to stratified double sampling. Therefore it is necessary to
know:
- The number of very ground factor sample plots which use average
stock as major factor. These sample plots should be set up n the field
and measured.
- The number of sample plots for calculating area proportions. These
sample plots are used to estimate the area proportions of each land
types and forest types. Normally, it is done by computer automatic
recognition and visual interpretation.
- Ground corrected sample plots. They are sub sample plots of double
sampling which are used to amend the area proportions and should be
laid in the area proportions sample plots. It is finished mainly in
the field and partly indoor such as water bodies.
A. Determining the number of ground sample plots
It is determined by the alternate coefficient and the precisions
of forest increment, forest depletion and forest average stock. The
formula is shown s follows:
N = (
t2 c2 / E2 % )
In
the function :
t is reliable index
c is alternate
coefficient
E is desired error
N the number of ground sample
plots
At last, it is determined by the largest number of sample
plots of forest increment, depletion and average stock plus a certain
amount of safety factor.
B. Determining the number of area
proportions sample plots
It is determined by the precision of
major land type and minimum land type. It can be calculated by following
function.
N = ( t2 ( 1-p ) / pE2 %)
p is the estimating proportions of appointed land type
E is
the allowable error land type area
t is the reliable index
N is
the number of area proportions sample plots
C. Determining the
number of ground corrected sample plots
It is determined by the
accuracy of automatic recognition and visual interpretation. It can be
calculated by following function:
n = N (1-q)
N is the
number of area proportions sample plots
q is the accuracy of
synthetic interpretation of each land types
n is the number of ground
corrected sample plots
- Layout of Sample Plots
- Determining the intervals of sample plots
The function
for calculating intervals of sample plots which are based on the
counted area proportions sample plots is shown as follows.
D =
ÖA / N
A is the whole area of
monitoring
N is the number of area proportions sample plots
D is
the intervals of sample plots.
Usually, it should be set up at
2 kilometer net intersections according to the caculated intervals.
- The layout of ground corrected sample plots
It is
systematically sampled as the ratio of n1/n2, where n1 is the number
of area proportions sample plots, n2 is the number of ground corrected
sample plots.
- The selection of ground inventory sample plots
The
forest mensuration sample plots are identified on the image map which
has determined area proportions sample plots. All the identified
forest menstruation sample plots can be ground inventory sample plots.
If the number of identified sample plots is much more than the
required number, the sample plots should be systematically deleted; if
the number is not enough to the required number, it should by
systematically added on the image map and be sampled again.
It is necessary to state that the number of area
proportions sample plots, ground corrected sample plots and ground
inventory sample plots decreases successively, but the lowest layer is
compatible with the highest layer. Interpretation of sample
plots
RGC method requires more than two independent interpretations
of Landsat image map. The interpreters need special technical training and
well understanding the image features and the state of forest resources of
the study area. There are three kinds of interpretation techniques:
- Computer automatic recognition;
- Visual interpretation;
- Computer aided visual interpretation.
The computer automatic
recognition can be used I the areas which with simple distribution of
forest types. The better results are usually get from computer aided
visual interpretation.
Attention must be paid to the influence of
seasons on interpretation marks. Generally, it is essential to set up
their own interpretation marks for different seasonal Landsat image.
In order to interpret the image map objectively, two groups of
interpreters should interpret the Landsat image independently. The sample
plots have same name but have different interpretation Results should be
distinguished by experts who have better understanding of remotely sensed
data and real status of the study area. All the interpreters should
interpret the image according to the interpretation marks without knowing
the ground investigation results to make sure that the interpretation is
objective enough.
Applications of GIS
The aim of
monitoring forest resources is to know the dynamic changes of forest
resources, that is the changes of forest quantity and quality as time
goes. Ensuring to investigate same sample plot continuously is the base of
increasing the estimating precision and reliability of forest dynamic
changes. In RGC method, GIS plays an important role. Its main functions
are:
- Ensuring the geometric precision of satellite image map, ensuring
the identity of inventory, area and the accuracy of whole area;
- Ensuring the accuracy of whole area:
- Aid to recognize forest types and land types.
Therefore, in
RGC, it is very important to establish a good and accurate GIS system.
Because of the long period and continuous inventory, the whole
parameters and data of the GIS system should be fully saved. In next
investigation, the renewal of data is mainly remotely sensed information,
but not and should not be geographic information.
Example of
the application of RGC method
In 1989, we used RGC method in West
Jilin province of China (about 10,000,000 hectares), obtained a good
monitoring result. The brief introduction is as follows:
Using 10
scences TM CCT tapes received in 1988, 86 scences topographic maps at the
scale of 1:100,000 to make satellite image maps. Designing 20,156 area
proportions sample plots, 1148 ground corrected sample plots, 432 ground
inventory sample plots. The precision of forest resources estimation is
about 90 percent.
Compared with traditional CFI system, field work
has been decreased by 50 percent, forest distribution maps of whole area
and satellite image maps at the scale of 1:50,000 provided, and 40 percent
of the cost saved.
Conclusions
In monitoring forest
resources, RGC method which combines remote sensing technique, GIS
technique and CFI technique ha obvious advantages. It enriches the content
of traditional forest resources monitoring, which is mainly based on CFI
system; improves the precision of estimation and increases economic
efficiency. It is a bright future in the areas where sufficient remote
sensing data can be obtained and little shadow exists.
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