Using Remote Sensing technology for dynamic
monitoring forest resources
- 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.
