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Forestry
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An approach to monitor pine caterpillar
using TM imagery
- Perpendicular Vegetation Index (PVI) Calculation.
Sunlight can still seep through masson pine forests even though they are very closed. This means that the spectral data of pine forest on TM image include certain information of Soil. It also jam the damage information. A couple of treatments including ratio-based indices and PVI were tested and the PVI was proved to be the most sensitive symptoms of the damage. Sample pixels in unaffected forests were of highest PVI values, while the value decreased from those lightly affected to those severely affected. Therefore using PVI to detect leaf area and biomass may be favorable. PVI is the distance of a point in TM3/TM4 two dimensional space to the soil (non-vegetation) line, while the soilline is fitted from the points of non-vegetation ground object in the two dimensional space [1]. For the chosen TM scenes of 1988 and 1989 the soil lines are
| and |
Y = 19.11 + 0.83X
Y = 11.1 + 1.03X |
respectively.
The PVI value for each forest pixel is calculated. The lower is the PVI value, The more severe is the pest problem.
- Normalization of PVI and Use in Classification
As PVI values were derived from non calibrated CCT data, which were still to influence of noise by atmospherical condition sensor behavior and elevation, etc., Normalization of PVI is necessary in order to make comparing analysis more reliable and precise. According to formula of Caloz (2).
NPVI = PVI/SE
Where SE is the deviation of the soil line. The NPVI values for images of 1988 and 1989 are calculated. Pixels with NPVI values between 96 to 120 belong to healthy forest with the caterpillar density less than 1 piece per tree and needle damage less then 10%. Light affected areas have NPVI in region of 75 to 95, with caterpillar density less than 3 pieces per tree and needle damage less than 10-30% . The severely damaged areas have NPVI vlues between 51 to 74, with caterpillar density lager than 3 pieces per tree and needle damage more than 30%. The damage map for the overwintering caterpillar in 1988 was made.
- Calculating and Classifying Greenness Change Index
In order to study the feasibility of extracted change information of damage, we conducted a precise geometrical correction according to ground control points. The NPVI value of 1989 minus that of 1988 was called Greenness Change Index (GCI), which indicated the increase of biomass resulted from normal growth of pine trees from 1988 to 1989 and the regenerated needles due to elimination of caterpillar by synthetical measures after the plague. It was obvious that GCI values were usually larger in severely affected area. Those in light or not affected areas were comparatively small. According to sample data study, GCI values in severely affected area were all larger than 20, light area 7 to 19. GCI values less than 7 were associated with healthy growing plants. In this way another damage map in the spring of 1988 was derived from data of both years.
- Output of Damage Map, Counting Areas of Each Damage Level
Two damage maps mentioned above showed similar distribution of effected forests as expected. GCI method was not favorable to be popularized to be popularized for its doubled costs of two temporal data and their processing work. So we took only the classified NPVI damage map as output by designating severely affected, lightly affected and unaffected areas with red, yellow and green, while non forest areas with black.
Results and conclusion
The final damage map and statistics of damage levels of 1988 indicates that total affected area reaches 52% of the total pine forest, among which severely affected area is larger than lightly affected area. They are 29% and 23% of total forest area respectively.
In order to assess the accuracy of this result, a hazard map based on field investigation conducted by the technician of that farm was reduced to the same scale as the TM image. In spite of the fact that the hazard map was drawn on the basis of forest subcompartment, while the TM damage map was derived pixel by pixel, these two maps give fairly good coincidence in results both in percentage of each damage level (cf. table), and in distribution status. This proves reliable and can meet the requirement of practical utilization in caterpillar prevention and control.
Table. Results of detecting pine caterpillar hazard
by two methods
| Hazard level |
Percent of total forest area |
| From TM image |
by ground survey |
| Server |
29.10 |
30 |
| Light |
22.84 |
28 |
| Not affected |
48.06 |
42 |
Literature
- Wang Jiesheng et al, Remote Sensing of Environment, China, Vol. 4,
No. 4, 1989. pp. 243-248.
- R. Caloz et al, Proceedings of IGARSS' 86 Symposium, Vol.III,p.p. 1471-1475.
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