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Analysis of interpretation properties of forest lands on various scale TM imagery1

Li Jiquan
Research Institute of Forest Resource information Techniques,
Chinese Academy of forestry


Abstract
This is paper described the radiation between TM image scales and its interpretation properties of forest lands and purposed optimal TM image scale for forest survey in "Three north Shelter forest Region".

General situation of study area and applied information source
Kangbao county is located at the north part of Zhangjiakou prefecture, Hebei province. Its geographical position is 41 25'-42 09 N and 1114 11 114 57 'E belong to the south east part of Mongolian Plateau and the elevation is from 1300 m to 1700 m the annual precipitation in 350 mm and its is very windy and dusty Kangbao County is a transit region from aired steeple to forest grasslands and the vegetation type is simple [1] is no natural forest in Kangbao County and small pieces of artificial forest are scattered nearby the villages and small towns however the covering area of arm land wind break forest network is extensive LANDSAT 5 false color composite TM image scale 1:1000,000 1:200,000 and 1:500,000 imaged in 8th Sep 1897 were employed in this research.

Forest interpretation and area measurement on TM imagery
  1. The timing of TM image employed in the research is very opportune by this time most crops have been harvested The outlines of forest lands and windbreak forest network are clearly discernible on TM images which affords is a favourable conditions to improve the forest interpretation precision(FRITP).The interpretation keys are as follows:

    forest lands----with obvious regular shape. The tone on 1:100,000 scaled TM image is fresh and light red colour; and on 1:200,000 and 1:500,000 scaled TM images is dark red color. Windbreak forest network----with clear netted and belt shape. The tone on 1:100,000 scaled TM images is red color and dark red color appears on 1:200,000 and 1:500,00 scaled TM images .

    Visual interpretation and plot delineation of forest lands and wind break forest network were carried out respectively on 1;100,000 and 1:200.000 as well as 1:500.000 scaled TM images according to the records obtained during fields survey in Kangbao County and relevant specialized maps and materials as reference.

  2. The 'KP-90 N" model digital planimeter was employed to measure the area of forest lands To counter of some forest area smaller than the minimum measurable area of planimeter (ie 10 ha. On 1:100.000 scaled interpretation map) the following method was adopted to solve the above problem i.e several adjacent small piece forests were constituted in to larger plot for area measurement, and a DOT-GRID with dense dots was employed to measure the weight of every small piece forest than their actual area could be calculated respectively.

Fig.1 TM image on the scale of 1:100,000


Fig.2 TM image on the scale of 1:200,000


Fig.3 TM image on the scale of 1:500,000

Examination of correct interpretaton percentage of FOREST plot (CIPFP) on TM images
In order examine the CIPFP (wind break forest network was not included here the lowing steps were taken.

  1. A random table [2] was employed to carry out sampling on 1:100.000 and 1:200.000 scaled TM images respectively The population on 1:100.000 and 1:200.000 scaled TM images are compared of 332 and 285 interpreted forest plots respectively the sampled forest them 50 forest plots each of them are randomly sampled to form two large samples. The sampled forest plots were marked on topographical map respectively according to their ordinal number and geographical position There are only 18 interpretable forest plots on 1:500:000 scaled TM image in Kangbao County therefore they were all marked on topographical map for examination.

  2. The CIPFP on 1:100.000 1:200.000 and 500,000 scaled TM images were examined respectively using 1:34.000 scaled serial photography taken in 1984 (after setting up the ground interpretation keys) and making reference to the geographical position of sampled forest plot marked on topographic map.

  3. Taking the area weight of each sample plot in to account the calculation of CIPFP is more reasonable on the basis of area.

    After examining to the random samples taken from the populations of 1:100.000 and 1:200.000 scaled TM images, the results of CIPFP were arranged in table1.

    Table 1. A schedule of CIPFP on TM images
    Scale of TM images 1 : 00,000 1 : 200,000
      Number of sample plots Total area(ha.) Percentage(%) Number of sample plots Total area(ha.) Percentage(%)
    Correct interpretation 46 827 92.3 47 971 91.4
    Erroneous interpretation 4 69 7.7 3 91 8.6
    Grand total 50 896 100.0 50 1,062 100.0

    All of 18 interpretable forest plots on 1:500,000 scaled TM image were examined however the area weight of correctly interpreted forest plots is large and that of erroneously interpreted forest plots is small in general in general therefore the results of CIPFP obtained from 1:500.000 scaled TM image was higher than that gained from random sampling So this result was used for reference only.
Results and Analysis
  1. The above examination of CIPFP on 1:100.000 and 1:200.000 and 1:500.000 scaled TM image enable us to preliminary find out the degree of interpretation accuracy within the extent of minimum delineation area (MDA) on this three kinds of TM image scale in order to systematically analyze interpretation properties of forest lands on Various scaled TM image (VSTMI) the 332 interpreted forest plots of Kangbao county were sorted out according to the specified area limits on this basis of ground resolution of TM images the number and area of forest plots within each area limits and their percentages occupied in total number and area of forest plots were calculated and a diagram was drawn for understanding the distribution patterns on the aspect of area and quantity in this region. It can be seen from table 2 and fig. 4.

    The small forest plots less than32 is 283b in number hat makes up 85.3% of the total number of forest plots and the total area of forest plots less than 32 hais 4,226 ha which makes up 45.9% of the grand total area forest plots in kangbao county so they greatly affect the FRITP on VSTMI.

    Table 2 Number and area of forest plots and their percentages within various area limits.
    Area limits(ha.) Number of forest plots Percentage(%) Total area(ha.) Percentage(%)
    ³3.0----<4.5 11 3.3 38 0.4
    ³4.5----<8.0 36 10.8 217 2.4
    ³8.0----<12.5 67 20.2 675 7.3
    ³12.5----<18.0 70 21.1 1042 11.3
    ³18.0----<24.5 61 18.4 1221 13.3
    ³24.5----<32.0 38 11.5 1033 11.2
    ³32.0----<40.5 27 8.1 980 10.6
    ³40.5----<50.0 4 1.2 177 1.9
    ³50.0----<100.0 13 3.9 852 9.2
    ³100.0 5 1.5 2990 32.4
    Grand total 332 100.0 9225 100.0



    Fig. 4 Diagram of forest distribution pattern sorted out according to the specified area limits.

  2. In order to further analyze FRITP on VSTMIaccording to the minimum area of forest plot that can be practically interpreted and delineated as well as interpretation experience the number and total area of interpreted forest plots on TM images with 9 kind scales were arranged in Table 3 and a diagram e\was drawn for finding out the loss of FRIM on VSTMI see table 3 and fig 5.

    It can be seen from Table 3 and Fig.5 show that the along with reduction of TM image's scales, information of some small artificial forest plots loses continuously and their interpretation properties of forest lands reduce rapidly . The table 3 and fig 5 show that loss percentages of the FRIM is low on 1:500.000 and 1:200.000 scaled TM are 3.3 % and 14.2% respectively and the loss of total area of forest plots are 0.4% respectively but beginning at 1:250.000 scaled TM image the loss percentage of FRIM image increase rapidly along with the reduction of TM image scale. This shows that using 1:250.000 or smaller scaled TM image to carry out FRIT can not satisfy the precision requirements of forestry production in forest lack region with small artificial forests scatteredly distributed so these scaled TM images can only be used for reference materials.

    Table 3. Statistical table of number and area of interpretable forest plots
    on TM images 9 kind scales.
    Scale of TM iamge Minimum delineated area(ha.) Number of forest plots Total area(ha.)
    1 : 100,000 3.0 332 9225
    1 : 150,000 4.5 321 9187
    1 : 200,000 8.0 285 8970
    1 : 250,000 12.5 218 8295
    1 : 300,000 18.0 148 7584
    1 : 350,000 24.5 87 6032
    1 : 400,000 32.0 48 4967
    1 : 450,000 40.5 22 4019
    1 : 500,000 50.0 18 3842



    Fig. 5 Diagram of loss percentages of forest information on variously scaled TM images.

  3. The above analysis results have displayed the extent of FRIM loss on VSTMI according to the previous experience of forest interpretation on variously scaled images and the examination results of CPIFP on 1:1000,000 and 1:200.000 scaled TM images it can be concluded that CIPFP are very close when the interpretation is carried out within the scope of respective minimum interpretable and delineative area of VSTMI therefore FRITP on several scaled TM images were reckoned on the basis of combination the mean of FRITP on VSTMI table 4 and fig. 6.

    The Changing trend of FRITP of VSTMI can be from fig. 6 which can provide reference for optimum scale selection of TM image for conducting FRIT three North shelter forest region and to accomplish the purpose of meeting the precision requirement of forestry production and scientific research as well as reducing the raising working efficiency to make economic benefits[3].
Conclusions
The above research results show that applying TM image to carry out FRIT is greatly restricted by TM image scale in forest lack region of China especially in small and scattered artificial forests dominantly distributed area such as Kanhbao county so some conclusions obtained as follows.
  1. The optimum scale of TM image for FRIT in this area id 1:100.000 the MDA can be 3 ha loss of FRIOM is very little is 92.3% A satisfying results can be gained if the FRIT is carried out on 1:500.000 scaled TM image the MDA is 4.5 ha and the FRITP can be 91.5%.

    Table 4. Table of reckoned FRITP of TM image with nine kind scales.
    Scale of TM image Keeping percentage of FRIM (%) CIPFP (%) FRITP (%) **
    1 : 100,000 100.0 * 92.3 92.3
    1 : 150,000 99.6 91.9 91.5
    1 : 200,000 97.2 91.4 88.9
    1 : 250,000 89.9 91.9 82.6
    1 : 300,000 82.2 91.9 75.6
    1 : 350,000 65.4 91.9 60.1
    1 : 400,000 53.8 91.9 49.5
    1 : 450,000 43.6 91.9 40.0
    1 : 500,000 41.7 91.9 38.3

    in the table :
    *------ The loss of FRIM on 1 : 100,000 scaled
    TM image is very small so, it is neglected here.i.e.
    suppose keeping percentage of FRIM on 1 : 100,000
    scaled TM image is 100.0%.
    **------ FRITP = keeping percentage of FRIM * CIPFP.



    Fig. 6 Diagram of FRITP of TM image with nine kind scales.

  2. Applying 1:200.000 scaled TM image conduct FIRT can meet the precision requirement of forestry production MDA is 8.0 ha the loss of FRIM is a few and its FRITP is 89.9% Moreover the costs of purchase information resource and the office work day for forest survey can be reduce and the economic benefit is higher.

  3. TM images with scales of 1:250:000 1:300.000 1:350.000 1:400.000 1:450.00 1.500.000 can not be used to carry out FRIT in three north Shelter forest region precision too much so the precision requirements of forestry production can not be satisfied.

    In the light of change trend of FRITP and VSTMI shown in diagram of FRITP there is possibility on applying 1:250.000 scaled TM image to conduct FRIT in the forest area with higher forest covering percentage therefore the research should be further carried out.
References
  • Zhu Junteng, Natural Resources in "Three North Shelter-Forest Region" and Integrated Agricultural Division, Forestry Publishing House(1985), pp 153-154.
  • Beijing Forestry College, Mathematical Statistics, Forestry Publishing House(1979), pp 416-417.
  • Donald T. Lauer and Andrew S. Benson, Classification of Forest Lands with Ultra-High Altitude Small Scale False Color Infrared Photography, Proceedings Symposium IUFRO S 6.05 Freiburg(1973), pp 156-158.
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1. Mr Li Yingguo and Ms. Wu. Honggan Bai Suhua (Research Institute of Resources Information, Chinese Academy of Forestry) partly participated in this research work.