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Forestry / Vegetation
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The Environmental Remote Sensing Monitoring and Research of JAINFENG Mountain Country Forest Park in HAINAN
we tried to make the image's brightness value in different years nearly consistent at the operations of image expansion, regarded the nearly unified average spectral brightness value of the years and the combinative property from examination as the main classificatory index. Secondly, we also emphasized the form's formative factors' classificatory principle, the landforms' formative factor's classificatory principle and the environmental background value classificatory principle, making them as one part of the whole classificatory principle and the classificatory index system. For example the natural primeval forest, first we took the Lansat image on dec. 1, 1992 as the consultative original manuscript, set up the expert model according to the classificatory principle, and then set up the unified interpretative standard. There was excessively more difference about the spectral property between the natural primeval forest and the perennial secondary forest, the difference was the former have its specific characteristics in the district, wood land environment and the geometric properties of map spot, we worked out the follow model standard in view of the above.
- Made the interpretative model according to the work experience at the forest region, the spectral combinative properties of the natural primeval forest, geometric properties characters and territorial environmental properties.
- The model's requirement was that the map spots of primeval forest (or the perennial secondary forest) must cover more than 80% of the whole map spots' area.
- The felled traces in the in the map sports were less than 5% (Band combination of TM7, TM4, TM3, spectral radiative values are 60, 156 and 12~60 in proper order), in other world, no clear felled traces.
- The map spots must have a certain standard, the single map spot's area was more that 40 hectares (20 x 20 pixels).
- The map shots, which was not qualified for the primeval forest and had TM7, TM4, TM3 spectral brightness values of 12, 60, 12~60 were all incorporated into the perennial secondary forest that recovered better.
- For the other object categories we set up the train sample area at the known area, made the thematic classificatory map by the method of supervised classification, and conducted the edit, the changing values, and the statistics of merged area consulting the classificatory principle for the classificatory map, then filtered the tiny map spots less than 15 pixels, making the map terse and sprightly.
We conducted changing value and classification for the mountain's shadows according to the Lansat image's veins of shadows, formation, the landforms that it belonged to, environmental back ground and other essential factors after the classification. For example, environmental back ground and other essential after the classification. For example, the shadow's environmental background was the region of the primeval forest, the landforms and topography wre precipitous, the inconvenient traffic, very little probability of the artificial destruction, so the shadow's value was changed to that of the primeval forest land, the rest may be also deducted by analogy for other categories. In a word, on the basic of considering the objective reality fully we did our best to the accuracy of changing value. In the classification data in the different years, heavily fired wood land, lightly fired wood land's naked land, form land, grassland of time and season. For example, the heavily fired wood land's spectral properties were very closed to the lightly fired wood land after some time. The naked land could also be converted into farm land, grassland or resume being shrubs with the passage of time and season. The farm land and the naked land also transformed each other along with the farming season. So, the above categories belonged to the shaky categories or being called dynamic categories, there were a little error in different years in categories or being called dynamic categories, there were a little error indifferent years in the course of statistics, but they represented the general category that was non-forest land.
2) The Analysis of the Spectral Property
All of the objects have their own particular spectral reflecting and radiating properties. The emissive and absorbing actions of material electromagnetic wave are caused by the vibration of atoms, the rotation of molecules and the transitions of energy level of electrons, they all are the least corpuscles that constitute material. The change from the high energy state to low energy state can release energy and radiate certain wavelength's electromagnetic wave, the change from low energy state to high energy state can absorb reflecting electromagnetic wave themselves, and we can recognize the different of spectral property of electromagnetic wave that is received by remote sensing instrument. The work's informational source was lansat-5TM data, we analyzed the spectral properties of the different categories of wood land from TM1 to TM7 and except TM6 (as figure 2). Obviously, the reflecting spectral curves of the different categories of objects changed evidently according to figure 2, especially at TM4 (0.76~0.90mm) the change showed the most outstanding. The spectral reflectance of the quasi-primeval forest(1) and the medium secondary forest(2) were both less than 50% at TM4, that not only represented the spectral properties of the health plant itself, but also represented that it related to higher canopy density of the wood land and higher moisture
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