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Abstract
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Article Information:
Scale Transformation of Forest Vegetation Coverage Based on Landsat TM and SPOT 5 Remote Sense Images Data
Li Hongzhi, Zhang Xiaoli, Wang Shuhan, Dong Sujing and Li Liangcai
Corresponding Author: Zhang Xiaoli
Submitted: January 8, 2015
Accepted: February 14, 2015
Published: July 30, 2015 |
Abstract:
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Landsat TM and SPOT 5 data are the most popular remote sense data in forest resource monitoring, though, both have advantages and disadvantages. Landsat TM images contain large scale, but with low accuracy; while high resolution Spot 5 images have high accuracy and small scale. We could combine the merits of accuracy and scale by scaling method in the study of forest vegetation cover. Hence, we use Landsat TM and SPOT 5 data to monitor the vegetation cover of three forest types (coniferous forest, broad-leaved forest mixed coniferous and broad-leaved forest) locating around Miyun reservoir in Miyun County in Beijing. These two different resolution images were scaled up by using mathematical statistics and modified the vegetation cover extracted from Landsat TM image by using scale conversion model. Upon testing, SPOT 5 images were used to resolve elements of Landsat TM images. We obtain statistic model from statistic results and information extracted from Landsat TM image. Statistic model may efficiently improve the accuracy of vegetation cover in Landsat TM image. In conclusion, basing on SPOT 5 and Landsat TM data, the scale conversion model has better performance. Combining element resolution and statistic model, we could apply high spatial resolution image to improve information accuracy of low spatial resolution image.
Key words: Element resolution, landsat TM, NDVI, scale conversion model, SPOT 5, vegetation cover,
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Cite this Reference:
Li Hongzhi, Zhang Xiaoli, Wang Shuhan, Dong Sujing and Li Liangcai, . Scale Transformation of Forest Vegetation Coverage Based on Landsat TM and SPOT 5 Remote Sense Images Data. Advance Journal of Food Science and Technology, (1): 19-27.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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