Multitemporal Analysis of Landsat Data for Forest Cover Changes Detection, Tab Lan National Park
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Abstract
This study was conducted according to two research objectives: 1) to categorize vegetation canopy density with forest canopy cover analysis; and 2) to observe patterns of forest canopy changes by multi-temporal LANDSAT imagery for protective forest –Tub Lan National Park (TLNP) and buffer distance 5 kilometers.In addition, vegetation canopy density classification was created from the integration between Soil Adjusted Vegetation Index (SAVI) and analysis of Forest Canopy Density (FCD) into a model of Soil Vegetation Canopy Density: (SVCD). The forest canopy density was classified into four levels: non-forested areas, low forest cover density, moderate forest cover density, and high forest cover density. The study found that study area was calculated in percentages of the overall areas at 15.75, 15.84, 13.80, and 54.61 respectively, with 85.07 percent of overall accuracy and 0.795 of kappa coefficient. Then, the method of vegetation canopy density classification in the first objective was used to categorize images from LANDSAT-5 TM and LANDSAT-8 OLI. Then, the 3-year pairs of vegetation canopy changes were measured by using the overlay analysis. The forest canopy changes pattern was classified into four patterns: deforestation, FCD Loss, FCD Gain, and no Change. It found that in the 20-year time since 1999-2019 was invaded and destroyed at approximately 15.35 square-kilometers per year and the ratio of loss of forest canopy density changes was averagely at 11.44 square-kilometers per year.
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References
IPCC. (2014). Climate Change 2014 Synthesis Report Summary Chapter for Policymakers. p. 31.
R. U. Ayres and J. Walter. (1991). The greenhouse effect: Damages, costs and abatement. Environmental & Resource Economic, Vol 1, pp.237-270.
R. L. Peters and J. D. S. Darling. (2016). The Greenhouse Effect and Nature Reserves. Bioscience, Vol. 35, no. : 11, pp. 707–717.
แผนพัฒนาเศรษฐกิจและสังคมแห่งชาติ ฉบับที่ 12 พ.ศ.2560-2564. (สืบค้นจาก https://www.nesdc.go.th/ewt_dl_link.php?nid=6422.)
J. W. Rouse, R. H. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan. (1974). Monitoring the vernal advancement and retrogration (Green Wave Effect) of natural vegetation. [Online]. Available: https://ntrs.nasa.gov/search.jsp?R=19740008955.
A. R. Huete. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. Vol. 25, no. : 3, pp. 295–309.
A. R. Huete, C. Justice, and W. van Leeuwen. (1999). MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document. [Online]. Available: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf.
A. Rikimaru, S. Miyatake, and P. Dugan. (1999). Sky is the limit for forest management tool. ITTO Tropical Forest Update. Vol. 9/3., pp. 6–9.
A. Rikimaru, P. S. Roy, and S. Miyatake. (2002). Tropical forest cover density mapping. Tropical Ecology. Vol. 43(1), no. : June, pp. 39–47.
G. Chander and B. Markham, (2003). Revised Landsat-5 TM Radiometrie Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, no. : 11 PART II, pp. 2674–2677.
USGS. (2016). Landsat 8 (L8) Data Users Handbook Version 2.0. [Online]. Available: https://prd-wret.s3-us-west2.
amazonaws.com/assets/palladium/
production/atoms/%0Afiles/LSDS1574_L8_Data_Users_Handbook_v4.0.pdf%0A.
A. Rikimaru and S. Miyatake. (1997). Development of Forest Canopy Density Mapping and Monitoring Model using Indices of Vegetation. Bare soil and Shadow. [Online]. Available:
http%5C%5Cwww.gisdevelopment.net/aars/acrs/1997/ts5/index.shtm%0Al
R. G. Congalton and K. Green. (1999). Assessing the accuracy of remotely sensed data : principles and practices. Boca Raton. FL: Lewis Publishers. pp. 137.
J. R. Landis and G. G. Koch. (1977). The measurement of observer agreement for categorical data. Biometrics, Vol. 33, no. : 1, pp. 159–174.