Capability of SPOT5-HRG data for forest density mapping (Case study: Deilaman forests in Guilan province)

Document Type : Research article

Authors

1 M.Sc. student, Faculty of Natural Resources, University of Tehran

2 Associate Prof., Faculty of Natural Resources, University of Tehran

3 Research Expert, Forest, Range and Watershed Management Organization of Iran

Abstract

In order to investigate the capability of SPOT5-HRG data for forest density mapping in Caspian forests, the data of this sensor with 5 and 10 spatial resolutions dated 2002 were analyzed. The study area with 10000 ha is located in south western of Amlash city in Guilan province. In addition to original bands, some synthetic bands using ratio, fusion and transformation methods were created and used. In order to accuracy assessment of classification results, a ground truth map covering 26% of total area was prepared based on seven aerial photos (1:40000) dated 2001. The aerial photos were orthorectified and mosaiced. A total of 2520 circle sample plots with one ha area were selected on the digital orthophotomosaic. Canopy closure percent of each plot was interpreted using a 45 dot grid. Satellite data were classified by supervised classification methods including minimum distance to mean (MD) and maximum likelihood (ML). The highest overall accuracy and kappa coefficient equal to 74% and 0.33 were obtained by maximum likelihood classifier with four classes (1-10%, 10-50%, 50-100% and non-forest). Third density class (50-100%) represented highest producer and user accuracy, 95% and 82%, respectively. Lower producer and user accuracy were related to first density class 11% and 32%, respectively. It could be concluded that due to low kappa coefficient (0.33), even if reaching to pretty good overall accuracy (74%), the result of classification was not desirable. To obtain a better result, it is suggested to test other classification methods like object-based. Using higher spectral resolution data are also offered.

Keywords

References

- احمدی ثانی، ن.، 1384. بررسی قابلیت سنجنده ASTER جهت تهیه نقشه تراکم جنگلهای زاگرس (مطالعه موردی: جنگلهای شهرستان مریوان). پایان‌نامه کارشناسی ارشد، دانشکده منابع طبیعی، دانشگاه تهران، 87 صفحه.
- پاکخصال، الف.، 1385. طبقه‌بندی تراکم تاج‌پوشش جنگل با استفاده از داده‌های سنجش از دوری در حوزه شفارود گیلان. پایان‌نامه کارشناسی ارشد، دانشکده منابع طبیعی، دانشگاه گیلان، 104 صفحه.
- ساروئی، س.، 1378. بررسی امکان طبقه‌بندی جنگل به‌لحاظ تراکم در جنگلهای زاگرس به‌کمک داده‌های ماهواره‌ای به روش رقومی. پایان‌نامه کارشناسی ارشد، دانشکده منابع طبیعی، دانشگاه تهران، 122 صفحه.
- ساعی، م. و آبکار، ع. ا.، 1383. تهیه نقشه تراکم جنگل با استفاده از فناوری سنجش از دور. همایش ژئوماتیک 88، سازمان نقشه‌برداری کشور.
- عبدالهی، ه.، 1387. مقایسه قابلیت داده‌های لندست 7 و IRS-P6 در تهیه نقشه تراکم تاج‌پوشش جنگلهای زاگرس (مطالعه موردی جنگلهای شهرستان جوانرود). پایان‌نامه کارشناسی ارشد، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، 105 صفحه.
- عبدی، ا.، اکبری، ح.، سوسنی، ج. و شیروانی، ز.، 1388. مقایسه شاخص‌های گیاهی در تعیین تراکم پوشش جنگلهای زاگرس با استفاده از داده‌های سنجنده ETM+. همایش ژئوماتیک 88، سازمان نقشه‌برداری کشور.
- معین ‌آزاد تهرانی، س. م.، 1385. ارزیابی مدل FCD جهت برآورد تراکم جنگل با استفاده از داده‌های لندست 7. پایان‌نامه کارشناسی ارشد، دانشکده منابع طبیعی دانشگاه تهران، 95 صفحه.
- Ashutosh, S., 2002. Principal component-based algorithm on multispectral remote sensing data for spectral discrimination of tree cover from other vegetation types. TCurrentScience, 82: 67-69T.
- Berberoglu, S. and Satir, O., 2008. Fuzzy classification of Mediterranean type using ENVISAT MERIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress Beijing 2008, Proceedings of Commission VIII.
- Bretschneider, T. and Kao, O., 2000. Image fusion in remote sensing. Open System Engineering Environment (OSEE): 1-8.
- Dorren, L.K., Maier, A.B. and Seijmonsbergen, A.C., 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183: 31-46.
- Eastman, J. R., 2006. IDRISI Andes Guide to GIS and Image Processing. CLARKUniversity, Version 15.00, 327 p.
- Jashi, C., Leeuw, J.D., Skidmore, A.K., Duren, I.C.V. and Oosten, H.V., 2006. Remotely sensed estimation of forest canopy density: a comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation, 8: 84-95.
- Johansen, K., Coops, N.C., Gergel, S.E. and Stange, Y., 2007. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment, 110 (1): 29-44.
- Roy, P.S., Sharma, K.P. and Jain, A., 1996. Stratification of density in dry deciduous forest using satellite remote sensing digital data-an approach based on spectral indices. Journal of Biosciences, 21 (5): 723-734.

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