To facilitate the identification of

To facilitate the identification of appropriate post-fire watershed treatments and minimize erosion effects after socio-economically
important fires, Interagency Burned Area Emergency Rehabilitation (BAER) teams produce initial timely estimates of the fire perimeter and
classifications of burn severity, forest mortality, and vegetation mortality. Accurate, cost-effective, and minimal time-consuming methods of
mapping fire are desirable to assist rehabilitation efforts immediately after containment of the fire. BAER teams often derive their products by
manually interpreting color infrared aerial photos and/or field analysis. Automated classification of multispectral satellite data are examined
to determine whether they can provide improved accuracy over manually digitized aerial photographs. In addition, pre-fire vegetation data
are incorporated to determine whether further gains in accuracy of mapped canopy consumption can be made. BAER team classifications
from the Cerro Grande Fire were compared to estimates of overstory consumption produced using a pre-fire vegetation classification, and a
change detection algorithm using bands 4 and 7 from July 1997 pre-fire Landsat Thematic Mapper (TM) and July 2000 post-fire Enhanced
Thematic Mapper (ETM) data. BAER team classifications are highly correlated to overstory consumption and should produce high Kappa
statistics when verified using the same dataset. Our three-class supervised classification of the change image incorporating a pre-fire
vegetation classification yielded the highest Kappa at 0.86. A three-class unsupervised classification of the change image yielded a lower
Kappa of 0.72. BAER team classifications yielded Kappas ranging from 0.38 to 0.63 using the same verification dataset.
D 2002 Elsevier Science Inc. All rights reserved.

Results (Indonesian) 1:

Untuk memudahkan identifikasi sesuai Das api pasca perawatan dan meminimalkan efek erosi setelah sosial ekonomipenting kebakaran, tim Interagency dibakar Area darurat rehabilitasi (BAER) menghasilkan perkiraan awal yang tepat dari perimeter api danklasifikasi membakar keparahan, hutan kematian, dan kematian vegetasi. Akurat, efektif, dan sedikit memakan waktu metodePemetaan kebakaran diinginkan untuk membantu upaya rehabilitasi segera setelah penahanan api. BAER tim sering berasal produk mereka olehsecara manual menafsirkan warna inframerah foto udara dan/atau bidang analisis. Otomatis Klasifikasi data satelit multispectral Diperiksauntuk menentukan apakah mereka dapat memberikan meningkatkan akurasi atas secara manual digital foto udara. Selain itu, pra-api vegetasi datadigabungkan untuk menentukan apakah lebih lanjut dalam akurasi dipetakan kanopi konsumsi dapat dibuat. Klasifikasi tim BAERdari api Grande Cerro dibandingkan dengan perkiraan konsumsi overstory diproduksi menggunakan klasifikasi vegetasi pra-api, danmengubah algoritma deteksi menggunakan band 4 dan 7 dari Juli 1997 pra-api Landsat Thematic Mapper (TM) dan Juli 2000 pasca api EnhancedThematic Mapper (ETM) data. Klasifikasi tim BAER sangat berkorelasi dengan konsumsi overstory dan harus menghasilkan tinggi KappaStatistik ketika diverifikasi menggunakan dataset sama. Klasifikasi diawasi tiga kelas kami gambar perubahan menggabungkan pra-apiklasifikasi vegetasi menghasilkan Kappa tertinggi di 0.86. Tiga kelas tanpa pengawasan klasifikasi gambar perubahan menghasilkan yang lebih rendahKappa 0.72. Klasifikasi tim BAER menghasilkan Kappas mulai dari 0.38 0.63 menggunakan dataset verifikasi sama.D 2002 Elsevier ilmu Inc Semua Hak, milik.