ABSTRAK Informasi luas area kebakaran sangat diperlukan sebagai salah satu pendekatan untuk penghitungan emisi gas rumah kaca. Data Landsat merupakan salah satu jenis citra penginderaan jauh optis resolusi menengah yang banyak dipergunakan untuk memetakan luas dan sebaran areal kebakaran. Tujuan penelitian adalah melakukan verifikasi hasil deteksi lahan bekas kebakaran hutan/lahan guna tersedianya hasil verifikasi burned area (BA) dari data Landsat-8 untuk dukungan penyusunan pedoman identifikasi BA. Pada penelitian ini dilakukan analisis verifikasi lahan bekas kebakaran yang diperoleh dari data satelit Landsat-8 sensor Operational Land Imager (OLI) menggunakan metode Normalized Burn Area (NBR). Data referensi yang digunakan dalam proses verifikasi adalah data lahan bekas kebakaran yang didelineasi dari citra SPOT-5. Citra ini memiliki resolusi spasial lebih tinggi dibandingkan dengan Landsat-8 OLI. Hasil penelitian menunjukkan bahwa tingkat akurasi Burned Area BA Landsat-8 OLI dengan metode ∆NBR memiliki nilai akurasi (overall accuracy) sebesar 87%, dengan commision error sebesar 2%, dan ommision error sebesar 11%. Tingkat akurasi burned area (BA) hasil estimasi dari data Landsat-8 dengan menggunakan metode ∆NBR memiliki nilai koefisien korelasi (r) 0,98 dengan persamaan Y = 0,928X-21,07 dan koefisien determinasi (R 2) = 0,96. Hasil ini menunjukkan bahwa sebesar 96% wilayah yang diklasifikasikan atau diestimasi sebagai wilayah yang terbakar adalah benar sebagai wilayah yang terbakar. Dengan demikian dapat disimpulkan bahwa metode ∆NBR yang diaplikasikan pada data Landsat-8 terbukti dapat digunakan untuk mendeteksi burned area. Kata Kunci: areal kebakaran, Landsat-8, Normalized Burn Area (NBR) ABSTRACT Information of burned area is needed as one among approaches on the calculation of greenhouse gas emissions. Landsat is one of the main types of remote sensing imageries frequently used to map the distribution of burned area.The purpose of this research is to verify the result of burned area (BA) analysis obtained from Landsat-8 satellite data acquired with Operational Land Imager (OLI) sensor. The results of verification burned area of the Landsat-8 to support the preparation of guidelines for the identification of BA. The BA analysis used Normalized Burn Area (NBR) method. The verification process used a manually digitized SPOT-5 image as the reference data, since it has higher spatial resolution than Landsat-8 OLI. The results of this study shows that BA Landsat-8 OLI using ΔNBR have accuracy values (Overall Accuracy) by 87%, with the commission error by 2%, and ommision error by 11%. The accuracy of BA which was estimated from Landsat-8 using ΔNBR has a correlation coefficient (r) of 0.98 with the equation Y = 0.928X-21.07 and the coefficient of determination (R2) = 0.96. These results indicate that 96% area classified or estimated as the burned area was real burned area. Thus, it can be concluded that the method ΔNBR applied on Landsat-8 proved it can be used to detect the burned area.

Abstract:The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on a burn-sensitive vegetation index based on MODIS daily values of near and middle infrared reflectance and makes use of active fire detection from multiple sensors. Validation is performed using reference burned area (BA) maps derived from Landsat imagery. Results are also compared with MODIS standard BA products. A monthly BA database for the Brazilian Cerrado is generated covering the period 2005-2014. Estimated value of BA is 1.3 times larger than the value derived from reference data, making the product suitable for applications in fire emission studies and ecosystem management. As expected the intra and inter-annual variability of estimated BA over the Brazilian Cerrado is in agreement with the regime of precipitation. This work represents the first step towards setting up a regional database of BA for Brazil to be developed in the

This paper presents results of the AQL2004 project, which has been develope within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLatif). The project intended to obtain monthly burned-land maps of the entire region, from Mexico to Patagonia, using MODIS (moderate-resolution imaging spectroradiometer) reflectance data. The project has been organized in three different phases: acquisition and preprocessing of satellite data; discrimination of burned pixels; and validation of results. In the first phase, input data consisting of 32-day composites of MODIS 500-m reflectance data generated by the Global Land Cover Facility (GLCF) of the University of Maryland (College Park, Maryland, U.S.A.) were collected and processed. The discrimination of burned areas was addressed in two steps: searching for "burned core" pixels using postfire spectral indices and multitemporal change detection and mapping of burned scars using contextual techniques. The validation phase was based on visual analysis of Landsat and CBERS (China-Brazil Earth Resources Satellite) images. Validation of the burned-land category showed an agreement ranging from 30% to 60%, depending on the ecosystem and vegetation species present. The total burned area for the entire year was estimated to be 153 215 km2. The most affected countries in relation to their territory were Cuba, Colombia, Bolivia, and Venezuela. Burned areas were found in most land covers; herbaceous vegetation (savannas and grasslands) presented the highest proportions of burned area, while perennial forest had the lowest proportions. The importance of croplands in the total burned area should be taken with reserve, since this cover presented the highest commission errors. The importance of generating systematic products of burned land areas for different ecological processes is emphasized.

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Abstract:We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.