A Multi-Resolution Multi-Temporal Technique for Detecting and Mapping Deforestation in the Brazilian Amazon Rainforest

Author(s)

Arai, Egidio ^[1] ; Shimabukuro, Yosio E. ^[1] ; Pereira, Gabriel ^[1] ; Vijaykumar, Nandamudi L. ^[2]

Affiliation

^[1] Natl Inst Space Res, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[2] Natl Inst Space Res, Lab Comp & Appl Math, BR-12227010 Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: REMOTE SENSING; v. 3, n. 9, p. 1943-1956, SEP 2011.
• Web of Science Citations: 18

Abstract

The analysis of rapid environment changes requires orbital sensors with high frequency of data acquisition to minimize cloud interference in the study of dynamic processes such as Amazon tropical deforestation. Moreover, a medium to high spatial resolution data is required due to the nature and complexity of variables involved in the process. In this paper we describe a multiresolution multitemporal technique to simulate Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image using Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The proposed method preserves the spectral resolution and increases the spatial resolution for mapping Amazon Rainfores deforestation using low computational resources. To evaluate this technique, sample images were acquired in the Amazon rainforest border (MODIS tile H12-V10 and ETM+/Landsat 7 path 227 row 68) for 17 July 2002 and 05 October 2002. The MODIS-based simulated ETM+ and the corresponding original ETM+ images were compared through a linear regression method. Additionally, the bootstrap technique was used to calculate the confidence interval for the model to estimate and to perform a sensibility analysis. Moreover, a Linear Spectral Mixing Model, which is the technique used for deforestation mapping in Program for Deforestation Assessment in the Brazilian Legal Amazonia (PRODES) developed by National Institute for Space Research (INPE), was applied to analyze the differences in deforestation estimates. The results showed high correlations, with values between 0.70 and 0.94 (p < 0.05, student's t test) for all ETM+ bands, indicating a good assessment between simulated and observed data (p < 0.05, Z-test). Moreover, simulated image showed a good agreement with a reference image, originating commission errors of 1% of total area estimated as deforestation in a sample area test. Furthermore, approximately 6% or 70 km(2) of deforestation areas were missing in simulated image classification. Therefore, the use of Landsat simulated image provides better deforestation estimation than MODIS alone. (AU)

• FAPESP's process: 10/07083-0 - Estimation and assimilation of aerosol and trace gases emissions in numerical models
• Grantee: Gabriel Pereira
• Support Opportunities: Scholarships in Brazil - Doctorate