Assessing land use/cover dynamics and exploring drivers in the Amazon's arc of deforestation through a hierarchical, multi-scale and multi-temporal classification approach

Author(s)

Garcia, Andrea S. ; Vilela, Vivian M. de F. N. ; Rizzo, Rodnei ; West, Paul ; Gerber, James S. ; Engstrom, Peder M. ; Ballester, Maria Victoria R.

• Document type: Journal article
• Source: REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT; v. 15, p. 14-pg., 2019-08-01.

Abstract

Land use and land cover (LULC) are intrinsically tied to ecological and social dynamics. Still, classifying LULC in ecotones, where landscapes are commonly heterogeneous and have a wide range of physiognomies, remains a challenge. Here we present a three-level hierarchical classification approach, using both Landsat and MODIS images, and both pixels and objects as units of information. We applied this multi-temporal and -spatial approach to classify land use in the Upper Xingu River Basin (similar to 170,000 km(2)), located in the arc of deforestation of the Brazilian Amazon. The first level includes five classes and differentiates managed land from native vegetation with high overall accuracy (93%). The second level has 11 classes (overall accuracy= 86%) and separates main land uses and native vegetation domains. The third level has 16 classes (overall accuracy= 83%) and addresses productivity of both managed and natural systems. We find that this new method presented here is more efficient than existing regional and global land cover products. Applying this approach to assess land cover transitions in the basin from 1985 to 2015, we find that agricultural production increased, yet manifested itself differently in the northern (Amazon biome) and southern (Cerrado biome) portions of the basin. Analyzing land use change in different levels, we identify that agricultural intensification occurred mainly in the Amazon while the Cerrado has undergone an expansion in agricultural area. The method presented here can be adapted to other regions, improving efficiency and accuracy of classifying land cover in heterogeneous landscapes. (AU)

• FAPESP's process: 13/20377-1 - Development of soybean virtual water map for the Upper Xingu basin, Mato Grosso - Brazil
• Grantee: Rodnei Rizzo
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 15/05103-8 - Dynamics of land use and land cover in the agricultural frontier of Brazilian Amazon: driving forces of changes and future scenarios
• Grantee: Andrea Santos Garcia
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 17/12787-6 - Dynamics of land use intensification in the agricultural frontier of Brazilian Amazon
• Grantee: Andrea Santos Garcia
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 17/12567-6 - Evaluation of the water balance in southeastern Amazon over the last two decades: employing a remote sensing based methodology to describe the regional water resources
• Grantee: Rodnei Rizzo
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 13/50180-5 - Xingu: integrating land use planning and water governance in Amazonia: towards improved freshwater security
• Grantee: Alex Vladimir Krusche
• Support Opportunities: Research Program on Global Climate Change - Thematic Grants