Mateus Batistella

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Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper(More)
Science has a critical role to play in guiding more sustainable development trajectories. Here, we present the Sustainable Amazon Network (Rede Amazônia Sustentável, RAS): a multidisciplinary research initiative involving more than 30 partner organizations working to assess both social and ecological dimensions of land-use sustainability in eastern(More)
Deforestation and colonization in Amazônia have attracted substantial attention. This article focuses on an area of 3,000 km 2 within the Brazilian State of Rondônia. Two adjacent settlements were compared to assess the role of their different designs in landscape change. Anari was planned following an orthogonal road network. Machadinho was designed with(More)
The mixed pixels in remotely sensed data are one of the main error sources resulting in poor classification accuracy using traditional classification methods. In order to improve classification accuracy, linear spectral mixture analysis (LSMA) has been used to handle the mixed pixel problems. This paper aims to achieve an appropriate processing routine of(More)
Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal(More)
Following an intense occupation process that was initiated in the 1960s, deforestation rates in the Brazilian Amazon have decreased significantly since 2004, stabilizing around 6000 km(2) yr(-1) in the last 5 years. A convergence of conditions contributed to this, including the creation of protected areas, the use of effective monitoring systems, and credit(More)
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover(More)
Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and(More)
Landsat Thematic Mapper (TM) data have been extensively used for land cover classification, but Terra ASTER and SPOT High Resolution Geometric (HRG) data applications are just beginning. This paper compares the capabilities of TM, ASTER, and HRG in land cover classification in the Amazon basin. Maximum likelihood classification was used for selected(More)
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