Mateus Batistella

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Interactions between distant places are increasingly widespread and influential, often leading to unexpected outcomes with profound implications for sustainability. Numerous sustainability studies have been conducted within a particular place with little attention to the impacts of distant interactions on sustainability in multiple places. Although distant(More)
  • Dengsheng Lu, Qi Chen, Mateus Guangxingwang, Maozhen Batistella, Gaia Vaglio Zhang, David Laurin +7 others
  • 2012
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)
Numerous classifiers have been developed and different classifiers have their own characteristics. Controversial results often occurred depending on the landscape complexity of the study area and the data used. Therefore, this paper aims to find a suitable classifier for the tropical land cover classification. Five classifiers – minimum distance classifier(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)
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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