Ribeiro, R. M., S. Amaral, A. M. V. Monteiro, and A. P. Dal'Asta. 2022. "Cities in the forest" and "cities of the forest": an environmental Kuznets curve (EKC) spatial approach to analyzing the urbanization-deforestation relationship in a Brazilian Amazon state. Ecology and Society 27(2):1. https://doi.org/10.5751/ES-13224-270201

Urbanization has altered the distribution of diseases of public health importance along gradients of human occupation. Adopting dichotomous urban/rural categories to explain differences in the risk of exposure, as well as the prevention of diseases is insufficient. In this paper, we present the potential of representations based on the gradient perspective to characterize the living spaces of municipalities where malaria is endemic in northwest Acre. Inventoried data in 40 localities in the Mâncio Lima and Rodrigues Alves municipalities and information on land use and land cover obtained from the TerraClass Database were used to characterize the urban spatial forms and their social content. Results showed a gradient of intensities: from municipal seats to the most connected localities through the road network to riverine communities. Based on the results, we hope to advance the discussion about the use of normative definitions of “urban” and “rural” for public policies and actions to control and eliminate malaria, considering the position of each locality in its own locally referenced urban gradient.

O fenômeno urbano na Amazônia contemporânea se estende pelo território e se estrutura por diversas formas socioespaciais, tais como as comunidades locais, que, ao formarem redes urbanas incipientes, têm características particulares definindo sua importância na estruturação do território regional. Neste artigo, é proposto o uso de técnicas quali-quantitativas para definir uma tipologia para as comunidades ribeirinhas do Baixo Tapajós (Pará): variáveis descritoras, obtidas por questionários de campo aplicados a 62 comunidades, entre Santarém e Itaituba, e submetidas à estatística multivariada. A partir da análise de componentes principais para 30 variáveis e agrupamento hierárquico, cinco grupos de comunidades foram identificados e descritos; resultado considerado consistente com as observações de campo. Variáveis relacionadas principalmente ao uso da terra e à infraestrutura condicionaram esta classificação. Comunidades em unidades de conservação, independente da localização na margem do Rio Tapajós, apresentaram situação distinta das demais, sendo que a presença do Estado, suportando programas governamentais, mostrou-se fundamental para manutenção de todas as comunidades

A key component of remote-sensing image analysis is image classification, which aims to categorize images into different classes using machine-learning methods. In many applications, machine-learning classifiers assign class probabilities to each pixel. These class probabilities serve as input for post-processing techniques that aim to improve the results of machine-learning algorithms. This paper proposes a new post-processing algorithm based on an empirical Bayes approach. We employ non-isotropic neighborhood definitions to capture the impact of borders between land classes in the statistical model. By incorporating expert knowledge, the algorithm improves the consistency of the classified map. This technique has proven its efficacy for large-scale data processing using image time-series analysis. The proposed method is a key component of a time-first, space-based approach for big Earth-observation data processing. It is available as open source as part of the R package sits.

Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera) images (5 m) merged with CBERS-CCD (Charge Coupled Device) images (20 m) were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.