Relação espectro-temporal entre índices de vegetação e a chuva na cidade do Rio de Janeiro

Abstract

Plataformas orbitais Disponibilidade hídrica Crescimento de floresta KEYWORDS Orbital platforms Water availability Forest growth RESUMO: O município do Rio de Janeiro nas últimas décadas vem crescendo desordenadamente, principalmente pela falta de políticas públicas de urbanização, o que vem diminuindo a densidade de áreas verdes. Assim, a relação espectro-temporal fundamentada em três índices de vegetação do satélite Landsat 5 (Normalized Difference Vegetation Index-NDVI, Soil Adjusted Vegetation Index-SAVI e o Leaf Area Index-LAI) e dados de chuva de estações convencionais foram avaliados com testes e índices estatísticos com o objetivo de obter a melhor correlação entre eles para o município do Rio de Janeiro, RJ, Brasil. Os resultados não apresentaram normalidade e homogeneidade de variância dos dados baseados nos testes de Kolmogorov Smirnov e Shapiro Wilk-WS, e Hartley e Bartlet, respectivamente. Foram identificados altos valores do coeficiente de correlação de Pearson (r), seguidos de alta variabilidade significativa dos índices (NDVI, SAVI e LAI), nos anos de 1984, 1985, 1989 e 1991. O índice que melhor representou a relação no período estudado, em comparação aos demais, foi o NDVI, apresentando em 60% dos anos observados a maior correlação com a chuva. Portanto, ele pode ser utilizado em estudos da dinâmica da vegetação no município. A regressão linear entre os índices e a chuva nos perídos de 1990 e 1991 apresentam tendências negativas e positivas para os índices de vegetação.

Validation of the net radiation through sebal algorithm in different classes of land use and occupation in Rio de Janeiro

“…In a study in the city of Santa Rita do Passa Quatro, São Paulo, Land use and occupation ( Figure 3A) and R n values ( Figure 3B) for the period studied, where the light areas indicate smaller R n values. There was a predominance of vegetation in the main mountain ranges (Pedra Branca, Tijuca and Gericinó) existing in MRJ (GOULART et al, 2015), followed by high R n values in the evaluated dates. According to Machado et al (2014) and Caula et al, (2016), vegetation areas have lower albedo values, reflecting a smaller percentage of RS↓, followed by a lower loss by RL↓ due to having lower temperatures, favoring the energy availability in the form of net radiation.…”

“…According to Machado et al (2014) and Caula et al, (2016), vegetation areas have lower albedo values, reflecting a smaller percentage of RS↓, followed by a lower loss by RL↓ due to having lower temperatures, favoring the energy availability in the form of net radiation. Lowland regions of MRJ are predominantly occupied with urban area and, consequently, there is little vegetation area (GOULART et al, 2015). Concomitantly, these areas showed low R n values ( Figure 3B).…”

Carbon monoxide trend in the city of Rio de Janeiro via mann-kendall and cusum tests

“…CRJ presents remnants areas of the Atlantic Forest domain, mostly protected by laws, mainly under Conservation Units (UC) (GOULART et al, 2015). In the last decades, we observed a territorial and urban expansion in the CRJ, accompanied by the increased number of formal and informal civil buildings.…”

“…It aims to regulate the levels of air pollutants that can be monitored and controlled in the country and have contributed to minimize the effects of air pollution and air quality in metropolitan regions (MR) (FREITAS et al, 2009;ZERI et al, 2016), mainly environmental degradation (GOULART et al, 2015) and respiratory and cardiac problems faced by society (CARVALHO et al, 2015).…”

EVI2 index trend applied to the vegetation of the state of Rio de Janeiro based on non-parametric tests and Markov chain

“…However, there is no rigorous control of Brazilian biomes in spatial scale, because biomes are constantly threatened by human activities such as farming, agriculture and real Estate occupation (ALEIXO et al, 2010). Given the need for evaluating the vegetation in large areas some vegetation indices were created the NDVI (Normalized Difference Vegetation Index) by Rouse et al (1974) and EVI (Enhanced Vegetation Index) by Huete et al (1997Huete et al ( , 1999Huete et al ( and 2002, both widely used in several dynamic studies on the vegetation (ZHANG et al, 2009;DELGADO et al, 2015;GOULART et al, 2015). Demarchi et al (2011) comment that sensory and spatial techniques help in the land use and occupation planning of a region; delimitation of PPAs, legal reserves and UCs; and maintenance of water resources and other areas recognized by the legislation as important for maintaining the environmental activities.…”