Uso da modelagem estatística para monitoramento da vegetação no Parque Nacional da Serra da Canastra, Minas Gerais / Use of statistical modeling for vegetation monitoring in the Serra da Canastra National Park, MG-DOI 10.5752/P.2318-2962.2014v24nespp120

Abstract

Artigo recebido para revisão do evento em 22/04/2014, aceito para publicação em 11/05/2014 e recebido para publicação em 01/06/2014Resumo O manejo sustentável e a conservação ambiental são elementos-chave para assegurar as condições de vida na Terra. Diversos pesquisadores buscam alternativas que mitiguem a ação antrópica sobre os recursos terrestres. O avanço da investigação espacial facilitou o acesso a imagens produzidas por satélites para a detecção de mudanças na vegetação. Os Índices de Vegetação, por exemplo, possuem enorme aplicabilidade para monitorar a cobertura vegetal. Um deles, o Normalized Difference Vegetation Index (NDVI), foi utilizado neste trabalho para avaliar as mudanças no vigor da biomassa da cobertura vegetal do Parque Nacional da Serra da Canastra (PNSC)-MG, entre os anos de 2008 e 2011. Aliada a estes índices, a modelagem estatística dá suporte estatístico de exatidão em pesquisas destinadas à conservação ambiental. Foi gerada uma série temporal de NDVI para o PNSC, com valores médios de dez pontos diferentes espalhados pelo parque. Com a finalidade de extrair maiores informações sobre a dinâmica temporal do índice, foi realizada a análise desta série temporal, a partir de técnicas apresentadas na literatura. Por fim, por meio da abordagem Box-Jenkins, foi ajustado um modelo estatístico ARIMA (1,1,2)(1,1,0) de comportamento e previsão da série temporal, que descreve a série investigada.Palavras-chave: Biodiversidade, Unidades de Conservação, Análise temporal, Índices de vegetação. AbstractThe sustainable management and conservation are key elements to ensure the conditions for life on Earth. Several researchers seeking alternatives that mitigate the anthropic activities on land resources. The advance of spacial research facilitated the access to images produced by satellites to detect changes in vegetation. The vegetation indexes, for example, have huge applicability to monitor vegetation cover. One of them, the Normalized Difference Vegetation Index (NDVI) was used in this study to evaluate the effect of changes in biomass of vegetation of the Serra da Canastra National Park, in Minas

Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes

“…It was possible to observe peaks of greater vegetative vigor and moments of fall in values, caused by dry periods (Figure 5). According to Chaves, Mataveli and Justino (2014), this variation is directly associated with precipitation, since the beginning of rains contributes to the increase of vegetative vigor in a region. Through the analysis of difference between pairs of variables obtained from stationary series transformed from original vegetation index data, it was identified that NDVI registered a greater tendency to present seasonality patterns in comparison with EVI data (Figure 6).…”

“…In this case, the null hypothesis was rejected. According to Chaves, Mataveli and Justino (2014) and Gow et al (2016), rejecting Ho hypothesis means that variation in vegetation behavior does not change substantially, indicating that there is a linearity of vegetative vigor present in region. In regions of lower vegetation density, such as transitions from rural to urban space, NDVI is more sensitive to seasonality.…”

“…This relationship was corroborated in studies presented by Kafer and Rex (2020) and Nora and Santos (2010), who reported a better correlation between NDVI and air temperature than precipitation due to the fact that forest formations have resistance to short periods of water scarcity. According to Chaves, Mataveli and Justino (2014), as the vegetation develops, the standard deviation of vegetation index data in a time series increases. The lowest values of these variables tend to appear in periods of drought, especially in winter, between August and October, being related to lack of rain, which causes a decrease in values of vegetation indexes.…”

“…In the Ho hypothesis, the total number of negative and positive signs is expected to be similar, so that a series is considered to be trendless (COX; STUART, 1955;DETZEL et al, 2011). The application of this technique has been developed in studies that use data from MODIS sensor (ARANTES et al, 2017) and in assessment of vegetation behavior for environmental planning and sustainable management with SPOT data (CHAVES; MATAVELI; JUSTINO, 2014).…”