Elsevier

Ecological Engineering

Volume 162, 1 April 2021, 106180
Ecological Engineering

Vegetation trends in a protected area of the Brazilian Atlantic forest

https://doi.org/10.1016/j.ecoleng.2021.106180 Get rights and content

Highlights

  • We analyze interannual and seasonal trends of vegetation behavior.

  • We correlate MODIS vegetation indices with TRMM precipitation data.

  • Both trends of increasing and decreasing greenness in our study area were observed.

  • There was a positive correlation between vegetation index and rainfall data.

  • The methodology can be used to biomes in protected areas around the world.

Abstract

Among worldwide tropical forest ecosystems, the Atlantic forest of Brazil stands out as a highly fragile area and a biodiversity hotspot, with a high degree of endemism. The Caparaó National Park is an important conservation area for this biome, where recent studies have found new species of animals and plants, including endemic and highly vulnerable ones. We analyzed interannual and seasonal trends in vegetation for Caparaó National Park and its buffer zone, using data from MODIS vegetation indices between the years 2001 and 2019. Our methods included both parametric (linear trend and linear correlation) and non-parametric (Mann-Kendall) analyses of interannual trends, wavelet transform, and seasonal trends analysis. We also studied the correlation of the vegetation index data with the variability of monthly rainfall data from the TRMM satellite. We generated linear regression analyses, with precipitation as the independent variable and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as the dependent variable, and repeated this procedure for time lags from 0 to 3 months. In accordance with the findings of the temporal profiles and Mann Kendall trend method identified a prevalence of decreasing trend in the values of vegetation indices in Caparaó National Park, with values higher than 90% of the regions where the result was significant, for the EVI and 80% for the NDVI. For the buffer zone, there is an increasing trend for the NDVI and EVI, with values higher than 50% of the regions where the result was significant. In most of the area, a positive slope was found in the buffer zone, while in the Caparaó National Park the slope was predominantly negative. The r values indicated tendencies predominantly positive for the buffer zone and predominantly negative for the area within the park. 66.9% of the results were positive trends for ndvi in the buffer zone, and 51.7% for EVI, both for r value and slope. In the park area, this trend was negative in 68.9% of the area for ndvi in both methods; 93.8% for the EVI and 93.9% for the NDVI.

These differences may be evidence of how vegetation responds to disturbances, once the vegetation in the buffer zone is fragmented and does not have the same protection as vegetation in the park area. The native vegetation in study area shows seasonal similarities, as detected by seasonal trend analysis and wavelets transform. The existence of two annual cycles is a common feature to the biome of Atlantic forest. R values for the correlation of precipitation with vegetation indices indicate high dependence of vegetation on precipitation in the study area. Values in the 0.4 to 0.8 class, represented almost 80% of the buffer zone for the NDVI and 90% for the EVI. For Caparaó National Park, this class accounted for 27% and 38% of the area for the NDVI and EVI, respectively. Vegetation in the protected area was less dependent on rainfall, which can be attributed to the fact that it can better meet its water needs through available water in the soil. In addition, due to anthropogenic pressures and a high degree of forest fragmentation, vegetation in the buffer zone may have less advanced successional stages. Our results collaborate with the range of vegetation studies in the Brazilian Atlantic forest. This methodology can be applied to larger areas around the world to provide information on vegetation trends conditions and influence of climatic variables.

Introduction

Forest ecosystems are areas of great environmental importance, as they provide various ecosystem services, such as soil water conservation, climate regulation, generation of natural habitats, decontamination of the environment, supply of food and materials (Tao et al., 2012; Nink et al., 2019; Díaz et al., 2020). Globally, forests absorb 15–20% of annual human carbon emissions (Quéré et al., 2018), most of which is stored on the surface of tree biomass or below the ground as carbon from the ground (Case et al., 2021). Thus, they are at the forefront of global initiatives for the mitigate of greenhouse gas emissions, since conserving remaining intact forests is important for carbon sequestration and prevention of future potential emissions (Jantz et al., 2014; Maxwell et al., 2019; Mitchell et al., 2017). Intact forest ecosystems have greater conservation benefits than degraded ones of a similar ecological type (Betts et al., 2019; Haddad et al., 2015), which is a strong argument to prioritize them for conservation management (Watson et al., 2018).

Although this value, forests are increasingly threatened by the expansion of human activities (Thompson et al., 2011; Venter et al., 2016). Forest degradation can occur through a fragmentation process, which in turn impacts biodiversity, biomass and, therefore, the forest's ability to provide many ecosystem services (Betts et al., 2019; Chaplin-Kramer et al., 2015; Potapov et al., 2012). In addition, recent climate changes can influence ecosystems, affecting the nature and distribution of species and, therefore, the ecosystems themselves (Vennetier and Ripert, 2009). This dynamic leads to changes in the offer of a range of ecosystem services (Parr et al., 2012) and generates impacts on economies and societies worldwide (Margulis et al., 2010; Nunes et al., 2012).

Some of the most threatened ecosystems in the world are the tropical forests of South America (Nunes et al., 2012). The Atlantic Forest is a type of tropical forest, which originally covered 1,345,300 km2, in three countries (92% of the total area in Brazil and the remaining 8% in Paraguay and Argentina), with more than 150 million in habitants. Although covering only 0.8% of the Earth's total surface, more than 5% of the world's vertebrate species have been cataloged in their area (Pinto et al., 2012). The richness of plant species is also high, with more than 20,000 species, of which 8000 are endemic (SOS Mata Atlântica and INPE, 2018). The Brazilian Atlantic Forest, which is one of the hotspots for biodiversity conservation (Forzza et al., 2012; Freitas et al., 2010; Ribeiro et al., 2009), has been threatened due to extensive land occupation for humanity. Due to the intense deforestation and human disturbances that occurred mainly in the first half of the 19th century (Dean, 1996), only about 13% of the native vegetation cover of the Atlantic Forest biome remains in Brazil (Fundação SOS Mata Atlântica / INPE, 2018). Nature reserves protect only 9% of the remaining forest (Ribeiro et al., 2009). In the state of Espírito Santo - Southeast Brazil, where the Atlantic Forest is predominant, there are only three natural reserves that provide integral protection, with at least 10,000 ha. One of these reserves is the Caparaó National Park (Santos et al., 2016a).

Continuous monitoring of vegetation is important to assess spatial and temporal variation in strength and dynamics, which can show severe fluctuations due to development activities and climate change (Vijith and Dodge-Wan, 2020). Among modern methods of monitoring terrestrial ecosystems, remote sensing stands out for its ability to provide information on large areas with high frequency acquisition and synoptic form (Richards, n.d.; Xie et al., 2008). Monitoring the Earth's surface changes using remote sensing has been widely applied in different applications such as changes in land use / cover (Demir et al., 2013; Huang et al., 2019), urban expansion / growth (Wang et al., 2018), hydrology (Luo et al., 2019), and change in vegetation (Kaliraj et al., 2012; Markogianni et al., 2013; Zhao et al., 2019). Since a large amount of data on the nature of the Earth's surface has been collected by remote sensing satellites at different spatial, spectral and temporal resolutions using appropriate band combinations, these data have become the main / primary sources used for the extraction of vegetation and its changes on the Earth's surface in recent decades (Amarnath et al., 2017; Boyte et al., 2018; Brown et al., 2013; Fensholt and Proud, 2012; Gärtner et al., 2014; Hilker et al., 2015; A Huete et al., 2002; Jamali et al., 2014; Moreira et al., 2019; O'Connell et al., 2014; Spiekermann et al., 2015; Zhang et al., 2017; Zhao et al., 2019).

Different vegetation indices derived from remote sensing, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), have been used in several studies. Vegetation indices are more sensitive than individual spectral bands in detecting vegetation characteristics (Bannari et al., 1995; Pu et al., 2008; Rao et al., 2015; Sims and Gamon, 2003). The vegetation indices most commonly used to monitor vegetation on a global and local scale are the Normalized Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) (Garroutte et al., 2016). NDVI is an index derived from visible and near-infrared reflectance and is associated with the fraction of solar radiation absorbed by plants during photosynthesis (Berger et al., 2019). This index increases the absorptive and reflective characteristics of the vegetation and provides a way to estimate the greeness and canopy (Rouse et al., 1974; Huete et al., 2002). The EVI employs near infrared, red region and reflectance in the blue bands and was developed to improve the detection of the vegetation signal in regions with higher biomass density and the influence of soil and atmosphere interference in the canopy response (Huete, 1997; Justice et al., 2002; Ponzoni; Shimabukuro; Kuplich, 2012).

In the Atlantic Forest biome, research on interannual and seasonal trends in native vegetation using remote sensing data is incipient. Studies of Brazilian Atlantic forest vegetation include studies of changes in land use/ cover (Bicudo da Silva et al., 2020); studies of climate and vegetation conditions (Chiminazzo et al., 2021; de Santana et al., 2020); threats such as fire (de Andrade et al., 2020), but to a lesser extent are studies of the trends observed by remote sensing images in this biome (Branco et al., 2019; Rebello et al., 2020). In this context, we analyzed the interannual and seasonal trends observed between 2001 and 2019, in two adjacent areas of native vegetation of the Brazilian Atlantic Forest, with different levels of protection: an area protected by law, where no human activities are allowed - Caparaó National Park - and the buffer zone of the Caparaó National Park, located in its surroundings, where human activities are moderately permitted. It is believed that the level of protection in these areas may contribute to differences in the responses of vegetation. Trends were analyzed in time series of vegetation index images from the Moderate Resolution Image Spectroradiometer (MODIS) sensor, which were subsequently correlated with the rain estimate data from the Tropical Rainfall Measuring Mission (TRMM) satellite.

Section snippets

Research area

For this study, we selected Caparaó National Park, located along the border between Espírito Santo state and Minas Gerais state in southeast Brazil. The Caparaó National Park is an important conservation area for the Brazilian Atlantic Forest biome. Recent studies have been concerned with cataloging existing species in their area, which reiterates the importance of maintaining this conservation area. Moreira et al. (2020) carried out an extensive research, in which the results indicated that

Vegetation trends analysis

Our analysis of the temporal behavior of vegetation indices shows a persistent trend toward decreased vigor of the vegetation in Caparaó National Park (Fig. 3). The same behavior was not observed for the representative native vegetation of forest fragments in the buffer zone, which suggests differences between the vegetation of the Atlantic forest between the two areas. These differences may be caused by different surrounding environments and how vegetation responds to disturbances. It is also

Discussion

Our results suggest that Caparaó National Park has been characterized by a general decrease in greenness from 2001 to 2019. This decrease occurs in proportion to the time; and EVI was the index that most strengthened this conclusion. The results found for the buffer zone indicate an opposite behavior to the behavior of vegetation in the park, with tendencies of increase in vegetative vigor. For this case, the most expressive results were obtained from the NDVI time series. These differences may

Conclusion

We successfully used MODIS and TRMM remote sensing data to our methodologies for studying vegetation in the Brazilian Atlantic forest biome. The results of this study according with other work, and show the need for continued collaboration, which could boost the search for strategies to promote greater conservation of this biome. Furthermore, this methodology is feasible to be applied to larger areas around the world and can provide information on the conditions of vegetation trends and the

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to extend their thanks to the Integrated System for National Agency and Space Administration (NASA) and the Ministério do Meio Ambiente- Brazil (MMA) for providing the necessary data for implementing this work. We also thank the Research and Innovation Support Foundation of the Espírito Santo (FAPES), the Coordination of university level Personnel Improvement (CAPES), the Graduate Program in Forest Science of the Federal University of Espirito Santo, and group research,

References (116)

  • B. Chen et al.

    Changes in vegetation photosynthetic activity trends across the Asia–Pacific region over the last three decades

    Remote Sens. Environ.

    (2014)
  • M.A. Chiminazzo et al.

    Marques Guimarães

  • C.F. de Andrade et al.

    Fire regime in Southern Brazil driven by atmospheric variation and vegetation cover

    Agric. For. Meteorol.

    (2020)
  • R.O. de Santana et al.

    The past, present and future of vegetation in the Central Atlantic Forest Corridor, Brazil

    Remote Sens. Appl. Soc. Environ.

    (2020)
  • R. Fensholt et al.

    Evaluation of Earth Observation based global long term vegetation trends - Comparing GIMMS and MODIS global NDVI time series

    Remote Sens. Environ.

    (2012)
  • R. Fensholt et al.

    Analysis of trends in the Sahelian “rain-use efficiency” using GIMMS NDVI, RFE and GPCP rainfall data

    Remote Sens. Environ.

    (2011)
  • C.D. Ferreira et al.

    The importance of the standardizing sampling methodology to detect altitudinal gradients in mountains: A study case for the resident bird community in a hotspot (Atlantic forest) and the Middle Domain Effect

    Acta Oecologica

    (2021)
  • S.R. Freitas et al.

    Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest

    For. Ecol. Manage

    (2010)
  • P. Gärtner et al.

    Object based change detection of central Asian Tugai vegetation with very high spatial resolution satellite imagery

    Int. J. Appl. Earth Obs. Geoinf.

    (2014)
  • T. Hilker et al.

    On the measurability of change in Amazon vegetation from MODIS

    Remote Sens. Environ.

    (2015)
  • C. Huang et al.

    Land use/cover change in the Three Gorges Reservoir area, China: Reconciling the land use conflicts between development and protection

    Catena

    (2019)
  • L. Hubert-Moy et al.

    Time-series spectral dataset for croplands in France (2006–2017)

    Data Br.

    (2019)
  • A. Huete et al.

    Overview of the radiometric and biophysical performance of the MODIS vegetation indices

    Remote Sens. Environ.

    (2002)
  • S. Jamali et al.

    Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel

    Remote Sens. Environ.

    (2014)
  • M. Lamchin et al.

    Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014

    Glob. Ecol. Conserv.

    (2020)
  • W. Luo et al.

    UAV based soil moisture remote sensing in a karst mountainous catchment

    Catena

    (2019)
  • B. Martínez et al.

    Vegetation dynamics from NDVI time series analysis using the wavelet transform

    Remote Sens. Environ.

    (2009)
  • F. Maselli

    Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data

    Remote Sens. Environ.

    (2004)
  • X. Meng et al.

    Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

    Int. J. Appl. Earth Obs. Geoinf.

    (2020)
  • N.B. Mishra et al.

    MODIS derived vegetation greenness trends in African Savanna: Deconstructing and localizing the role of changing moisture availability, fire regime and anthropogenic impact

    Remote Sens. Environ.

    (2015)
  • A. Moreira et al.

    Wavelet approach applied to EVI/MODIS time series and meteorological data

    ISPRS J. Photogramm. Remote Sens.

    (2019)
  • E.L. Nunes et al.

    Monitoring carbon assimilation in South America’s tropical forests: Model specification and application to the Amazonian droughts of 2005 and 2010

    Remote Sens. Environ.

    (2012)
  • J. O’Connell et al.

    A monitoring protocol for vegetation change on Irish peatlandand heath

    Int. J. Appl. Earth Obs. Geoinf.

    (2014)
  • P.V. Potapov et al.

    Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data

    Remote Sens. Environ.

    (2012)
  • M.J. Pringle et al.

    Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery

    Int. J. Appl. Earth Obs. Geoinf.

    (2012)
  • M.C. Ribeiro et al.

    The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation

    Biol. Conserv.

    (2009)
  • A.R. Santos et al.

    Geotechnology and landscape ecology applied to the selection of potential forest fragments for seed harvesting

    J. Environ. Manage.

    (2016)
  • L.H. Silva Rotta et al.

    The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil

    Int. J. Appl. Earth Obs. Geoinf.

    (2020)
  • D.A. Sims et al.

    Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features

    Remote Sens. Environ.

    (2003)
  • B.S. Sobral et al.

    PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State, Brazil

    Atmos. Res.

    (2020)
  • R. Spiekermann et al.

    Woody vegetation and land cover changes in the Sahel of Mali(1967–2011)

    Int. J. Appl. Earth Obs. Geoinf.

    (2015)
  • M. Tabarelli et al.

    Prospects for biodiversity conservation in the Atlantic Forest: Lessons from aging human-modified landscapes

    Biol. Conserv.

    (2010)
  • R. Taghizadeh-Mehrjardi et al.

    Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

    Geoderma

    (2021)
  • A. Bannari et al.

    A review of vegetation indices

    Remote Sens. Rev

    (1995)
  • M.G. Betts et al.

    Extinction filters mediate the global effects of habitat fragmentation on animals

    Science (80-.)

    (2019)
  • S.P. Boyte et al.

    Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

    GIScience Remote Sens.

    (2018)
  • Brasil. Ministério do meio ambiente

    Plano de ação para prevenção e controle do desmatamento e das queimadas. Brasília, setembro de

    (2010)
  • Brasil. Sistema Nacional de Unidades de Conservação

    Lei 9985 de 18 de julho de 2000. Coletânea de legislação ambiental, Odete Medauar

    (2011)
  • P.V. Campos et al.

    Plant diversity and community structure of Brazilian Páramos

    J. Mt. Sci.

    (2018)
  • M.J. Case et al.

    Forests of the future: Climate change impacts and implications for carbon storage in the Pacific Northwest, USA

    For. Ecol. Manage

    (2021)
  • Cited by (7)

    View all citing articles on Scopus
    View full text