Camila Américo dos Santos 1, Victor Rei de Carvalho 1, Victor de Melo Pinheiro 1, Dandara Bernardino Bezerra 1, Ruan Vargas 1, Paulo Roberto Alves dos Santos 1, Fábio Ferreira Dias 1
1
Universidade Federal Fluminense, Niterói
Presented in
V Biosystems Engineering Workshop - WEB 5.0 November 05-07, 2019 - Niterói - RJ, Brazil
Abstract
The mangrove is considered an ecosystem of great ecological, social and economic importance, which is distributed along the Brazilian coastline. However, it has been suffering from the disorderly growth of urban areas and their potentially polluting activities, as well as environmental and climate change across the globe. Geotechnology tools are of paramount importance for environmental monitoring and evaluation studies, with relatively low cost. The objective of this research was to conduct a study of the dynamics of the characteristic mangrove vegetation that are located in the Guaratiba Administrative Region, Rio de Janeiro, as well as the disordered occupation of the region, with a temporal cut of 29 years (1990 to 2019) and application of the Anthropic Transformation Index (ATI) in the Reserve area and its surroundings. The materials used were multispectral images from the Landsat series (5 and 8), complemented by field research in 2015 and 2018. This base served as support for the classification of land use and land cover, using techniques Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-Up Index (NDBI). The results show that the mangrove areas grew, even though there was a huge growth of the built areas in the region. The ATI for the region is low, showing that the area is poorly degraded, even with high growth in urban areas.
Keywords: Remote sensing; Mangrove; NDVI; NDBI; ATI
INTRODUCTION
Coastal regions can be considered one of the most threatened on the planet, to undergo constant change and anthropogenic use of natural resources. Approximately two thirds of the world's population lives in these regions, where the major metropolises are established (KAWASHIMA et al., 2016). Coastal areas, including mangroves, are important regions of transitions between terrestrial and marine environments that are of great social, environmental and ecological importance, providing key environmental services (SOUZA et al, 2018). With the advancement of remote sensing and satellite-based studies, satisfactory results can be found and costs of mapping and detection of environmental, urban, agricultural, and so on can be reduced. Geoprocessing brings users closer to this data for processing of their applications, where the tools for spatial analysis are provided (CARVALHO et al., 2015). The aplication of these tools for the analysis of land use becomes of great importance regarding the possibility of observing the temporal variability of the region. Thus, we have an important mechanism for studies focused on environmental analysis, diagnosis of dynamism in coastal space, adaptation to climate change, among other purposes (CHOW, 2017).
One of the main mangrove remnants of the municipality of Rio de Janeiro is located in Sepetiba Bay, where it is composed of two features: the mangrove forest and hypersaline plains (SOARES, 2007). The study area
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is located in the Guaratiba region, covering the Guaratiba, Pedra de Guaratiba and Barra de Guaratiba neighborhoods. Until the last century, the area was essentially rural, but with growth in Barra da Tijuca and Recreio do Bandeirantes, the neighborhoods in this region became more attractive to real estate (MENEZES, 2010).
We aim to conduct a study of the dynamics of characteristic vegetation of mangroves in the region, and the disorderly occupation, cut with a time of 29 years (1990 and 2019). They were used as bases satellite images of Landsat series (TM 5 and 8 OLI) for its more consistent time series and their free availability.
MATERIALS AND METHODS
For the construction of land use and land cover classification, satellite images were used for the 1990s (Landsat 5 TM) and 2019 (Landsat 8 OLI), where two new bands were elaborated from two indices by normalized difference: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built- Up Index (NDBI). From them were made the outlines of the classes of use and land cover, which were manually vectorized by digital means in Arcgis software. With the classification, it was possible to estimate the total areas in km². Each class received a weight for the implementation of the Anthropic Transformation Index (ATI).
The delimitation of the study area was made from a 3 km buffer of the Guaratiba State Biological Reserve area, covering the neighborhoods of Barra de Guaratiba, Pedra de Guaratiba and Guaratiba, including parts of Grumari, Vargem Grande and Recreio dos Bandeirantes.
Figure 1: Area of study. Source: Author.
The NDVI and NDBI are made from the spectral bands of the Landsat 5 TM and 8 OLI images. Both range from -1 to 1. For NDVI, the expression used is (ZHA, et al, 2003):
NDVI = (NIR - RED) / (NIR + RED) (1)
90 For NDBI, the expression used is (ZHA, et al, 2003):
NDBI = (SWIR - NIR) / (SWIR + NIR) (2)
Where: SWIR corresponds to the shortwave infrared band.
Thus, it was possible to delimit the land use and land cover classes, where they were divided into 10 classes: Mangrove, Hypersaline areas, Built-up areas; Tidal channels; River; Sand bank; Restinga; Vegetation; Areas without vegetation and beach. They were defined manually, by digital means, in Arcgis software, where the features were observed from visual interpretation. The surface areas were calculated for each of the classes in square kilometers.
ATI was proposed to check the actions of human pressures on the RBG. It is calculated from land use and land cover classes to quantify the degree of anthropogenic transformation in an area by the expression (BEVEN, 1988):
ATI= ∑(%Use X Weight) / 100 (3)
Where: Use the corresponding area in % of a given land use and land cover and Weight is the weight given to the different types of use and the degree of coverage anthropogenic change ranging from 1 to 10; 10 indicates the highest pressures.
RESULTS AND DISCUSSION
As a result, we obtained the data of NDVI and NDBI region for the dates 1990 and 2019, the use classification and land cover and the respective areas calculated.
Figure 2: Color False Composition with NDVI. GSBR buffer with a 3km buffer for the 1990s (left) and
2019 (right). The darkest shade in the vegetation is the mangrove areas, which are predominantly within the GSBR. Areas without vegetation within the GSBR correspond to hypersaline areas. Notable is the increase in built-up areas around GSBR. Source: Author.
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Figure 3: Color False Composition with NDBI. It can be observed more clearly the hypersaline areas and
built areas. Areas without vegetation outside the RBG are also well highlighted. Source: Author. Typical mangrove vegetation occupies extensive areas in the innermost region of Sepetiba Bay. The hypersaline areas are located almost exclusively in the GSBR. The increase of the mangrove is also explained by the reserve, by the legal protections attributed to it. However, another noticeable phenomenon is the colonization of hypersaline areas by mangrove forest. Soares et al. (2007) explains that mangrove forests and hypersaline areas have opposite growth and suppression behavior. As one area grows, the other decreases. This occurred for a few years, but eventually the mangrove forests began to migrate to hypersaline areas. This phenomenon is described by the author as a consequence of rising sea level.
The proposed NDBI and NDVI methods are able to map their respective areas of interest accurately. It is noticeable in the generated images the distinction between mangrove areas, built-up areas and hypersaline areas. However, the analyst needs to be very knowledgeable about the study site, as there are similar spectral responses to vegetation-free, hypersaline and built-up areas. With this, it was of great importance for vectoring the aid of classes and use of ground cover, which served as a base.
The vectoring of the areas was performed from these four images, generating the following results:
Table 1: Total area in square kilometers (km²) for the classification of land use and land cover in
Guaratiba region and neighboring districts in the years 1990 and 2019, with the variation between the two years. Source: Author.
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With these results, the areas in km² were transformed into percentage values. Also, weights were assigned to each class. Mateo (1991) states that these values may be indicated by the researcher, or by a multidisciplinary view of various specialists (Ribeiro et al., 2017), who have knowledge of which classes have the most anthropogenic impact. In this study, these classes are urban areas and areas without vegetation.
The ATI classification followed the attribution made by Cruz et al. (1998), where: Slightly degraded (0 to 2,5); Regular (2,5 to 5); Degraded (5 to 7,5) and Very Degraded (7,5 to 10).
Table 2: Total areas of the classes in percentage, with their respective weights, and the result
of the anthropic transformation index - ATI. Source: Author.
Despite the large increase in built-up areas in the region, ATI values remain within the “regular” range, achieving minimal growth within this timeframe. As stated earlier, this result can be attributed to the legal protections attached to the reserve. Barra de Guaratiba, for example, is a tourist spot, which encourages local
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authorities to preserve the environment. Guaratiba went through a great process of disordered urban growth, being the closest place to Barra da Tijuca and Recreio dos Bandeirantes. Many construction workers moved to the location to meet the demand for labor that existed in the most exclusive areas of the West Zone of Rio de Janeiro (MENEZES, 2010).
Even getting a very low value in ATI, implying that the region would not be suffering a great anthropic impact, it is possible to be alert to urban growth. This can be explained by the spatial resolution of the images, because some points have more spaced urban areas, where there are occasional occupations (ROCHA, CRUZ; 2009). In the field, it was observed that there is increasing anthropogenic pressure on the GSBR boundary, where irregular housing was built and, after a few years, local authorities granted land tenure to families. The range from 1990 to 2019 shows a growth of 163%, where we can see that, in addition to part of the native vegetation areas outside the reserve, the areas without vegetation were transformed into built-up areas. However, some of these areas without vegetation were recovered, becoming native vegetation.
CONSIDERATIONS
In addition to the two dates already made, it is intended to make two further classifications for the years 2000 and 2010. Data validation will still be performed by Pearson's correlation coefficient.
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