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Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió, Brazil
Land Use Policy 87 (2019) 104026
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Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió, Brazil
T
Washington Luiz Félix Correia Filhoa, Dimas de Barros Santiagob, ⁎ José Francisco de Oliveira-Júniora,c, Carlos Antonio da Silva Juniord, a
Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), 57072-260, Maceió, Alagoas, Brazil Postgraduate Program in Meteorology, Unidade Acadêmica de Ciências Atmosféricas (UACA), Federal University of Campina Grande (UFCG), 58429-140, Campina Grande, Paraíba, Brazil c Postgraduate Program in Biosystems Engineering (PGEB), Federal Fluminense University (UFF), Niterói, Rio de Janeiro, 24220-900, Brazil d State University of Mato Grosso (UNEMAT), Department of Geography, Sinop, Mato Grosso, 78555-000, Brazil b
ARTICLE INFO
ABSTRACT
Keywords: Urban heat Island Orbital products Vegetation indices
The development / urbanization process of Brazilian cities in the last decade occurred since the creation of the Programa de Aceleração de Crescimento (PAC) and Programa Minha Casa Minha Vida (PMCMV). In this study, we evaluated the impact of these programs on urban sprawl in the northern part of the Alagoan capital, Maceió, located in the Northeast of Brazil, by using orbital products from remote sensing (Normalized Difference Vegetation Index-NDVI, Normalized Difference Built-up Index-NDBI e Land Surface Temperature-LST) during the period from 1987 to 2017. Descriptive statistics (minimum, maximum, mean and standard deviation) and Pearson correlation analysis were applied to the NDVI, NDBI and LST in the selected years (1987, 1998, 2003 and 2017). The results obtained indicated significant changes in land use and land cover in the last thirty years verified by the NDVI with a gradual decrease in vegetation cover in areas north, east and northeast of the northern zone of Maceió over time, which it was also found by the NDBI. This gradual replacement of green areas by residential and commercial developments resulted in a significant increased LST, from 26.40 ± 1.63 °C in 1987 to 32.73 ± 3.20 °C in 2017, and in the latter, some regions indicated values higher than 35 °C and the difference between 2017-1987 oscillated between 4–10 °C. The PAC and PMCMV programs contributed significantly to a change in land use and land cover, which increased the extent of urbanized areas as well as changes in the local microclimate.
1. Introduction The growth of urban centers in a disorganized way, especially in developing countries, leads to several changes in land use and land cover (LULC) – (Li et al., 2018), due to the replacement of vegetation by materials with high heat storage capacity (Santiago and Gomes, 2016; Alves et al., 2019). As a consequence, there is an increase in the thermal sensation in urbanized areas, which in turn causes changes in the local microclimate and the regional climate, and thus in the thermal discomfort of the population (Guha et al., 2018; Li et al., 2018). In Brazil, the development/urbanization process of the cities occurred in the last decade is due to the creation of public policies called Programa de Aceleração de Crescimento (PAC) by Law nº 11,578/2007 (Brazil, 2007) and Programa Minha Casa Minha Vida (PMCMV) by Law nº 11,977/2009 (Brazil, 2009). Both programs implemented by the
⁎
Brazilian Government aimed to boost the country economic growth and infrastructure, which benefited millions of families, offering comfort and well-being to the population (Cardoso and Leal, 2010). According to Menezes & Mourão (2017), approximately US$ 75 billion (R$ 300 billion in current values) were invested in the PMCMV, in about four million housing units throughout the national territory. In the Northeast of Brazil (NEB), in the state capital of Alagoas, Maceió, was built 10,092 thousand units, and there are still 5932 thousand units in progress in 2018 (Maceió, 2018), with a total investment of US$ 400 million (R$ 1.5 billion). However, the reflection of this accelerated development without adequate urban planning can lead to several problems, such as alteration in LULC (Akbari et al., 2016), followed by environmental impacts: water pollution with dumping of waste (Moura, 2014; Tamano et al., 2015; Panagopoulos et al., 2016; Silva et al., 2017), and in extreme cases, public health problems caused by local
Corresponding author. E-mail address: [email protected] (C.A.d. Silva Junior).
https://doi.org/10.1016/j.landusepol.2019.104026 Received 5 January 2019; Received in revised form 6 May 2019; Accepted 29 May 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.
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thermal discomfort (Panagopoulos et al., 2016; Lin et al., 2018).These changes can be evaluated by different approaches in the literature, for example by the use of mesoscale models (Oleson et al., 2011), by network of conventional and automatic meteorological stations (Bernard et al., 2017) and mainly by the use of Remote Sensing - RS products (Zha et al., 2003, Guha et al., 2018). The use of RS allows us to identify the impact of these changes on the landscape in multiscale, particularly with the use of vegetation indices, due to its capacity to detect presence (when values higher than 0) and vegetative absence (values lower than 0), for example, the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974; Guha et al., 2018). The NDVI allows to monitor changes in the photosynthetic activity of the plants (Huete et al., 2002), to characterize dry episodes (Zhao et al., 2018), to diagnose the LULC (Fu and Weng, 2016; Guha et al., 2018), and to evaluate the environmental modifications from spectral patterns (Mancino et al., 2014). On the other hand, the Normalized Difference Built-Up Index (NDBI) assesses urban development from the built-up area (Zha et al., 2003, Varhney, 2013, García and Pérez, 2016; Guha et al., 2018). García and Pérez (2016) used the NDBI to map soil types in Spain, and the authors found that this clearly distinguishes urbanized areas of gardens, xerophylic vegetation around the city. The LULC effects are reflected in the local thermal comfort and well-being, and such changes can be detected by the Land Surface Temperature (LST) (Guha et al., 2018; Li et al., 2018). The use of LST also allows to evaluate the impact of the LULC changes on local microclimate and its effects on the thermal comfort of the population (Santiago and Gomes, 2016; Guha et al., 2018; Lin et al., 2018). In Maceió, there are few studies that address the impacts of urban sprawl in a disorderly way (Araújo and Di Pace, 2010; Santiago and Gomes, 2016). However, none of these were carried out with focus on Federal Government Programs (PAC and PMCMV), followed by other unsuccessful local public policies. Therefore, the main objective is to evaluate the impact of the urban advance on the land use and land cover, and the soil surface temperature in the municipality of Maceió over 30 years. Next, the study is subdivided into four topics: topic 2 will deal on the description of the study area with the exposure of demographic and climatic information in the northern zone of Maceió. Topic 3 approaches the methodology, which deals with obtaining, manipulating and extracting the orbital and LST indices for the characterization of changes in land use and land cover in the northern zone of the municipality of Maceió. The topic 4 shows the main results from the descriptive statistics and thematic maps and ring tree that point out the areas with the greatest changes in the orbital indices and the LST. And the last, topic 5, will point out the highlights obtained in this study.
Pace, 2010). Regarding rainfall, the municipality records annual values between 1500 and 2200 mm with two well-defined seasons: a) dry, from September to February; b) rainy, from March to August (Araújo and Di Pace, 2010). 3. Material and methods 3.1. Acquisition of orbital images In order to evaluate the urban expansion of the northern part of the city of Maceió, it was acquired images of the Landsat 5/Thematic Mapper (TM) and 8/Operational Land Imager (OLI) sensor systems from the electronic address https://earthexplorer.usgs.gov/, managed by the United States Geological Survey - USGS (USGS, 2018). Initially, four images from the orbit 214 and position 67 with the minimum of clouds on the study area were used, aiming at evaluating the spacetemporality of the urbanization in the north / west zone of Maceió over 30 years (1987–2017). Images correspond to periods: 09/07/1987 (beginning of series), 09/21/1998, 09/03/2003 (before the implantation of PMCMV) and 12/14/2017 (after the PMCMV). Further details are shown in Table 1. After obtaining the images, the study area was delimited from the software QGIS version 2.18 (QGis Development Team, 2009), the north zone of Maceió City has 78.68 km2. Then, the images from the Landsat 5 and 8 satellites were submitted to the following steps: 1) organization; 2) pre-processing; 3) manipulation; e 4) extraction of the values from Land Surface Temperature (LST), Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). All steps were performed in R environment software version 3.41 (R Development Team, 2017), from the raster package version 2.6–7 (Hijmans, 2017). 3.2. NDVI For the extraction of the NDBI and NDVI data a few subsequent steps were performed (Fig. 2). One of them was the preprocessing of the spectral bands obtained by the Landsat 5/TM and 8/OLI sensor satellites, respectively, followed by conversion of the digital number (ND) in values of radiance and monochromatic reflectance. The bands used for each index are described in Table 2. The ND conversion into spectral radiance (L i ) was obtained by Eq. (1) (Markham and Baker, 1987):
L i = ai +
2. Study area
bi
ai * ND, for Landsat 5 images 255
or
The city of Maceió is located in the NEB region and has an area of 509.55 km2 (1.84% of the total area of the state) (Fig. 1), and has an estimated population of 1012 million people (IBGE, 2018), and a Gross Domestic Product (GDP) per capita of US$ 4.77 billion (R$ 18.30 billion) (IBGE, 2018). Maceió is subdivided into seven administrative regions, to which was evaluated administrative regions six and seven, referring to the north zone of the city, denominated of high part of Maceió, composed of seven neighborhoods: i) Antares, ii) Benedito Bentes, iii) Cidade Universitária, iv) Clima Bom, v) Santa Lúcia, vi) Santos Dumont and vii) Tabuleiro dos Martins. Currently, these neighborhoods concentrate approximately 355 thousand people (approximately 35% of the total population of the Alagoan capital) – (SEPLAG, 2018). According to the Köppen classification, the climate of Maceió is tropical hot and humid, corresponding to As' type, characterized by small variations in the thermal amplitude throughout the year. Average monthly temperatures range from 22.9 °C to 27.9 °C, with minimum and maximum temperatures of 19.0 °C and 31.0 °C, while the annual average relative humidity is about 78%, respectively (Araújo and Di
L i = ai +
bi ai * ND, for Landsat 8 images 65535
(1)
where a and b are the minimum and maximum spectral radiances (W m−2 sr1 μm-1), as shown in Table 2, detected by TM and OLI of the Landsat series; i corresponds to bands 3, 4, 5 and 6 (thermal band) of the Landsat 5 T M sensor (bands 4, 5 and 6 of the OLI sensor and bands 10 and 11 of the Thermal Infrared Sensor (TIRS) of Landsat 8). The following equation was used to calculate the correlation coefficients between the reflected light flux and the incident solar radiation flux, was obtained according to Eq. (2) (Allen et al., 2002): i
=
*
i
(2)
ESUN i * cos * dr −2
-1
-1
in which, L i is spectral radiance (W m sr μm ), ESUN i is the spectral solar irradiance of each band at the top of the atmosphere (W m−2 sr-1 μm-1), shown in Table 2, θ is the solar zenith angle and d is the distance Earth -Sun. After obtaining the reflectance factors, the NDBI was calculated, being developed by Zha et al. (2003). This index detects built-up areas, 2
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Fig. 1. Location of the northern part of the city of Maceió, capital of the State of Alagoas, Brazil. The linear cut has a length of 14 km, and runs through the neighborhoods of Clima Bom, Tabuleiro dos Martins and Benedito Bentes. Table 1 Date and time of acquisition of the orbital image, elevation angle (in º) and solar azimuth (in º), cloud cover (in %), Landsat 5 and 8 images. Path/Row
214/067
Date of aquisition
Julian Day (DJ)
Time
Sun Elevation (°)
Sun Azimuth (°)
Cloud Cover (%)
09/07/1987 09/21/1998 09/03/2003 12/14/2017
250 264 246 348
11:56:31 12:08:40 12:07:09 12:30:02
50.39 56.41 51.83 61.07
67.25 73.13 63.63 120.36
12.00 23.00 11.00 9.10
from Eq. (3):
NDBI =
( (
3.3. Land surface temperature (LST) NIR )
SWIR SWIR
+
NIR )
The LST was obtained from the following steps, the calculation of the emissivity of the terrestrial surface extracted from the NDVI, from the proposed methodology of Sobrino et al. (2004) by Eq. (5):
(3)
where ρSWIR and ρNIR correspond to the reflectances of the near-infrared and shortwave infrared bands. The NDBI varies between −1 and +1, positive values correspond to built-up areas and bare soil, while negative values correspond to vegetation and water bodies. After obtaining the NDBI, the NDVI was calculated. This index is associated with the photosynthetic activity of the vegetation obtained by Eq. (4) (Rouse et al., 1974; Huete et al., 2002; Guha et al., 2018):
( NDVI = (
R)
NIR NIR
+
R)
d = (1
s )*(1
Pv )* F *
(5)
v
v is the vegetation emissivity, s is the soil emissivity, F is the mean value factor (F is 0.55 according to Lim et al., 2012), Pv is the proportion of the vegetation obtained by Eq. (6) (Quintano et al., 2015), described below:
NDVI NDVImin NDVImax + NDVImin
Pv = (4)
2
(6)
The emissivity was obtained by Eq. (7):
where ρNIR and ρR correspond, respectively, to the reflectances of the near-infrared and red bands. The NDVI varies between -1 and +1, when the values are positive correspond the areas with vegetative vigor (terrestrial surface) according to the photosynthetic activity of the plants and their density in the area considered (pixel); while negative values reflect water bodies and presence of clouds.
=
v
* Pv +
s *(1
Pv ) + d
(7)
In the evaluation of the urbanization effect, the LST was used. For this, it is necessary to calculate the monochromatic radiance (Eq. 2) of the band 6 of the TM sensor from Landsat 5/TM (Band 10 of the TIRS1 sensor for the 8/OLI). After obtaining the spectral radiation, BT was 3
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Fig. 2. Flowchart of the steps for obtaining, extracting, manipulating and illustrating the results regarding changes in land use and land occupation for the northern area of Maceió.
constant (1.38*10-23 J/K), and
calculated by Eq. (8) (Guha et al., 2018):
BT = ln
(
K2 K1 pi
)
3.4. Kernel density
273.16
+1
The mathematical expression of the kernel density method is expressed in Eq. (10) by (Smith et al., 2015):
(8)
wherein BT é the brightness temperature given in Celsius (°C), for the Landsat 5 satellite the values of K1 and K2 correspond to 607.76 and 1260.56, respectively, and for Landsat 8 were 774.88 and 1321.08, respectively. The LST was obtained by Eq. (9):
LST =
fˆ (s, b) = n 1*b
( ) *ln( ) BT P
n 2
K*
(s
i=1
Si ) b
(10)
Wherein, n = total number of observations; b = smoothing parameter (i.e., the bandwidth), which can be varied by the user; s = coordinate vector indicating where the function is being estimated; si = coordinate vector representing each observation;
BT 1 + W*
is the emissivity of the earth's surface.
(9)
W is the effective wavelength (11.475 µ m), P = h * c/ s (1.438*10−2 mK), h = Planck constant, c = ligth speed, s = Boltzmann
Table 2 Description of the TM, OLI and TIRS bands of Landsat 5 and 8 with respective calibration coefficients (ai e bi) and spectral solar irradiation (Eλi). TM Bands Description
Landsat 5 Wavelengths
Band 3 – Red Band 4 – Near Infrared (NIR) Band 5 – Shortwave Infrared (SWIR 1) Banda 6 – Thermal
0.630–0.690 0.790–0.900 1.550–1.750 10.40–12.50
OLI bands Description
Landsat 8 Wavelengths
Band Band Band Band
4 – Red 5 – Near Infrared (NIR) 6 – Shortwave Infrared (SWIR 1) 10 – Thermal Infrared (TIRS 1)
0.636–0.673 0.851–0.879 1.566–1.651 10.60–11.19
4
Calibration coefficients ai
bi
−1.17 −1.51 −0.37 1.24
264.00 221.00 30.20 15.30
Calibration coefficients ai
bi
−51.54 −31.54 −7.84 0,10
624.17 381.96 94.99 22,00
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After the descriptive analysis and the Pearson's correlation, the spatial distributions of NDVI, NDBI and LST were evaluated to verify their characteristics and influences over 30 years, which are discussed below.
Table 3 Classification of the NDBI and NDVI, for the classes: vegetation, water bodies and built-up area. Indices
Vegetation Water Bodies Built-up area
4.2. Spatial distribution of NDVI
NDBI
NDVI
< −0.01 −0.01 to 0.01 > 0.01
> 0.30 < 0.00 0.01 to 0.30
From the thematic maps obtained by the NDVI, significant changes in land use and occupation over the 30 years from the selected images (1987, 1998, 2003 and 2017) are observed, especially when comparing the year 2017 (Fig. 4a) in relation to 1987 (Fig. 4d). In the first decade, between the periods of 09/07/1987 (Fig. 4e) and 21/9/1998 (Fig. 4b), there was a significant decrease in the photosynthetic activity in the west (W) and east (E) areas of the zone north of the city of Maceió, characterized by vegetated areas (represented by green color), whose maximum value is 0.79. However, a higher concentration of NDVI negative values in the central region, with a minimum of 0.38, is associated with the expansion of the city of Maceió in this region (represented by the purple color). In the evaluation of the period of 9/3/2003 (Fig. 2c), there are changes throughout the region in the built-up area class with an increase from 31.65 km2 to 34.89 km2 (from 40.22% to 44.34%), due to the expansion of housing complexes, exposed soil and road corridors. All were intensified during implementation and executions of Federal Government Programs (PAC and PMCMV). In contrast, there is a gradual reduction of the vegetation category, from 46.49 km2 to 42.86 km2 (reduction of area from 59.09% to 54.48%). This migration from the southern to northern of Maceió is due in part to the saturation of housing and commercial building, so the only way was to expand to the northern and northeastern of the city (Santiago and Gomes, 2016). On the other hand, the expansion to the north and northeast of the city of Maceió was an alternative to minimize the degree of physic-environmental vulnerability of Maceió, the south and southeast zones present a high degree and as a consequence the occurrence of intense rains (Moura, 2014; Nascimento et al., 2018), and generate material damages, and in some cases, human losses. In this way, the only solution was the expansion of the population to the north, because it presents a low degree of vulnerability, which reduces these harmful effects to the population (Nascimento et al., 2018). This expansion caused significant changes in LULC, mainly in the upper region, and also in the northeast (NE) and southwest (SW) portions of the region, corresponding to the neighborhoods of Benedito Bentes and Antares, respectively. In addition, there was no change in land use and occupation in the Environmental Protection Area (EPA) Catolé and Fernão Velho located in the western region of the study area, located between the cities of Maceió and Satuba. This EPA is a conservation unit created by Law nº. 5347/ 1992 (IMA, 1992), supervised by the Instituto do Meio Ambiente (IMA) and the Companhia de Saneamento de Alagoas (CASAL), and protected by the Environmental Police Battalion (BPA). This EPA has a strategic role for the city of Maceió, because there are two sources, the Aviação and Catolé rivers, responsible for supplying 30% of the south and southeast of the city of Maceió (IMA, 1992). In relation to the last decade, as of 12/14/2017, there was a significant change in the LULC, verified by built-up area class, homogeneously distributed. This rapid expansion was the result of the implementation of several commercial developments associated with the expansion of housing complexes after the PMCMV, part of the PAC, implemented in 2009 (Menezes and Mourão, 2017). Also notable for the sugar and alcohol industry crisis was a gradual reduction of sugarcane cultivation areas over the last few years on the north zone of Maceió, located in the north and northeast portions of the region, which was gradually replaced by housing developments, verified by vegetation class with a reduction from 50.72 Km2 to 21.35 Km2 (reduction from 64.46% to 27.14%) − (Table 4). The results obtained on the changes in use and occupation in Maceió was discussed in Santiago and Gomes (2016). The authors found that the evolution of the
K = density function that satisfies the following condition given by Eq. (11):
K(s)*ds = 1
(11)
The radius used in the study was 0.03 km. 4. Results and discussion 4.1. Descriptive analysis of NDBI, NDVI and LST In this article, the descriptive statistics of the 4 images will be presented. It was observe differences between the minimum (maximum) values of NDBI and NDVI with values between −0.38 to −0.17 (0.64 to 0.75) and −0.45 to −0.42 (0.23 to 0.57), respectively. Regarding the mean, the values ranged from 0.23 to 0.36 for NDVI, and −0.01 to 0.11 for NDBI. In the case of LST, significant differences were observed in the minimum (maximum) with variations between 12.83 °C to 21.49 °C (30.41 °C to 40.82 °C), while the mean ± standard deviation varies between 24.71 ± 1.68 °C (in 2003) and 32.73 ± 3.20 °C (in 2017) (Table 3). From the Pearson's correlation (Fig. 3), the relationship between the NDBI and NDVI indices shows a positive correlation with values higher than 0.32. Based on the data, it is noted that the strong correlations occur in associations of the same year, in 1987 the correlation between them is 0.83, in 1998 the value is 0.84, in 2003 the value is 0.99, and in 2017 the value is 0.78. However, when these indices correlate with the LST, there is an opposite association, that is, the increased LST is associated with a decreased NDBI and NDVI indices. In addition, the correlation between the variables has a greater prominence between the years 1987 and 1998, with negative values higher than 0.6.
Fig. 3. Pearson's correlation analysis between orbital products, NDBI, NDVI and LST from 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017. 5
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Fig. 4. Time evolution of the LST for the north zone of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/2017.
urban area contributes to the decrease of NDVI, with values between 0.01-0.40, mainly in 2011.
Maceió (Fig. 5). The NDBI spatial pattern presents aspects and characteristics similar to those verified by NDVI, however, in the opposite way. In the literature, its differential refers to the distinctions between water bodies, vegetation and built-up area (Zha et al., 2003; Varhney, 2013; Guha et al., 2018). When analyzing the patterns of variability between 9/7/1987 (Fig. 3a) and 9/21/1998 (Fig. 3b), there was a
4.3. Spatial distribution of NDBI After the NDVI, the NDBI was also evaluated for the north zone of 6
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Table 4 Descriptive analysis of the NDVI and NDBI, and LST, referring to days images: 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017. NDVI
07-09-1987 21-09-1998 03-09-2003 14-12-2017
NDBI
LST
Min/Max
Mean ± DP
Min/Max
Mean ± DP
Min/Max
Mean ± DP
−0.39/0.79 −0.43/0.79 −0.28/0.78 −0.58/0.86
0.43 0.42 0.39 0.36
−0.39/0.79 −0.43/0.79 −0.28/0.78 −0.58/0.86
0.43 0.42 0.39 0.36
15.64/34.86 21.50/32.86 12.84/30.42 16.85/40.83
26.39 27.28 24.71 32.72
± ± ± ±
0.15 0.15 0.16 0.13
significant decrease of the vegetation class (-0.80 > NDBI < −0.01, represented in shades of green), with an area of 25.47 Km2 to 20.66 Km2 (from 32.37% to 26.26%)-(Table 4), identified at the eastern (E) and northeastern (NE) ends of the northern zone of Maceió. Such reduction in both portions possibly associated to the population expansion with the implantation of new housing estates, between the neighborhoods of Benedito Bentes and Antares. While in the western portion, located in the EPA region located between the neighborhoods of Clima Bom, Fernão Velho and Santos Dumont, there is a smaller decrease as previously verified by NDVI, with the highest negative values of NDBI. In relation to the built-up area (NDBI with values higher than 0.01, illustrated in shades of yellow color), the central region of the north zone of Maceió stands out in relation to the others. However, such expansion contributes to the housing constructions irregular in slopes or valleys without proper urban planning (Freitas, 2017; Coutinho et al., 2018; Nascimento et al., 2018). These occupations generate problems for the State Civil Defense, because they are areas with slope and that during the rainy season occurs landslide, similar to several metropolitan regions of Brazil (Maior and Cândido, 2014; de Mello et al., 2014; Garcia et al., 2016; Marchioro et al., 2016; Nascimento et al., 2018; Toniazzo et al., 2018). In relation to the periods 9/3/2003 and 12/14/2017, the NDBI presented gradual changes in comparison to the period 9/7/1987, however, in 2017 it is not possible to verify the real built-up area due to presence of clouds, but there is an increase of the built-up area class from 50.15 Km2 to 54.79 Km2 (from 63.78% to 69.63%). Despite this reduction of vegetation class from 25.47 Km2 to 21.18 Km2 (32.37 to 26.93%)-(Table 4), there was an increase in water bodies class from 3.06 Km2 to 5.18 Km2 (from 3.89% to 6.59%), there was no reduction of both vegetation and water bodies in the EPA area, even with reports of occurrences of deforestation in the region. In addition, it was verified the increase of vegetation in slope areas located in the neighborhood of Antares with negative values of NDBI higher than 0.01.
± ± ± ±
0.15 0.15 0.16 0.13
± ± ± ±
1.63 1.77 1.68 3.20
showed that the highest LST values occurred between 1998 and 2003, as minimum values between 24.3 °C and 21.9 °C and maximum between 32.2 °C and 32.4 °C, respectively. In the period 12/14/2017, there was a significant change in the areas of the LST ranges, mainly between 34.0 °C and 40.0 °C, with an area of 32.88 km2 (with 54.61% of the total area), mainly in regions where there were changes in the LULC, as, for example, the substitution of sugar cane cultivation by housing estates. These results corroborate with the results of Santiago and Gomes (2016), which found that the increase in LST is due to the development of the urban mesh, especially in 2011, with LST > 35 °C in regions with higher density. When comparing LST variation between 1987 and 2017, there are regions where the difference in LST ranges between 2 °C and 15 °C. An interesting fact in this study was that even with changes around the EPA, the increase in LST was only 0.5 °C–2 °C due to dense vegetation located in the region, while in the areas occupied with residential and commercial areas this increase varies between 5 °C and 15 °C. 4.5. Evaluation of neighborhoods based on orbital products (NDBI, NDVI and LST) For a more detailed evaluation of the effect of these changes on land use and land occupation along the northern zone of Maceió, a transect was analyzed between A and B along the study area (Fig. 1) in each of the four selected images (Fig. 7), with extension of 14 km. This line crosses three neighborhoods with Clima Bom (between km 3 and km 5), Tabuleiro dos Martins (between km 5 and km 8) and Benedito Bentes (between km 8 and km 14). It is found that NDVI (Fig. 7a) and NDBI (Fig. 7b) show similar patterns, with small differences in relation to their intensities. According to the NDVI, the Clima Bom neighborhood presents a strong oscillation over the 30 years between km 1.7 and 2.1, with a decrease from 0.6 (1987) to 0.2 (2017), there is also an increase between km 2.2 and 2.7, with values from 0.2 (1987) to 0.7 (2017). In the case of the Tabuleiro dos Martins, there was a more marked reduction, with some sites with a reduction between 0.4 and 0.5, with NDVI values between 0.5 in 1987 to values of 0.1 and 0.2 in 2017. In the case of the Benedito Bentes, there was no strong oscillations along the line. At times, the difference between 2017 and 1987 is minimal. In relation to NDBI (Fig. 7b), the patterns are similar to those verified by NDVI, to which such differences are hardly discernible. These patterns obtained by the NDBI and NDVI can be detected by the LST (Fig. 7c) with well-highlighted differences, mainly by the intensity values. It is noted over time the considerable increase of LST in 1987, the oscillation is around 24.0 °C–29.6 °C. Among the images of 1987, 1998 and 2003, the differences between LST do not exceed 5 °C, with variation between 25 °C and 30 °C. However, in 2017, the LST difference compared to previous years is on average between 4 °C and 10 °C, with values between 28.8 °C and 37.2 °C, in the km 6.6–6.8 reach values higher than 37 °C. These differences are motivated by the densification of commercial and housing developments. In addition, the reduction of vegetated areas in the cities propitiates the increased temperature. In 2017, the expressive increased LST along the line is highlighted. The gradual replacement of green or vegetated areas by materials
4.4. Spatial distribution of LST The LULC effects detected by NDVI and NDBI are reflected in the LST results, which increased significantly over the thirty years, associated to the expansion to the north zone of Maceió due to population growth. In the analyzed period the cloud interference (white circles) is verified, but does not compromise the analyses realized (Fig. 6). The image of the 9/3/2003 presented the lowest values of LST, between 24.0 °C and 29.0 °C, with an area of 67.05 km2 (with 85.22% of the total area). In addition, it is verified that the periods of 9/7/1987 and 9/21/1998 show an considerable increase in LST, of the range between 24.0 °C and 29.0 °C for 29.0 °C and 34.0 °C (Table 4), with a decrease (increase) from 71.20 Km2 to 61.71 Km2 (2.80 Km2 to 14.60 Km2), with a percentage increase from 91.76% to 78.43% (3.56% to 18.56%) in the first (second) range. This increase is related to the replacement of the vegetation class for the built-up area class (asphalt or concrete) or bare soil, which retains a greater amount of heat (Akbari et al., 2016; Santiago and Gomes, 2016; Alves et al., 2019, in pressAlves et al., 2019Alves et al., 2019, in press). Araújo and Di Pace (2010) evaluated LST in the city of Maceió between 1990 and 2003. They 7
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Fig. 5. Temporal evolution of the NDBI of the North Zone of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/ 2017.
used in city constructions modifies the local microclimate, thereby increasing soil heat flux, as well as air temperature (Lin et al., 2018). In addition, the variables that are more sensitive to the thermal component also suffered changes in the local pattern, such as: relative humidity and wind speed (Wang et al., 2018), followed by local rainfall
pattern (Goulart et al., 2015; Delgado et al., 2017). These differences of LST obtained between 2017 and 1987 corroborate the results obtained by Santiago and Gomes (2016), who in 2011 due to the large verticalizations and expansions of a disorderly form of land occupation in the south zone of Maceió, temperature of 10 °C between the urban and rural 8
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Fig. 6. Temporal evolution of the LST of the north of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/2017.
areas, motivated by the replacement of vegetated areas.
region, with a marked decrease of regions categorized as vegetation, mainly by the substitution of areas of sugarcane cultivation of sugarcane plants and fragments of the Atlantic Forest, for regions with several residential and commercial developments in the north zone of Maceió. In the decennial scale, a gradual change was detected in the central region of the north zone of Maceió, corresponding to
5. Conclusions Based on products from orbital products (NDBI, NDVI and LST) during the years 1987 to 2017, the LULC changes are clear on study 9
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Fig. 7. Linear temporal evaluation of orbital products: (a) NDVI, (b) NDBI and (c) LST for the period: 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017, in the neighborhoods Clima Bom, Tabuleiro dos Martins and Benedito Bentes.
neighborhood of the Tabuleiro dos Martins due to the expansion of construction of several housing estates, driven Minha Casa Minha Vida Program, as of 2009. The two Federal Government Programs contributed significantly to the LULC modification in the region. This expansion resulted in an increase in the urban network and road corridors, as evidenced by NDBI. The region of greatest change was the neighborhood of the Benedito Bentes, with the implementation of commercial and residential developments, which benefited positively all sectors of the city. However, such growth has contributed to some problems, such as: increase of dwellings in irregular areas, changes on the LULC and, finally, a significant increase in LST, especially in 2017, with values above 35 °C. In the end, these changes pointed by orbital products followed by unsuitable urban planning led to profound changes in the quality of life of the population, due to changes in local microclimate patterns. As a way to minimize these changes, it must proposes an increase in urban afforestation, the maintenance of the still existing landscapes and urban reorganization in the region in order to minimize environmental impacts on the population.
inundação no trecho paulista da bacia do Rio Paraíba do Sul e nascentes do Cadastro Ambiental Rural. Urban Rev. Bras. Gest. Urbana 10, 614–623. https://doi.org/10. 1590/2175-3369.010.003.ao09. Delgado, R.C., Oliveira-Júnior, J.F., Gois, G., Teodoro, P.E., Rodrigues, R.A., 2017. Synoptic events associated with the land surface temperature in Rio de Janeiro. Biosci. J. 33, 1038–1048. https://doi.org/10.14393/BJ-v33n4a2017-33990. (On line). Freitas, C.F.S., 2017. Ilegalidade e degradação em Fortaleza: os riscos do conflito entre a agenda urbana e ambiental brasileira. Revista Brasileira de Gestão Urbana 6, 109–125. Fu, P., Weng, Q., 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sens. Environ. 184, 175–187. https://doi.org/10.1016/j.rse.2016.06.019. Garcia, M.I.M., Jara, S.D.M., Schlee, M.B., da Silva Júnior, O.P., Netto, A.L.C., 2016. Uso do Solo e Vulnerabilidade Socioambiental na Sub-Bacia do Alto Rio das Pedras (Rio de Janeiro/RJ): Subsídios para Regeneração de Rios Neotropicais. Revista do Departamento de Geografia 32, 29–38. https://doi.org/10.11606/rdg.v32i0.115801. García, P., Pérez, E., 2016. Mapping of soil sealing by vegetation indexes and built-up index: a case study in Madrid (Spain). Geoderma 268, 100–107. https://doi.org/10. 1016/j.geoderma.2016.01.012. Goulart, A.C.O., Delgado, R.C., de Oliveira-Júnior, J.F., de Gois, G., Oliveira, E.S., 2015. Relação espectro-temporal entre índices de vegetação e a chuva na cidade do Rio de Janeiro. Revista de Ciências Agrárias (Belém) 58, 277–283. https://doi.org/10.4322/ rca.1990. Guha, S., Govil, H., Dey, A., Gill, N., 2018. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur. J. Remote. Sens. 51, 667–678. https://doi.org/10.1080/22797254.2018. 1474494. Hijmans, R.J., 2017. Raster: Geographic Data Analysis and Modeling. R Package Version 2 6–7, 2017. https://CRAN.R-project.org/package=raster. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213. https://doi.org/10.1016/S0034-4257(02) 00096-2. IBGE - Instituto Brasileiro de Geografia e Estatística, 2018. Produto Interno Bruto da cidade de Maceió. (Accessed 23 November 2018). https://cidades.ibge.gov.br/ brasil/al/maceio/panorama. IMA - Instituto do Meio Ambiente, 1992. Lei de Nº. 5347/1992 – Criação e delimitação da Área de Proteção Ambiental do Catolé e Fernão Velho. (Accessed 23 November 2018). http://www.ima.al.gov.br/wp-content/uploads/2015/03/Lei-nb0-5.347_92. pdf. Li, G., Zhang, X., Mirzaei, P.A., Zhang, J., Zhao, Z., 2018. Urban heat island effect of a typical valley city in China: responds to the global warming and rapid urbanization. Sustain. Cities Soc. 38, 736–745. https://doi.org/10.1016/j.scs.2018.01.033. Lim, H.S., Jafri, M., Abdullah, K., Alsultan, S., 2012. Application of a simple monowindow land surface temperature algorithm from Landsat ETM over Al Qassim, Saudi Arabia. Sains Malays. 41 (7), 841–846. Lin, B.B., Egerer, M.H., Liere, H., Jha, S., Bichier, P., Philpott, S.M., 2018. Local-and landscape-scale land cover affects microclimate and water use in urban gardens. Sci. Total Environ. 610, 570–575. https://doi.org/10.1016/j.scitotenv.2017.08.091. Maceió, 2018. Maceió terá mais 2.460 moradias do Minha Casa Minha Vida. (Accessed 28 November 2018). http://www.maceio.al.gov.br/2018/10/obras-de-novosconjuntos-habitacionais-tem-inicio-em-maceio/. Maior, M.M.S., Cândido, G.A., 2014. Avaliação das metodologias brasileiras de vulnerabilidade socioambiental como decorrência da problemática urbana no Brasil. Cadernos Metrópole 16, 239–262. Mancino, G., Nolè, A., Ripullone, F., Ferrara, A., 2014. Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. iForest-Biogeosciences and Forestry 7, 75–84. https://doi. org/10.3832/ifor0909-007. Marchioro, E., Silva, G.M., Correa, W.D.S.C., 2016. A Zona de Convergência do Atlântico Sul e a precipitação pluvial do município de Vila Velha (es): repercussões sobre as inundações. Revista do Departamento de Geografia 31, 101–117. https://doi.org/10.
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Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió, Brazil
T
Washington Luiz Félix Correia Filhoa, Dimas de Barros Santiagob, ⁎ José Francisco de Oliveira-Júniora,c, Carlos Antonio da Silva Juniord, a
Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), 57072-260, Maceió, Alagoas, Brazil Postgraduate Program in Meteorology, Unidade Acadêmica de Ciências Atmosféricas (UACA), Federal University of Campina Grande (UFCG), 58429-140, Campina Grande, Paraíba, Brazil c Postgraduate Program in Biosystems Engineering (PGEB), Federal Fluminense University (UFF), Niterói, Rio de Janeiro, 24220-900, Brazil d State University of Mato Grosso (UNEMAT), Department of Geography, Sinop, Mato Grosso, 78555-000, Brazil b
ARTICLE INFO
ABSTRACT
Keywords: Urban heat Island Orbital products Vegetation indices
The development / urbanization process of Brazilian cities in the last decade occurred since the creation of the Programa de Aceleração de Crescimento (PAC) and Programa Minha Casa Minha Vida (PMCMV). In this study, we evaluated the impact of these programs on urban sprawl in the northern part of the Alagoan capital, Maceió, located in the Northeast of Brazil, by using orbital products from remote sensing (Normalized Difference Vegetation Index-NDVI, Normalized Difference Built-up Index-NDBI e Land Surface Temperature-LST) during the period from 1987 to 2017. Descriptive statistics (minimum, maximum, mean and standard deviation) and Pearson correlation analysis were applied to the NDVI, NDBI and LST in the selected years (1987, 1998, 2003 and 2017). The results obtained indicated significant changes in land use and land cover in the last thirty years verified by the NDVI with a gradual decrease in vegetation cover in areas north, east and northeast of the northern zone of Maceió over time, which it was also found by the NDBI. This gradual replacement of green areas by residential and commercial developments resulted in a significant increased LST, from 26.40 ± 1.63 °C in 1987 to 32.73 ± 3.20 °C in 2017, and in the latter, some regions indicated values higher than 35 °C and the difference between 2017-1987 oscillated between 4–10 °C. The PAC and PMCMV programs contributed significantly to a change in land use and land cover, which increased the extent of urbanized areas as well as changes in the local microclimate.
1. Introduction The growth of urban centers in a disorganized way, especially in developing countries, leads to several changes in land use and land cover (LULC) – (Li et al., 2018), due to the replacement of vegetation by materials with high heat storage capacity (Santiago and Gomes, 2016; Alves et al., 2019). As a consequence, there is an increase in the thermal sensation in urbanized areas, which in turn causes changes in the local microclimate and the regional climate, and thus in the thermal discomfort of the population (Guha et al., 2018; Li et al., 2018). In Brazil, the development/urbanization process of the cities occurred in the last decade is due to the creation of public policies called Programa de Aceleração de Crescimento (PAC) by Law nº 11,578/2007 (Brazil, 2007) and Programa Minha Casa Minha Vida (PMCMV) by Law nº 11,977/2009 (Brazil, 2009). Both programs implemented by the
⁎
Brazilian Government aimed to boost the country economic growth and infrastructure, which benefited millions of families, offering comfort and well-being to the population (Cardoso and Leal, 2010). According to Menezes & Mourão (2017), approximately US$ 75 billion (R$ 300 billion in current values) were invested in the PMCMV, in about four million housing units throughout the national territory. In the Northeast of Brazil (NEB), in the state capital of Alagoas, Maceió, was built 10,092 thousand units, and there are still 5932 thousand units in progress in 2018 (Maceió, 2018), with a total investment of US$ 400 million (R$ 1.5 billion). However, the reflection of this accelerated development without adequate urban planning can lead to several problems, such as alteration in LULC (Akbari et al., 2016), followed by environmental impacts: water pollution with dumping of waste (Moura, 2014; Tamano et al., 2015; Panagopoulos et al., 2016; Silva et al., 2017), and in extreme cases, public health problems caused by local
Corresponding author. E-mail address: [email protected] (C.A.d. Silva Junior).
https://doi.org/10.1016/j.landusepol.2019.104026 Received 5 January 2019; Received in revised form 6 May 2019; Accepted 29 May 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.
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thermal discomfort (Panagopoulos et al., 2016; Lin et al., 2018).These changes can be evaluated by different approaches in the literature, for example by the use of mesoscale models (Oleson et al., 2011), by network of conventional and automatic meteorological stations (Bernard et al., 2017) and mainly by the use of Remote Sensing - RS products (Zha et al., 2003, Guha et al., 2018). The use of RS allows us to identify the impact of these changes on the landscape in multiscale, particularly with the use of vegetation indices, due to its capacity to detect presence (when values higher than 0) and vegetative absence (values lower than 0), for example, the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974; Guha et al., 2018). The NDVI allows to monitor changes in the photosynthetic activity of the plants (Huete et al., 2002), to characterize dry episodes (Zhao et al., 2018), to diagnose the LULC (Fu and Weng, 2016; Guha et al., 2018), and to evaluate the environmental modifications from spectral patterns (Mancino et al., 2014). On the other hand, the Normalized Difference Built-Up Index (NDBI) assesses urban development from the built-up area (Zha et al., 2003, Varhney, 2013, García and Pérez, 2016; Guha et al., 2018). García and Pérez (2016) used the NDBI to map soil types in Spain, and the authors found that this clearly distinguishes urbanized areas of gardens, xerophylic vegetation around the city. The LULC effects are reflected in the local thermal comfort and well-being, and such changes can be detected by the Land Surface Temperature (LST) (Guha et al., 2018; Li et al., 2018). The use of LST also allows to evaluate the impact of the LULC changes on local microclimate and its effects on the thermal comfort of the population (Santiago and Gomes, 2016; Guha et al., 2018; Lin et al., 2018). In Maceió, there are few studies that address the impacts of urban sprawl in a disorderly way (Araújo and Di Pace, 2010; Santiago and Gomes, 2016). However, none of these were carried out with focus on Federal Government Programs (PAC and PMCMV), followed by other unsuccessful local public policies. Therefore, the main objective is to evaluate the impact of the urban advance on the land use and land cover, and the soil surface temperature in the municipality of Maceió over 30 years. Next, the study is subdivided into four topics: topic 2 will deal on the description of the study area with the exposure of demographic and climatic information in the northern zone of Maceió. Topic 3 approaches the methodology, which deals with obtaining, manipulating and extracting the orbital and LST indices for the characterization of changes in land use and land cover in the northern zone of the municipality of Maceió. The topic 4 shows the main results from the descriptive statistics and thematic maps and ring tree that point out the areas with the greatest changes in the orbital indices and the LST. And the last, topic 5, will point out the highlights obtained in this study.
Pace, 2010). Regarding rainfall, the municipality records annual values between 1500 and 2200 mm with two well-defined seasons: a) dry, from September to February; b) rainy, from March to August (Araújo and Di Pace, 2010). 3. Material and methods 3.1. Acquisition of orbital images In order to evaluate the urban expansion of the northern part of the city of Maceió, it was acquired images of the Landsat 5/Thematic Mapper (TM) and 8/Operational Land Imager (OLI) sensor systems from the electronic address https://earthexplorer.usgs.gov/, managed by the United States Geological Survey - USGS (USGS, 2018). Initially, four images from the orbit 214 and position 67 with the minimum of clouds on the study area were used, aiming at evaluating the spacetemporality of the urbanization in the north / west zone of Maceió over 30 years (1987–2017). Images correspond to periods: 09/07/1987 (beginning of series), 09/21/1998, 09/03/2003 (before the implantation of PMCMV) and 12/14/2017 (after the PMCMV). Further details are shown in Table 1. After obtaining the images, the study area was delimited from the software QGIS version 2.18 (QGis Development Team, 2009), the north zone of Maceió City has 78.68 km2. Then, the images from the Landsat 5 and 8 satellites were submitted to the following steps: 1) organization; 2) pre-processing; 3) manipulation; e 4) extraction of the values from Land Surface Temperature (LST), Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). All steps were performed in R environment software version 3.41 (R Development Team, 2017), from the raster package version 2.6–7 (Hijmans, 2017). 3.2. NDVI For the extraction of the NDBI and NDVI data a few subsequent steps were performed (Fig. 2). One of them was the preprocessing of the spectral bands obtained by the Landsat 5/TM and 8/OLI sensor satellites, respectively, followed by conversion of the digital number (ND) in values of radiance and monochromatic reflectance. The bands used for each index are described in Table 2. The ND conversion into spectral radiance (L i ) was obtained by Eq. (1) (Markham and Baker, 1987):
L i = ai +
2. Study area
bi
ai * ND, for Landsat 5 images 255
or
The city of Maceió is located in the NEB region and has an area of 509.55 km2 (1.84% of the total area of the state) (Fig. 1), and has an estimated population of 1012 million people (IBGE, 2018), and a Gross Domestic Product (GDP) per capita of US$ 4.77 billion (R$ 18.30 billion) (IBGE, 2018). Maceió is subdivided into seven administrative regions, to which was evaluated administrative regions six and seven, referring to the north zone of the city, denominated of high part of Maceió, composed of seven neighborhoods: i) Antares, ii) Benedito Bentes, iii) Cidade Universitária, iv) Clima Bom, v) Santa Lúcia, vi) Santos Dumont and vii) Tabuleiro dos Martins. Currently, these neighborhoods concentrate approximately 355 thousand people (approximately 35% of the total population of the Alagoan capital) – (SEPLAG, 2018). According to the Köppen classification, the climate of Maceió is tropical hot and humid, corresponding to As' type, characterized by small variations in the thermal amplitude throughout the year. Average monthly temperatures range from 22.9 °C to 27.9 °C, with minimum and maximum temperatures of 19.0 °C and 31.0 °C, while the annual average relative humidity is about 78%, respectively (Araújo and Di
L i = ai +
bi ai * ND, for Landsat 8 images 65535
(1)
where a and b are the minimum and maximum spectral radiances (W m−2 sr1 μm-1), as shown in Table 2, detected by TM and OLI of the Landsat series; i corresponds to bands 3, 4, 5 and 6 (thermal band) of the Landsat 5 T M sensor (bands 4, 5 and 6 of the OLI sensor and bands 10 and 11 of the Thermal Infrared Sensor (TIRS) of Landsat 8). The following equation was used to calculate the correlation coefficients between the reflected light flux and the incident solar radiation flux, was obtained according to Eq. (2) (Allen et al., 2002): i
=
*
i
(2)
ESUN i * cos * dr −2
-1
-1
in which, L i is spectral radiance (W m sr μm ), ESUN i is the spectral solar irradiance of each band at the top of the atmosphere (W m−2 sr-1 μm-1), shown in Table 2, θ is the solar zenith angle and d is the distance Earth -Sun. After obtaining the reflectance factors, the NDBI was calculated, being developed by Zha et al. (2003). This index detects built-up areas, 2
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Fig. 1. Location of the northern part of the city of Maceió, capital of the State of Alagoas, Brazil. The linear cut has a length of 14 km, and runs through the neighborhoods of Clima Bom, Tabuleiro dos Martins and Benedito Bentes. Table 1 Date and time of acquisition of the orbital image, elevation angle (in º) and solar azimuth (in º), cloud cover (in %), Landsat 5 and 8 images. Path/Row
214/067
Date of aquisition
Julian Day (DJ)
Time
Sun Elevation (°)
Sun Azimuth (°)
Cloud Cover (%)
09/07/1987 09/21/1998 09/03/2003 12/14/2017
250 264 246 348
11:56:31 12:08:40 12:07:09 12:30:02
50.39 56.41 51.83 61.07
67.25 73.13 63.63 120.36
12.00 23.00 11.00 9.10
from Eq. (3):
NDBI =
( (
3.3. Land surface temperature (LST) NIR )
SWIR SWIR
+
NIR )
The LST was obtained from the following steps, the calculation of the emissivity of the terrestrial surface extracted from the NDVI, from the proposed methodology of Sobrino et al. (2004) by Eq. (5):
(3)
where ρSWIR and ρNIR correspond to the reflectances of the near-infrared and shortwave infrared bands. The NDBI varies between −1 and +1, positive values correspond to built-up areas and bare soil, while negative values correspond to vegetation and water bodies. After obtaining the NDBI, the NDVI was calculated. This index is associated with the photosynthetic activity of the vegetation obtained by Eq. (4) (Rouse et al., 1974; Huete et al., 2002; Guha et al., 2018):
( NDVI = (
R)
NIR NIR
+
R)
d = (1
s )*(1
Pv )* F *
(5)
v
v is the vegetation emissivity, s is the soil emissivity, F is the mean value factor (F is 0.55 according to Lim et al., 2012), Pv is the proportion of the vegetation obtained by Eq. (6) (Quintano et al., 2015), described below:
NDVI NDVImin NDVImax + NDVImin
Pv = (4)
2
(6)
The emissivity was obtained by Eq. (7):
where ρNIR and ρR correspond, respectively, to the reflectances of the near-infrared and red bands. The NDVI varies between -1 and +1, when the values are positive correspond the areas with vegetative vigor (terrestrial surface) according to the photosynthetic activity of the plants and their density in the area considered (pixel); while negative values reflect water bodies and presence of clouds.
=
v
* Pv +
s *(1
Pv ) + d
(7)
In the evaluation of the urbanization effect, the LST was used. For this, it is necessary to calculate the monochromatic radiance (Eq. 2) of the band 6 of the TM sensor from Landsat 5/TM (Band 10 of the TIRS1 sensor for the 8/OLI). After obtaining the spectral radiation, BT was 3
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Fig. 2. Flowchart of the steps for obtaining, extracting, manipulating and illustrating the results regarding changes in land use and land occupation for the northern area of Maceió.
constant (1.38*10-23 J/K), and
calculated by Eq. (8) (Guha et al., 2018):
BT = ln
(
K2 K1 pi
)
3.4. Kernel density
273.16
+1
The mathematical expression of the kernel density method is expressed in Eq. (10) by (Smith et al., 2015):
(8)
wherein BT é the brightness temperature given in Celsius (°C), for the Landsat 5 satellite the values of K1 and K2 correspond to 607.76 and 1260.56, respectively, and for Landsat 8 were 774.88 and 1321.08, respectively. The LST was obtained by Eq. (9):
LST =
fˆ (s, b) = n 1*b
( ) *ln( ) BT P
n 2
K*
(s
i=1
Si ) b
(10)
Wherein, n = total number of observations; b = smoothing parameter (i.e., the bandwidth), which can be varied by the user; s = coordinate vector indicating where the function is being estimated; si = coordinate vector representing each observation;
BT 1 + W*
is the emissivity of the earth's surface.
(9)
W is the effective wavelength (11.475 µ m), P = h * c/ s (1.438*10−2 mK), h = Planck constant, c = ligth speed, s = Boltzmann
Table 2 Description of the TM, OLI and TIRS bands of Landsat 5 and 8 with respective calibration coefficients (ai e bi) and spectral solar irradiation (Eλi). TM Bands Description
Landsat 5 Wavelengths
Band 3 – Red Band 4 – Near Infrared (NIR) Band 5 – Shortwave Infrared (SWIR 1) Banda 6 – Thermal
0.630–0.690 0.790–0.900 1.550–1.750 10.40–12.50
OLI bands Description
Landsat 8 Wavelengths
Band Band Band Band
4 – Red 5 – Near Infrared (NIR) 6 – Shortwave Infrared (SWIR 1) 10 – Thermal Infrared (TIRS 1)
0.636–0.673 0.851–0.879 1.566–1.651 10.60–11.19
4
Calibration coefficients ai
bi
−1.17 −1.51 −0.37 1.24
264.00 221.00 30.20 15.30
Calibration coefficients ai
bi
−51.54 −31.54 −7.84 0,10
624.17 381.96 94.99 22,00
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After the descriptive analysis and the Pearson's correlation, the spatial distributions of NDVI, NDBI and LST were evaluated to verify their characteristics and influences over 30 years, which are discussed below.
Table 3 Classification of the NDBI and NDVI, for the classes: vegetation, water bodies and built-up area. Indices
Vegetation Water Bodies Built-up area
4.2. Spatial distribution of NDVI
NDBI
NDVI
< −0.01 −0.01 to 0.01 > 0.01
> 0.30 < 0.00 0.01 to 0.30
From the thematic maps obtained by the NDVI, significant changes in land use and occupation over the 30 years from the selected images (1987, 1998, 2003 and 2017) are observed, especially when comparing the year 2017 (Fig. 4a) in relation to 1987 (Fig. 4d). In the first decade, between the periods of 09/07/1987 (Fig. 4e) and 21/9/1998 (Fig. 4b), there was a significant decrease in the photosynthetic activity in the west (W) and east (E) areas of the zone north of the city of Maceió, characterized by vegetated areas (represented by green color), whose maximum value is 0.79. However, a higher concentration of NDVI negative values in the central region, with a minimum of 0.38, is associated with the expansion of the city of Maceió in this region (represented by the purple color). In the evaluation of the period of 9/3/2003 (Fig. 2c), there are changes throughout the region in the built-up area class with an increase from 31.65 km2 to 34.89 km2 (from 40.22% to 44.34%), due to the expansion of housing complexes, exposed soil and road corridors. All were intensified during implementation and executions of Federal Government Programs (PAC and PMCMV). In contrast, there is a gradual reduction of the vegetation category, from 46.49 km2 to 42.86 km2 (reduction of area from 59.09% to 54.48%). This migration from the southern to northern of Maceió is due in part to the saturation of housing and commercial building, so the only way was to expand to the northern and northeastern of the city (Santiago and Gomes, 2016). On the other hand, the expansion to the north and northeast of the city of Maceió was an alternative to minimize the degree of physic-environmental vulnerability of Maceió, the south and southeast zones present a high degree and as a consequence the occurrence of intense rains (Moura, 2014; Nascimento et al., 2018), and generate material damages, and in some cases, human losses. In this way, the only solution was the expansion of the population to the north, because it presents a low degree of vulnerability, which reduces these harmful effects to the population (Nascimento et al., 2018). This expansion caused significant changes in LULC, mainly in the upper region, and also in the northeast (NE) and southwest (SW) portions of the region, corresponding to the neighborhoods of Benedito Bentes and Antares, respectively. In addition, there was no change in land use and occupation in the Environmental Protection Area (EPA) Catolé and Fernão Velho located in the western region of the study area, located between the cities of Maceió and Satuba. This EPA is a conservation unit created by Law nº. 5347/ 1992 (IMA, 1992), supervised by the Instituto do Meio Ambiente (IMA) and the Companhia de Saneamento de Alagoas (CASAL), and protected by the Environmental Police Battalion (BPA). This EPA has a strategic role for the city of Maceió, because there are two sources, the Aviação and Catolé rivers, responsible for supplying 30% of the south and southeast of the city of Maceió (IMA, 1992). In relation to the last decade, as of 12/14/2017, there was a significant change in the LULC, verified by built-up area class, homogeneously distributed. This rapid expansion was the result of the implementation of several commercial developments associated with the expansion of housing complexes after the PMCMV, part of the PAC, implemented in 2009 (Menezes and Mourão, 2017). Also notable for the sugar and alcohol industry crisis was a gradual reduction of sugarcane cultivation areas over the last few years on the north zone of Maceió, located in the north and northeast portions of the region, which was gradually replaced by housing developments, verified by vegetation class with a reduction from 50.72 Km2 to 21.35 Km2 (reduction from 64.46% to 27.14%) − (Table 4). The results obtained on the changes in use and occupation in Maceió was discussed in Santiago and Gomes (2016). The authors found that the evolution of the
K = density function that satisfies the following condition given by Eq. (11):
K(s)*ds = 1
(11)
The radius used in the study was 0.03 km. 4. Results and discussion 4.1. Descriptive analysis of NDBI, NDVI and LST In this article, the descriptive statistics of the 4 images will be presented. It was observe differences between the minimum (maximum) values of NDBI and NDVI with values between −0.38 to −0.17 (0.64 to 0.75) and −0.45 to −0.42 (0.23 to 0.57), respectively. Regarding the mean, the values ranged from 0.23 to 0.36 for NDVI, and −0.01 to 0.11 for NDBI. In the case of LST, significant differences were observed in the minimum (maximum) with variations between 12.83 °C to 21.49 °C (30.41 °C to 40.82 °C), while the mean ± standard deviation varies between 24.71 ± 1.68 °C (in 2003) and 32.73 ± 3.20 °C (in 2017) (Table 3). From the Pearson's correlation (Fig. 3), the relationship between the NDBI and NDVI indices shows a positive correlation with values higher than 0.32. Based on the data, it is noted that the strong correlations occur in associations of the same year, in 1987 the correlation between them is 0.83, in 1998 the value is 0.84, in 2003 the value is 0.99, and in 2017 the value is 0.78. However, when these indices correlate with the LST, there is an opposite association, that is, the increased LST is associated with a decreased NDBI and NDVI indices. In addition, the correlation between the variables has a greater prominence between the years 1987 and 1998, with negative values higher than 0.6.
Fig. 3. Pearson's correlation analysis between orbital products, NDBI, NDVI and LST from 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017. 5
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Fig. 4. Time evolution of the LST for the north zone of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/2017.
urban area contributes to the decrease of NDVI, with values between 0.01-0.40, mainly in 2011.
Maceió (Fig. 5). The NDBI spatial pattern presents aspects and characteristics similar to those verified by NDVI, however, in the opposite way. In the literature, its differential refers to the distinctions between water bodies, vegetation and built-up area (Zha et al., 2003; Varhney, 2013; Guha et al., 2018). When analyzing the patterns of variability between 9/7/1987 (Fig. 3a) and 9/21/1998 (Fig. 3b), there was a
4.3. Spatial distribution of NDBI After the NDVI, the NDBI was also evaluated for the north zone of 6
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Table 4 Descriptive analysis of the NDVI and NDBI, and LST, referring to days images: 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017. NDVI
07-09-1987 21-09-1998 03-09-2003 14-12-2017
NDBI
LST
Min/Max
Mean ± DP
Min/Max
Mean ± DP
Min/Max
Mean ± DP
−0.39/0.79 −0.43/0.79 −0.28/0.78 −0.58/0.86
0.43 0.42 0.39 0.36
−0.39/0.79 −0.43/0.79 −0.28/0.78 −0.58/0.86
0.43 0.42 0.39 0.36
15.64/34.86 21.50/32.86 12.84/30.42 16.85/40.83
26.39 27.28 24.71 32.72
± ± ± ±
0.15 0.15 0.16 0.13
significant decrease of the vegetation class (-0.80 > NDBI < −0.01, represented in shades of green), with an area of 25.47 Km2 to 20.66 Km2 (from 32.37% to 26.26%)-(Table 4), identified at the eastern (E) and northeastern (NE) ends of the northern zone of Maceió. Such reduction in both portions possibly associated to the population expansion with the implantation of new housing estates, between the neighborhoods of Benedito Bentes and Antares. While in the western portion, located in the EPA region located between the neighborhoods of Clima Bom, Fernão Velho and Santos Dumont, there is a smaller decrease as previously verified by NDVI, with the highest negative values of NDBI. In relation to the built-up area (NDBI with values higher than 0.01, illustrated in shades of yellow color), the central region of the north zone of Maceió stands out in relation to the others. However, such expansion contributes to the housing constructions irregular in slopes or valleys without proper urban planning (Freitas, 2017; Coutinho et al., 2018; Nascimento et al., 2018). These occupations generate problems for the State Civil Defense, because they are areas with slope and that during the rainy season occurs landslide, similar to several metropolitan regions of Brazil (Maior and Cândido, 2014; de Mello et al., 2014; Garcia et al., 2016; Marchioro et al., 2016; Nascimento et al., 2018; Toniazzo et al., 2018). In relation to the periods 9/3/2003 and 12/14/2017, the NDBI presented gradual changes in comparison to the period 9/7/1987, however, in 2017 it is not possible to verify the real built-up area due to presence of clouds, but there is an increase of the built-up area class from 50.15 Km2 to 54.79 Km2 (from 63.78% to 69.63%). Despite this reduction of vegetation class from 25.47 Km2 to 21.18 Km2 (32.37 to 26.93%)-(Table 4), there was an increase in water bodies class from 3.06 Km2 to 5.18 Km2 (from 3.89% to 6.59%), there was no reduction of both vegetation and water bodies in the EPA area, even with reports of occurrences of deforestation in the region. In addition, it was verified the increase of vegetation in slope areas located in the neighborhood of Antares with negative values of NDBI higher than 0.01.
± ± ± ±
0.15 0.15 0.16 0.13
± ± ± ±
1.63 1.77 1.68 3.20
showed that the highest LST values occurred between 1998 and 2003, as minimum values between 24.3 °C and 21.9 °C and maximum between 32.2 °C and 32.4 °C, respectively. In the period 12/14/2017, there was a significant change in the areas of the LST ranges, mainly between 34.0 °C and 40.0 °C, with an area of 32.88 km2 (with 54.61% of the total area), mainly in regions where there were changes in the LULC, as, for example, the substitution of sugar cane cultivation by housing estates. These results corroborate with the results of Santiago and Gomes (2016), which found that the increase in LST is due to the development of the urban mesh, especially in 2011, with LST > 35 °C in regions with higher density. When comparing LST variation between 1987 and 2017, there are regions where the difference in LST ranges between 2 °C and 15 °C. An interesting fact in this study was that even with changes around the EPA, the increase in LST was only 0.5 °C–2 °C due to dense vegetation located in the region, while in the areas occupied with residential and commercial areas this increase varies between 5 °C and 15 °C. 4.5. Evaluation of neighborhoods based on orbital products (NDBI, NDVI and LST) For a more detailed evaluation of the effect of these changes on land use and land occupation along the northern zone of Maceió, a transect was analyzed between A and B along the study area (Fig. 1) in each of the four selected images (Fig. 7), with extension of 14 km. This line crosses three neighborhoods with Clima Bom (between km 3 and km 5), Tabuleiro dos Martins (between km 5 and km 8) and Benedito Bentes (between km 8 and km 14). It is found that NDVI (Fig. 7a) and NDBI (Fig. 7b) show similar patterns, with small differences in relation to their intensities. According to the NDVI, the Clima Bom neighborhood presents a strong oscillation over the 30 years between km 1.7 and 2.1, with a decrease from 0.6 (1987) to 0.2 (2017), there is also an increase between km 2.2 and 2.7, with values from 0.2 (1987) to 0.7 (2017). In the case of the Tabuleiro dos Martins, there was a more marked reduction, with some sites with a reduction between 0.4 and 0.5, with NDVI values between 0.5 in 1987 to values of 0.1 and 0.2 in 2017. In the case of the Benedito Bentes, there was no strong oscillations along the line. At times, the difference between 2017 and 1987 is minimal. In relation to NDBI (Fig. 7b), the patterns are similar to those verified by NDVI, to which such differences are hardly discernible. These patterns obtained by the NDBI and NDVI can be detected by the LST (Fig. 7c) with well-highlighted differences, mainly by the intensity values. It is noted over time the considerable increase of LST in 1987, the oscillation is around 24.0 °C–29.6 °C. Among the images of 1987, 1998 and 2003, the differences between LST do not exceed 5 °C, with variation between 25 °C and 30 °C. However, in 2017, the LST difference compared to previous years is on average between 4 °C and 10 °C, with values between 28.8 °C and 37.2 °C, in the km 6.6–6.8 reach values higher than 37 °C. These differences are motivated by the densification of commercial and housing developments. In addition, the reduction of vegetated areas in the cities propitiates the increased temperature. In 2017, the expressive increased LST along the line is highlighted. The gradual replacement of green or vegetated areas by materials
4.4. Spatial distribution of LST The LULC effects detected by NDVI and NDBI are reflected in the LST results, which increased significantly over the thirty years, associated to the expansion to the north zone of Maceió due to population growth. In the analyzed period the cloud interference (white circles) is verified, but does not compromise the analyses realized (Fig. 6). The image of the 9/3/2003 presented the lowest values of LST, between 24.0 °C and 29.0 °C, with an area of 67.05 km2 (with 85.22% of the total area). In addition, it is verified that the periods of 9/7/1987 and 9/21/1998 show an considerable increase in LST, of the range between 24.0 °C and 29.0 °C for 29.0 °C and 34.0 °C (Table 4), with a decrease (increase) from 71.20 Km2 to 61.71 Km2 (2.80 Km2 to 14.60 Km2), with a percentage increase from 91.76% to 78.43% (3.56% to 18.56%) in the first (second) range. This increase is related to the replacement of the vegetation class for the built-up area class (asphalt or concrete) or bare soil, which retains a greater amount of heat (Akbari et al., 2016; Santiago and Gomes, 2016; Alves et al., 2019, in pressAlves et al., 2019Alves et al., 2019, in press). Araújo and Di Pace (2010) evaluated LST in the city of Maceió between 1990 and 2003. They 7
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Fig. 5. Temporal evolution of the NDBI of the North Zone of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/ 2017.
used in city constructions modifies the local microclimate, thereby increasing soil heat flux, as well as air temperature (Lin et al., 2018). In addition, the variables that are more sensitive to the thermal component also suffered changes in the local pattern, such as: relative humidity and wind speed (Wang et al., 2018), followed by local rainfall
pattern (Goulart et al., 2015; Delgado et al., 2017). These differences of LST obtained between 2017 and 1987 corroborate the results obtained by Santiago and Gomes (2016), who in 2011 due to the large verticalizations and expansions of a disorderly form of land occupation in the south zone of Maceió, temperature of 10 °C between the urban and rural 8
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Fig. 6. Temporal evolution of the LST of the north of Maceió, corresponding to the years: (a) 09/07/1987; (b) 09/21/1998; (c) 09/03/2003 and (d) 12/14/2017.
areas, motivated by the replacement of vegetated areas.
region, with a marked decrease of regions categorized as vegetation, mainly by the substitution of areas of sugarcane cultivation of sugarcane plants and fragments of the Atlantic Forest, for regions with several residential and commercial developments in the north zone of Maceió. In the decennial scale, a gradual change was detected in the central region of the north zone of Maceió, corresponding to
5. Conclusions Based on products from orbital products (NDBI, NDVI and LST) during the years 1987 to 2017, the LULC changes are clear on study 9
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Fig. 7. Linear temporal evaluation of orbital products: (a) NDVI, (b) NDBI and (c) LST for the period: 09/07/1987, 09/21/1998, 09/03/2003 and 12/14/2017, in the neighborhoods Clima Bom, Tabuleiro dos Martins and Benedito Bentes.
neighborhood of the Tabuleiro dos Martins due to the expansion of construction of several housing estates, driven Minha Casa Minha Vida Program, as of 2009. The two Federal Government Programs contributed significantly to the LULC modification in the region. This expansion resulted in an increase in the urban network and road corridors, as evidenced by NDBI. The region of greatest change was the neighborhood of the Benedito Bentes, with the implementation of commercial and residential developments, which benefited positively all sectors of the city. However, such growth has contributed to some problems, such as: increase of dwellings in irregular areas, changes on the LULC and, finally, a significant increase in LST, especially in 2017, with values above 35 °C. In the end, these changes pointed by orbital products followed by unsuitable urban planning led to profound changes in the quality of life of the population, due to changes in local microclimate patterns. As a way to minimize these changes, it must proposes an increase in urban afforestation, the maintenance of the still existing landscapes and urban reorganization in the region in order to minimize environmental impacts on the population.
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