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Article

Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census

by
Cássio Filipe Vieira Martins
1,
Franciele Caroline Guerra
2,*,
Anderson Targino da Silva Ferreira
2,3 and
Roger Dias Gonçalves
1,4,*
1
Program in Water Resources (PRORH), Federal University of Sergipe, São Cristóvão 49107-230, SE, Brazil
2
Institute of Geosciences, State University of Campinas, Campinas 13083-855, SP, Brazil
3
Oceanographic Institute, University of São Paulo, São Paulo 05508-120, SP, Brazil
4
Department of Geology, Federal University of Sergipe, São Cristóvão 49107-230, SE, Brazil
*
Authors to whom correspondence should be addressed.
Earth 2025, 6(3), 87; https://doi.org/10.3390/earth6030087
Submission received: 24 April 2025 / Revised: 6 July 2025 / Accepted: 23 July 2025 / Published: 1 August 2025
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Abstract

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy.

Graphical Abstract

1. Introduction

Remote sensing techniques have become indispensable in urban studies due to their extensive global applications in monitoring urban environments [1,2,3]. Recent studies underscore the pivotal role of remote sensing in tracking urban development [4], assessing light pollution [5], characterizing land cover types, and analyzing urban spatial patterns [6]. Furthermore, the application of remote sensing technologies has proven invaluable for urban tree inventory and mapping, offering efficient and accurate methods for assessing canopy cover, species identification, and overall urban forest health [7,8]).
The use of vegetation indices derived from satellite imagery significantly enhances urban studies by providing quantitative metrics to characterize vegetation cover and its dynamics over time. The Normalized Difference Vegetation Index (NDVI) is among the most utilized due to its efficacy in distinguishing vegetated from non-vegetated areas, capitalizing on differences in red and near-infrared (NIRv) spectral reflectance that are characteristic of vegetation. This feature of NDVI enables a more precise representation of the vegetation’s proportionate reflectance within a pixel. Research has validated the utility of NDVI in evaluating urban vegetation health, tracking changes in vegetation coverage, and analyzing the spatial distribution of vegetation in urban settings, thereby supporting urban environmental management and planning [9,10].
Approaches that integrate multiple vegetation indices have been employed for a more comprehensive analysis of urban vegetation cover, allowing for the extraction of detailed vegetation characteristics, such as texture, shape, and spatial context from remote sensing images [11,12]. These combined approaches leverage the strengths of different vegetation indices to provide a more comprehensive understanding of urban vegetation and its environmental impacts. This is particularly relevant in urban environments, where high spectral and spatial variability pose challenges for segmenting and classifying individual pixels [13].
With urbanization rising globally, the need to understand the role of urban vegetation in the health and quality of life of urban residents [14,15] and visitors [16] has become increasingly evident [17,18]. Remote sensing offers a range of tools and techniques to identify, map, and analyze urban vegetation, facilitating a detailed assessment of the ecosystem services it provides, such as local climate regulation, air quality improvement, and urban noise reduction, as done in Brazil [19].
Whether through NDVI [20], SAVI [21], or combinations of the indices, these quantitative metrics provide valuable insights for researchers, urban planners, and environmental managers interested in fostering greener and healthier urban environments. Furthermore, the use of complementary LiDAR data can enhance the accuracy and effectiveness of vegetation object segmentation, providing detailed information about the height and three-dimensional structure of urban vegetation [22,23,24].
With the advancement of technology and the development of new methodologies, detailed vegetation maps have become invaluable tools in the planning and management of urban vegetation [25]. As cities face increasingly complex challenges related to environmental sustainability and urban resilience, remote sensing offers powerful tools to support sustainable urban planning, natural resource management, and informed decision-making to promote greener and more livable cities [26,27,28].
The CBERS-4A satellite, a significant outcome of Sino-Brazilian cooperation in space technology, offers unique capabilities for urban remote sensing, particularly in support of urban planning and green space management. Its high-resolution optical imagery, including the WPM camera (2m) and MUX camera (16m), provides an invaluable resource for detailed monitoring of urban environments. While its role in tracking broad urban development and land use changes are established [29], the research status concerning its specific application in urban tree inventory and mapping remains notably underexplored. Despite this, early and promising studies indicate that CBERS-4A’s advanced spectral and spatial resolutions are highly effective for mapping urban vegetation intricately. This capability presents new and significant opportunities for more accurate classification of urban vegetation types, contributing to more efficient and sustainable management of urban green areas. The current research landscape suggests a substantial potential for CBERS-4A to bridge existing gaps in detailed urban forest assessments, offering a cost-effective and continuous data source for this critical area of urban planning.
For instance, CBERS-4A offers unique capabilities, particularly in some Brazilian research contexts, such as vegetation index estimation [30], demonstrating the practical applicability of CBERS-4A in estimating NDVI by utilizing its wide-scan multispectral and panchromatic camera to monitor vegetation health and coverage. Additionally, ref. [28] emphasizes the need to utilize high-resolution sensors, like CBERS-4A, to overcome the limitations of previous studies in terms of land degradation data accuracy. These approaches underscore the transformative potential of CBERS-4A, not only in technical terms but also as a strategic tool for continuous improvement in environmental monitoring and management practices, highlighting its importance in supporting conservation and sustainable forest management.
This context further underscores the importance of using remote sensing to monitor territorial transformations driven by urbanization, particularly in light of the demographic and environmental changes observed in Brazilian cities. Over the decades, these cities have experienced a significant rural exodus, with approximately three-quarters of the rural population migrating to urban centers. This process has profoundly altered the previously dominant natural landscape, a national pattern also observed in the state of Sergipe, specifically in its capital, Aracaju [31,32,33,34].
Aracaju is located at geographic coordinates approximately −10.9472 latitude and −37.0731 longitude (Figure 1). The city covers a territorial area of 182.163 km2, with a resident population of 602,757 people and a population density of 3308.89 inhabitants per km2 [35].
Designed as a grid or chessboard layout in 1855, the city of Aracaju is geomorphologically situated within a fluvio-marine plain, comprising layers of seasonal semideciduous forest and pioneer formations, such as restingas and mangroves. Amidst urban development, the municipality features a condensed and altered ecosystem, with a vegetative cover of approximately 1772.66 hectares, comprising 10% of its territory [36].
Situated within the Atlantic Forest Biome, this urban municipality features pioneer formations of mangroves and restingas, characterized by the presence of fixed dunes, interdunal coastal zones, and fields with shrubby-arboreal vegetation, including denser island formations of vegetation.
As a result of the rural exodus affecting this region, Aracaju has undergone significant changes, including the introduction of exotic species for agricultural purposes, such as the coconut palm (Cocos nucifera) and Brachiaria grass (Urochloa brizantha). In addition to this floristic alteration, the area is experiencing progressive urbanization, the growing establishment of gated communities, and urban infrastructure, which are responsible for replacing the natural landscape and vegetation [36,37].
This municipality has been implementing green spaces within the urban context—on public roads, sidewalks, squares, urban parks, and protected areas—to promote social integration, sanitary and ecosystem benefits, the restoration of local vegetation, and wildlife preservation.
Despite being a planned city, managing Aracaju’s urban trees only began in the 1970s, establishing a specific sector within the city hall. However, it was not until 2014 that the Municipal Urban Forestry Plan was created [38]. To better target areas lacking green spaces, the Municipal Environmental Department (SEMA) conducted an inventory of urban tree planting through census sampling in 2022.
Tree planting in the municipality of Aracaju, predominantly found in squares and public parks, such as the City Park, APA Morro do Urubu, Tramandaí Ecological Park, and Poxim Ecological Park, is characterized by low diversity and a predominance of exotic species. These species contribute to a floristic homogeneity throughout the city, a reality that conflicts with the objectives of the Municipal Urban Forestry Plan developed by the Aracaju Environmental Department [38,39].
Due to the high costs associated with this activity and the need for periodic updates, this study aims to map urban tree planting areas using accessible geotechnologies to support the implementation of the Municipal Urban Forestry Plan. The methodology presented does not intend to replace a tree inventory conducted through census, but it can be implemented as an auxiliary tool for monitoring the arboreal evolution.
Ultimately, this study may guide other municipalities by introducing an additional, low-cost arboreal inventory tool that can be an auxiliary resource to the on-site inventory process.

2. Materials and Methods

Satellite images from the CBERS-4A were acquired, dated 25 June 2022, using bands 3 (red) and 4 (near-infrared) with a spatial resolution of 8 m, to calculate the Normalized Difference Vegetation Index—NDVI [20,40]. The resolution of these images was a critical factor in the development of this study, given that the target was the canopy of individual trees or clusters of trees. In the GIS phase, a comparative analysis of the results obtained through remote sensing in this study was conducted alongside a spatial correlation analysis with data generated by [41]. It is important to note that the primary datasets used in this project are all from the base year 2022: the CBERS-04A image, the Aracaju tree inventory from the Environmental Department (SEMA), and the population data of Aracaju from the Brazilian Institute of Geography and Statistics [35].
The tree inventory conducted by SEMA (2022), over approximately two years, involved a comprehensive census of trees located in public areas throughout the municipality. The data were systematically organized by administrative zones and neighborhoods. This inventory, made available online through the department’s official platform, provided a key data source for the present study. Inclusion criteria for the inventory considered only individuals with a Diameter at Breast Height (DBH) of 5 cm or greater and a minimum height of 1.5 m.
Orbital data acquisition was conducted through the download of CBERS-4A imagery, specifically bands 3 (B3) and 4 (B4), which best covered the study area (available from the Brazilian National Institute for Space Research database, INPE [42]). The selected images are georeferenced in the SIRGAS 2000 datum, using the UTM projection, Zone 24L, with a spatial resolution of 8 m for both bands. To streamline digital image processing, a raster clip was performed using a mask layer of the municipality of Aracaju, utilizing Quantum GIS (QGIS) software version 3.4.12 with Grass 7.6.1. The following text outlines the primary characteristics of the CBERS-4A satellite’s Wide Area Multispectral and Panchromatic Camera (WPM).
The CBERS-4A satellite camera comprises a panchromatic band (0.45–0.90 µm) and multispectral bands in the blue (0.45–0.52 µm), green (0.52–0.59 µm), red (0.63–0.69 µm), and near-infrared (NIR) ranges (0.77–0.89 µm). The sensor acquires imagery over a 92 km swath with spatial resolutions of 2 m (panchromatic) and 8 m (multispectral). Although lacking a side-view mirror, the system supports high data rates of 1800.8 Mbps (panchromatic) and 450.2 Mbps (multispectral). These specifications enable detailed detection and mapping of tree canopies in heterogeneous urban environments. The high spatial resolution enables individual crown delineation and cluster analysis, which are crucial for accurate assessment of vegetation cover and sustainable management of urban green spaces.
To calculate the Normalized Difference Vegetation Index (NDVI), a simple ratio was computed between the near-infrared spectral band (0.77–0.89 µm) and the red band (0.63–0.69 µm) using Equation (1).
N D V I = ( B 4 − B 3 ) / ( B 4 + B 3 ) .
As a result, NDVI enhances the contrast of the reflected radiation within the spectral ranges of the aforementioned bands, directly correlating with photosynthetic activity or impacts related to anthropogenic activity [43].
Harder et al. [44] and Nucci [45] suggest that only green areas located in urban zones and public spaces should be considered for direct quantification of the municipal urban environment’s quality. Accordingly, this study calculated the Green Area Index (GAI) for the entire territorial extent of Aracaju, as well as for areas delimited by thoroughfares (streets and avenues). For both area calculations, vegetation cover was assessed using pixels with NDVI values ranging from 0.20 to 1, processed in a GIS environment using QGIS software.
To validate the 0.2 NDVI threshold adopted for the identification of wooded areas, 1228 direct observations of NDVI values were performed across different typologies of confirmed vegetation, encompassing isolated trees and canopies in avenues, squares, parks, and coastal areas. This involved direct validation on the NDVI image, where values were extracted from known tree canopies and isolated trees.
An accuracy assessment was conducted by comparing the results obtained in this study with the official tree census inventory provided by the Municipal Environmental Department, which served as a ground-truth reference for validating the remote sensing-derived products.
Regarding the Green Area Percentage (GAP), the green space within the municipality of Aracaju, including squares, parks, and native vegetation, was calculated. Thus, for the calculation of the GAP, Equation (2) is as follows:
G A P A r a c a j u =   ( N D V I   0.2 − 1 N D V I − 1 − 0.2 )   ∗   100  
For the calculation of the Green Area Index (GAI), we use the following Equation (3):
G A I R o a d w a y   = ( N D V I   0.2 − 1 p o p u l a t i o n )
All data processing and analyses were conducted in accordance with established geospatial and remote sensing methodologies, ensuring scientific rigor and reproducibility. The integration of satellite imagery, GIS tools, and official urban forestry inventories provided a robust methodological framework to support the identification and evaluation of urban vegetation. This approach lays a solid foundation for subsequent interpretation and discussion of the results.

3. Results and Discussion

Figure 2 illustrates the spatial distribution of NDVI across the entire municipality of Aracaju, reflecting a higher tree density: (i) near-surface water resources (where water shows NDVI values between −0.14 and −0.30), marked by the presence of mangroves (0.48–0.69); (ii) in the urban expansion zone to the south of the municipality, where a dense vegetative cover by restinga species is observed (0.45–0.61).
For greater clarity and distinction between urban tree cover (streets, thoroughfares, avenues, and recreational areas) and natural vegetation (restinga, mangroves, and Atlantic Forest), an NDVI cut was made using the mask layer identified as ‘buffer arrangement’ (with an eight-meter edge from the curb, Figure 3). The shapefile extension is provided by the Brazilian Institute of Geography and Statistics (IBGE) for the year 2021 [35].
The empirical validation of the 0.2 NDVI threshold, adopted for identifying wooded areas, was conducted through 1228 direct observations of NDVI values across various confirmed vegetation typologies, including isolated trees and canopies in avenues, squares, parks, and coastal areas. These observations revealed that the highest NDVI values were consistently recorded in denser canopies, particularly in squares and parks, indicating more vigorous vegetation. Conversely, NDVI values bordering the 0.2 index were predominantly associated with isolated trees and the edges of canopies, where foliage density or the presence of non-vegetal substrate is more pronounced. Furthermore, a simple sensitivity analysis demonstrated that when transitioning from a wooded area to its immediate edge and to adjacent non-vegetated regions, NDVI values dropped abruptly, consistently reaching levels below 0.09. This observational distinction and the behavior of NDVI in transition areas reinforce the discriminatory capacity of the 0.2 threshold, validating it as an effective and robust criterion for delineating the presence of tree cover, and ensuring the reliability of the remote sensing methodology employed in characterizing urban vegetation. The model’s sensitivity to the NDVI cutoff threshold is evidenced by the percentage variation in the recorded values. Reductions in the threshold to 0.18 and 0.16 result in increases of 7.47% and 8.24%, respectively, in the measured values. Conversely, elevations in the threshold to 0.22 and 0.24 lead to decreases of 6.91% and 6.64%, respectively, in the observed values.
After overlaying the NDVI layer with the digital image from Google Satellite, available through the Quick Map Services tool in QGIS, it was possible to identify and correlate NDVI classes with values equal to or greater than 0.2 with the corresponding presence of vegetation. The NDVI threshold values identified in this study are consistent with those previously established in research focused on shrubland vegetation characterization, thereby validating this threshold range for this vegetation type [46,47]. Consequently, NDVI was reclassified into two categories: (i) equal to or greater than 0.2 and (ii) less than 0.2 µm, represented by green and white colors, respectively, in Figure 4.
With this information, efforts were made to extract elements that provide a better understanding of the distribution of green areas within the municipal system (Table 1). The initial calculation performed was the spatial occupation of the NDVI classes across the entire territory and within the public roadway system alone.
The Green Area Percentage (GAP) within the municipality of Aracaju was calculated by considering vegetated areas, such as public squares, urban parks, and remnants of native vegetation. The analysis revealed that vegetated areas constitute 12.31% of the municipality’s total land area, indicating a relatively low proportion of urban green space in relation to the built environment.
The Green Area Index (GAI) for public roadways in Aracaju is 4.27 m2 per inhabitant. When recreational areas are included in the analysis, this value increases to 6.57 m2 per inhabitant, underscoring the substantial contribution of recreational spaces to the overall availability of accessible urban vegetation.
Accordingly, following the guidelines set by the Brazilian Society of Urban Forestry [48], which stipulates a minimum value of 15 m2 per inhabitant for public green spaces designated for recreation, the municipality of Aracaju exhibits a deficit of 10.73 m2 per inhabitant considering only the green area in public roadways and 8.43 m2 per inhabitant when including the green spaces in recreational parks.
To increase the Green Area Index to the minimum required by the SBAU, Figure 5 displays the spaces occupied by urban tree cover and the areas with a scarcity of trees where planting should be focused according to the municipal Tree Planting Plan.
For a qualitative and quantitative assessment of areas with a tree deficit, the Green Area Index (GAI) was evaluated by dividing the territory into neighborhoods (Table 2). The results indicate that the most tree-covered neighborhoods are those within urban expansion areas or regions still retaining rural characteristics. Only in the Capucho neighborhood, for which Brazilian Institute of Geography and Statistics data were acquired for 2021, does the GAI exceed the standards set by the SBAU. In the other areas, the GAI falls below one-third of the established per capita standard for recreational space.
According to the neighborhood delineation (Figure 1), excluding recreational areas (Table 2), the neighborhoods of Matapoã, Areia Branca, Mosqueiro, and São José dos Náufragos encompass 47.24% of the urban tree cover in the capital, leaving a sparse distribution of the remaining trees across the other neighborhoods.
If we add to this analysis the following green spaces: City Park in the Porto Dantas neighborhood; Sementeira Park in the Jardins neighborhood; Cajueiros Park and Atalaia Shoreline in Coroa do Meio neighborhood; the 13 de Julho Promenade in the 13 de Julho neighborhood; the Industrial District Shoreline in Industrial neighborhood; and the Exhibition Park in José Conrado de Araújo neighborhood, we obtain the following green area indices: (i) Porto Dantas: 133.13; (ii) Coroa do Meio: 48.87; (iii) 13 de Julho: 0.16; Jardins: 27.32; (iv) Industrial: 3.64; and, José Conrado de Araújo: 0.44.
In comparison, Table 3 presents the green areas mapped in this study alongside census data obtained from the Municipal Environmental Department. To standardize the parameters for analysis and comparison, the average canopy diameter has been set at 8 m for roadways and 15 m for parks and recreational areas, consistent with the spatial resolution of the CBERS-04A imagery.
According to Table 3, the best data fit was observed for individuals considering all roadways, parks, and recreational areas in Aracaju, with a data confidence level of 98.9%. Furthermore, based on the same sample data, the tree-covered areas calculated through remote sensing techniques showed a confidence of 88.8% in their results. This difference may be attributed to the presence of various canopies in parks and recreational areas, compared to isolated individuals in urban streets. Another factor to consider regarding these differences is the heterogeneity between the tree canopies on streets and in recreational areas, where the latter have more developed individuals with a DBH (Diameter at Breast Height) over 60 cm, compared to a DBH of at least 5 cm on city streets. Individuals must also have a height of at least 1.5 m to be included in the sample.
This study validates the efficacy of CBERS-4A imagery for urban tree inventory, demonstrating its accessible and yet underexplored potential. However, a direct comparative analysis with established NDVI datasets from conventional sensors, such as Sentinel-2, MODIS, or Lidar, was outside the scope of the current research. Future investigations are encouraged to integrate such comparisons, as they would undoubtedly provide valuable insights into sensor performance and enhance the understanding of urban vegetation dynamics across varying urban landscapes. A comparative analysis involving NDVIb is also valid, as supported by a study carried out by Martins et al. (2022) [49].

4. Conclusions

This study highlighted the effectiveness of remote sensing combined with GIS geoprocessing techniques, offering a considerably lower-cost alternative than on-site tree inventories. Utilizing bands 3 and 4 of the Sino-Brazilian CBERS-4A satellite to compute the Normalized Difference Vegetation Index (NDVI) proved to be a simple yet powerful methodology, aligning closely with official tree census data and providing promising results for identifying areas lacking urban tree cover in Aracaju, the capital of Sergipe in Brazil. These results not only achieved the set objectives but were also validated with high confidence levels, reinforcing the applicability of this method as a valuable tool for environmental public policy planning and supplementing on-site assessments.

Author Contributions

Conceptualization, C.F.V.M., F.C.G., and R.D.G.; methodology, C.F.V.M., and R.D.G.; validation, C.F.V.M., R.D.G., and A.T.d.S.F.; formal analysis, R.D.G.; investigation, C.F.V.M., F.C.G., R.D.G., and A.T.d.S.F.; resources, C.F.V.M., F.C.G., R.D.G., and A.T.d.S.F.; data curation, R.D.G.; writing—original draft preparation, C.F.V.M., F.C.G., R.D.G., and A.T.d.S.F.; writing—review and editing, R.D.G., C.F.V.M., A.T.d.S.F., and F.C.G.; visualization, C.F.V.M., F.C.G., A.T.d.S.F., and R.D.G.; supervision, R.D.G.; project administration, C.F.V.M.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Acknowledgments

We would like to acknowledge support from Econsult—Environmental Consulting®. This study was also made possible using freely available data from the Cbers database. Municipal data observations supported by the SEMA and IBGE are available on digital websites.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic contextualization of the municipality of Aracaju, Sergipe, Brazil. This map details the location and political-administrative delimitation of the municipality of Aracaju, capital of the state of Sergipe. The study area is divided into 48 districts or neighborhoods, which are numerically identified and described in the accompanying legend. Geographically, Aracaju is characterized by its coastal position, bounded to the east by the Atlantic Ocean and surrounded by adjacent municipalities: Barra dos Coqueiros (North), Nossa Senhora do Socorro (Northwest), São Cristóvão (West), and Itaporanga d’Ajuda (South). Additional cartographic insets provide spatial hierarchy, positioning the state of Sergipe within Brazilian territory (upper left) and the municipality of Aracaju within the state context (lower left), thus establishing the area’s delimitation for remote sensing studies and urban analysis. All spatial data presented in this map are referenced to DATUM SIRGAS 2000 in the Universal Transverse Mercator (UTM) coordinate system, Zone 24S.
Figure 1. Geographic contextualization of the municipality of Aracaju, Sergipe, Brazil. This map details the location and political-administrative delimitation of the municipality of Aracaju, capital of the state of Sergipe. The study area is divided into 48 districts or neighborhoods, which are numerically identified and described in the accompanying legend. Geographically, Aracaju is characterized by its coastal position, bounded to the east by the Atlantic Ocean and surrounded by adjacent municipalities: Barra dos Coqueiros (North), Nossa Senhora do Socorro (Northwest), São Cristóvão (West), and Itaporanga d’Ajuda (South). Additional cartographic insets provide spatial hierarchy, positioning the state of Sergipe within Brazilian territory (upper left) and the municipality of Aracaju within the state context (lower left), thus establishing the area’s delimitation for remote sensing studies and urban analysis. All spatial data presented in this map are referenced to DATUM SIRGAS 2000 in the Universal Transverse Mercator (UTM) coordinate system, Zone 24S.
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Figure 2. Spatial distribution of the Normalized Difference Vegetation Index (NDVI) within the Municipality of Aracaju, Sergipe, Brazil. This map illustrates the variability of vegetation cover across Aracaju, derived from satellite imagery. The main panel presents the calculated NDVI values, ranging from −1 to +1, where higher positive values (green tones) indicate dense, healthy vegetation, and lower values (red/orange tones) represent areas with sparse vegetation, bare soil, or water bodies. The municipal boundaries and district divisions (“Aracaju districts”) are overlaid to provide geographical context. The Sergipe, Poxim, and Santa Maria Rivers, along with the Vaza Barris River, are also indicated, influencing the local vegetation patterns. The two inset maps on the left display the raw satellite imagery bands CBERS 4A-B4 (Near-Infrared) and CBERS 4A-B3 (Red), which were utilized in the computation of the NDVI, thereby demonstrating the primary data source for the index derivation. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
Figure 2. Spatial distribution of the Normalized Difference Vegetation Index (NDVI) within the Municipality of Aracaju, Sergipe, Brazil. This map illustrates the variability of vegetation cover across Aracaju, derived from satellite imagery. The main panel presents the calculated NDVI values, ranging from −1 to +1, where higher positive values (green tones) indicate dense, healthy vegetation, and lower values (red/orange tones) represent areas with sparse vegetation, bare soil, or water bodies. The municipal boundaries and district divisions (“Aracaju districts”) are overlaid to provide geographical context. The Sergipe, Poxim, and Santa Maria Rivers, along with the Vaza Barris River, are also indicated, influencing the local vegetation patterns. The two inset maps on the left display the raw satellite imagery bands CBERS 4A-B4 (Near-Infrared) and CBERS 4A-B3 (Red), which were utilized in the computation of the NDVI, thereby demonstrating the primary data source for the index derivation. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
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Figure 3. Delimitation of analysis areas for urban tree inventory in the municipality of Aracaju, Sergipe, Brazil. This map illustrates the extent of urbanized areas and areas of interest for the study, which served as the basis for clipping the NDVI imagery. The main panel (left) displays the road network and recreational areas (in black) across [35] the entire municipality of Aracaju, highlighting the urban distribution. The inset map (right) provides an enlarged detail of the City Park region, exemplifying the application of street buffers and the inclusion of urban green spaces. This consolidated layer was employed to precisely delineate the study area, ensuring that the NDVI analysis was focused exclusively on urban and recreational regions with potential for tree canopy cover. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
Figure 3. Delimitation of analysis areas for urban tree inventory in the municipality of Aracaju, Sergipe, Brazil. This map illustrates the extent of urbanized areas and areas of interest for the study, which served as the basis for clipping the NDVI imagery. The main panel (left) displays the road network and recreational areas (in black) across [35] the entire municipality of Aracaju, highlighting the urban distribution. The inset map (right) provides an enlarged detail of the City Park region, exemplifying the application of street buffers and the inclusion of urban green spaces. This consolidated layer was employed to precisely delineate the study area, ensuring that the NDVI analysis was focused exclusively on urban and recreational regions with potential for tree canopy cover. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
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Figure 4. Distribution of vegetal cover in the municipality of Aracaju, Sergipe, Brazil, obtained through NDVI and Urban Area Mask. This map illustrates the application of the “streets and recreational areas” layer as a mask to refine the Normalized Difference Vegetation Index (NDVI) analysis within the public areas of the municipality of Aracaju. The main panel (left) highlights, in green, areas classified as “wooded,” corresponding to pixels with NDVI values equal to or greater than 0.20. This filtering allows for the visualization of regions with higher arboreal vegetation density within the urban environment. The inset map (right) provides an enlarged detail of the City Park region, evidencing the concentration of tree cover within an urban recreational area. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
Figure 4. Distribution of vegetal cover in the municipality of Aracaju, Sergipe, Brazil, obtained through NDVI and Urban Area Mask. This map illustrates the application of the “streets and recreational areas” layer as a mask to refine the Normalized Difference Vegetation Index (NDVI) analysis within the public areas of the municipality of Aracaju. The main panel (left) highlights, in green, areas classified as “wooded,” corresponding to pixels with NDVI values equal to or greater than 0.20. This filtering allows for the visualization of regions with higher arboreal vegetation density within the urban environment. The inset map (right) provides an enlarged detail of the City Park region, evidencing the concentration of tree cover within an urban recreational area. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
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Figure 5. Classification of arboreal and non-arboreal cover in the study areas of Aracaju Municipality, Sergipe, Brazil. This map details the spatial distribution of vegetation cover, categorized based on Normalized Difference Vegetation Index (NDVI) values within public areas and areas of interest for the urban tree census. Areas classified as “wooded” are represented in green, indicating pixels with NDVI ≥ 0.20. In contrast, “non-wooded” areas, shown in red, correspond to pixels with NDVI < 0.20, suggesting a lack of dense vegetation or the presence of other surfaces (bare soil, buildings, etc.). The main panel presents the general distribution for the municipality, with the overlaid “Aracaju districts” boundaries. Three inset maps provide an enlarged detail of representative districts: Porto Dantas (upper right), Centro (middle right), and Matapoã (lower right), illustrating the variability of vegetation cover in different urban and peri-urban contexts and the impact of research area delimitation. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
Figure 5. Classification of arboreal and non-arboreal cover in the study areas of Aracaju Municipality, Sergipe, Brazil. This map details the spatial distribution of vegetation cover, categorized based on Normalized Difference Vegetation Index (NDVI) values within public areas and areas of interest for the urban tree census. Areas classified as “wooded” are represented in green, indicating pixels with NDVI ≥ 0.20. In contrast, “non-wooded” areas, shown in red, correspond to pixels with NDVI < 0.20, suggesting a lack of dense vegetation or the presence of other surfaces (bare soil, buildings, etc.). The main panel presents the general distribution for the municipality, with the overlaid “Aracaju districts” boundaries. Three inset maps provide an enlarged detail of representative districts: Porto Dantas (upper right), Centro (middle right), and Matapoã (lower right), illustrating the variability of vegetation cover in different urban and peri-urban contexts and the impact of research area delimitation. All spatial data presented in this map are referenced to DATUM SIRGAS 2000, in the UTM coordinate system, Zone 24S.
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Table 1. Area of arboreal and non-arboreal cover in the municipality of Aracaju, Sergipe, Brazil, classified by different geographic contexts. This table presents the quantification of surface areas based on Normalized Difference Vegetation Index (NDVI) values, categorized as “Arboreal” (NDVI ≥ 0.2 km2) and “Non-Arboreal” (NDVI < 0.2 km2). The data are subdivided into three scopes of analysis: the entire municipality of Aracaju; public roadways, excluding recreational areas; and public roadways with the inclusion of selected recreational areas (13 de Julho Promenade, Atalaia Shoreline, Industrial District Riverside, Sementeira Park, City Park, João Cleophas Exhibition Park, and Cajueiros Park). All areas are expressed in square kilometers (km2), providing a quantitative metric of vegetation cover distribution in each scenario.
Table 1. Area of arboreal and non-arboreal cover in the municipality of Aracaju, Sergipe, Brazil, classified by different geographic contexts. This table presents the quantification of surface areas based on Normalized Difference Vegetation Index (NDVI) values, categorized as “Arboreal” (NDVI ≥ 0.2 km2) and “Non-Arboreal” (NDVI < 0.2 km2). The data are subdivided into three scopes of analysis: the entire municipality of Aracaju; public roadways, excluding recreational areas; and public roadways with the inclusion of selected recreational areas (13 de Julho Promenade, Atalaia Shoreline, Industrial District Riverside, Sementeira Park, City Park, João Cleophas Exhibition Park, and Cajueiros Park). All areas are expressed in square kilometers (km2), providing a quantitative metric of vegetation cover distribution in each scenario.
For the Entire Municipality of Aracaju
NDVI < 0.2Non-vegetated137.814 km2
NDVI ≥ 0.2Vegetated44.349 km2
Roadways excluding recreational areas
NDVI < 0.2Non-vegetated29.628 km2
NDVI ≥ 0.2Vegetated2.574 km2
Roadways including recreational areas (13 de Julho Promenade, Atalaia Shoreline, Industrial District Riverside, Sementeira Park, City Park, João Cleophas Exhibition Park, and Cajueiros Park)
NDVI < 0.2Non-vegetated32.201 km2
NDVI ≥ 0.2Vegetated3.965 km2
Table 2. Distribution of urban tree cover by neighborhood in the municipality of Aracaju, Sergipe, Brazil. This table quantifies the urban tree cover in square meters (m2) for each district (neighborhood) within Aracaju, Brazil. Two primary metrics are presented: “Urban Tree Cover (m2)” represents the tree canopy area strictly within the road network and urbanized zones of each neighborhood, while “Urban Tree Cover (m2) including Recreational Areas” additionally incorporates tree cover from identified parks and recreational spaces. The “Population (Inhabitants)” column provides demographic data for each neighborhood, sourced from the Brazilian Institute of Geography and Statistics [35]. The “Green Area Index (GAI)” is presented both excluding and including recreational areas, offering a normalized metric of green space availability per inhabitant. The data are organized by neighborhood, enabling the analysis of the heterogeneity of tree cover and its relationship with population density across different territorial units of the municipality. The total sums for “Urban Tree Cover (m2)” and “Urban Tree Cover (m2) including Recreational Areas” are provided in the last row.
Table 2. Distribution of urban tree cover by neighborhood in the municipality of Aracaju, Sergipe, Brazil. This table quantifies the urban tree cover in square meters (m2) for each district (neighborhood) within Aracaju, Brazil. Two primary metrics are presented: “Urban Tree Cover (m2)” represents the tree canopy area strictly within the road network and urbanized zones of each neighborhood, while “Urban Tree Cover (m2) including Recreational Areas” additionally incorporates tree cover from identified parks and recreational spaces. The “Population (Inhabitants)” column provides demographic data for each neighborhood, sourced from the Brazilian Institute of Geography and Statistics [35]. The “Green Area Index (GAI)” is presented both excluding and including recreational areas, offering a normalized metric of green space availability per inhabitant. The data are organized by neighborhood, enabling the analysis of the heterogeneity of tree cover and its relationship with population density across different territorial units of the municipality. The total sums for “Urban Tree Cover (m2)” and “Urban Tree Cover (m2) including Recreational Areas” are provided in the last row.
Neighborhood
(Aracaju District)		Urban Tree Cover (m2)Urban Tree Cover (m2) Including Recreational AreasPopulation (Inhabitants)Green Area Index (GAI)Green Area Index (GAI) Including Recreational Areas
Matapoã281,250281,250
Areia Branca285,704285,704
Mosqueiro254,852254,852
São José dos Náufragos275,313275,313
Santa Maria425,702425,702
Robalo159,312159,312
Jabotiana163,181163,1819715 *12.4612.46
Gameleira79,92879,928
Aruana62,24166,891
Coroa do Meio50,827109,35014,9503.3248.87
Capucho40,02440,02488948.4348.43
Aeroporto41,33141,33191754.494.49
Industrial22,03733,38915,074 *2.563.64
Inácio Barbosa45,15745,15777414.954.95
Porto Dantas35,0721,295,70397433.58133.13
Soledade31,04331,04377774.294.29
Farolândia33,20933,20935,3360.940.94
Marivan34,76834,76891752.962.96
Atalaia27,52571,62410,4642.482.48
São Conrado95,46495,46423,622 *1.071.07
Jardins25,281256,28251754.7127.32
17 de Março30,06430,064
Luzia17,55617,55621,9240.710.71
Cidade Nova15,22815,22818,5380.830.83
Santos Dumont12,72612,72625,0610.490.49
Lamarão10,68710,68766551.591.59
18 do Forte11,16011,16021,0250.450.45
Ponto Novo11,73111,73122,0440.420.42
Santo Antônio22,90322,90311,9500.720.72
Grageru8766876616,2270.530.53
Japãozinho11,60511,60574411.131.13
América7869786915,9620.490.49
Dom Luciano5759575918,5380.280.28
Siqueira Campos7321732115,7050.290.29
Getúlio Vargas5148514871880.610.61
São José3603360359400.610.61
Suíssa2988298811,7800.250.25
José Conrado de Araújo3101572413,8810.210.44
Jardim Centenário2147214713,9190.150.15
Centro1787178781170.220.22
Novo Paraíso1618161811,6270.140.14
Bugio1560156015,5580.10.1
13 de Julho137221,39983840.160.16
Salgado Filho94194142980.220.22
Pereira Lobo76576554430.140.14
Cirurgia49549557670.090.09
Palestina74974942170.10.1
Olaria36843684
TOTAL2,672,5544,305,460
* Census carried out by the city of Aracaju in 1996. Available online: https://www.aracaju.se.gov.br/aracaju/bairros_populacao/ (accessed on 11 February 2024).
Table 3. Comparison of urban tree cover between the current study and census data in the municipality of Aracaju, Sergipe, Brazil. This table presents a comparative analysis of the area covered by arboreal vegetation and the estimated number of individual trees, contrasting the results obtained by the present study (utilizing remote sensing and NDVI) with census data acquired by the Municipal Secretariat of Environment (SEMA) [38]. Metrics are provided for three location categories: Roadways, Parks and Recreational Areas, and a combined total for all Roadways, Parks, and Recreational Areas. For the estimation of individual trees, the average area occupied by the canopy of an individual tree in roadways (50.24 m2) and in parks/recreational areas (177 m2) was considered, as detailed in the footnotes. The “Confidence (Current Study vs. Census)” column expresses the percentage of agreement between the two datasets, providing an evaluation of the accuracy of the applied remote sensing method. Areas are expressed in square kilometers (km2) and square meters (m2), and the number of individuals in discrete units.
Table 3. Comparison of urban tree cover between the current study and census data in the municipality of Aracaju, Sergipe, Brazil. This table presents a comparative analysis of the area covered by arboreal vegetation and the estimated number of individual trees, contrasting the results obtained by the present study (utilizing remote sensing and NDVI) with census data acquired by the Municipal Secretariat of Environment (SEMA) [38]. Metrics are provided for three location categories: Roadways, Parks and Recreational Areas, and a combined total for all Roadways, Parks, and Recreational Areas. For the estimation of individual trees, the average area occupied by the canopy of an individual tree in roadways (50.24 m2) and in parks/recreational areas (177 m2) was considered, as detailed in the footnotes. The “Confidence (Current Study vs. Census)” column expresses the percentage of agreement between the two datasets, providing an evaluation of the accuracy of the applied remote sensing method. Areas are expressed in square kilometers (km2) and square meters (m2), and the number of individuals in discrete units.
LocationTree-Covered Area (Current Study)Tree-Covered Area (Census)Confidence (Current Study vs. Census)
Roadways2.672 km2
53,185
individuals **		2,813,289.28 m2
55,997 individuals		94.9%
Parks and Recreational Areas *1.632 km2
9220
individuals ***		1,013,679 m2
5727 individuals		62.1%
Roadways, Parks, and Recreational Areas4,305,460 m2
62,405
individuals		3,826,968.28 m2, 61,724 individualsTree-Covered Area: 88.8%
Individuals: 98.9%
* Locations include 13 de Julho Promenade, Atalaia Shoreline, Industrial District Riverside, Sementeira Park, City Park, João Cleophas Exhibition Park, and Cajueiros Park. ** Individuals: considering an average area occupied by the canopy of an individual in roadways as 50.24 m2. *** Individuals: considering an average area occupied by the canopy of an individual in parks and recreational areas as 177 m2.
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Martins, C.F.V.; Guerra, F.C.; Ferreira, A.T.d.S.; Gonçalves, R.D. Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census. Earth 2025, 6, 87. https://doi.org/10.3390/earth6030087

AMA Style

Martins CFV, Guerra FC, Ferreira ATdS, Gonçalves RD. Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census. Earth. 2025; 6(3):87. https://doi.org/10.3390/earth6030087

Chicago/Turabian Style

Martins, Cássio Filipe Vieira, Franciele Caroline Guerra, Anderson Targino da Silva Ferreira, and Roger Dias Gonçalves. 2025. "Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census" Earth 6, no. 3: 87. https://doi.org/10.3390/earth6030087

APA Style

Martins, C. F. V., Guerra, F. C., Ferreira, A. T. d. S., & Gonçalves, R. D. (2025). Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census. Earth, 6(3), 87. https://doi.org/10.3390/earth6030087

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