Remote sensing image processing application to identify spatial units of human occupation along Trans-Amazonian Highway (BR-230) in Pará state

Transcription

1 Remote sensing image processing application to identify spatial units of human occupation along Trans-Amazonian Highway (BR-230) in Pará state Bruna Virginia Neves¹ João Arthur Pompeu Pavanelli¹ Vanessa Priscila Camphora¹ ¹ Instituto Nacional de Pesquisas Espaciais - INPE Caixa Postal São José dos Campos - SP, Brasil brunavneves@dpi.inpe.br, {jpompeu, vpcamphora}@dsr.inpe.br Abstract. Observe cities and communities is important to investigate the urban phenomena in the Amazon because they configure socio-spatial forms linked to the urbanization process. Identifying these population nuclei can provide information about where the population is concentred and offer clues as how it relates to the space and environment and, therefore, how urban is constructed. The aim of this work is to apply remote sensing image processing techniques to identify spatial units of human occupation along Trans-Amazonian Highway (BR-230) in Pará state, in the municipalities of Altamira, Brasil Novo, Medicilândia and Uruará, inside a 15 km buffer from the Highway. Four Landsat-5 Thematic Mapper orthorrectfied scenes from 2011 were processed using SPRING. The processing steps consisted in mosaic the scenes, the application of dilation filter, segmentation and maximum likelihood classification. The validation was performed with Kappa coefficient based on manual classification of middle resolution RapidEye images (5 metres) and ancillary IBGE data. A total 23 spatial units of human occupation were mapped with TM process and the validation showed a Kappa coefficient of , which is considered a good agreement between the classification and the reference. The application of dilation filter during the processing was efficient to identify spatial units of human occupation in the study site, although some misclassified pixels occurred mainly in small patches of human occupation. Future sensors will provide freely available high resolution images that can contribute to the extensive urban studies in Amazon region. Keywords: Dilation filter, Segmentation, Maximum Likelihood classifier, filtro de dilatação, segmentação, MaxVer. 1. Introduction Investigations concerning Amazonian urban phenomena have pointed questions about how urban is expressed and understood in the region. Thus it is necessary to consider ways to study that better fit with the presented phenomena, assumed in this work as the extensive urbanization concept, proposed by Monte-Mór (2006). According to the author, distinction of Amazonian urban and rural is increasingly blurred because Amazonian urbanization extends along the territory beyond structured and formal cities and villages, including other sociospatial units of human occupation. (Dal'Asta apud Cardoso & Lima, 2006) Amaral et al. (2013) claim that to observe cities and communities are important because they configure socio-spatial forms linked to the urbanization process. Identifying these population nuclei can provide information about where the population is concentred and offer clues as how it relates to the space and environment and, therefore, how urban is constructed. Thus identifying spatial units of human occupation is an important step to understand extensive urban in Amazon region, once these population nuclei expresses the Amazonian urban phenomena. Orbital remote sensing image process techniques have been widely used to identify areas of human occupation, e.g. Dal Asta et al. (2012) successfully used Landsat-5 Thematic Mapper (TM), CBERS High Resolution CCD Camera (CCD) and CBERS High Resolution Camera (HRC) images to identify and characterize spatial units of human occupation in the Sustainable Forest District of the BR-163 Highway in Pará state. Thereby the aim of this work is to apply remote sensing image processing techniques to identify spatial units of human occupation along Trans-Amazonian Highway (BR-230) in Pará state.

2 2. Methods 2.1. Study site The study site comprehends the municipalities of Altamira, Brasil Novo, Medicilândia and Uruará, along the Trans-Amazonian Highway in Pará state. A 15 km buffer was created for both sides along BR-230 in these municipalities to delimit the study site, where spatial units of human occupation were mapped (Figure 1). Figure 1. (A) Pará state in Brazil; (B) Municipalities of Altamira, Brasil Novo, Medicilândia and Uruará; (C) Study site along Trans-Amazonian Highway (BR-230) in the indicate municipalities. 2.2 Image Processing According to Menezes and Almeida (2012) digital image processing consists in performing mathematical operations in the data aiming their transformation to obtain better spectral and spatial images which is most appropriated for particular applications. It means that each remote sensing images processing technique is specific for each application, that best suits the user s needs. To identify the spatial units of human occupation in the study site, a total of four scenes from Thematic Mapper (TM) sensor, onboard Landsat-5 satellite, were used as show on Table 1. These images have spatial resolution of 30 metres and TM sensor collects radiometric information in six multispectral bands, ranging from 0.45 µm to 2.35 µm, and one thermal band, which cover wavelengths from µm to µm. Only the spectral bands 3 ( µm), 4 ( µm) and 5 ( µm) were used in this processing.

3 Orthorectified Thematic Mapper images from 2011 were obtained from United States Geological Survey (USGS) catalogue, freely available in the website: Cloud cover reduced the amount of available scenes, restricting the use of most recent images, such as those obtained from Landsat-8 s Operational Land Imager (OLI) sensor. Table 1. TM Landsat 5 scenes for the study area along Trans-Amazon Highway. WRS Date WRS Date 226/ / / / The images were processed in Georeferenced Information Processing System SPRING version (Câmara et al., 1996). SPRING is a project designed by the National Institute for Space Research (INPE) in partnership with other public and private companies, and it is freely available for download in the website: A database was created for the project, where TM-3, TM-4 and TM-5 bands of the four scenes were imported and a mosaic of the scenes was created. There are many image processing tools in SPRING, such as contrast, filtering, segmentation and classification, among other functions. Intending to detach the spatial units of human occupation from vegetation and other land use classes, the non-linear morphologic dilation filter was applied. According to Dal Asta et al. (2012) human occupations in the Amazon region leads bare soil areas, which contrasts with vegetation, mainly in infrared spectral bands. Filtering is one of the various digital images processing technique that is based on mathematical and statistical parameters and it is useful when the user need to emphasize or to reduce information in the image. The filters use operations that involve pixels in the context of the neighbourhood, considering some geometrical spaces in the image, instead of transformations in isolated pixels. Therefore, filters are techniques that operate in the spatial domain of the image (Menezes & Rosa, 2012). As targets in remote sensing images shows tendency to have homogeneity inside some spaces, there is always strong interdependence of the values of pixels in a neighbourhood. The interdependence is useful to selectively highlight geometric details in the images, such as abrupt changes or edges between apparently homogenous areas. Morphologic filters are non-linear filters that act precisely in the geometric properties of the digital images; meanwhile the non-linear morphological dilation filter causes the expansions of lighter areas over darker areas in the image, and is used for noise reduction and segmentation for image classification (Menezes & Rosa, 2012). The dilation filter was applied to TM-5 band, in which the visual contrast between human occupation areas and vegetation and land uses are lower than in TM-3. After this step, the segmentation based on region growing algorithm was applied in TM-3, TM-4 and in the dilated TM-5, with a similarity threshold of 25 and an area threshold of 30. The region growing is a technique where regions are joined together starting from individual pixels and growing interactively until all the pixels of the image are processed and merged in similar regions. The similarity threshold sets the minimum value to which two regions are considered spectrally similar based on statistical tests to average digital values, and the threshold area is the minimum area in pixels needed to individualize a particular region (Bins et al., 1996). The segmented image was classified with the Maximum Likelihood algorithm, using three classes, adapted from Leitold et al. (2013): forest, non-forest and urban, that comprehends the spatial units of human occupation. Maximum Likelihood classification

4 assumes that the statistics for each class in each band are normally distributed and calculates the probability that one specific pixel belongs to a specific class (Richards & Xia, 2006). The output of the classification in SPRING is a raster containing the given classes. To extract the areas classified as urban, the classified raster was converted to vector and whenever necessary, visual interpretation and editing were made in order to remove misclassified areas, represented by isolated pixels, clouds and cloud shadows. The other classes were not used in this work but they are stored in the database for future projects. Thus, it was assumed that this mask represented the spatial units of human occupation in the specific study site along Trans-Amazonian Highway. 2.3 Evaluation of the classification The identification of the spatial units of human occupation obtained from Maximum Likelihood classification was evaluated with Kappa index, which is based on comparing the results of the classifier with information assumed as truth (Congalton & Green, 1999), such as ground points collected in field work or regions of interest collected from the image itself but with secondary information from better spatial resolution images. The information used as truth to compare with TM classification was derived from RapidEye images and IBGE ancillary data. A total of ten RapidEye images (Table 2) were used to cover all of the spatial units of human occupation identified with TM classification. These images have 5 m of spatial resolution and were obtained from the Ministry of the Environment of Brazil (MMA) and National Institute for Space Research (INPE) catalogue, which is not freely available. Table 2. RapidEye images used to evaluate TM classification. WRS Date WRS Date 1/ / / / / / / /376 Not available 6/ / Based on these RapidEye middle resolution images the urban patches were manually classified. To distinguish every possible spatial unit of human occupation from other land use and land cover typologies, all the polygons representing the urban patches in TM images were checked in the RapidEye images. IBGE Locality Register, that contains information about the municipality, name and coordinates of Brazilian localities, was consulted to improve the manual identification and map of the urban patches. Because of cloud cover and a lack of images in MMA/INPE catalogue, probable spatial units of human occupation mapped with TM images and registered at IBGE localities couldn t be manually classified in RapidEye images. These areas were removed of the rasters that were used to evaluate the overall accuracy. 3. Results and discussion The application of dilation filter in TM3 bands of the four Landsat-5 scenes, followed by segmentation and Maximum Likelihood classification, enabled to identify 23 spatial units of human occupation along Trans-Amazonian Highway in the municipalities of Altamira, Brasil Novo, Medicilândia and Uruará, which is consistent with IBGE Locality Register and in general the classification fitted well with human occupation areas visually and manually classified with RapidEye images. The Figure 2 shows all the spatial units of human

5 occupation identified with Landsat-5 TM images processing steps that are represented in the map as the urban class. Figure 2. Spatial units of human occupation identified with TM images represented by the urban class. As the dilation filter expands lighter areas over darker areas in the image, this process applied to TM-5 showed to be efficient in identifying patches of human occupation in this Amazonian region because in this spectral band the urban areas shows some texture different from deforested areas, meanwhile forest appears darker because of energy absorption, as illustrated in Figure 3, using the city of Uruará as example. Figure 3. (A) TM band 5 with no process evidencing the urban patch texture in the centre of the image, surrounded by deforested areas in gray colour with smooth texture and forest patches in darker colours; (B) Dilation filter applied to TM-5 highlighting the urban texture contrasting to other land uses in the landscape; (C) RapidEye image colour composition R(3)G(2)B(1) with urban polygon in red colour showing the result of the classified dilationfiltered TM5 image.

6 In the figure it is possible to realize that when the filter is applied the urban patches are highlighted from the other surrounding land use classes (Figure 2B). These surrounding land use classes could cause spectral confusion on the classification algorithm if no filter was applied, depending on the similarity and area thresholds used for image segmentation and on the obtained training areas for the classifier. The overlaying of the urban polygon and 5 m spatial resolution RapidEye image shows that this image processing technique well delimited the boundary of Uruará city, as well as other larger cities, such as Altamira (Figure 4) but it also misclassified some areas, mainly along the highway. Figure 4. Boundaries of Altamira city. Even though larger urban patches boundaries were nicely delimited, smaller ones could not be well discriminated because of TM 30 metres spatial resolution. This is evidenced in the Figure 5 using as example the boundary of Vila Planalto locality, in the municipality of Uruará, obtained from Landsat-5 TM classification when compared with RapidEye images colour composition R(3)G(2)B(1). Figure 5. Misclassified boundary of Vila Planalto, Uruará municipality.

7 The major limitation of this image processing technique for mapping spatial units of human occupation along Trans-Amazonian Highway is contradictory related to the dilation characteristic of the filter itself, which evidenced some areas of bare soil and also included some stretches of the highway in the urban class (Figure 6). This misclassification led to more intense manual image interpretation and vector editing, indicating that this limitation has to be overcome if an automatic process is needed for operational monitoring of the extensive urban network in Amazonian regions. As urban areas in Amazon are frequently related to bare soils that can indicate street layouts, vacant lots and other land uses (Dal Asta et al., 2012), this problem could be solved with other processing techniques, such as band subtraction or band ratios. Figure 6. Trans-Amazonian Highway bare soil area misclassified as urban patch. Thus the accuracy assessment of the urban patches classification resulted in a Kappa Index = , which is considered a good agreement between the classification and the reference. 4. Conclusion The application of dilation filter was efficient to identify spatial units of human occupation in the study site, although some misclassified pixels occurred mainly in small patches of human occupation. According to IBGE, there are 23 spatial units of human occupation in the study site and all of them could be identified with Landsat-5 TM image processing. Even though larger urban patches boundaries were nicely delimited, smaller ones could not be well discriminated because of TM 30 metres spatial resolution. However for the purpose of a preliminary identification of the spatial units of human occupation in this Amazonian region this result is considered satisfactory, as a future step could be an intraurban analysis with high resolution images. CBERS-4 foreseen to be launched next December will provide freely available high resolution images that may contribute to the extensive urban studies in Amazon region. Finally, field work is required to enhance the mapping accuracy and to include in the validation the areas with cloud cover and with no RapidEye images available.

8 5. References Amaral, S.; Dal'Asta, A.P.; Brigatti, N. Comunidades ribeirinhas como forma socioespacial de expressão urbana na Amazônia : uma tipologia para a região do Baixo Tapajós ( Pará-Brasil ). REBEP, p , Bins, L.S.; Fonseca, L.M.G.; Erthal, G.J.; Mitsuo, F., II. Satellite imagery segmentation: A region growing approach. In Proceedings of VII Simpósio Brasileiro de Sensoriamento Remote (SBSR), Salvador, Brazil, 1996; pp Câmara, G.; Souza, R. C. M.; Freitas, U. M.; Garrido, J. SPRING: Integrating remote sensing and GIS by objectoriented data modelling. Computers & Graphics, v. 20, n. 3, p , Dal Asta apud Cardoso, A. C. D.; Lima, J. J. F. Tipologias e padrões de ocupação urbana na Amazônia Oriental: para que e para quem? In: CARDOSO, A. C. D. (Ed.). O rural e o urbano na Amazônia. Diferentes olhares e perspectivas. Belém-PA: EDUFPA, 2006, p Congalton, R.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL: CRC/Lewis Press, p. Dal Asta, A. P.; Amaral, S.; Monteiro, A. M. V. O Rio e as cidades : uma análise exploratória de dependências e alcances das comunidades do Arapiuns ( Pará-Brasil ) e a formação do urbano na Amazônia. p. 1 20, Dal Asta, A. P.; Brigatti, N.; Amaral, S.; Escada, M. I. S.; Monteio, A. M. V. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery. Remote Sens.,v. 4, p , 2012 Leitold, V.; Polizel, S. P.; Moreira, M. A. Mapeamento do uso e cobertura da terra a partir da segmentação e classificação orientada a objetos em imagens do TM/Landsat-5: caso de estudo de parte do município de Brasil Novo PA. In: Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 16, Anais... São José dos Campos: INPE, 2013 Artigos, p CD-ROM, On-line. ISBN: Disponível em: < Acesso em 10/09/2014. Menezes, P. R.; Almeida, T. Introdução ao processamento de imagens de sensoriamento remoto. Brasília: UNB, p. Menezes, P. R.; Rosa, A. N. C. S. Filtragem. In: Menezes, P. R.; Almeida, T. (Org.). Introdução ao processamento de imagens de sensoriamento remoto. Brasília: UNB, p. Monte-mór, R. L. M. O que é o urbano, no mundo contemporâneo. Belo Horizonte, UFMG/Cedeplar, 14p Richards, J.A.; Jia, X. 2006, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, 4th ed., pp.453.