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Urban sprawl characterization and its impact on peri-urban agriculture in Sidi Bel Abbes, Algeria, using multi-date landsat imagery

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Abstract

Identifying and characterizing the spatiotemporal dynamics of urban areas is mandatory in many disciplines, e.g., in environmental studies, infrastructure, and agriculture. The current research work investigates the overall trends of urban development and agricultural land changes of Sidi Bel Abbes city, for the years 1987, 1999, 2009 and 2019. To this end, Landsat archives have been considered due to its excellent temporal coverage, appropriate spatial dimension for urban characterization, and free data access. Multiple classifiers decision fusion technique was performed to characterize the spatial distribution of the built-up areas followed by agricultural land changes estimation through the Normalized Difference Vegetation Index. The results reveal that over the last three decades, a significant increase of built-up lands has been settled at the expense of peri-urban agriculture where the net-growth rate (%) of built-up areas and agricultural land per year between 1987 to 1999, 1999 to 2009 and 2009 to 2019 were + 4.2, + 2.83, + 2.11 and -1.66, -0.94, -0.69 respectively. The multi-temporal assessment of urban change and orientation permitted the identification of slow (southeast) and fast (north-east) sprawling areas. In addition, to understand the built-up expansion effects on the agricultural land-cover, urban sprawl was analyzed and the results show that the settlement areas generally increased in the whole study period, which was around 39.01% of the total area (7077 ha) and similarly, peri-urban agriculture areas decreased by approximately 32% from the arable lands (4648 ha).

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References

  • Abdaoui, G. R., Tabet, A. A., Bouaicha, F., Bousmaha, A., & Bouchemal, S. (2020). Sprawl, Specificity and Dynamics of Inter-Municipal Urban Agglomerations of the Souf Valley (South East Algeria): Using GIS Techniques. International Journal of Innovation and Applied Studies, 29(4), 991–1014.

    Google Scholar 

  • Akbar, T. A., Hassan, Q. K., Ishaq, S., Batool, M., Butt, H. J., & Jabbar, H. (2019). Investigative spatial distribution and modelling of existing and future urban land changes and its impact on urbanization and economy. Remote Sensing, 11(2), 105.

    Article  Google Scholar 

  • Akram, S. M., Ahmed, S. S., Nabil, M., Zohra, B. F., & Halim, G. A. (2019). Réflexion sur la conception d’un système d’informations foncières à partir des données Multi-sources. Bulletin des Sciences Géographiques, (32).

  • Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (vol. 964). US Government Printing Office.

  • Ardjoum, S. (2015). Usage et apport des sig dans l’identification, la cartographie, la dynamique et les politiques de gestion, de protection et de mise en valeur des espaces periurbains de la commune de sidi bel abbés (Algérie nord-ouest) [Ph.D. Thesis].

  • As-Syakur, A. R., Adnyana, I. W. S., Arthana, I. W., & Nuarsa, I. W. (2012). Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing, 4(10), 2957–2970.

    Article  Google Scholar 

  • Ayambire, R. A., Amponsah, O., Peprah, C., & Takyi, S. A. (2019). A review of practices for sustaining urban and peri-urban agriculture: Implications for land use planning in rapidly urbanising Ghanaian cities. Land Use Policy, 84, 260–277.

    Article  Google Scholar 

  • Bannari, A., Morin, D., Bonn, F., & Huete, A. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120.

    Article  Google Scholar 

  • Baouche, F. (2014). L’évolution du foncier agricole en Algérie à travers les réformes [Ph.D. Thesis]. Poitiers.

  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.

    Article  Google Scholar 

  • Benabdelkader, M., Saifi, R., & Saifi, H. (2021). Sustainable Agriculture in Some Arab Maghreb Countries (Morocco, Algeria, Tunisia). In Agro-Environmental Sustainability in MENA Regions (pp. 233–261). Springer.

  • Bendjelid, A. (1997). Les stratégies familiales face à la crise de l’emploi à Sidi Bel Abbès: Perception de l’espace urbain et diversité de l’informel dans une ville moyenne algérienne. Insaniyat/إنسانيات. Revue Algérienne D’anthropologie Et De Sciences Sociales, 1, 43–62.

    Google Scholar 

  • Bentekhici, N., & Yousfi, D. (2013). Analyse de l’étalement urbain de la ville de Mostaganem à l’aide des données satellitales multi-dates. Bulletin des sciences géographiques de l’INCT, 17(1), 51–62.

  • Bouhata, R., Kalla, M., Bensekhria, A., & Habibi, Y. (2016). The spatio-temporal analysis of urban expansion of Biskra city (South Eastern Algeria) by the use of landsat satellite images. Annals of the University of Oradea, Geography Series/Analele Universitatii din Oradea, Seria Geografie, 26(2), 159–166.

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 1.

  • Butt, A., Shabbir, R., Ahmad, S. S., Aziz, N., Nawaz, M., & Shah, M. T. A. (2015). Land cover classification and change detection analysis of Rawal watershed using remote sensing data. Journal of Biodiversity and Environmental Sciences, 6(1), 236–248.

    Google Scholar 

  • Chen, F., Yang, S., Yin, K., & Chan, P. (2017). Challenges to quantitative applications of Landsat observations for the urban thermal environment. Journal of Environmental Sciences, 59, 80–88.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 1.

  • Das, S., & Angadi, D. P. (2022). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, 87(3), 2101–2123.

    Article  Google Scholar 

  • Debru, J., & Brand, C. (2019). Theoretical approaches for effective sustainable urban food policymaking. In Designing Urban Food Policies (pp. 75–105). Springer.

  • Demaze, M. T. (2010). Un panorama de la télédétection de l’étalement urbain. ESO Travaux et Documents, 29, 29.

  • Dhanaraj, K., & Angadi, D. P. (2020). Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India. GeoJournal, 87, 1133–1159. https://doi.org/10.1007/s10708-020-10302-4

  • Diaf, A., Ben Yelles, H., Benhanifia, K., & Yousfi, D. (2007). Le Mitage des Terrains Agricoles par !’Urbanisation Application à la ville de Sidi Bel Abbès. Bulletin Des Sciences Géographiques De l’INCT, 11(2), 53–60.

    Google Scholar 

  • Dridi, H., Bendib, A., & Kalla, M. (2015). Analysis of urban sprawl phenomenon in Batna city (Algeria) by remote sensing technique. Analele Universităţii Din Oradea, Seria Geografie, 2, 211–220.

    Google Scholar 

  • Dube, T., Rampheri, B. M., & Shoko, C. (2022). GIS and remote sensing analytics: Assessment and monitoring. In Fundamentals of Tropical Freshwater Wetlands (pp. 661–678). Elsevier.

  • Estoque, R. C., & Murayama, Y. (2017). Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS Journal of Photogrammetry and Remote Sensing, 133, 18–29.

    Article  Google Scholar 

  • Florczyk, A., Corban, C., Ehrlich, D., Carneiro, F. S. M., Kemper, T., Maffenini, L., Melchiorri, M., Pesaresi, M., Politis, P., Schiavina, M., Sabo, F., & Zanchetta, L. (2019). GHSL Data Package 2019. JRC Publications Repository. https://doi.org/10.2760/290498.

  • Franz, M. J., VanWormer, J. J., Crain, A. L., Boucher, J. L., Histon, T., Caplan, W., Bowman, J. D., & Pronk, N. P. (2007). Weight-loss outcomes: A systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. Journal of the American Dietetic Association, 107(10), 10.

  • Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008). Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors, 8(4), 2136–2160.

    Article  Google Scholar 

  • Hamza, M. B., Abbassia, A., & Mohammed, B. (2022). Urban sprawl and expansion of road networks and its impacts on the environment using sensor and socio-economic data: Macta watershed, western Algeria. Journal of Geology, Geography and Geoecology, 31(1), 31–44.

    Article  Google Scholar 

  • Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment, 86(3), 3.

  • Hostert, P., Griffiths, P., Linden, S. van der, & Pflugmacher, D. (2015). Time series analyses in a new era of optical satellite data. In Remote Sensing Time Series (pp. 25‑41). Springer.

  • Howarth, P. J., & Boasson, E. (1983). Landsat digital enhancements for change detection in urban environments. Remote Sensing of Environment, 13(2), 149–160.

    Article  Google Scholar 

  • Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., & Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sensing, 9(1), 95.

    Article  Google Scholar 

  • Ismael, H. M. (2021). Urban form study: The sprawling city—review of methods of studying urban sprawl. GeoJournal, 86(4), 1785–1796.

    Article  Google Scholar 

  • Kadri, D., & Khalfallah, B. (2022). Impact of urban expansion on urban transport in the city, study case: City of Bordj Bou Arreridj, Algeria. Analele Universităţii Din Oradea, Seria Geografie, 32(1), 63–73.

    Article  Google Scholar 

  • Khallef, B., & Brahamia, K. (2019). Application des indices de télédétection à la cartographie des zones urbaines et des sols nus: Cas de la ville de Guelma (Algérie) (vol. 22). Comité de lecture du BSG.

  • Kokhan, S., & Vostokov, A. (2020). Using vegetative indices to quantify agricultural crop characteristics. Journal of Ecological Engineering, 21(4), 120–127. https://doi.org/10.12911/22998993/119808

  • Kou, W., Liang, C., Wei, L., Hernandez, A. J., & Yang, X. (2017). Phenology-based method for mapping tropical evergreen forests by integrating of MODIS and landsat imagery. Forests, 8(2), 34.

    Article  Google Scholar 

  • Leeson, G. W. (2018). The growth, ageing and urbanisation of our world. Journal of Population Ageing, 11(2), 107–115.

    Article  Google Scholar 

  • Lefebvre, H., Levert, M.-J., & Imen, K. (2011). Un accompagnement personnalisé d’intégration communautaire en soutien au développement de la résilience: Vers un modèle. Développement Humain, Handicap et Changement Social/Human Development, Disability, and Social Change, 19(1), 1.

  • Li, Z., Li, X., Wei, D., Xu, X., & Wang, H. (2010). An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences, 2, 964–969.

    Article  Google Scholar 

  • Lin, L., Hao, Z., Post, C. J., & Mikhailova, E. A. (2022). Monitoring ecological changes on a rapidly urbanizing island using a remote sensing-based ecological index produced time series. Remote Sensing, 14(22), 5773.

    Article  Google Scholar 

  • Maity, B., Mallick, S. K., & Rudra, S. (2020). Spatiotemporal dynamics of urban landscape in Asansol municipal corporation, West Bengal, India: A geospatial analysis. GeoJournal, 87, 1619–1637. https://doi.org/10.1007/s10708-020-10315-z

  • Mendas, A., Mebrek, A., & Mekranfar, Z. (2021). Comparison between two multicriteria methods for assessing land suitability for agriculture: Application in the area of Mleta in western part of Algeria. Environment, Development and Sustainability, 23(6), 9076–9089.

    Article  Google Scholar 

  • Missoumi, M. A., Desponds, D., & Hadeid, M. (2018). Suivi de l’évolution de la tache urbaine à travers la classification multidates de l’indice de cuirasse. Le cas d’Oran (Algérie) de 1987 à 2016. Photo-Interprétation. European Journal of Applied Remote Sensing, 54(3–4), 3–4.

  • Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.

    Article  Google Scholar 

  • Müller, H., Rufin, P., Griffiths, P., Siqueira, A. J. B., & Hostert, P. (2015). Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sensing of Environment, 156, 490–499.

    Article  Google Scholar 

  • Nemouchi, H., & Zeghiche, A. (2021). Oran: Des terres agricoles sacrifiées pour un urbanisme sauvage. Belgeo. Revue belge de géographie, 1. https://doi.org/10.4000/belgeo.46093

  • Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A. M., Newell, J. D., Friedl, M. A., & Herold, M. (2012). A global land-cover validation data set, part I: Fundamental design principles. International Journal of Remote Sensing, 33(18), 5768–5788.

    Article  Google Scholar 

  • ONS. (2011). Armature urbaine—Collections Statistiques No. 163. Série S: Statistiques Sociales (Office National des Statistiques). https://www.ons.dz/spip.php?rubrique356

  • ONS. (2013). Démographie algérienne, no. 658 (Office National des Statistiques).

  • Qian, Y., Zhou, W., Nytch, C. J., Han, L., & Li, Z. (2020). A new index to differentiate tree and grass based on high resolution image and object-based methods. Urban Forestry & Urban Greening, 53, 126661.

    Article  Google Scholar 

  • Rabehi, W. (2018). Détermination spatio-temporelle de l’expansion urbaine sur la baie d’Alger et impact environnemental sur la bande côtière. [Ph.D. Thesis]. Ecole nationale supérieure des sciences de la mer et de l’aménagement du ….

  • Rizvi, R. H., Yadav, R. S., Singh, R., Datt, K., Khan, I. A., & Dhyani, S. K. (2009). Spectral analysis of remote sensing image for assessment of agroforestry areas in Yamunanagar district of Haryana. In National Symposium on “Advances in Geo-spatial Technologies with Special Emphasis on Sustainable Rainfed Agriculture”, RRSSC, Nagpur (vol. 7).

  • Safia, A., Mohamed, B., Lotfi, B. T., Farouk, M., & Samir, M. M. (2015). Contribution to the Characterization of Peri-urban Natural Areas of Sidi Bel Abbes Town (Northwestern Algeria).

  • Saouli, R. A., Benhassine, N., & Oularbi, A. (2020). A spatio-temporal retrospective of the urban sprawl of Annaba (Algeria). Journal of Fundamental and Applied Sciences, 12(2), 825–844.

    Article  Google Scholar 

  • Schmitt, M., Hughes, L., Qiu, C., & Zhu, X. X. (2019). Aggregating cloud-free Sentinel-2 images with Google earth engine. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2-W7, 145–152. https://doi.org/10.5194/isprs-annals-IV-2-W7-145-2019

  • Semmoud, B., & Ladhem, A. (2015). L’agriculture périurbaine face aux vulnérabilités foncières en Algérie. Territoire en mouvement Revue de géographie et aménagement. Territory in Movement Journal of Geography and Planning, 25–26. https://doi.org/10.4000/tem.2845

  • Serra, P., Saurí, D., & Salvati, L. (2018). Peri-urban agriculture in Barcelona: Outlining landscape dynamics vis à vis socio-environmental functions. Landscape Research, 43(5), 613–631.

    Article  Google Scholar 

  • Shah, A., Ali, K., & Nizami, S. M. (2021). Spatio-temporal analysis of urban sprawl in Islamabad, Pakistan during 1979–2019, using remote sensing. GeoJournal, 87, 2935–2948. https://doi.org/10.1007/s10708-021-10413-6

  • Shah, A., Ali, K., & Nizami, S. M. (2022). Spatio-temporal analysis of urban sprawl in Islamabad, Pakistan during 1979–2019, using remote sensing. GeoJournal, 87(4), 2935–2948.

    Article  Google Scholar 

  • Simon, D. (2008). Urban environments: Issues on the peri-urban fringe. Annual Review of Environment and Resources, 33(1), 167–185.

    Article  Google Scholar 

  • Thanh Noi, P., & Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18(1), 18.

    Article  Google Scholar 

  • Turker, M., & Ozdarici, A. (2011). Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: A comparison study. International Journal of Remote Sensing, 32(24), 9735–9768.

    Article  Google Scholar 

  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 1353691. https://doi.org/10.1155/2017/1353691

  • Yamani, L., & Trache, S. M. (2020). Contournement des instruments d’urbanisme dans l’urbanisation de l’agglomération mostaganémoise (Algérie). Cybergeo: European Journal of Geography. https://doi.org/10.4000/cybergeo.34731

  • Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.

    Article  Google Scholar 

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Acknowledgements

I would like to thank my supervisor, Souiah Sid Ahmed for supporting this research. I would like to thank my co-authors and my colleagues at the Centre des Techniques Spatiales for their assistance and guidance during the preparation of this paper.

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  1. Université Oran2 Mohamed Ben Ahmed, Laboratoire Espace Géographique et Aménagement du Territoire (EGEAT), EL M’naouer, BP 1015, 31000, Oran, Algeria

    Djamel Mansour & Sid Ahmed Souiah

  2. Agence Spatiale Algérienne (ASAL), Centre des Techniques Spatiales (CTS), 14 Rue Omar Aissaoui El Hamadia, 16000, Algiers, Algeria

    Djamel Mansour, Mohammed El Amin Larabi & Khadidja Bakhti

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I would like to inform you, that this work is part of my doctoral thesis. Doctoral training (LMD) is currently governed by the texts following regulations: (1) Executive Decree No. 08-265 of August 19 2008 scheme carrying studies with a view to obtaining a bachelor's degree, master's degree and the diploma of doctorate. (2) Executive Decree No. 10-231 of October 2 2010 on the status of the doctoral student. (3) Order No. 191 of July 16, 2012 setting the organization of third-grade training course for obtaining the diploma of doctorate, amended and supplemented by the order No. 345 of October 17, 2012.

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Appendix

Appendix

Classification Algorithm

Random forest

RF algorithm defined as the most well-known ensembles learning methodology that can be used in different tasks such as prediction out-of-sample rapidly and ranking capably of the importance of features (Belgiu & Drăguţ, 2016; Breiman, 2001). Generally, RF is created by randomly selected samples (bootstrap sampling). As output classification, the results are obtained by selecting the MV technique from all the used Decision Trees (DT).

Support vector machine

SVM is one of the most ML techniques used in RS data classification tasks that can generalize with limited training samples (Mountrakis et al., 2011). The benefits of using SVM come from two aspects such as the discovery of a large margin linear boundary in the new space and converting the original training set’ space into a new very high-dimensional new space. In addition to this, SVM can optimize the non-separable using ‘kernel stick’.

k-nearest neighbor

The k-NN is defined as a Machine Learning (ML) classifier used for memory-based and nonparametric learning, as well as instance-based learning for regression and classification tasks (Thanh Noi & Kappas, 2017). In the classification process, k-NN classifies a given pixel employing the MV of its neighbors in the feature space.

Maximum likelihood classifier

MLC is a well-known algorithm for the RS community and is commonly used for classification tasks. Individual pixels are assigned to the class, which can maximize the likelihoods of the data set. As a pixel-based technique, this approach does not take contextual information about the classes of neighboring classes into account in labeling a pixel (Turker & Ozdarici, 2011).

Neural networks

NN is one of the best performing ML techniques. Since NN is non-parametric (meaning they do not assume a Gaussian distribution), it has been successfully applied to RS data sets. For this research, a Multi-Layer Perceptron (MLP) with one hidden layer was applied (Qian et al., 2020).

Decision fusion

All these rules can be used to combine classifiers on measurement level:

  • Minimum: Select the minimum score of each class provided by each classifier and allocates the input pattern to that class with the maximum score.

  • Maximum: Select the maximum score of each class provided by each classifier and allocates the input pattern to the class with the maximum score.

  • Product: Multiplies the score from each classifier and allocates the class label with the maximum score to the specified input pattern.

  • Sum: Adds the score provided by each base classifier and assigns the class label with the maximum score to the specified input pattern.

  • Average: Select the mean of the scores of each class between the classifiers and allocates the input pattern to the class with the maximum score.

  • Median: Calculate the scores’ median of each class from the classifiers and assigns the input pattern to the class with the maximum score.

  • Majority voting: It is an elementary combiner, together with the average and the product rules, majority vote is the most used one. As mentioned beforehand, for several ML methods, the majority voting is selected as the optimal combiner.

  • Elimination: To combine classifiers, all of them must produce the same result, else rejected. This mechanism can be reformulated as voting where all of them vote for the same class.

  • Otsu: An automatic thresholding method.

  • K-means: Automatic clustering method. There are different ways of classifiers combination.

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Mansour, D., Souiah, S.A., Larabi, M.E.A. et al. Urban sprawl characterization and its impact on peri-urban agriculture in Sidi Bel Abbes, Algeria, using multi-date landsat imagery. GeoJournal 88, 4671–4695 (2023). https://doi.org/10.1007/s10708-023-10875-w

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