Dynamics of land-use Change using Geospatial Techniques From 1986 to 2019: A Case Study of High Oum Er-Rbia Watershed (Middle Atlas Region)
DOI:
https://doi.org/10.18006/2022.10(2).369.378Keywords:
Google Earth Engine (GEE), Random Forest (RF), Environmental changes, Spatio-temporal changes, Remote sensing, Landsat, Land use mapAbstract
This work aims to expose the contribution of the use of the cloud google earth Engine (GEE) platform, in particular the capacity of optical monitoring by remote sensing to assess the impact of environmental changes on the evolution of natural resources in the Middle Atlas region. To achieve this goal, the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform was used. The spatial resolution of the images used is 30 meters for the TM 5 sensor (Thematic Mapper) and the OLI 8 sensor (Operational Land Imager). Further, the google earth engine platform is used primarily to download and prepare the images for the dates 1986, 2000, and 2019, then a supervised classification with the Random Forest (RF) algorithm to produce land use maps of selected dates with an overall accuracy exceeding 80%. This was followed by the production of maps and change matrices for the periods 1986-2000 and 2000-2019. The results obtained have shown a decline in grassland, forest land, and water body in parallel with an increase in the following classes: buildings, farmland, and arboriculture during the last 30 years. In addition, elevation was the most important characteristic variable for land-use classification in the study area. Obtained results provide theoretical support for adjusting and optimizing land use in the High Oum Er-Rbia watershed.
References
Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office. DOI: https://doi.org/10.3133/pp964
Belgiu, M., & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. DOI: https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bootsma, E. J. (2021). Mapping time-series evapotranspiration for agricultural applications (Unpublished Dissertation).
Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5–32. DOI: https://doi.org/10.1023/A:1010933404324
De Waele, J., & Melis, M. T. (2009). Geomorphology and geomorphological heritage of the Ifrane–Azrou region (Middle Atlas, Morocco). Environmentalgeology, 58(3), 587-599. DOI: https://doi.org/10.1007/s00254-008-1533-4
El-Bouqdaoui, K. (2007). Approche méthodologique de l’évaluation du risque potentiel d’érosion des sols du bassin versant du Srou (Moyen Atlas, Maroc) à l’aide de la télédétection et du SIG.
El Jazouli, A., Barakat, A., Khellouk, R., Rais, J., & El Baghdadi, M. (2019). Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications: Society and Environment, 13, 361-374. DOI: https://doi.org/10.1016/j.rsase.2018.12.004
Gashaw, T., Tulu, T., Argaw, M., Worqlul, A.W., Tolessa, T., & Kindu, M. (2018). Estimating the impacts of land use/land cover changes on ecosystem service values: The case of the Andassa watershed in the Upper Blue Nile basin of Ethiopia. Ecosystem Services, 31, 219–228. DOI: https://doi.org/10.1016/j.ecoser.2018.05.001
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing Environment, 202(1), 18–27. DOI: https://doi.org/10.1016/j.rse.2017.06.031
Hoshikawa, K., & Umezaki, M. (2014). Effects of terrain-induced shade removal using global DEM data sets on land-cover classification. International Journal of Remote Sensing, 35(4), 1331-1355. DOI: https://doi.org/10.1080/01431161.2013.876122
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.
Millennium Ecosystem Assessment. (2005). Ecosystems and Human Well-being. Opportunities and challenges for Business and Industry. . Retrieved from https://www.millenniumassessment.org/ documents/document.353.aspx.pdf.
Na, X.D., Zhang, S.Q., Li, X.F., Yu, H.A., & Liu, C.Y. (2010).Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. Photogrammetric Engineering & Remote Sensing, 76, 833–840. DOI: https://doi.org/10.14358/PERS.76.7.833
Ran, Q., Hao, Y., Xia, A., Liu, W., et al. (2019). Quantitative assessment of the impact of physical and anthropogenic factors on vegetation spatial-temporal variation in Northern Tibet. Remote Sensing, 11(10), 1183. DOI: https://doi.org/10.3390/rs11101183
Rasool, R., Fayaz, A., ul Shafiq, M., Singh, H., & Ahmed, P. (2021). Land use land cover change in Kashmir Himalaya: Linking remote sensing with an indicator based DPSIR approach. Ecological Indicators, 125, 107447. DOI: https://doi.org/10.1016/j.ecolind.2021.107447
Ren, M., Zeng, W., Yang, B., & Urtasun, R. (2018). Learning to reweight examples for robust deep learning. In International conference on machine learning (pp. 4334-4343). PMLR. Retrieved from http://proceedings.mlr.press/v80/ren18a/ren18a.pdf
Ren, Y., Lü, Y., Fu, B., & Zhang, K. (2017). Biodiversity and ecosystem functional enhancement by forest restoration: A meta‐analysis in China. Land Degradation & Development, 28(7), 2062-2073. DOI: https://doi.org/10.1002/ldr.2728
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the
effectiveness of a random forest classifier for land-cover classification. ISPRS journal of photogrammetry and remote sensing, 67, 93-104. DOI: https://doi.org/10.1016/j.isprsjprs.2011.11.002
Rogan, J., & Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in planning, 61(4), 301-325. DOI: https://doi.org/10.1016/S0305-9006(03)00066-7
Pu, D. C., Sun, J. Y., Ding, Q., Zheng, Q., Li, T. T., & Niu, X. F. (2020). Mapping urban areas using dense time series of landsat images and google earth engine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 403-409. DOI: https://doi.org/10.5194/isprs-archives-XLII-3-W10-403-2020
Schürmann, A., Kleemann, J., Fürst, C., & Teucher, M. (2020). Assessing the relationship between land tenure issues and land cover changes around the Arabuko Sokoke Forest in Kenya. Land Use Policy, 95, 104625. DOI: https://doi.org/10.1016/j.landusepol.2020.104625
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152-170. DOI: https://doi.org/10.1016/j.isprsjprs.2020.04.001
Tsai, Y. H., Stow, D., An, L., Chen, H. L., Lewison, R., & Shi, L. (2019). Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve. Applied Geography, 111, 102077. DOI: https://doi.org/10.1016/j.apgeog.2019.102077
Waske, B., & Braun, M. (2009). Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS journal of photogrammetry and remote sensing, 64(5),450-457. DOI: https://doi.org/10.1016/j.isprsjprs.2009.01.003
Downloads
Published
How to Cite
License
Copyright (c) 2022 Journal of Experimental Biology and Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
