Monitoring Land Use and Land Cover Changes Using Remote Sensing Techniques and the Precipitation-Vegetation Indexes in Morocco

Ait El Haj, Fatiha; Ouadif, Latifa; Akhssas, Ahmed

doi:10.12912/27197050/154937

Artykuł - szczegóły

Tytuł artykułu

Monitoring Land Use and Land Cover Changes Using Remote Sensing Techniques and the Precipitation-Vegetation Indexes in Morocco

Autorzy

Ait El Haj Fatiha , Ouadif Latifa , Akhssas Ahmed

Treść / Zawartość

Pełne teksty:

27_Ait_El_Haj_Monitoring_EEET_2023_1.pdf

Pobierz

Identyfikatory

DOI

10.12912/27197050/154937

Warianty tytułu

Języki publikacji

Abstrakty

The study of land use and land cover change (LULC) is essential for the development of strategies, monitoring and control of the ecosystem. The present study aims to describe the dynamics of land cover and land use, and specially the impact of certain climatic parameters on the distribution of vegetation and land cover. For this study, multi-temporal remote sensing data are used to monitor land cover changes in Morocco, using a set of Landsat images, including Landsat 7 (ETM+), Landsat 5 (TM), and Landsat 8 (OLI), captured during the period 2000–2020, those changes were determined by adopting the maximum likelihood (ML) classification method. The classification results show good accuracy values in the range of 90% (2000), 80% (2007), 82% (2010), 93% (2020). The LU/LC change detection showed a decrease of agricultural and forest areas in the order of 5% between the year 2000 and 2020, and an increase of bare soil of 5% to 6%, and a notable change in urban area from 97.31 ha (0.03%) in 2000 to 2988.2637 ha (0.82%) in 2020. The overall results obtained from LULC show that the vegetation cover of the study area has undergone major changes during the study period. In order to monitor the vegetation status, an analysis of the precipitation-vegetation interaction is essential. The normalized difference vegetation index (NDVI) was determined from 2000 to 2020, to identify vegetation categories and quantify the vegetation density in the Lakhdar sub-basin. The obtained NDVI was analyzed using climatic index SPI (Normalized Precipitation Index) based on rainfall data from five stations. The correlation study between NDVI and SPI indices shows a strong linear relation between these two indicators especially while using an annual index SPI12 however, the use of NDVI index based on remote sensing provides a significant result while assessing vegetation. The results of our study can be used for vegetation monitoring and sustainable management of the area, since it is one of the largest basins in the country.

Słowa kluczowe

LULC land use change land cover change remote sensing GIS kappa landsat NDVI normalized difference vegetation index SPI Standardized Precipitation Index

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2023

Tom

Vol. 24, iss. 1

Strony

272--286

Opis fizyczny

Bibliogr. 42 poz., rys., tab.

Twórcy

autor

Ait El Haj Fatiha

aitelhaj@emi.ac.ma

L3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco

https://orcid.org/0000-0002-7515-1415

autor

Ouadif Latifa

L3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco

https://orcid.org/0000-0002-4613-1124

autor

Akhssas Ahmed

L3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco

https://orcid.org/0000-0001-9275-558X

Bibliografia

1. Abd El-Kawy, O.R., Rød, J.K., Ismail, H.A., Suliman, A.S. 2011. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), 483–494. https://doi.org/10.1016/j.apgeog.2010.10.012
2. Abebe, G., Getachew, D., Ewunetu, A. 2022. Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences, 4(1). https://doi.org/10.1007/s42452-021-04915-8
3. Acharki, S., El Qorchi, F., Arjdal, Y., Amharref, M., Bernoussi, A.S., Ben Aissa, H. 2022. Soil erosion assessment in Northwestern Morocco. Remote Sensing Applications: Society and Environment, 25, 100663.
4. Anand, A. 2012. Unit 14 Accuracy Assessment. Processing and Classification of Remotely Sensed Images, January 2017, 59–77. https://www.researchgate.net/publication/319963167_processing_and_classification_of_remotely_sensed_images
5. AHT Group, F., Ag, A.H.T.G., Avril, R. 2016. Diagnostic du sous-bassin de Lakhdar.
6. Lounis, B., Aïssa, A.B. 2005. Processus de Correction Radiométrique Relative «PCRR» Appliqué Aux Images Landsat TM Multi- Dates. 3rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, 1–7.
7. Barakat, A., Khellouk, R., El Jazouli, A., Touhami, F., Nadem, S. 2018. Monitoring of forest cover dynamics in eastern area of Béni-Mellal Province using ASTER and Sentinel-2A multispectral data. Geology, Ecology, and Landscapes, 2(3), 203–215. https://doi.org/10.1080/24749508.2018.1452478
8. Baral, G.P., Mcdougall, K., Chong, A. 2011. Review of Australian Land Use Mapping and Land Management Practice. 21, 199–210.
9. Bahri, E.M. 2007. Essai de cartographie des espèces forestières dominantes dans le Moyen Atlas ( Maroc ) à partir des données ASTER To cite this version: HAL Id: hal-01946833. January.
10. Bijaber, N., Hadani, D., El, Saidi, M., Svoboda, M.D., Wardlow, B.D., Hain, C.R., Poulsen, C.C., Yessef, M., Rochdi, A. 2018. Developing a Remotely Sensed Drought Monitoring Indicator for Morocco. https://doi.org/10.3390/geosciences8020055
11. Bordi, I., Fraedrich, K., Sutera, A. 2009. Observed drought and wetness trends in Europe: An update. Hydrology and Earth System Sciences, 13(8), 1519–1530. https://doi.org/10.5194/hess-13-1519-2009
12. Chowdhury, M., Hasan, M.E., Abdullah-Al-Mamun, M.M. 2020. Land use/land cover change assessment of Halda watershed using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Science, 23(1), 63–75. https://doi.org/10.1016/j.ejrs.2018.11.003
13. Congedo, L. 2020. Semi-Automatic Classification Plugin Documentation Release 7.9.5.1. User Manual, August, 1–225.
14. Chavez, P.S., Jr. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ., 24, 459–479.
15. Dodamani, B.M., Anoop, R., Mahajan, D.R. 2015. Agricultural Drought Modeling Using Remote Sensing. International Journal of Environmental Science and Development, 6(4), 326–331. https://doi.org/10.7763/ijesd.2015.v6.612
16. Faruque, M.J., Hasan, M.Y., Islam, K.Z., Young, B., Ahmed, M.T., Monir, M.U., Shovon, S. M., Kakon, J.F., Kundu, P. 2022. Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment, 25(July 2021), 100699. https://doi.org/10.1016/j.rsase.2022.100699
17. Haboudane, D. 2007. Deforestation detection and monitoring in cedar forests of the Moroccan Middle-Atlas Mountains. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain 2007, 4327–4330
18. Hasan, S., Shi, W., Zhu, X., Abbas, S., Khan, H.U.A. 2020. Future simulation of land use changes in rapidly urbanizing South China based on land change modeler and remote sensing data. Sustainability (Switzerland), 12(11), 4–6. https://doi.org/10.3390/su12114350
19. Gurgel, R.S., Farias, P.R.S., De Oliveira, S.N. 2017. Land use and land cover mapping and identification of misuse in the permanent preservation areas in the Tailândia Municipality - PA. Semina: Ciencias Agrarias, 38(3), 1145–1160. https://doi.org/10.5433/1679-0359.2017v38n3p1145
20. Hasan, S., Shi, W., Zhu, X., Abbas, S., Khan, H.U.A. 2020. Future simulation of land use changes in rapidly urbanizing South China based on land change modeler and remote sensing data. Sustainability (Switzerland), 12(11), 4–6. https://doi.org/10.3390/su12114350
21. Maina J., Wandiga S., Gyampoh B. Charles K.K.G. 2020. Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing: A Case Study of Kieni, Central Kenya. Journal of Remote Sensing & GIS, 9(1), 1–5. https://doi.org/10.35248/2469-4134.20.9.270
22. Jaouda, I., Akhssas, A., Ouadif, L., Bahi, L., Lahmili, A. 2018. Stabilite des talus et impact sur le reseau routier: cas du bassin versant d’Ouergha (Maroc). MATEC Web of Conferences, 149. https://doi.org/10.1051/matecconf/201714902052
23. Jazouli, A., El Barakat, A., Khellouk, R., Rais, J., Baghdadi, M.E. 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 (Dec. 2018), 361–374. https://doi.org/10.1016/j.rsase.2018.12.004
24. Jiang, J., Tian, G. 2010. Analysis of the Impact of Land use/Land cover change on Land Surface Temperature with Remote Sensing. Procedia Environmental Sciences, 2(5), 571–575. https://doi.org/10.1016/j.proenv.2010.10.062
25. Ji, L., Peters, A.J. 2004. Forecasting Vegetation Greenness With Satellite and Climate Data. IEEE Geoscience and Remote Sensing Letters, 1(1), 3–6. https://doi.org/10.1109/LGRS.2003.821264
26. Kumar, S., Shwetank, Jain, K. 2020. A multi-temporal landsat data analysis for land-use/land-cover change in haridwar region using remote sensing techniques. Procedia Computer Science, 171, 1184–1193.
27. Kafy, A.A, Faisal, A.A., Shuvo, R.M., Naim, M.N.H., Sikdar, M.S., Chowdhury, R.R., Islam, M.A., Sarker, M.H.S., Khan, M.H.H., Kona, M.A. 2021. Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21 (Dec. 2020), 100463. https://doi.org/10.1016/j.rsase.2020.100463
28. Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M., El, Hmaidi, A., El, Ouali, A., El, Randazzo, G., Teodoro, A.C. 2018. Land use/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in azrou forest, in the central middle atlas of Morocco. Environments - MDPI, 5(12), 1–16. https://doi.org/10.3390/environments5120131
29. Mohd Hasmadi, I., Pakhriazad, H.Z., Shahrin, M.F. 2009. Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Malaysiann Journal of Society and Space, 5(1), 1–10.
30. Onuche, N.C. (n.d.). Remote sensing and gis based application for land use mapping and urban renewal in Hayin Dogo, Samaru By.
31. Organisation Météorologique Mondiale. 2012. Guide d’utilisation de l’indice de précipitations normalisé. Temps. Climat. Eau, 1090, 17. http://www.droughtmanagement.info/literature/WMO_standardized_precipitation_index_user_guide_fr_2012.pdf
32. Ozesmi, S.L., Bauer, M.E. 2002. Satellite Remote Sensing of Wetlands. Wetland Ecology and Management, 10, 381-402. http://dx.doi.org/10.1023/A:1020908432489
33. Pang, C., Yu, H., He, J., Xu, J. 2013. Deforestation and changes in landscape patterns from 1979 to 2006 in Suan County, DPR Korea. Forests, 4, 968–983.
34. Purwanto, Utomo, D.H., Kurniawan, B.R. 2016. Spatio Temporal Analysis Trend of Land Use and Land Cover Change Against Temperature Based on Remote Sensing Data in Malang City. Procedia – Social and Behavioral Sciences, 227, 232–238. https://doi.org/10.1016/j.sbspro.2016.06.066
35. Rasool, R., Fayaz, A., Shafiq, M.U, 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. https://doi.org/10.1016/j.ecolind.2021.107447
36. Rokhmatullah, Hernina, R., Yandi, S. 2019. Drounght Analysis by Using Standarized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) at Bekasi Regency in 2018. IOP Conference Series: Earth and Environmental Science, 280(1). https://doi.org/10.1088/1755-1315/280/1/012002
37. Shalaby, A., Tateishi, R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28–41. https://doi.org/10.1016/j.apgeog.2006.09.004
38. Souidi, H., Ouadif, L., Bahi, L., Elhachmi, D., Edderkaoui, R. 2020. Application of stochastic process & cellular automata integrated to GIS for land monitoring: Coastal Chaouia, Morocco. International Journal of Advanced Research in Engineering and Technology, 11(5), 245–252. https://doi.org/10.34218/IJARET.11.5.2020.026
39. Tadese, M., Kumar, L., Koech, R., Kogo, B.K. 2020. Mapping of land-use/land-cover changes and its dynamics in Awash River Basin using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 19, 100352. https://doi.org/10.1016/j.rsase.2020.100352
40. Tan, K.C., Lim, H.S., MatJafri, M.Z., Abdullah, K. 2010. Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509–1521. https://doi.org/10.1007/s12665-009-0286
41. Vanjare, A., Omkar, S.N., Senthilnath, J. 2014. Satellite Image Processing for Land Use and Land Cover Mapping. International Journal of Image, Graphics and Signal Processing, 6(10), 18–28. https://doi.org/10.5815/ijigsp.2014.10.03
42. Xie, F., Lin, Y., Ren, W. 2011. Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets. Ecological Modelling, 222(2), 232–240. https://doi.org/10.1016/j.ecolmodel.2010.08.011

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-f9978362-7c6a-45b5-99eb-dc70a9be5395