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Land Surface Temperature in Response to Land Use/Cover Change Based on Remote Sensing Data and GIS Techniques: Application to Saïss Plain, Morocco

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In order to analyze the impact of land use and land cover change on land surface temperature (LST), remote sensing is the most appropriate tool. Land use/cover change has been confirmed to have a significant impact on climate through various aspects that modulate LST and precipitation. However, there are no studies which illustrate this link in the Fez-Meknes region using satellite observations. Thus, the aim of this study was to monitor LST as a function of the land use change in the Saïss plain. In the study, 12 Landsat images of the year 2019 (one image per month) were used to represent the variation of LST during the year, and 2 images per year in 1988, 1999 and 2009 to study the interannual variation in LST. The mapping results showed that the land use/cover in the region has undergone a significant evolution; an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. On the basis of statistical analyses, LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of land use type. The relationship between LST and NDVI shows a negative correlation (LST decreases when NDVI increases). This explains the increase in LST in rangelands and arable land, while it decreases in irrigated crops and arboriculture.
Rocznik
Strony
100--112
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Sidi Mohamed Ben Abdallah University, FST-Fez, Route d´Imouzzer, Fez, B.P. 2202, Morocco
autor
  • Sidi Mohamed Ben Abdallah University, FLSHS, Fez, Morocco
  • Sidi Mohamed Ben Abdallah University, ENSA, Fez, Morocco
  • Hassania School for Public Works Engineering, Casablanca, Morocco
Bibliografia
  • 1. Artis, D.A., Carnahan, W.H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sens. Environ. 12, 313–329.
  • 2. Bhaga, T.D., Dube, T., Shekede, M.D., Shoko, C., (2020). Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review. Remote Sens. 12, no. 24: 4184. https://doi.org/10.3390/rs12244184.
  • 3. Bonn, F. (1996). Précis de télédétection, Volume 2 – Application thématiques. Presses de l’Université du Québec/ AUPELF, 633.
  • 4. Bontemps S. (2004). Cartographie et interprétation de l’évolution du développement territorial par télédétection spatiale au Cambodge. Mémoire de fi n d’études de la faculté des Sciences Agronomiques, UCL, Louvain-La-Neuve, 111.
  • 5. Courault, B., Seguin, & Olioso, A., (2005). Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. Irrig Drainage Syst 19, 223–249. https://doi.org/10.1007/s10795–005–5186–0.
  • 6. El Hadraoui Y. (2013). Étude diachronique de l’occupation du sol et de modélisation des processus érosifs du bassin versant du Bouregreg (Maroc) à partir des données de l’Observation de la Terre. Mémoire d’Ingénieur CNAM, École Supérieure des Géomètres et Topographes, 89.
  • 7. Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja, L.E., Meehl, G.A. et Washington, W.M. (2005). The importance of land-cover change in simulating future climates. Science, 1674–1678.
  • 8. Joshi, R.C., Ryu, D., Sheridan, G.J., and Lane, P.N. J., A new Remote Sensing-based vegetation water stress index: Temperature Vegetation Water Stress Index (TVWSI), EGU General Assembly, (2020), Online, 4–8 May 2020, EGU2020–12308, https://doi.org/10.5194/egusphere-egu2020–12308.
  • 9. Li, Z.L., Tang, B., Wu, H., (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment 131, pp 14–37.
  • 10. Nivedha D.S., Jasmineniketha M., Geetha P. and Soman K.P. (2017). Agricultural drought analysis for Thuraiyur taluk of Tiruchirappali District using NDVI and land surface temperature data. 11th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, 155–159.
  • 11. Qin, Z., and Karnieli, A., (1999). Progress in remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. Inter. J. of Rem. Sens 20: 2367–2393.
  • 12. Sajjad, K.Ajay, H. Stephen, (2009). Estimating soil moisture using remote sensing data: A machine learning approach, Advances in Water Resources,Volume 33, Issue 1, pp.69–80,( 2010), https://doi.org/10.1016/j.advwatres10.008.
  • 13. Schmugge, T.J., Kustas, W.P., Ritchie J.C., Jackson T. J. and Al Rango. (2002). Remote sensing in hydrology. Advances in Water Resources, 25 (12), 1367–1385.
  • 14. Sobrino, J.A., Jiménez-Munoz, J.C. et Paolini, L., (2004). Land surface temperature retrieval from Landsat TM5. Remote Sensing of Environment, no. 4, vol 90, 434–440.
  • 15. USGS, (2019a). Landsat collection 1 level 1 product definition. EROS Sioux Falls, South Dakota, USA, 32.
  • 16. USGS. (2019b). Landsat 8 (L8) Data Users Handbook Version 5.0. EROS Sioux Falls, South
  • 17. USGS. (2019c). Landsat 7 (L7) Data Users Handbook Version 2.0. EROS Sioux Falls, South Dakota, USA, 114.
  • 18. Yagoub. H., (2015). Cartographie et suivi du couvert végétal des zones semi-arides par l’imagerie satellitaire. Doctorat Es-Science, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, Faculté de Physique, 150.
  • 19. Yu M. and M. Cheng., (2010). Ts/NDVI space based drought monitoring study from satellite remote sensing data in Heilongjiang. 2010 World Automation Congress, Kobe, 23–28.
  • 20. Zhou, J., Li, J., Zhang, Li., Hu, D. and Zhan, W., (2012). Intercomparaison of methods for estimating land surface temperature from Landsat-5 TM image in an arid region with low water vapor in the atmosphere. International Journal of Remote Sensing 33(8), 2582–2602.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f93fe6ed-40ef-4195-8a50-5d7bcbe666b5
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