Links between Land Use Change, Land Surface Temperature and Partridge Distribution – An Analysis of Environmental Factors

• Autorzy: El Fallah Kamal , Ouhakki Hicham , Adiba Atman , El Kharrim Khadija , Belghyti Driss

Identyfikatory

DOI

10.12912/27197050/175269

• Języki publikacji: EN

Abstrakty

EN

The purpose of this research was to investigate the intricate connections among land use change, land surface temperature, and the distribution of partridges (Alectoris barbara), employing a comprehensive analysis of various environmental factors. Indeed, a variety of geospatial techniques have been used to analyze the spatio-temporal trends in temperature as a function of different classes of vegetation cover, and the geographic distribution of ecological niches for this species in Meknes province was modeled using Maxent 3.2 (Maximum Entropy) software. The study spanned a 22-year timeframe, from 2000 to 2021, during which alterations in each land use category were identified through the utilization of various sensors, incorporating Landsat 7 ETM+ and Landsat 8 OLI/TIRS in the analysis. The results induced a significant change in the land surface temperature (LST) with a range of 15.85–36.20°C, 12.76–38.24°C and 25.73–47.79°C for the years 2000, 2010 and 2020, respectively. However, this change was negatively correlated with the normalized difference vegetation index (NDVI). This decline in vegetation, in turn, manifests as a significant factor contributing to the diminution of partridge distribution. By empirically establishing these connections, the research not only underscores the impact of temperature-induced vegetation changes on partridge habitat but also enhances comprehension of the intricate ecological dynamics governing species distribution in the context of evolving land use patterns.

Słowa kluczowe

EN

ecological niche; Alectoris barbara; temporal analysis; land surface temperature; maximum entropy

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 1
• Strony: 260--275
• Opis fizyczny: Bibliogr. 56 poz., rys., tab.

Twórcy

autor

El Fallah Kamal

• Laboratory of Natural Resources and Sustainable Development, Department of Biology, Faculty of Science, University Ibn Toufail, BP 133-14000, Kenitra, Morocco

• https://orcid.org/0009-0003-3510-2402

autor

Ouhakki Hicham

• Laboratory of Organic Chemistry, Catalysis and Environment, Department of Chemistry, Faculty of Science, University Ibn Toufail, BP 133-14000, Kenitra, Morocco

autor

Adiba Atman

• Regional Agricultural Research Center of Meknes, National Institute of Agricultural Research, Avenue Ennasr, P.O. Box 415, Rabat 10090, Morocco

• https://orcid.org/0000-0002-4525-0682

autor

El Kharrim Khadija

• Laboratory of Natural Resources and Sustainable Development, Department of Biology, Faculty of Science, University Ibn Toufail, BP 133-14000, Kenitra, Morocco

• https://orcid.org/0000-0002-0962-8764

autor

Belghyti Driss

• Laboratory of Natural Resources and Sustainable Development, Department of Biology, Faculty of Science, University Ibn Toufail, BP 133-14000, Kenitra, Morocco

• https://orcid.org/0000-0002-2465-8742

Bibliografia

• 1. Alademomi A.S., Okolie C.J., Daramola O.E., Agboola R.O., Salami T.J. 2020. Assessing the relationship of LST, NDVI and EVI with land cover changes in the Lagos Lagoon environment. Quaestiones Geographicae, 39(3), 87–109. https://doi.org/10.2478/quageo-2020-0025
• 2. Alademomi A.S., Okolie C.J., Daramola O.E., Akinnusi S.A., Adediran E., Olanrewaju H.O., Alabi A.O., Salami T.J., Odumosu J. 2022. Correction to: The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria. Applied Geomatics, 14(3), 571–572. https://doi.org/10.1007/s12518-022-00446-y
• 3. Al-Taisan W.A. 2022. A remote sensing approach for displaying the changes in the vegetation cover at Az Zakhnuniyah Island at Arabian Gulf, Saudi Arabia. Scientifica, 1–14. https://doi.org/10.1155/2022/2907921
• 4. Ayanlade A., Aigbiremolen M.I., Oladosu O.R. 2021. Variations in urban land surface temperature intensity over four cities in different ecological zones. Scientific Reports, 11(1), 1–17. https://doi.org/10.1038/s41598-021-99693-z
• 5. Bai L., Long D., Yan L. 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research. https://doi.org/10.1029/2018WR024162
• 6. Barbieri T. 2018. A multi-temporal analyses of land surface temperature using Landsat-8 Data and open-source software: the case study of Modena, Italy. https://doi.org/10.3390/su10051678
• 7. Bodart C., Eva H., Beuchle R., Raši R., Simonetti D., Stibig H.J., Brink A., Lindquist E., Achard F. 2011. Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 555–563. https://doi.org/10.1016/j.isprsjprs.2011.03.003
• 8. Brier J., lia-dwi J. 2020. Modélisation de répartition d’espèces aviaires et de feux en forêt boréale du Québec dans un contexte de changement climatique. 21(1), 1–9. http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203
• 9. Chattopadhyay B., Forcina G., Garg K.M., Irestedt M., Guerrini M. Barbanera F., & Rheindt F.E. 2021. Novel genome reveals susceptibility of popular gamebird, the red-legged partridge (Alectorisrufa, Phasianidae), to climate change. Genomics, 113(5), 3430–3438. https://doi.org/10.1016/j.ygeno.2021.08.010
• 10. Chen L., Li M., Huang F., Xu S. 2013. Relationships of LST to NDBI and NDVI in Changsha-Zhuzhou-Xiangtan area based on MODIS data. 6th International Congress on Image and Signal Processing (CISP 2013), Cisp, 840–845. http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLKX200902018.htm
• 11. Chikerema S.M., Murwira A., Matope G., Pfukenyi D.M. 2013. Spatial modelling of Bacillus anthracis ecological niche in Zimbabwe. Preventive Veterinary Medicine, 111(1–2), 25–30. https://doi.org/10.1016/j.prevetmed.2013.04.006
• 12. Deng Y., Wang S., Bai X., Tian Y., Wu L., Xiao J., Chen F., Qian Q. 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports, 8(1), 1–12. https://doi.org/10.1038/s41598-017-19088-x
• 13. DGLA 2015. General monograph of the Fez - Meknes region. https://collectivites-territoriales.gov.ma/fr/node/738
• 14. Emmerson M., Morales M.B., Oñate J.J., Batáry P., Berendse F., Liira J., Aavik T., Guerrero I., Bommarco R., Eggers S., Pärt T., Tscharntke T., Weisser W., Clement L., Bengtsson J. 2016. How agricultural intensification affects biodiversity and ecosystem services. Advances in Ecological Research, 55, 43–97. https://doi.org/10.1016/bs.aecr.2016.08.005
• 15. Fabri-ruiz S. 2019. Modèles de distribution et changements environnementaux : Application aux faunes d ’ échinides de l ’ océan Austral et écorégionalisation To cite this version : HAL Id : tel-02063427 Modèles de distribution et changements environnementaux : Application au.
• 16. Farah A., Algouti A., Algouti A., Ifkirne M., Rafik A. 2021. Remote sensing for spatio-temporal mapping of land surface temperature and surface energy fluxes in the Bouregreg-Chaouia Region of Morocco. Journal of Environmental and Agricultural Studies, 2(1), 23–35. https://doi.org/10.32996/jeas.2021.2.1.4
• 17. Fontanelli K., Carla R., Fiorucci F., Santurri L. 2012. Surface soil moisture evaluation by a multitemporal satellite approach. International Geoscience and Remote Sensing Symposium (IGARSS), July, 686–689. https://doi.org/10.1109/IGARSS.2012.6351486
• 18. Gavrilović M., Pjević M., Borisov M., Marinković G., Petrović V.M. 2019. Analysis of Climate Change in the Area of Vojvodina-Republic of Serbia and Possible Consequences. Journal of Geographical Research, 2(2), 11–19. https://doi.org/10.30564/jgr.v2i2.952
• 19. Hu X., Ren H., Tansey K., Zheng Y., Ghent D., Liu X., Yan L. 2019. Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agricultural and Forest Meteorology, 279(August), 107707. https://doi.org/10.1016/j.agrformet.2019.107707
• 20.Jain D., Areendran G., Raj K., Gupta V.D., Sahana M. 2021. Comparison of ahp and maxent model for assessing habitat suitability of wild dog (cuon alpinus) in pench tiger reserve, madhya pradesh. In Environmental Science and Engineering (Issue October). https://doi.org/10.1007/978-3-030-56542-8_14
• 21.Johnson A.R., Wiens J.A., Milne B.T., Crist T.O. 1992. Animal movements and population dynamics in heterogeneous landscapes. Landscape Ecology, 7(1), 63–75. https://doi.org/10.1007/BF02573958
• 22. Kaluskar S., Johnson C.A., Blukacz-Richards E.A., Ouellet F., Kim D.K., Arhonditsis, G. 2020. A stochastic modelling framework to accommodate the inter-annual variability of habitat conditions for Peary caribou (Rangifer tarandus pearyi) populations. Ecological Informatics, 56, 101013.
• 23. Kumar D., Soni A., Kumar M. 2022. Retrieval of land surface temperature from Landsat-8 thermal infrared sensor data. Journal of Human, Earth, and Future, 3(2), 159–168. https://doi.org/10.28991/HEF-2022-03-02-02
• 24. Li A., Xia C., Bao C., Yin G. 2019. Using MODIS land surface temperatures for permafrost thermal modeling in beiluhe basin on the Qinghai-Tibet plateau. Sensors (Switzerland), 19(19). https://doi.org/10.3390/s19194200
• 25. Li B., Liang S., Liu X., Ma H., Chen Y., Liang T., He T. 2021. Estimation of all-sky 1 km land surface temperature over the conterminous United States. In Remote Sensing of Environment (Vol. 266). https://doi.org/10.1016/j.rse.2021.112707
• 26. Li Z., Wu H., Duan S., Zhao W., Ren H., Liu X., Leng P., Tang R., Ye X., Zhu J., Sun Y., Si M., Liu M., Li J., Zhang X., Shang G., Tang B., Yan G., Zhou C. 2023. Satellite remote sensing of global land surface temperature: definition, methods, products, and applications. Reviews of Geophysics, 61(1). https://doi.org/10.1029/2022rg000777
• 27. Liu W., Meng Q., Allam M., Zhang L., Hu D., Menenti M. 2021. Driving factors of land surface temperature in urban agglomerations: A case study in the pearl river delta, china. Remote Sensing, 13(15), 1–25. https://doi.org/10.3390/rs13152858
• 28. Malik M.S., Shukla J.P., Mishra S. 2019. Relationship of LST, NDBI and NDVI using landsat-8 data in Kandaihimmat watershed, Hoshangabad, India. Indian Journal of Geo-Marine Sciences, 48(1), 25–31.
• 29. 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
• 30. Mujabar S.P. 2019. Spatial-temporal variation of land surface temperature of Jubail Industrial City, Saudi Arabia due to seasonal effect by using Thermal Infrared Remote Sensor (TIRS) satellite data. Journal of African Earth Sciences, 155(June 2017), 54–63. https://doi.org/10.1016/j.jafrearsci.2019.03.008
• 31. Naikoo M. W., Rihan M., Ishtiaque M., Shahfahad. 2020. Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets. Journal of Urban Management, 9(3), 347–359. https://doi.org/10.1016/j.jum.2020.05.004
• 32. Nasiri V., Deljouei A., Moradi F., Sadeghi S. M.M., Borz S.A. 2022. Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods. Remote Sensing, 14(9). https://doi.org/10.3390/rs14091977
• 33. Nguyen Q.K., Trinh, L.H., Dao, K.H., Dang N.D. 2019. Land Surface Temperature Dynamics in Dry Season 2015-2016 According to Landsat 8 Data in the South-East Region of Vietnam. Geography, Environment, Sustainability, 12(1), 75–87. https://doi.org/10.24057/2071-9388-2018-06
• 34. Northrup J.M., Vander Wal E., Bonar M., Fieberg J., Laforge M.P., Leclerc M., Prokopenko C.M., Cerber, B.D. 2022. Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution. Ecological Applications, 32(1), 1–31. https://doi.org/10.1002/eap.2470
• 35. Ogunjobi K.O., Adamu Y., Akinsanola A.A., Orimoloye I.R. 2018. Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. Royal Society Open Science, 5(12). https://doi.org/10.1098/rsos.180661
• 36. Parmar S., Thakur P.K., Chauhan M., Lata R. 2022. Land Use Land Cover Change Detection and Its Impact on Land Surface Temperature of Malana Watershed Kullu, Himachal Pradesh, India. May, 235–263. https://doi.org/10.1007/978-981-16-7731-1_11
• 37. Préau C., Trochet A., Bertrand R., Isselin-Nondedeu F. 2018. Modeling potential distributions of three european amphibian species comparing enfa and maxent. Herpetological Conservation and Biology, 13(1), 91–104.
• 38. Qin A., Liu B., Guo Q., Bussmann R.W., Ma F., Jian Z., Xu G., Pei S. 2017. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10, 139–146. https://doi.org/10.1016/j.gecco.2017.02.004
• 39. Qin Z., Karnieli A. 1999. Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. International Journal of Remote Sensing, 20(12), 2367–2393. https://doi.org/10.1080/014311699212074
• 40. Raherilalao M.J. 2001. Effets de la fragmentation de la forêt sur les oiseaux autour du Parc National de Ranomafana (Madagascar). Revue d’Ecologie (La Terre et La Vie), 56(4), 389–406. https://doi.org/10.3406/revec.2001.2374
• 41. Rai V., Upadhyay R.K., Thakur N.K. 2012. Complex population dynamics in heterogeneous environments: Effects of random and directed animal movements. International Journal of Nonlinear Sciences and Numerical Simulation, 13(3–4), 299–309. https://doi.org/10.1515/ijnsns-2012-0115
• 42. Rongali G., Keshari A.K., Gosain A.K., Khosa R. 2018. Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data. Journal of Geovisualization and Spatial Analysis, 2(2). https://doi.org/10.1007/s41651-018-0021y
• 43. Sajib M.Q.U., Wang T. 2020. Estimation of land surface temperature in an agricultural region of Bangladesh from landsat 8: Intercomparison of four algorithms. Sensors (Switzerland), 20(6). https://doi.org/10.3390/s20061778
• 44. Sandholt I., Rasmussen K., Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2–3), 213–224. https://doi.org/10.1016/S0034-4257(01)00274-7
• 45. Stralberg D., Arseneault D., Baltzer J.L., Barber Q.E., Bayne E.M., Boulanger Y., Whitman E. 2020. Climate‐change refugia in boreal North America: what, where, and for how long?. Frontiers in Ecology and the Environment, 18(5), 261-270.
• 46. Tang X., Yuan Y., Li X., Zhang J. 2021. Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China. Frontiers in Plant Science, 12(April). https://doi.org/10.3389/fpls.2021.652500
• 47. Tariq A., Riaz I., Ahmad Z., Yang B., Amin M., Kausar R., Andleeb S., Farooqi M.A., Rafiq M. 2020. Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environmental Earth Sciences, 79(1), 1–15. https://doi.org/10.1007/s12665-019-8766-2
• 48. Toffa Y., Idohou R., Fandohan A.B. 2022. Modélisation de la distribution des espèces en Afrique : état de l’art et perspectives. Physio-Géo, Volume 17, 43–65. https://doi.org/10.4000/physio-geo.13738
• 49. Ullah W., Ahmad K., Ullah S., Tahir A.A., Javed M.F., Nazir A., Abbasi A.M., Aziz M., Mohamed A. 2023. Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region. Heliyon, 9(2), e13322. https://doi.org/10.1016/j.heliyon.2023.e13322
• 50. Urbani F., D’Alessandro P., Biondi M. 2017. Using maximum entropy modeling (MaxEnt) to predict future trends in the distribution of high altitude endemic insects in response to climate change. Bulletin of Insectology, 70(2), 189–200.
• 51. USGS 2020. Landsat 8-9 Operational Land Imager (OLI) - Thermal Infrared Sensor (TIRS) Collection 2 Level 2 (L2) Data Format Control Book (DFCB). Operational Land Imager (OLI) - Thermal Infrared Sensor (TIRS) Data Format Control Book (DFCB. U.S. Geological Survey.
• 52. Viana C.M., Oliveira S., Oliveira S.C., Rocha J. 2019. Land use/land cover change detection and urban sprawl analysis. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier Inc. https://doi.org/10.1016/b978-0-12-815226-3.00029-6
• 53. Xing Z., Li Z.L., Duan S.B., Liu X., Zheng X., Leng P., Gao M., Zhang X., Shang G. 2021. Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations. ISPRS J Photogrammetry and Remote Sensing, 178, 51–67. https://doi.org/10.1016/j.isprsjprs.2021.05.017
• 54. Yibo Y., Ziyuan C., Xiaodong Y., Simayi Z., Shengtian Y. 2021. The temporal and spatial changes of the ecological environment quality of the urban agglomeration on the northern slope of Tianshan Mountain and the influencing factors. Ecological Indicators, 133(November), 108380. https://doi.org/10.1016/j.ecolind.2021.108380
• 55. Yu X., Guo X., Wu Z. 2014. Land surface temperature retrieval from landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829–9852. https://doi.org/10.3390/rs6109829
• 56. Zhang X., Kasimu A., Liang H., Wei B., Aizizi Y. 2022. Spatial and temporal variation of land surface temperature and its spatially heterogeneous response in the urban agglomeration on the northern slopes of the Tianshan Mountains, Northwest China. International journal Environment Research Public Health, 19(20). https://doi.org/10.3390/ijerph192013067

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).