GEOSPATIAL ASSESSMENT OF LAND SURFACE TEMPERATURE IN OWO FOREST RESERVE AREA, ONDO STATE NIGERIA
DOI:
https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v7i2.43318Keywords:
Land Surface Temperature, Normalized Difference Vegetation Index, Spatial, Landsat Imagery, Single Window (SW) algorithmAbstract
Nigeria’s forest reserves no matter where they are located act as the last succour for the entire citizenry by rendering ubiquitous services that significantly contribute to her economy. This study was intended at assessing the Land Surface Temperature (LST) in Owo Forest Reserve Area (FRA) with a view to sustainable forest management. The essential objectives set for the research include: (i.) assessing the vegetation changes in Owo Forest Reserve Area (ii.) evaluating the LST in Owo forest reserve and (iii.) relating changes in vegetation cover to LST in other to ascertain whether the observed difference in vegetation cover has significant effect and contribution either positively or otherwise to LST values obtained in Owo forest reserve. Recorded spatial coordinates of some selected points constitute the primary data while the secondary data were the satellite dataset, which includes: the Operational Landsat Imager, Enhanced Thematic Mapper, and Thematic Mapper of different years (1991, 2002, 2014, and 2020) used for mapping the vegetation change in the forest reserves. Specifically, thermal bands of the Landsat image in conjunction with the Normalized Difference Vegetation Index (NDVI) were utilized for mapping the LST which is the hallmark of the research. Various data acquired were processed according to the best practices. The results obtained showed that moderate vegetation (shrubs and scattered trees) has been increasing, which signifies that these categories of vegetation in Owo forest reserve area is not depleting for the whole period of study. It was also discovered from the results garnered from 1991 to 2020 that areas with vegetation (Dense, moderate, and sparse) had the least LST values while the forecast probable future LST values for the year 2030 are in the purview of 24.22°C(minimum) and 53.98°C (maximum) which may have negative implications on the forest reserve. The research recommends a significant increase in the rate of tree planting and preserving green areas (vegetation) in order to mitigate the upsurge of LST while laws guiding illegal logging should be upheld with the utmost tenacity.
References
Agbor, C.F. and Makinde, E.O. (2018). Land Surface Temperature Mapping Using Geoinformation Techniques. Geoinformatics FCE CTU. doi:10.14311/gi.17.1.2.
Aik, D. H. J., Ismail, M. H. Muharam, F. M. and Alias, M. A. (2021). Evaluating the impacts of land use land cover changes across topography against land surface temperature in Cameron Highlands. Available at https://www.researchgate.net/publication/350040633. Visited on 17 June 2023
Avdan, U. and Jovanovska, J. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Hindawi Publishing Corporation, Journal of Sensors. Article ID 1480307. doi.org/10.1155/2016/1480307.
Balew, A. and Korme, T. (2020). Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Sciences. doi.org/10.1016/j.ejrs.2020.02.001.
Climate signals(2023). Global land surface temperature trends and climate change. Available at https://www.climatesignals.org/climate-signals/land-surface-temperature-increase. Visited on 14/6/23
Crago, R. D. and Qualls, R.J. (2014). Use of land surface temperature to estimate surface energy fluxes: Contributions of Wilfried Brutsaert and collaborators, Water Resour. Res., 50, 3396–3408. doi:10.1002/2013WR015223.
El Garouani, M., Amyay, M., Lahrach, A., Oulidi, H. (2021). Land Surface Temperature in Response to Land Use/Cover Change Based on Remote Sensing Data and GIS Techniques: Application to Saïss Plain, Morocco. Journal of Ecological Engineering. ISSN 2299–8993. doi.org/10.12911/22998993/139065.
European Space Agency (2021). Land Surface Temperature. Available at https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3slstr/overview/geophysical-measurements/land-surface-temperature visited on 2nd May, 2023
Fahad, A., Bassam, A. A., & Hazem, T. A. (2023). Impact of land use dynamics on land surface temperature using optical remote sensing data integrated with statistical analysis in Riyadh, Saudi Arabia. Advances in Space Research. Science Direct https://doi.org/10.1016/j.asr.2023.04.051
Hashim, H., Abd Latif, Z., and Adnan, N.A (2019). Urban Vegetation Classification with NDVI Threshold Value Method with Very High Resolution (VHR) Pleiades Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W16. 6th International Conference on Geomatics and Geospatial Technology (GGT 2019), 1–3 October 2019, Kuala Lumpur, Malaysia. https://doi.org/10.5194/isprs-archives-XLII-4-W16-237-2019.
Hasnat G.N.T (2021) A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery. Frontier Forest Global Change 4:687988. doi: 10.3389/ffgc.2021.68798
Ibrahim, I., Abu samah, A., Fauzi, R., and Noor, N. (2016). The land surface temperature impact to land cover types. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLI-B3. 871-876. DOI:10.5194/isprs-archives-XLI-B3-871-2016.
James, J.M., Abubakar, T. and Gundiri, L.R. (2020). Impact of Deforestation on Land Surface Temperature in Northern Part of Bauchi State, Nigeria. FUTY Journal of the Environment. Photogrammetry, Remote Sensing and spatial Information Science. Vol XL-1/W5. http://creativecommons.org/licenses/by/4.0/.
Jeevalakshmi, D., Narayana, S., and Manikiam, B. (2017). Land Surface Temperature Retrieval from LANDSAT data using Emissivity Estimation. International Journal of Applied Engineering Research. ISSN 0973-4562
Lamidi, A. J. and Ijaware, V .A (2022). Land Suitability for None-Rice Cultivation Areas in Ekiti State Using a GIS- Based Analytic Hierarchy Process Approach. In European Journal of Environment. and Earth Science.(EJ-GEO) vol. 3, issue 5. ISSN:2684-446X(online). United Kingdom www.sciencepublishinggroup.com/j/ijaas
Meteobox (2021). Owo, Ondo. https://meteobox.com/nigeria/owo/. Visited on 5th June 2021
Mohamadi, B., Chen, S., Balz, T., Gulshad, K. and McClure, S.C. (2019). Normalized Method for Land Surface Temperature Monitoring on Coastal Reclaimed Areas. MPDI. Sensors. doi:10.3390/s19224836.
Mohd Jaafar, W., Abdul Maulud, K.N., Kamarulzaman, A.M., Raihan, A., Sah, S.M., Ahmad, A., Saad, S.N., Mohd Azmi, A.T., Syukri, N.K., Khan, W.R. (2020) The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia. Forests. MPDI. doi:10.3390/f11060670
National Aeronautical Space Agency (2008, October). Land Surface Temperature Product. https://modis.gsfc.nasa.gov/gallery/individual.php?db_date=2008-10-27. Visited on 6/5/2021
Ngie, A., Abutaleb, K., Ahmed, F., Taiwo, A., Darwish, A.A., and Ahmed, A. (2016). An Estimation of Land Surface Temperatures from Landsat ETM+ images for Durban, South Africa. Rwanda Journal Life and Natural Sciences: Special issue II. doi.org/10.4314/rj.v1i1S.2D.
Odunuga, S. and Badru, G. (2015). Landcover Change, Land Surface Temperature, Surface Albedo and Topography in the Plateau Region of North-Central Nigeria. Land. ISSN 2073-445X
Othman, M.A. Ash’aari & Ranli, M. (2018). Tropical deforestation monitoring using NDVI from MODIS satellite: a case study in Pahang, Malaysia. Environmental Science, Mathematics IOP Conference Series: Earth and Environmental Science DOI:10.1088/1755-1315/169/1/012047 Corpus ID: 134751947
Tang, B., Zhao, X., and Zhao, W. (2018). Local Effects of Forests on Temperatures across Europe. MPDI. Remote sensing. doi:10.3390/rs10040529.
Shatnawi, N. and Abu Qdais, H. (2019). Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling. International Journal of Remote Sensing. doi: 10.1080/01431161.2018.1557792.
Weatherspark (2021). Average weather in Owo, Nigeria. https://weateher sprk.com/y/51416/Average-weather-in-Owo-Nigeria-Year-Round
Weng, Q., Lu, D., and Schubring, J. (2003). Estimation of land surface temperature vegetation abundance relationship for urban heat island studies. Elsevier. Remote Sensing of Environment – Elsevier. doi:10.1016/j.rse.2003.11.005.
WorldView-3 Overview(2022) - Earth Online. Available at
https://earth.esa.int/eogateway/missions/worldview-3/description?text=2022.Visited
on 6/9/2022
Zhi, Y., Shan, L., Ke, L., and Yang, R. (2020). Analysis of Land Surface Temperature Driving Factors and Spatial Heterogeneity Research Based on Geographically Weighted Regression Model. Hindawi. Volume 2020, Article ID 2862917. https://doi.org/10.1155/2020/2862917.
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