這項研究結合了遙感數據和方法來分析馬薩帕-曼齊尼的城市化進程，斯威士蘭中部地區（以前稱為斯威士蘭）多年來經歷了巨大的城市化進程，社會經濟指標和自然特徵的變化得到了證實。本研究旨在利用遙感技術調查 Manzini-Matsapha 地區從 1997 年到 2022 年的城市化趨勢和相關環境影響，研究分析了地表溫度 (LST)、歸一化植被指數 (NDVI)、歸一化植被指數 (NDVI)、差異建成指數 (NDBI)、土地利用土地覆蓋 (LULC) 和城市熱島，以了解環境變化和城市擴張之間的相互作用。該研究利用 LULC、LST 和社會經濟數據來形成關於城市化影響的具體而全面的觀點。該研究分析了社會經濟變量，以衡量城市化趨勢，繪製 LULC 地圖以分解其對 LST 的影響，為選定的研究時期繪製 LST 地圖，並將其與兩個生物物理參數 (即 NDVI 和 NDBI) 相關聯。除此之外，該研究還利用植被調整植被指數 (VANUI)，使用 LST 閾值和夜間燈光對 UHI 區域進行分類。該方法利用 1997-1998、2006-2007、2014-2015 和 2021-2022 這四個時間段的衛星圖像進行多時相分析。數據處理包括圖像預處理、NDVI 和 NDBI 的推導、LST 計算和使用其他學者建立的算法進行 LULC 分類。結果顯示，城市區域覆蓋範圍呈增加趨勢，像素間高 LST 差異相應增加，這也表明城市熱島區域有所增加。該研究表明，植被覆蓋率下降、建成區和高地表溫度像素之間存在明顯的關係。這一結果強調了納入綠色空間以減輕城市熱島影響的必要性。該研究面臨一些限制，例如衛星和社會經濟數據的空間分辨率不同以及時間範圍變化很大。鑑於這些，該研究確實為曼齊尼-馬薩法城市化對環境的影響提供了極其有價值的見解。總之，該研究揭示了遙感在監測城市化及其相關環境影響方面發揮的重要作用。通過利用這種動態，可以制定適當的策略來盡量減少這種不利影響。

This study incorporates remote sensing data and methods to analyze urbanization in Matsapha-Manzini, this central region of Eswatini (formerly known as Swaziland) has undergone tremendous urbanization over the years with a proven growth in socio economic indicators and changes in physical characteristics. This research aims to investigate urbanization trends and the associated environmental impacts in the Manzini-Matsapha area dating back to 1997 until 2022 by employing remote sensing techniques, the research analyzes the land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land use land cover (LULC) and urban heat islands in a bid to understand the interplay between environmental changes and urban expansion. The research leverages LULC, LST and socio-economic data to develop a concrete and comprehensive view of urbanization impacts. The study analyzed the socio-economic variables to gauge the trend of urbanization, develop LULC maps to break down their influence on LST, develop LST maps for the chosen study periods and correlate them to the two biophysical parameters, namely NDVI and NDBI. Above that, the study classified UHI zones using an LST threshold and night time lights by making use of the vegetated adjusted vegetation index (VANUI). The method made use of a multi-temporal analysis using satellite imagery at four time periods, 1997-1998, 2006-2007, 2014-2015 and 2021-2022. The processing of the data included image preprocessing, derivation of NDVI and NDBI, calculation of LST and LULC classification using established algorithms from other scholars. The result depicted an increasing trend in urban area coverage with a corresponding rise in high LST difference amongst pixels, which also showed a rise in UHI zones. The study showed a clear relationship amongst reduced vegetation coverage, built-up areas and high LST pixels. This result emphasizes on the need for incorporation of green spaces to mitigate UHI effects. The study faced some limitations such as the varying spatial resolutions and largely varying temporal scope of the satellite and socio-economic data. In light to these, the study does provide extremely valuable insights into the environmental impacts of urbanization in Manzini-Matsapha. In conclusion, the study brings forth the vital role that remote sensing plays in monitoring urbanization and the associated environmental impacts. By utilizing such dynamics, appropriate strategies can be developed as to how to minimize such adverse effects.

Ali Shah, S., Kiran, M., Nazir, A., & Ashrafani, S. H. (2022). EXPLORING NDVI AND NDBI RELATIONSHIP USING LANDSAT 8 OLI/TIRS IN KHANGARH TALUKA, GHOTKI. Malaysian Journal of Geosciences, 6(1), 08–11. https://doi.org/10.26480/mjg.01.2022.08.11
Burges, C. J. C., & Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121–167. https://www.microsoft.com/en-us/research/publication/a-tutorial-on-support-vector-machines-for-pattern-recognition/
Central Statistical Office. (2019). The 2017 Population and Housing Census Volume 3.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Congalton, R. G., & Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data. CRC Press. https://doi.org/10.1201/9781420055139
Demographia. (2023). Demographia World Urban Areas. http://www.demographia.com/db-worldua.pdf
Dervisoglu, A. (2023). Investigation of the Efficiency of Satellite-Derived LST Data for Mapping the Meteorological Parameters in Istanbul. Atmosphere, 14(4), 644. https://doi.org/10.3390/atmos14040644
Efe, S. I., & Eyefia, A. O. (2014). Urban Effects on the Precipitation of Benin, Nigeria. American Journal of Climate Change, 03(01), 8–21. https://doi.org/10.4236/ajcc.2014.31002
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21), 5860–5879. https://doi.org/10.1080/01431161.2017.1342050
Environmental Protection Agency. (2016). Heat Island Impacts. 2016. http://www.epa.gov/hiri/impacts/index.htm
Eswatini Electricity Company. (2023). Eswatini Electricity Company 2023.
Eswatini Environment Authority. (2021). Review and Update of the State of Environment Report Ministry of Tourism and Environmental Affairs Restoring the Environment for Climate Resilient Economic Recovery The Kingdom of Eswatini 2020. Eswatini Environment Authority. www.eea.org.sz
Founda, D., & Santamouris, M. (2017). Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). Scientific Reports, 7(1), 10973. https://doi.org/10.1038/s41598-017-11407-6
Google. (n.d.). VNP46A1: Viirs Daily Gridded Day Night Band 500m Linear Lat Lon grid night. Retrieved June 18, 2024, from https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A1#description
Grover, A., & Singh, R. (2015). Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai. Environments, 2(2), 125–138. https://doi.org/10.3390/environments2020125
Hamada, S., Tanaka, T., & Ohta, T. (2013). Impacts of land use and topography on the cooling effect of green areas on surrounding urban areas. Urban Forestry & Urban Greening, 12(4), 426–434. https://doi.org/10.1016/j.ufug.2013.06.008
Jabbar, H. K., Hamoodi, M. N., & Al-Hameedawi, A. N. (2023a). Urban heat islands: a review of contributing factors, effects and data. IOP Conference Series: Earth and Environmental Science, 1129(1), 012038. https://doi.org/10.1088/1755-1315/1129/1/012038
Jabbar, H. K., Hamoodi, M. N., & Al-Hameedawi, A. N. (2023b). Urban heat islands: a review of contributing factors, effects and data. IOP Conference Series: Earth and Environmental Science, 1129(1), 012038. https://doi.org/10.1088/1755-1315/1129/1/012038
Jacobs, S., David, O. O., & Wyk, A. S.-V. (2023). The Impact of Urbanization on Economic Growth in Gauteng Province, South Africa. International Journal of Economics and Financial Issues, 13(2), 1–11. https://doi.org/10.32479/ijefi.13899
Jerry, R., Rija, R., Jean, R., Lahatra, R., & Fils, R. (2018). Temperature Retrieval of Lake Itasy Using Remote Sensing. Resources and Environment, 8(6), 241–244. https://doi.org/10.5923/j.re.20180806.01
Kadhim, N., Mourshed, M., & Bray, M. (2016). Advances in remote sensing applications for urban sustainability. Euro-Mediterranean Journal for Environmental Integration, 1(1), 7. https://doi.org/10.1007/s41207-016-0007-4
Katie Baynes. (n.d.). Visible Infrared Imaging Radiometer Suite. Retrieved February 18, 2024, from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/
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. https://doi.org/10.1016/j.procs.2020.04.127
Li, M., Gao, Q., & Yu, T. (2023). Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters. BMC Cancer, 23(1), 799. https://doi.org/10.1186/s12885-023-11325-z
Macarof, P., & Statescu, F. (2017). Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environment and Sustainable Development, 11(2), 141–150. https://doi.org/10.1515/pesd-2017-0032
Mohajerani, A., Bakaric, J., & Jeffrey-Bailey, T. (2017). The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete. Journal of Environmental Management, 197, 522–538. https://doi.org/10.1016/j.jenvman.2017.03.095
Municipal Council of Manzini. (2019). Municipal-Council-of-Manzini-2019-Annual-Report.
Neinavaz, E., Skidmore, A. K., & Darvishzadeh, R. (2020). Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. International Journal of Applied Earth Observation and Geoinformation, 85, 101984. https://doi.org/10.1016/j.jag.2019.101984
Nguyen, T., Lin, T.-H., & Chan, H.-P. (2019). The Environmental Effects of Urban Development in Hanoi, Vietnam from Satellite and Meteorological Observations from 1999–2016. Sustainability, 11(6), 1768. https://doi.org/10.3390/su11061768
OECD/UN ECA/AfDB. (2022). Africa’s Urbanisation Dynamics 2022. OECD. https://doi.org/10.1787/3834ed5b-en
Pedalo. (n.d.). The Kingdom of Eswatini. Retrieved June 21, 2024, from https://www.thekingdomofeswatini.com/central-eswatini/
PricewaterhouseCoopers. (2011). Protecting human health and safety during severe and extreme heat events A national framework.
Quintano, C., Fernández-Manso, A., Calvo, L., Marcos, E., & Valbuena, L. (2015). Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. International Journal of Applied Earth Observation and Geoinformation, 36, 1–12. https://doi.org/10.1016/j.jag.2014.10.015
Rahman, Md. N., Rony, Md. R. H., Jannat, F. A., Chandra Pal, S., Islam, Md. S., Alam, E., & Islam, A. R. Md. T. (2022). Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools. Climate, 10(1), 3. https://doi.org/10.3390/cli10010003
Swaziland Electricity Company. (2014). Swaziland Electricity Company Annual Report 2013/14.
Tan, P. Y., Wong, N. H., Tan, C. L., Jusuf, S. K., Chang, M. F., & Chiam, Z. Q. (2018). A method to partition the relative effects of evaporative cooling and shading on air temperature within vegetation canopy. Journal of Urban Ecology, 4(1). https://doi.org/10.1093/jue/juy012
United Nations Human Settlements Programme (UN-Habitat). (2022). Envisaging the Future of Cities. Website: www.unhabitat.org
United States Geological Survey. (2019a). Landsat Missions. https://www.usgs.gov/landsat-missions/landsat-satellite-missions
United States Geological Survey. (2019b). What Is Remote Sensing ? https://www.earthdata.nasa.gov/learn/backgrounders/remote-sensing#sensors
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
Zhang, Q., Schaaf, C., & Seto, K. C. (2013). The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment, 129, 32–41. https://doi.org/10.1016/j.rse.2012.10.022