利用地理空间技术分析埃塞俄比亚西部Gida Kiremu、Limu和Amuru地区地表温度

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY Artificial Intelligence in Agriculture Pub Date : 2022-01-01 DOI:10.1016/j.aiia.2022.06.002
Mitiku Badasa Moisa , Bacha Temesgen Gabissa , Lachisa Busha Hinkosa , Indale Niguse Dejene , Dessalegn Obsi Gemeda
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引用次数: 4

摘要

植被退化和荒地扩大仍然是全球面临的主要环境问题。利用陆地表面温度(LST)、归一化植被指数(NDVI)、归一化贫瘠指数(NDBaI)和修正归一化水分指数(MNDWI)进行相关分析,量化变化关系。本文利用地理空间技术分析了埃塞俄比亚西部Gida Kiremu、Limu和Amuru地区地表温度与NDVI、NDBaI和ndwi的关系。利用Landsat TM 1990、Landsat ETM+ 2003和Landsat OLI/TIRS 2020的热波段和多光谱波段估算了所有指数。利用散点图分析地表温度与NDVI、NDBaI和MNDWI的相关性。因此,NDBaI与LST呈正相关(R2 = 0.96)。而NDVI和MNDWI与LST呈显著负相关(R2 = 0.99, 0.95)。结果表明:研究期间,由于植被覆盖减少和裸地增加,地表温度升高了5°C;最后,我们的研究结果建议决策者和环境分析人员应重视植被覆盖、水体和湿地在减缓气候变化中的重要性,特别是研究区的地表温度。
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Analysis of land surface temperature using Geospatial technologies in Gida Kiremu, Limu, and Amuru District, Western Ethiopia

Degradation of vegetation cover and expansion of barren land are remained the leading environmental problem at global level. Land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Barren Index (NDBaI), and Modified Normalized Difference Water Index (MNDWI) were used to quantify the changing relationships using correlation analysis. This study attempted to analyze the relationship between LST and NDVI, NDBaI, and MNDWI using Geospatial technologies in Gida Kiremu, Limu, and Amuru districts in Western Ethiopia. All indices were estimated by using thermal bands and multispectral bands from Landsat TM 1990, Landsat ETM+ 2003, and Landsat OLI/TIRS 2020. The correlation of LST with NDVI, NDBaI and MNDWI were analyzed by using scatter plot. Accordingly, the NDBaI was positive correlation with LST (R2 = 0.96). However, NDVI and MNDWI were substantially negative relationship with LST (R2 = 0.99, 0.95), respectively. The result shows that, LST was increased by 5 °C due to decline of vegetation cover and increasing of bare land over the study periods. Finally, our result recommended that, decision-makers and environmental analysts should give attention on the importance of vegetation cover, water bodies and wetland in climate change mitigation, particularly, LST in the study area.

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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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