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Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran
Canadian Journal of Remote Sensing ( IF 2.6 ) Pub Date : 2019-11-02 , DOI: 10.1080/07038992.2019.1704622
Bakhtiar Feizizadeh 1, 2 , Hejar Shahabi Sorman Abadi 1 , Khalil Didehban 1 , Thomas Blaschke 3 , Franz Neubauer 4
Affiliation  

Abstract Land surface temperature (LST) and soil moisture are important factors in environmental hazard modeling. The main objective of this research is to derive the LST and a soil moisture index (SMI) from thermal satellite images. A split-window algorithm is applied to derive the spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8 satellite in four consecutive years (2015–2018) to serve as input for the LST analysis. First, the normalized difference vegetation index (NDVI) is computed from which an emissivity index is calculated using an object-based threshold technique. This is followed by the calculation of the LST via a split-window algorithm. Subsequently, the SMI is modeled to reflect the relationship between the surface temperature and the vegetation cover. A spatial analysis investigates the relationship between the LST and SMI with known geological faults. The results indicate that the areas with low-temperature and high-moisture overlap with fault zones. The authors discuss to what degree fault zones can be detected or predicted based on LST and SMI.

中文翻译:

伊朗马什哈德县基于物体的热遥感故障检测分析

摘要 地表温度(LST)和土壤湿度是环境灾害建模中的重要因素。本研究的主要目标是从热卫星图像中推导出 LST 和土壤水分指数 (SMI)。应用裂窗算法从 Landsat 8 卫星连续四年(2015-2018 年)的两个热红外 (TIR) 波段推导出光谱辐射率和发射率,作为 LST 分析的输入。首先,计算归一化差异植被指数 (NDVI),使用基于对象的阈值技术从中计算发射率指数。然后通过拆分窗口算法计算 LST。随后,对 SMI 进行建模以反映地表温度与植被覆盖之间的关系。空间分析研究了 LST 和 SMI 与已知地质断层之间的关系。结果表明,低温高湿区与断裂带重叠。作者讨论了基于 LST 和 SMI 可以检测或预测到何种程度的断层带。
更新日期:2019-11-02
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