Abstract

Long-term MODIS time series Land Surface Temperature (LST) observations over coal fire affected areas were utilized for detection and characterization of coal fire as a function of its temporal intensity variation (e.g. growing, temporally consistent or diminishing) on annual basis in Jharia coalfield. LST pixel time series (LPTS) vectors were generated for selected coal fire and non-coal fire locations using 782 LST maps of the duration 2001 − 2017. LPTS vectors were decomposed to extract the nonlinear trend component using Seasonal Trend decomposition based on Loess model. Background-referenced trends were generated for coal fire pixels. Slope, p-value of Mann-Kendall test and average annual deviation parameters were calculated for annually segmented background-referenced coal fire trends to characterize coal fire on annual basis and to separate coal fire induced anomalous trend and non-coal fire trends. The results were validated with that of published literature.

摘要

在 Jharia 煤田，煤火影响区域的长期 MODIS 时间序列地表温度 (LST) 观测被用于检测和表征煤火作为其时间强度变化（例如增长、时间一致或减少）的函数. 使用 2001-2017 年期间的 782 张 LST 地图为选定的煤火和非煤火位置生成 LST 像素时间序列 (LPTS) 向量。使用基于黄土模型的季节趋势分解分解 LPTS 向量以提取非线性趋势分量。为煤火像素生成了背景参考趋势。斜率，p- 计算每年分段的背景参考煤火趋势的 Mann-Kendall 检验值和平均年偏差参数，以每年表征煤火并区分煤火引起的异常趋势和非煤火趋势。结果与已发表的文献进行了验证。