Analysis of the Relationship between Vegetation and Radar Interferometric Coherence

Yuxi Cao, Peixian Li (), Dengcheng Hao, Yong Lian, Yuanjian Wang and Sihai Zhao
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Yuxi Cao: School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Peixian Li: School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Dengcheng Hao: School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Yong Lian: Dongqu Coal Mine, Shanxi Coking Coal Group Co., Ltd., Taiyuan 030024, China
Yuanjian Wang: School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Sihai Zhao: School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Sustainability, 2022, vol. 14, issue 24, 1-18

Abstract: To effectively reduce the impact of vegetation cover on surface settlement monitoring, the relationship between normalized difference vegetation index (NDVI) and coherence coefficient was established. It provides a way to estimate coherence coefficient by NDVI. In the research, a new method is tried to make the time range coincident between NDVI results and coherence coefficient results. Using the coherence coefficient results and the NDVI results of each interference image pair in the study area, the mathematical relationship between NDVI and the coherent coefficient was established based on statistical analysis of the fitting results of the exponential model, logarithmic model, and linear model. Four indicators were selected to evaluate the fitting results, including root mean square error, determinant coefficient, prediction interval coverage probability, and prediction interval normalized average width. The fitting effect of the exponential model was better than that of the logarithmic model and linear model. The mean of error was −0.041 in study area ROI1 and −0.126 in study area ROI2.The standard deviation of error was 0.165 in study area ROI1 and 0.140 in study area ROI2. The fitting results are consistent with the coherence coefficient results. The research method used the NDVI results to estimate the InSAR coherence coefficient. This provides an easy and efficient way to indirectly evaluate the interferometric coherence and a basis in InSAR data processing. The results can provide pre-estimation of coherence information in Ningxia by optical images.

Keywords: interferometry; correlation; curve fitting; NDVI (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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