Rapid monitoring of reclaimed farmland effects in coal mining subsidence area using a multi-spectral UAV platform.

Abstract

In eastern China, coal mining has damaged a large amount of farmland, posing a great threat to food security. Backfilling with coal waste, fly ash, and sediments from rivers is an effective method to restore farmland. This study was conducted at the reclaimed area (RA) and the undisturbed area (UA) in Shandong Province, China. Soil and plant analyzer development (SPAD) of corn was selected as an indicator of crop growth. Multi-spectral data was obtained by the unmanned aerial vehicle equipped with a camera. By analyzing the correlation between SPAD and spectral bands, the common vegetation index is improved. Different regression methods were used to construct the SPAD inversion model. The distribution of corn SPAD was monitored to objectively evaluate reclamation technology. The results are as follows: (1) the vegetation index improved using the red-edge band has a higher correlation with SPAD, and the largest coefficient of determination (R2) value is 0.779; (2) the optimum inversion model for both jointing stage (R2 = 0.676) and milky stage (R2 = 0.661) is the linear regression model; the optimum model for both tasseling stage (R2 = 0.809) and filling stage (R2 = 0.830) is the partial least squares regression model; (3) the SPAD inversion map of RA and UA obtained by the optimum model shows that the corn grown in RA is slightly better than in UA. This study realized the rapid and efficient monitoring of the reclamation effects based on multi-spectral imagery and verified the feasibility of backfilling reclamation with Yellow River sediment in coal mining subsidence areas.