Dust pollution is severe in some mining areas in China due to rapid industrial development. Dust deposited on the vegetation canopy may change its spectra. However, a relationship between canopy spectra and dust amount has not been quantitatively studied, and a pixel-scale condition for remote sensing application has not been considered yet. In this study, the dust dispersion characteristics in an iron mining area were investigated using the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) regulatory model (AERMOD). Further, based on the three-dimensional discrete anisotropic radiative transfer (DART) model, the spectral characteristics of vegetation canopy under the dusty condition were simulated, and the influence of dustfall on vegetation canopy spectra was studied. Finally, the dust effect on vegetation spectra at the canopy scale was extended to a pixel scale, and the response of dust effect on vegetation spectra at the pixel scale was determined under different fractional vegetation covers (FVCs). The experimental results show that the dust pollution along a haul road was more severe and extensive than that in a stope. Taking dust dispersion along the road as an example, the variation of vegetation canopy spectra increased with the height of dust deposited on the vegetation canopy. At the pixel scale, a lower vegetation FVC would weaken the influence of dust on the spectra. The results derived from simulation spectral data were tested using satellite remote sensing images. The tested result indicates that the influence of dust retention on the pixel spectra with different FVCs was consistent with that created with the simulated data. The finding could be beneficial for those making decisions on monitoring vegetation under dusty conditions and reducing dust pollution in mining areas using remote sensing technology.

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. This paper presents an energy-efficient 3D path planning algorithm based on an improved A* algorithm and the particle swarm algorithm in complex field scenes. The evaluation function of the A* algorithm was improved to be suitable for complex field scenes. The slope parameter and friction coefficient were respectively used in the evaluation function to represent different terrain features and landcover types. The selection of expanding nodes in the algorithm depends not only on the minimum distance but also on the minimum consumption cost. Furthermore, the turning radius factor and slope threshold factor of vehicles were added to the definition of impassable points in the improved A* algorithm, so that the accessibility of path planning could be guaranteed by excluding some bends and steep slopes. To meet the requirements for multi-target path planning, the improved A* algorithm was used as the fitness function of the particle swarm algorithm to solve the traveling salesman problem. The experimental results showed that the proposed algorithm is capable of multi-target path planning in complex field scenes. Furthermore, the path planned by this algorithm is more passable and more energy efficient. In this experimental environment model, the average energy-saving efficiency of the path planned by the improved algorithm is 14.7% compared to the traditional A* algorithm. This would be beneficial to the development of ecotourism and geological exploration.