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Forest fires can destroy millions of acres of land at shockingly fast speeds. The forest fire points identification algorithm is the most critical step in the forest fire monitoring process. Most traditional forest fire monitoring methods use fixed thresholds, ignoring background pixels, and have low recognition rates, which could lead to many problems, such as false reporting and low recognition rate. This paper proposes and tests an adaptive forest fire points identification algorithm using Himawari-8 data. By calculating the three-dimensional histogram of brightness temperature, an adaptive threshold that can automatically identify potential forest fire points is obtained. Based on this three-dimensional Otsu method, the contextual test algorithm has also been adopted to specify forest fire points. The experimental results show that the omission rate of the improved algorithm is about 10% lower than that of the previous algorithm in small-scale fire incidents. The improved algorithm can quickly and effectively extract fire point information, and it is also sensitive to small and low-temperature fires, which provides an efficient means for monitoring fire disasters.

This paper presents a terrain-related method to simultaneously correct the global error trends and local linear/nonlinear terrain-related errors of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), which have not been focused in most of the previous methods. To meet this goal, an adaptive strategy for modelling the SRTM DEM errors is first proposed, especially over mountainous areas, based on the Bayesian information criterion. Then, the M-estimator, instead of the ordinary least squares solver, is utilized to estimate the model parameters of the constructed model to improve the estimation robustness. The proposed method was tested over the Zhangjiajie areas of China, where the ground surface terrain varies from plains to steep mountains. The results show that the errors of the SRTM DEM over the region of interest decrease from 10.1 m to approximately 8.1 m after correction with the proposed method, indicating an improvement of approximately 20%. In addition, compared with two existing and common methods that can respectively correct the global error trends and local linear terrain-related errors of the SRTM DEM, the accuracy of the corrected SRTM DEM improves by approximately 29% and 27.9% over mountainous areas with slopes larger than 20° when the proposed method is used. Moreover, the proposed method will be beneficial to the correction of other airborne or spaceborne DEM products, especially over mountainous areas.

Stringent physical resource constraints in unmanned aerial vehicle (UAV) communications bring new challenges to energy efficient transmission. In this paper, we study energy efficient beamforming for UAV communications, where a wideband hybrid polarized antenna array with tapped-delay-lines (TDLs) structure is deployed on the UAV. By exploiting hybrid structured, TDLs, and polarized antenna arrays, the strict requirements of hardware size and cost on UAV can be satisfied. It is shown that the energy efficient beamforming with the adopted array can be formulated as a WMMSE-based parameter optimization problem, which however is still a challenging nonconvex problem due to unit modulus constraints in analog beamformer. To overcome the nonconvex nature, an iterative algorithm is proposed, in which the optimization problem is iteratively solved through three convex quadratic programming problems with convex equality or inequality constraints. Finally, the numerical results demonstrate that the proposed wideband beamforming algorithm can significantly improve the energy efficiency, compared with the gradient projection algorithm.