Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection and the imbalance of training data. To address these issues, we propose a novel framework integrating conventional methods into CNN for wildfire smoke detection, which consisted of a candidate smoke region segmentation strategy and an advanced network architecture, namely wildfire smoke dilated DenseNet (WSDD-Net). Candidate smoke region segmentation removed the complex backgrounds of the wildfire smoke images. The proposed WSDD-Net achieved multi-scale feature extraction by combining dilated convolutions with dense block. In order to solve the problem of the dataset imbalance, an improved cross entropy loss function, namely balanced cross entropy (BCE), was used instead of the original cross entropy loss function in the training process. The proposed WSDD-Net was evaluated according to two smoke datasets, i.e., WS and Yuan, and achieved a high AR (99.20%) and a low FAR (0.24%). The experimental results demonstrated that the proposed framework had better detection capabilities under different negative sample interferences.

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with $R^2$ = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.

Unmanned air vehicle (UAV) systems for performing forestry applications have expanded in recent decades and have great economic benefits. They are validated to be more appealing than traditional platforms in various aspects, such as repeat rate, spatial resolution, and accuracy. This paper consolidates the state-of-the-art unmanned systems in the forestry field with a major focus on UAV systems and heterogeneous platforms, which are applied in a variety of forestry applications, such as wood production, tree quantification, disease control, wildfire management, wildlife conservation, species classification, etc. This review also studies practical applications under multiple forestry environments, including wild and managed forests, grassland, urban green parks, and stockyards. Special forest environments and terrains present customized demands for unmanned systems. The challenges of unmanned systems deployment are analyzed from environmental characterization, maneuverability and mobility improvement, and global regulatory interpretation. To better apply UAV systems into forestry, future directions are analyzed in terms of mobility enhancement and customized sensory adaption, which need to be further developed for synchronizing all possible agents into automatic functioning systems for forestry exploration.