Efficiently and accurately measuring forest structure is of great significance for high-quality assessment of forest resources. Backpack laser scanning (BLS) has become a common device to acquire forest structural information due to its low cost and high time efficiency. However, complex forest environments bring challenges to BLS-based forest mapping, which faces problems with incomplete data and poor mapping accuracy. In this article, we design a disassembly-free dual-scanner BLS system for complete and accurate forest mapping. We first execute a high precision automatic self-calibration of dual laser scanners by means of angle compensation and the fixed rotation angle. Then, a simultaneous localization and mapping (SLAM) framework by combining the natural feature of trees and Inertial Measurement Unit (IMU) measurements is proposed, in which IMU provides priori motion estimation and motion compensation for dual scanners, and the natural feature of the forest is used to correct motion. The proposed method is validated in three small-scale forest plots with size of 0.1 ha. Experimental results show well performance in terms of mapping accuracy, where the mean errors and the root square mean errors are less than 3.0 cm in both horizontal and vertical directions. Our study demonstrates the effectiveness of the proposed strategy and has the potential to perform accurate and complete mapping in understory.

Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies.