As the main bearing area of the ecological crisis in resource-rich cities, it is essential for the urban fringe to enhance regional ecological security during a city’s transformation. This paper takes Daqing City, the largest oilfield in China’s cold land, as an example. Based on remote sensing image data from 1980 to 2017, we use the DPSIR (Driving forces, Pressure, State, Impact, Response) framework and spatial auto-correlation analysis methods to assess and analyze the landscape eco-security change of the study area. From the perspective of time–space, the study area is partitioned, and control strategies are proposed. The results demonstrate that: (1) The landscape eco-security changes are mainly affected by oilfield exploitation and ecological protection policies; the index declined in 1980–2000 and increased in 2000–2017. (2) The landscape eco-security index has obvious spatial clustering characteristics, and the oil field is the main area of warning. (3) The study area determined the protection area of 1692.07 km2, the risk restoration area of 979.64 km2, and proposed partition control strategies. The results are expected to provide new decision-making ideas in order to develop land use management and ecological plans for the management of Daqing and other resource shrinking cities.
The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) was able to capture the full waveform of backscattered laser pulse. However, the accuracy of the surface information extracted from the waveform was vulnerable to background noise. In this paper, a piecewise adaptive lq-norm trend filtering method is proposed for the GLAS full waveform denoising on the basis of trend filtering. To minimize the loss of useful signal while removing the noise, the proposed method adaptively assigns different norms to the smooth constraints according to the local signal energy. The filtered results can then be obtained by iteratively minimizing the hybrid-norm loss function. The proposed method is tested on both the simulated waveforms and real GLAS waveform data. In the simulated experiments, the quantitative evaluation is conducted with the filtered waveforms, as well as the results after waveform decomposition. For comparison, the most commonly used waveform filtering methods, i.e. Gaussian filtering, wavelet transform, Empirical model decomposition and 1 trend filtering, are involved in the experiments. The results show that the proposed method outperforms the mainstream methods on waveform filtering, in terms of removing noise and preserving the shape and energy amplitude of the GLAS waveforms.
Owing to the widespread use of GPS-enabled devices, sensing road information from vehicle trajectories is becoming an attractive method for road map construction and update. Although the detection of intersections is critical for generating road networks, it is still a challenging task. Traditional approaches detect intersections by identifying turning points based on the heading changes. As the intersections vary greatly in pattern and size, the appropriate threshold for heading change varies from area to area, which leads to the difficulty of accurate detection. To overcome this shortcoming, we propose a deep learning-based approach to detect turns and generate intersections. First, we convert each trajectory into a feature sequence that stores multiple motion attributes of the vehicle along the trajectory. Next, a supervised method uses these feature sequences and labeled trajectories to train a long short-term memory (LSTM) model that detects turning trajectory segments (TTSs), each of which indicates a turn occurring at an intersection. Finally, the detected TTSs are clustered to obtain the intersection coverages and internal structures. The proposed approach was tested using vehicle trajectories collected in Wuhan, China. The intersection detection precision and recall were 94.0% and 91.9% in a central urban region and 94.1% and 86.7% in a semi-urban region, respectively, which were significantly higher than those of the previously established local G* statistic-based approaches. In addition to the applications for road map development, the newly developed approach may have broad implications for the analysis of spatiotemporal trajectory data.
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