Shangri-La is located in the eastern part of the Qinghai-Tibet Plateau, which has a fragile ecology. The plateau grassland has suffered from irreversible degradation under the influence of human activities. To address this issue, the Sentinel-2A data obtained is used in this study to calculate the RVI and build an inversion model of grassland degradation grade with GDI data, which was used to obtain the area and proportion of grassland degradation. Landscape indexes were then calculated for different degradation grades of grassland to examine the correlation between roads and degraded grassland in spatial distance and the spatial distribution characteristics of different degradation grades of grassland. The results show that the grassland area in Shangri-La was 2207.94 km2, of which the heavily degraded area reaches 824.03 km2, exceeding the undegraded grassland area by 172.62 km2, indicating that the grassland degradation is severe. From south to north, the proportion of heavily degraded and moderately degraded grassland in townships gradually decreased, while the proportion of lightly degraded and undegraded grassland gradually increased. The townships with high percentages of degraded grassland were predominantly located in the southern area, where there was a dense road network and well-developed transport networks, particularly along National Highway 214, which is the main road in Shangri-La. Conversely, townships with low percentages are generally located in the north with dispersed roads and sparse transport lines. The study’s outcomes are significant in providing a better understanding of the current status of grassland degradation and promoting the sustainable utilization of grassland resources in Shangri-La.
Although a satellite laser altimetry Geoscience Laser Altimeter System (GLAS) can directly measure ground elevation, the measurement accuracy of ground elevation is generally limited over mountainous vegetated areas. Currently, most methods for ground elevation estimations that use GLAS data fail to obtain an accurate ground elevation in sloped areas. Therefore, this study aimed to propose a continuous wavelet transform (CWT) based method for better estimating ground elevation from GLAS data over mountainous vegetated areas. First, the CWT was applied to each GLAS waveform for peak detection. Second, all potential ground peaks were correctly identified using GLAS waveform parameters in conjunction with auxiliary digital elevation model data. Third, the true ground peak was determined from CWT results according to several strict rules, and then the ground elevation was calculated based on the position of the true ground peak. Finally, these ground elevation estimates were validated by the digital terrain model derived from airborne discrete-return LiDAR data in Genhe in the Neimeng province of China. Additionally, the CWT-based method was also compared with previous methods, which estimate ground elevation from GLAS data over mountainous vegetated areas based on a Gaussian decomposition. It was found that our CWT-based method can reduce the root-mean-square error of ground elevation estimates by up to 0.8 m. Overall, this study can provide an available solution for estimating ground elevation from large-footprint waveform LiDAR data over mountainous vegetated areas.
Ice, cloud, and land elevation satellite (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) multibeam micropulse photoncounting light detection and ranging (LiDAR) can be effectively applied to extract forest canopy height. However, the ICESat-2/ATLAS photon point cloud interfered with the signal-to-noise ratio (SNR), fraction vegetation coverage (FVC), and terrain slope. The main challenge of this research is to extract high-precision canopy heights. Therefore, this article improves the canopy height extraction method based on the ICESat-2/ATL08 theoretical algorithm. First, an adaptive filter, Threshold Segmentation based on Spatial Clustering and Bimodal Reconstruction (TS-SCABR), is proposed, which can adapt to different SNR scenarios. Then, combined with the gradient method, the discontinuous data are detrended in sections to eliminate the edge mutation problem of the detrended data. Based on the detrended data, the iterative filtering algorithm of the local terrain is employed to fit the ground curve, and the mutation detection and empirical mode decomposition (EMD)-digital smoothing polynomial (DISPO) filtering remove the pseudoground photons to identify the data of ground and nonground photons accurately. Finally, the percentile statistics method is utilized to extract the canopy-top photons from the nonground photons according to their elevation difference. The results indicate that, under different natural conditions, the improved algorithm has better adaptability than the previous algorithm. Compared with the original ATL08 ATBD algorithm, the canopy height accuracy is significantly improved, especially in low FVC and high slope scenarios. When the FVC is lower than 25%, $R_{2}$ increases by 50.3%, and the root mean square error (RMSE) is reduced by 2.175 m, and when the slope is higher than 45°, it increases by 41.7%, and the RMSE is reduced by 2.159 m. Therefore, the algorithm has apparent advantages in inverting the canopy height in a mountainous environment with lush forests.
Abstract GLA14, one of the products of the spaceborne Light Detection and Ranging (LiDAR) sensor Geoscience Laser Altimeter System (GLAS), provides six Gaussian decomposition waveforms that represent different vertical layers of the ground target in a laser spot. In this article, we have extracted the relative height of ground targets from peak positions of the GLAS waveform, carried out the field validations, analysed the trend of building height in Beijing and then multiplied the building height and the percentage of building area within a pixel of the land-use/land-cover classification map to get the annual change of total floor space of buildings in Beijing. Based on the total floor space of buildings (TFSB) released by the National Bureau of Statistics of China (NBSC), we have established a linear regression model between the GLAS-estimated total floor space in Beijing and the data provided by NBSC. The results show that the building height and (TFSB) in Beijing increased from 2003 to 2008. The method proposed in this article expands research on urban change from a two-dimensional plane to a three-dimensional space to improve research accuracy, and is complementary to current remote-sensing methods. Acknowledgements This work was supported by the National Basic Research Programme (973 programme) of China (2010CB951701, 2009CB723900) and the National Natural Science Foundation of China (No. 40861009). We also thank the National Snow and Ice Data Centre for distributing the GLAS data.
With the increment of the amount of meteorological hazard recently, storms and severe weather have become the leading cause of power outages. The present study aims to provide an alternative approach to identifying and evaluating the risk of meteorological hazards in the power system. An integrated Cloud-GIS and supporting technologies for the rapid muti-sensor data fusion and risk assessment are introduced in the paper, and the integrated evaluation system including four layers satisfies the meteorological hazards assessment requirement. Then a model is designed for assessing meteorological risk for power system depending on hazard-formative factors, vulnerability, and anti-disaster capacity. The case study has been employed in East China Grid, and the experimental results show that the new methodology offers accuracy risk level, with which the operators can establish and implement control measures to control these hazards.
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