Influence of front slope gradient and the angle between tunnel and slope on stability of tunnel portal

Shallow buried and unsymmetrically loaded tunnel is the most common type of mountain tunnel portal. Currently, most of the studies mainly focus on the stability of side slope, while the investigation on the deformation of the front slope after tunnel excavation is relatively less. In this paper, the influence of front slope gradient and the angle between tunnel and slope on stability of tunnel portal was analyzed by FLAC3D. The results show that: When the front slope angle is greater than 40°, the slope deformation caused by tunnel excavation is larger with the increase of slope angle. In order to ensure the safety of construction, we should not only pay attention to the treatment of the front slope before the tunnel excavation, but also pay attention to the monitoring of the front slope deformation during the excavation of the tunnel portal section. And the most suitable entry angle between tunnel axis and the front slope is 0°, which can control the vertical displacement of relatively large deformation. The research conclusions can provide some reference for the excavation and support design of mountain tunnel portal

An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.https://doi.org/10.3390/rs1304071

An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas

Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China

Due to the spatial heterogeneity of land surfaces, downscaling is an important issue in the development of carbon cycle models when evaluating the role of ecosystems in the global carbon cycle. In this study, a downscaling algorithm was developed to model gross primary productivity (GPP) at 500 m in a time series over rugged terrain, which considered the effects of spatial heterogeneity on carbon flux simulations. This work was carried out for a mountainous area with an altitude ranging from 2606 to 4744 m over the Gongga Mountain (Sichuan Province, China). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product at 1 km served as the primary dataset for the downscaling algorithm, and the 500 m MODIS GPP product was used as the reference dataset to evaluate the downscaled GPP results. Moreover, in order to illustrate the advantages and benefits of the proposed downscaling method, the downscaled results in this work, along with ordinary kriging downscaled results, spline downscaled results and inverse distance weighted (IDW) downscaled results, were compared to the MODIS GPP at 500 m. The results showed that (1) the GPP difference between the 500 m MODIS GPP and the proposed downscaled GPP results was primarily in the range of [−1, 1], showing that both vegetation heterogeneity factors (i.e., LAI) and topographic factors (i.e., altitude, slope and aspect) were useful for GPP downscaling; (2) the proposed downscaled results (R2 = 0.89, RMSE = 1.03) had a stronger consistency with the 500 m MODIS GPP than those of the ordinary kriging downscaled results (R2 = 0.43, RMSE = 1.36), the spline downscaled results (R2 = 0.40, RMSE = 1.50) and the IDW downscaled results (R2 = 0.42, RMSE = 1.10) for all Julian days; and (3) the inconsistency between MODIS GPP at 500 m and 1 km increased with the increase in altitude and slope. The proposed downscaling algorithm could provide a reference when considering the effects of spatial heterogeneity on carbon flux simulations and retrieving other fine resolution ecological-physiology parameters (e.g., net primary productivity and evaporation) over topographically complex terrains

Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas

Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area

Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area