Utilization of urban underground space has become a vital approach to alleviate the strain on urban land resources, and to optimize the structure and pattem of the city. It is also very important to improve the city environment, build livable city and increase the capacity of the city. Based on the analysis of existing evaluation methods and their problems, a method for evaluating underground space resources based on a negative list of adverse factors affecting underground space development is proposed, to be primarily used in urban planning stages. A list of the adverse factors is established, including limiting factors, constraining factors and influencing factors. Taking Xi’an as an example, using a geographical information system platform, a negative list of adverse factors for the underground space resources in Xi’ an City are evaluated, and preventive measures are proposed. Natural resources, exploitable resources, and the potential growth of exploitable underground space resources are evaluated. Underground space assessment in the different development stages of the city, collaborative utilization and safety evaluation for multiple subsurface resources, environmental impact and assessment, as well as evaluation methods based on big data and intelligent optimization algorithms are all discussed with the aim of serving city planning and construction. 相似文献
Mongolia is an important part of the Belt and Road Initiative “China-Mongolia-Russia Economic Corridor” and a region that has been severely affected by global climate change. Changes in grassland production have had a profound impact on the sustainable development of the region. Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions. Three estimation models were established using a statistical analysis method based on EVI, MSAVI, NDVI, and PsnNet from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and measured data. A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation (model accuracy 78%). This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015. The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study; a slight overall increasing trend was observed. For the first five years, the grassland production decreased slowly, whereas in the latter five years, significant fluctuations were observed. The grassland production (per unit yield) gradually increased from the southwest to northeast. In most provinces of the study area, the production was above 1000 kg ha -1, with the largest production in Hentiy, at 3944.35 kg ha -1. The grassland production (total yield) varied greatly among the provinces, with Kent showing the highest production, 2341.76×10 4 t. Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.