Search

Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network's performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation. [ABSTRACT FROM AUTHOR]

The economic and social development evaluation system with the Gross Domestic Product (GDP) as the leading indicator is no longer applicable to the current social progress in China. It is essential to carry out an assessment of the Gross Ecosystem Product (GEP) to integrate ecological benefits into the economic and social evaluation system and promote sustainable socio-economic development. This study took Guangxi, an important province in South China, as the study area. We used four periods of land use and land cover data (LULC), meteorological data, soil data and yearbook statistics to construct a GEP assessment framework based on geographic information system (GIS) and remote sensing (RS) technologies. We accounted for the provisioning services, regulating services, and tourism services provided by Guangxi in 2005, 2010, 2015, and 2020 and analyzed the region's and municipalities' spatial–temporal pattern characteristics and trends of change in GEP. In addition, this study also discusses the relationship between GEP and GDP. The results showed that many important products and services provided by natural ecosystems in Guangxi had enormous economic benefits. GEP had increased from CNY 15,657.37 billion in 2005 to CNY 36,677.04 billion in 2020, and the distribution of GEP showed obvious spatial heterogeneity. The value of ecosystem regulation services was about 65–89% of GEP, which is the main component of GEP. From 2005 to 2020, natural ecosystem protection and socio-economic development have achieved coordinated development in Guangxi. GEP and GDP showed upward trends in general. Although Guangxi is relatively backward in terms of economic development, the scientific quantification of the unrealized value of the services provided by the ecosystem through GEP accounting makes it possible to transform ecological advantages into economic advantages. It could help the local government and people to re-recognize the value of ecological resources and realize the beautiful vision of lucid waters and lush mountains as invaluable assets. [ABSTRACT FROM AUTHOR]

Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO. [ABSTRACT FROM AUTHOR]