Jianjun Liu’s research while affiliated with Northwest A&F University and other places

With the rapid development of urban area of Xi’an in recent years, the contradiction between ecological environmental protection and urban development has become prominent. The traditional remote sensing classification method has been unable to meet the accuracy requirements of urban vegetation monitoring. Therefore, how to quickly and accurately conduct dynamic monitoring of urban vegetation based on the spectral component characteristics of vegetation is urgent. This study used the data of Landsat 5 TM and Landsat 8 OLI in 2011, 2014 and 2017 as main information source and LSMM, region of variation grid analysis and other methods to analyse the law of spatial-temporal change of vegetation components in Xi’an urban area and its influencing factors. The result shows that: (1) The average vegetation coverage of the study area from 2011 to 2017 reached more than 50 %, meeting the standard of National Garden City (great than 40 %). The overall vegetation coverage grade was high, but it had a decreasing trend during this period. (2) The vegetation in urban area of Xi’an experienced a significant change. From 2011 to 2017, only 30 % of the low-covered vegetation, 24.39 % of the medium-covered vegetation and 20.15 % of the high-covered vegetation remained unchanged, while the vegetation in the northwest, northeast, southwest and southeast of the edge of the city’s third ring changed significantly. (3) The vegetation quality in urban area of Xi’an has decreased from 2011 to 2014 with 6.9 % of vegetation coverage reduced; while from 2014 to 2017, the overall vegetation quality of this area has improved with 2.1 % of the vegetation coverage increased, which was mainly attributed to urban construction and Urban Green Projects. This study not only can obtain the dynamic change information of urban vegetation quickly, but also can provide suggestions and data support for urban planning of ecological environmental protection.

Clarifying the spatial heterogeneity of urban heat island (UHI) effect is of great significance for promoting sustainable urban development. A GeoDetector was used to detect the influential natural and society factors. Natural factors (normalized difference vegetation index (NDVI), soil-regulating vegetation index (SAVI), normalized building index (NDBI), and modified normalized difference water index (MNDWI)) as well as society factors (road density (RDD), and population density (POPD)) were selected as driving factors to be tested for their explanatory power for land surface temperature (LST). Results indicated that the Moran’s I index value for the LST of the built-up area is 0.778. The top three factors influencing the LST were NDBI, NDVI, and SAVI, the explanatory power of which was 0.7593, 0.6356, and 0.6356, respectively. The interactive explanatory power for NDBI and MNDWI was 0.8108 and for NDBI and RDD was 0.8002, these two interactions are double enhanced interaction relationships. The results of this study play a guiding role in the development of urban thermal environment regulation schemes and ecological environment planning.

Stability and productivity are important indicators used to measure the state of forest ecosystems. Artificial forests populations with reasonable structures and strong stability are critical for ecosystem productivity. Previous studies have focused on individual factors, while the mechanisms of how multiple factors affect population productivity remain unknown. We used 57 plots in a Chinese pine (Pinus tabuliformis) plantation to investigate 23 stand factors and analyzed the relationships among site factors, population structure, population stability, and population productivity using partial least square-structural equation modeling (PLS-SEM). The results showed that the population productivity of the plantation was directly affected by the population stability latent variable but indirectly affected by the site conditions latent variables (indirect effect path coefficient = 0.249) and forest structure (indirect effect path coefficient = 0.222). However, the site conditions latent variable was the main factor directly affecting the population stability latent variables; the total effect was 0.511 (direct effect path coefficient = 0.307, indirect effect path coefficient = 0.204), and the influence of forest structure on population stability was lower than that of the site conditions latent variable (direct effect path coefficient = 0.454). The factor with the greatest weight among the site conditions latent variable was slope (0.747), indicating that slope contributes the most to latent variables related to forest population stability. Among all variables affecting the forest stability latent variables, forest density had the highest weight value (0.803), and the weight value of forest mortality was lower than that of forest density. The weights of the latent variables associated with population structure from high to low were canopy density, the uniform angle index, and the spatial competition index, indicating that competition for space had the lowest influence on the population stability latent variables. The results provide new insights and ideas for quantifying relationships among different driving factors and a basis for scientific and rational plantation management.