Dynamic Changes and Influencing Factors of Vegetation in the "Green Heart" Zone of the Chang-Zhu-Tan Urban Agglomeration during the Past 21 Years.

As a policy, protected green space in the rapidly developing the Chang-Zhu-Tan Urban Agglomeration is of great practical significance to study the vegetation changes and influencing factors in the Green Heart area. In this paper, data processing, grading and area statistics were carried out for the maximum value of normalized differential vegetation index (NDVI) from 2000 to 2020. Combined with Theil-Sen median trend analysis and Mann-Kendall, the change trend of long-time series NDVI was studied, and investigation of NDVI influencing factors, processes and mechanisms using geographical detectors. The results showed that: (1) The spatial distribution characteristics of NDVI in the study area were high in the middle and inlaid transition between adjacent grades. Except for the low grades, the distribution of NDVI in other grades was relatively scattered, and the overall trend of NDVI change was rising. (2) Population density was the main factor affecting NDVI changes, with an explanatory power of up to 40%, followed by elevation, precipitation and minimum temperature. (3) The influence of influencing factors on the change of NDVI was not the result of independent action of a single factor, but the result of the interaction between human factors and natural factors, and the factor combinations with greater interaction had significant differences in the spatial distribution of NDVI.

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

QuickBird image-based estimation of tree stand density using local maxima filtering method: A case study in a Beijing forest.

The stand density of trees affects stand growth and is useful for estimating other forests structure parameters. We studied tree stand density in Jiufeng National Forest Park in Beijing. The number of spectral local maxima points (NSLMP) calculated within each sample plot was extracted by the spectral maximum filtering method using QuickBird imagery. Regression analysis of NSLMP and the true stand density collected by ground measurements using differential GPS and the total station were used to estimate stand density of the study area. We used NSLMP as an independent variable and the actual stand density as the dependent variable to develop separate statistical models for all stands in the coniferous forest and broadleaf forest. By testing the different combination of Normalized Difference Vegetation Index (NDVI) thresholds and window sizes, the optimal selection was identified. The combination of a 3 × 3 window size and NDVI ≥ 0.3 threshold in coniferous forest produced the best result using near-infrared band (coniferous forest R2 = 0.79, RMSE = 12.60). The best combination for broadleaf forest was a 3 × 3 window size and NDVI ≥ 0.1 with R2 = 0.44, RMSE = 9.02 using near-infrared band. The combination of window size and NDVI threshold for all unclassified forest was 3 × 3 window size and NDVI ≥ 0.3 with R2 = 0.70, RMSE = 11.20 using near-infrared band. A stand density planning map was constructed using the best models applied for different forest types. Different forest types require the use of different combination strategies to best extract the stand density by using the local maximum (LM). The proposed method uses a combination of high spatial resolution imagery and sampling plots strategy to estimate stand density.