Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?

Abstract

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.