Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km². Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

The mixed Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Masson’s pine (Pinus massoniana Lamb.), and hardwood forest is a major forest type in China and of national and international importance in terms of its provision of both timber and ecosystem services. However, over-harvesting has threatened its long-term productivity and sustainability. We examined the impacts of timber harvesting intensity on soil physical and chemical properties 10 and 15 years after cutting using the research plots established with a randomized block design. We considered five treatments, including clear cutting and low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1) intensities of selective cutting with non-cutting as the control. The impact on overall soil properties derived from principal component analysis showed increasing with a rise in cutting intensity, and the most critical impact was on soil nutrients, P and K in particular. Soil nutrient loss associated with timber harvesting even at a low cutting intensity could lead to nutrient deficits in this forest although most of the soil physical properties could be recovered under the low and medium intensities of cutting. These results indicate that clear cutting and the selective cutting of extra-high and high intensities should be avoided in this type of forest in the region.

Sprawling road networks cutting through forested areas continue to be a potent catalyst of deforestation in tropical regions. Yet, pantropical assessments limited by the lack of high-resolution global maps of tropical forest loss induced by road networks. Here, we harnessed the road dataset from the Global Roads Inventory Project and the forest loss dataset from Global Forest Change, to produce global tropical high-resolution maps of road impact index spanning 2001 to 2020. We find that the forest area within a 1-km distance from roads accounts for about one-sixth, but its proportion of forest loss is nearly one-third, the road impact index is 2.45 times higher than those beyond the zone. The road impact index of all countries shows a decreasing trend with the distance from roads and increasing from 2001 to 2020. Our findings emphasize the urgent need for globalized efforts to protect the intact forests and rehabilitate degraded forests along roadside.

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 (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.

Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.

The mixed Cunninghamia lanceolata (Lamb.) Hook., Pinus massoniana Lamb., and hardwood forest in southeastern China is a major assemblage in natural secondary forests, and of national and international importance in terms of both timber and ecosystem services. However, over-harvesting has threatened its long-term sustainability, and there is a knowledge gap relating to the effect of harvesting on the ecosystem. After conifer species were selected for harvesting, the mixed Chinese fir, pine, and hardwood forest was changed into mixed evergreen broadleaf forest. In this context, we observed the restoration dynamics of plant communities over a period of 15 years (1996 to 2011) with different levels of harvesting intensity, including selective harvesting at low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1%) intensities, as well as clear-cut harvesting (100.0%), with non-harvesting as the control, based on permanent sample plots established in a randomized block design in these forests in southeastern China. The impact on the richness, diversity, and evenness of plant species derived from descriptive statistical analyses was shown to initially increase, and then decrease, with an increase in harvesting intensity. The most critical impacts were on the richness, diversity, and evenness of shrub and herb species. Richness, diversity, and evenness of plant species recovered and increased under selective harvesting at low and medium intensities, while these parameters had not recovered and significantly decreased under selective harvesting at high and extra-high intensities, as well as with clear-cut harvesting. The impact on the plant community stability was derived from the stability test method of the improved Godron M. The plant community stability was closest to the point of stability (20/80) under selective harvesting at medium intensity, followed by selective harvesting at low intensity. The plant community stability was far from the point of stability (20/80) under selective harvesting at high and extra-high intensities, as well as with clear-cut harvesting. Of these treatments, clear-cut harvesting had the greatest effect with regard to reducing stability. Therefore, these results indicate that the selective harvesting at low and medium intensities is conducive to preserve or increase the species diversity and community stability. In order to prioritize promoting plant species diversity, clear-cut harvesting and selective harvesting at high and extra-high intensities should be avoided with regard to this type of forest in this region. This study sheds light on the practice of forest operation in the study region and subtropical forests with the same environment.