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• Local space–time Moran's I from NDVI can monitor forest disturbance and recovery. • Elasticity, malleability and trend were selected as forest resilience indicators. • Dense satellite images are successful at deriving forest resilience. Resilience is the capacity of an ecosystem to respond and recover from damage or stress. It can inherently exhibit the cycle and feedback of the disturbed ecosystem recovery process for a specified period. The current availability of dense and consistent time series of satellite images holds the promise of monitoring forest resilience. The aim of this study is to establish forest resilience indicators using dense Landsat time series and assess forest resilience in response to the maximum disturbance magnitude in the hilly red soil region, Hengyang Basin, Southern China. To achieve this, Landsat images of the study area from 1987 to 2017 were collected. Normalized Difference Vegetation Index (NDVI), i.e., proxy of forest green characteristics, number of patch (NP), average patch size (PS), patch perimeter-area ratio (PPAR) and aggregation index (AI), i.e., proxies of forest landscape metrics were calculated from Landsat images. And then elasticity (i.e., the time and rate of recovery), malleability (i.e., degree of deviation from an initial state) and trend (i.e., the pattern of change) as the indicators of forest resilience were constructed. The local space–time Moran's I (STI) based on NDVI residual space–time Moran's I (STI) was employed to characterize the forest disturbance and recovery process. The results revealed the following. Firstly, the STI calculated from dense time series NDVI residuals are successful at monitoring the forest disturbance recovery process, regardless of whether changes were dramatic or subtle. Secondly, the most forest disturbances (i.e., > 75%) occurred in the late 1980s and early 1990s. NDVI and landscape metrics also differed in their response to disturbances; NDVI, PPAR and AI are more malleable to disturbance than PS and NP are. Finally, approximately 40% of the disturbed forest had the strong elasticity with a short recovery time (i.e., a year). We conclude that measuring forest resilience via vegetation greenness and landscape metrics using dense time series satellite images is practical as an operational tool for policy makers, landowners, and national park managers. Moreover, insights into ecological dynamics are emerging from capturing the process showing the difference both before and after the change. [ABSTRACT FROM AUTHOR]

• Establishing indicators to describe landscape structure patterns and processes. • Explaining the ecological significance of spatio-temporal variation indicators. • Reasoning landscape structure variation in Changting County. Terrestrial ecosystems are constantly changing at various spatial and temporal scales due to natural and/or anthropogenic factors. It is therefore of great interest to develop effective indicators based on spatio-temporal variations to detect, map, and monitor ecosystem changes on the continental and global scales. In this study, spatio-temporal variation indicators were established to characterize landscape structure changes based on temporal analysis combined with spatial information obtained using dense Landsat time series in Changting County, Fujian, Province, China. All available Landsat images from 1987 to 2017 were collected. The fractal dimension (FD) of the spatial neighbor normalized difference vegetation index (NDVI) derived from the Landsat images was calculated. Four change patterns (stable, increasing, decreasing, and wave) were explored using the ordinary least-squares method between abrupt change points performed by a Mann–Kendall test on the FD time series to characterize the spatio-temporal dynamics. The results showed that 49.98% of the landscape structure in the study area decreased over the 31-year study period. The forest and water were dominated by decreasing patterns (67.00% and 56.27%); the urban land and cropland were dominated by wave patterns (50.07% and 50.95%); bare land regions were dominated by wave patterns and increasing patterns (50.52% and 31.38%). Wide and discontinuous landscape structures with high heterogeneity were replaced by landscape patches. The regional landscape structure heterogeneity increased in the bare land regions and other homogeneous landscape structures, while the decreases may be coupled with internal landscape structure changes. It was concluded that the spatial and temporal variations of the change patterns and change processes determined based on the FD of the NDVI in the spatial neighbor region are systematic indicators and can be used to detect landscape structure changes successfully and provide insights into dynamic trends. [ABSTRACT FROM AUTHOR]

• A water optical classification method was developed using density peaks. • The class-specific retrieval models of surface water turbidity were established. • Long-term dynamics of surface water turbidity was tracked using satellite images. • Interaction of water level and rainfall is probably the main environmental driver. Surface water turbidity (SWT), as a low-cost proxy of surface suspended sediment, is important for characterizing the hydro-ecological process and light availability in the lake or reservoir ecosystem. In this study, we proposed the combined use of HJ-1 charge-coupled device imaging and field observation to track the long-term SWT dynamics with environmental changes in Lakes Gaoyang, Hanfeng, and Changshou of the Three Gorges Reservoir, China. In situ remote sensing reflectance spectra were utilized to develop the characteristic spectral indexes for the SWT estimation in different water optical classes separated by a density peaks-based classification. Significant correlations were found between the red-, four-band, band ratio spectral indexes and SWT (determination coefficient >0.71 and root-mean-square error <8.32 nephelometric turbidity unit), suggesting a crucial role of the class-specific retrieval models for the SWT estimation in optically complex waters. The proposed method was further used to monitor the spatio-temporal SWT dynamics over the three lakes from 2008 to 2019, demonstrating that the significant SWT decline in Lakes Gaoyang and Hanfeng and the relatively stable trend in Lake Changshou during the 11-year period. Specifically, the SWT decreasing trends may be attributed to the water level linkage mechanism of Three Gorges and Wuyang Dams. In addition, analyses with simultaneous environmental factors showed that the seasonal and inter-annual variations of SWT appear to be closely correlated with water level and rainfall. Long-term remote tracking of the SWT dynamics presented in this study could provide new insight and reference for reservoir management in the post-Three Gorges Project Era. [ABSTRACT FROM AUTHOR]