Eco-friendly Sn-based perovskites show significant potential for high-performance second near-infrared window light-emitting diodes (900 nm – 1700 nm). Nevertheless, achieving efficient and stable Sn-based perovskite second near-infrared window light-emitting diodes remains challenging due to the propensity of Sn²⁺ to oxidize, resulting in detrimental Sn⁴⁺-induced defects and compromised device performance. Here, we present a targeted strategy to eliminate Sn⁴⁺-induced defects through moisture-triggered hydrolysis of tin tetrahalide, without degrading Sn²⁺ in the CsSnI₃ film. During the moisture treatment, tin tetrahalide is selectively hydrolyzed to Sn(OH)₄, which provides sustained protection. As a result, we successfully fabricate second near-infrared window light-emitting diodes emitting at 945 nm, achieving a performance breakthrough with an external quantum efficiency of 7.6% and an operational lifetime reaching 82.6 h.

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest’s short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies.

Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and associated phenology features between planted forests and other vegetation types. In this study, taking advantage of the Google Earth Engine (GEE) platform and taking the Ningxia Hui Autonomous Region in northwestern China as an example, we developed an approach to map planted forests in an arid region by applying long-term features of the NDVI derived from dense Landsat time series. Our land cover map achieved a satisfactory accuracy and relatively low uncertainty, with an overall accuracy of 93.65% and a kappa value of 0.92. Specifically, the producer (PA) and user accuracies (UA) were 92.48% and 91.79% for the planted forest class, and 93.88% and 95.83% for the natural forest class, respectively. The total planted forest area was estimated as 3608.72 km² in 2020, accounting for 20.60% of the study area. The proposed mapping approach can facilitate assessment of the restoration effects of ecological engineering and research on ecosystem services and stability of planted forests.

A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km²), while the highest negative effects were observed in 2017 (54.54 km²). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures.

Attentions over the health of evergreen vegetation are increasing owing to frequently occurrence of recent disturbance events (i.e. soil erosion, logging activities, and afforestation). However, vegetation indices that characterize canopy greenness have limitations in spectral saturation for representing the growth states of densely vegetated areas, and the continuous acquisition of satellite-derived vegetation functional metrics depends on the availability of clear image observations. This study investigated the vegetation health dynamics (1993–2012) in the red soil regions of southern China using satellite observations based task-oriented metrics, including the Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Aboveground Biomass Carbon (ABC). The results indicated that the total number of pixels with significant changes (SC) was 214, 1,186, and 794 for the NDVI, VWC, and ABC indices, respectively. More than 90% of the SC pixels in the three metrics exhibited increasing trends, which were primarily observed in mountainous areas. Pixels that exhibited a continuously declining trend were discretely distributed throughout the entire study area. Among the SC pixels, vegetation in major parts of the study area was disturbed by abrupt events. In the NDVI, VWC, and ABC, the frequency of abrupt changes increased after 2000, coinciding with the launch of the Natural Forest Conservation Program (NFCP) in 2000–2001. For regions with abrupt changes, four patterns were further observed based on the indices: the continued increases (pattern-1), sustained decreases (pattern-2), recovery growth after an initial decline (pattern-3), and significant decreases after initial growth (pattern-4). Pattern-1 appeared more frequently than the other three patterns. This study indicates that vegetation in most areas was optimally developed and exhibited a healthier tendency compared with previous growth states. Notably, the presence of an increasingly unhealthy vegetation state was observed in the northeastern region of the study area. Satellite derived datasets and synergetic use of indicators contribute to understanding the changes in the vegetation health in the entire red soil regions in southern China. Thus, this study acts as a reference for forest management and soil erosion control.

Investigating how the productivity dynamics of planted forests vary over time is important for understanding the resilience of forests against disturbance and for maximizing ecological restoration and replanting efforts. In this study, the patterns of interannual variability in net primary production (NPP) were analyzed for planted forests as indicated by the inverse of the coefficient of variation (ICV) time series at a ten-year moving window on the Loess Plateau, China, from 2000 to 2021. The spatial–temporal patterns were defined based on the increase or decrease trend obtained using the ordinary least squares method between abrupt change points performed by a Mann–Kendall test in an ICV time series, as follows: only one linear trend, increase (LI), and decrease (LD); at least two trends, increase firstly and decrease lastly (ID) and decrease firstly and increase lastly (DI); and other trends. The results showed that 82.74% of the ICV on the Loess Plateau displayed LD and ID patterns, indicating an increasing variability of forest productivity in this region. Overall, 73.83% of the ICV had a lower degree of rate decrease in the last phase than during the initial increase. Thus, the variability was in an early stage of increasing degree. The ICV time series showed an LI pattern in the eastern Gansu and the southern Shanxi, indicating a decreased variability, due partly to the improved forest restoration. When the plantation age was considered, the newly planted forests (less than 19 a) exhibited a decreasing variability, indicating the proactive role of forest management and restoration in averting environmental disruptions in dry environments.