The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m⁻²·month⁻¹ (30–80%) and 0.13–0.35 kg C·m⁻²·year⁻¹ (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km² (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe.

Addressing the problems of misclassification and omissions in urban vegetation fine classification from current remote sensing classification methods, this research proposes an intelligent urban vegetation classification method that combines feature engineering and improved DeepLabV3+ based on unmanned aerial vehicle visible spectrum images. The method constructs feature engineering under the ReliefF algorithm to increase the number of features in the samples, enabling the deep learning model to learn more detailed information about the vegetation. Moreover, the method improves the classical DeepLabV3+ network structure based on (1) replacing the backbone network using MoblieNetV2; (2) adjusting the atrous spatial pyramid pooling null rate; and (3) adding the attention mechanism and the convolutional block attention module. Experiments were conducted with self-constructed sample datasets, where the method was compared and analyzed with a fully convolutional network (FCN) and U-Net and ShuffleNetV2 networks; the migration of the method was tested as well. The results show that the method in this paper is better than FCN, U-Net, and ShuffleNetV2, and reaches 92.27%, 91.48%, and 85.63% on the accuracy evaluation indices of overall accuracy, MarcoF1, and mean intersection over union, respectively. Furthermore, the segmentation results are accurate and complete, which effectively alleviates misclassifications and omissions of urban vegetation; moreover, it has a certain migration ability that can quickly and accurately classify the vegetation.

Plant communities in urban wetland parks (UWP) have significant eco-healthcare benefits in terms of regulating the climate and improving the human living environment. However, factors influencing the regulation of eco-healthcare benefits are unclear. Taking Huaxi Ten Mile Beach National Urban Wetland Park as an example, the urban wetland park comprehensive healthcare index (UPCHI) was constructed based on an outdoor survey and indoor analysis to evaluate the UWP’s eco-healthcare benefits. Pathway analysis was used to investigate how climatic, geographic, and plant factors interact to affect the UPCHI. The results show that, over the whole year, tree–shrub–herb showed the best performance in terms of reducing PM_2.5, PM₁₀, and noise, as well as raising negative air ion concentrations; however, human comfort performed the worst. The UPCHI was generally beyond level Ⅲ (0.49–0.58) in the spring and summer, indicating that there are eco-healthcare benefits. Overall, the deciduous tree–shrub–herb community had the highest annual mean UPCHI, and more than half of the plant communities’ eco-healthcare benefits were class Ⅱ, which is very beneficial for eco-healthcare. The main direct factors on UPCHI were illumination intensity (0.68) and tree height (0.90), while canopy height (0.64–0.59) and tree crown radius/canopy height (0.72–0.14) directly or indirectly influenced UPCHI. The distance from the edge of the mountain (−0.39–−0.322) had a direct negative, but minor, effect on UPCHI. This study will assist residents with selecting suitable times and places for wetland recreation and healthcare activities, and it offers a valuable reference for the future planning and design of UWP plant communities.

Since tree morphological structure is strongly influenced by internal genetic and external environmental factors, accurate simulation of individual morphological–structural changes in trees is the premise of forest management and 3D simulation. However, existing studies have few descriptions, and the research on the impact of growth environments and stand spatial structures on tree morphological structure and growth is still limited. In our study, we constructed a comprehensive grade model of spatial structure (CGMSS) to comprehensively evaluate individual tree growth states of the stands and grade them from 0 to 10 correspondingly. In addition, we developed a Chinese fir morphological structure growth model based on CGMSS, and dynamically simulate the growth variations of Chinese fir stands. The results showed that the overall stand prediction accuracy of CGMSS-based Chinese fir diameter at breast height, tree height, crown width and under-living branch height growth models was more than 94%. According to the analysis of the comprehensive grade of spatial structure (CGSS) of trees in the stand, except for the prediction accuracy and systematic error of the under-living branch height growth model at the CGSS = 3–5 levels, the systematic error of the Chinese fir growth model at each level was lower than 21.2%, and the prediction accuracy was greater than 73%. Compared with the spatial structural unit (SSU)-based Chinese fir growth model proposed by Ma et al., all growth models fit better at all levels, except for the CGMSS-based Chinese fir tree height and under-living branch height growth models that fit significantly lower than the SSU-based Chinese fir growth model at CGSS = 3–5 levels. In this study, the main conclusion is that the simulation results of CGMSS’s Chinese fir morphological structure growth model are closer to the real growth state of trees, achieving accurate simulation of differential growth of trees in different growth dominance degrees and spatial structure states in forest stands, making visualized forest management more effective and realistic.

This paper explores how landscape risk and habitat quality vary in coastal areas with strong anthropogenic disturbance based on a case study. We analyze the temporal–spatial variations of habitat quality and ecological risk in the coastal region by adopting the methods of theInVEST model and the ecological risk index. The correlations of habitat quality and ecological risk with landscape metrics are subsequently quantified. The results indicated the presence of obvious distance gradients in relation to the deterioration of habitat quality and the increase in ecological risk. Moreover, the gradient area close to the coastline exhibits significant habitat quality and ecological risk changes. The majority of landscape metrics show positive correlations with habitat quality and ecological risk, and these correlations vary with the distance gradients. Since the rapid urbanization of the coastal region, the marked expansion of built-up land and decrease in natural landscapes has significantly impacted the landscape pattern index and, consequently, changed the habitat quality and ecological risk level.