Yinghui Zhao

1. Accurate estimation of forest above-ground biomass (AGB) plays a crucial role in the context of climate change as it can help reduce the uncertainty in emissions estimation (Le Toan et al. 2011)...

Understanding plant phenology dynamics is essential for ecosystem health monitoring and climate change impact assessment. This study generated 4-day, 500 m land surface phenology (LSP) in Northeast China (NEC) from 2001 to 2021 using interpolated and Savitzky–Golay filtered kernel normalized difference vegetation index (kNDVI) derived from MODIS. Spatial patterns, trends, and climate responses of phenology were analyzed across ecoregions and vegetation. Marked spatial heterogeneity was noted: forests showed the earliest start of season (SOS, ~125^th day) and longest growing season (LOS, ~130 days), while shrublands had the latest SOS (~150^th day) and shortest LOS (~96 days). Grasslands exhibited strong east–west gradients in SOS and EOS. From 2001 to 2021, SOS of natural vegetations in NEC advanced by 0.23 d/a, EOS delayed by 0.12 d/a, and LOS extended by 0.38 d/a. Coniferous forests, especially evergreen needle-leaved forests, exhibited opposite trends due to cold-resistant traits and an earlier EOS to avoid leaf cell freezing. Temperature was the main driver of SOS, with spring and winter temperatures influencing 48.8% and 24.2% of the NEC region, respectively. Precipitation mainly affected EOS, especially in grasslands. Drought strongly influences SOS, while precipitation affects EOS. This study integrates high-resolution phenology utilizing the kNDVI with various seasonal climate drivers, offering novel insights into vegetation-specific and ecoregion-based phenological dynamics in the context of climate change.

In the context of global climate change, the sensitivity of ecosystem services (ESs) and their relationships to aridity are increasing, and the areas affected by aridity continue to expand. Previous research has largely focused on ES changes in arid regions under intensified aridity conditions and has overlooked humid and semi-humid regions, which are more sensitive to water loss. In this study, we applied a generalized additive model (GAM) to analyze the nonlinear responses of ES trade-offs to aridity in humid and semi-humid regions. The results indicated that at an aridity index (AI) of approximately 0.26, the trade-off intensity of two ES pairs—carbon sequestration vs. water yield and habitat quality vs. water yield—reached a peak. Additionally, structural equation modeling (SEM) based on threshold partitioning revealed significant shifts in ES trade-off drivers below and above the AI threshold. In areas with AI < 0.26, worsening meteorological and soil conditions exacerbated ES trade-offs, whereas in areas with AI > 0.26, competition for water resources between human activities and vegetation became the primary driver of intensified trade-offs. These findings highlight the need for targeted management strategies to maintain ecosystem stability in humid and semi-humid regions and provide valuable guidance for responding to increasing aridity risks.

Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. The approach utilizes hyperspectral, unmanned aerial vehicle laser scanning data, and ground survey data from Maoershan Forest Farm in Heilongjiang Province, China. The study consists of two main processes: (1) combining semantic segmentation algorithms (U-Net and Deeplab V3 Plus) with watershed transform (WTS) for tree crown detection (U-WTS and D-WTS algorithms); (2) resampling the original images to different pixel densities (16 × 16, 32 × 32, and 64 × 64 pixels) and inputting them into five 3D-CNN models (ResNet10, ResNet18, ResNet34, ResNet50, VGG16). For tree species classification, the MSFB combined with the CNN models were used. The results show that the U-WTS algorithm achieved a recall of 0.809, precision of 0.885, and an F-score of 0.845. ResNet18 with a pixel density of 64 × 64 pixels achieved the highest overall accuracy (OA) of 0.916, an improvement of 0.049 over the original images. After incorporating MSFB, the OA improved by approximately 0.04 across all models, with only a 6% increase in model parameters. Notably, the floating-point operations (FLOPs) of ResNet18 + MSFB were only one-eighth of those of ResNet18 with 64 × 64 pixels, while achieving similar accuracy (OA: 0.912 vs. 0.916). This framework offers a scalable solution for large-scale tree species distribution mapping and forest resource inventories.