Mountain forest greenness varies across seasons, yet growing-season averages can mask season-specific relationships with climate variability. We assessed seasonal and elevational variation in historical forest greenness within areas classified as forest in 2024, temporal variation in spring NDVI–climate relationships, and the association between nighttime light (NTL) intensity and forest greenness in Changbai Mountain from 2000 to 2024. We used MODIS-derived normalized difference vegetation index (NDVI), 1 km temperature and precipitation datasets, elevation zones, and annual nighttime light (NTL) data. We applied a forest mask derived from the 2024 Annual China Land Cover Dataset (CLCD) and retained 1 km pixels with at least 70% forest cover. NDVI increased significantly over the aggregate growing season (slope = 0.0011 NDVI yr−1, R2 = 0.584, p < 0.001), summer (slope = 0.0006 NDVI yr−1, R2 = 0.188, p = 0.030), and autumn (slope = 0.0019 NDVI yr−1, R2 = 0.280, p = 0.007). Autumn showed the largest estimated slope; this ranking alone does not establish statistical differences among seasonal slopes. Spring showed no significant long-term trend (slope = 0.0011 NDVI yr−1, R2 = 0.087, p = 0.152), but spring NDVI showed a strong positive correlation with temperature at the regional scale (r = 0.760, p < 0.001), with positive correlations maintained across all elevation zones. In spring regression models, temperature showed a larger standardized coefficient magnitude than precipitation across elevation zones, with all variance inflation factors ≤ 1.22. Exploratory five-year sliding-window correlations suggested temporal variation in spring NDVI–climate relationships, although each estimate was based on only five annual observations. At low elevation, the NTL–NDVI regression was significant (R2 = 0.198, p = 0.026), whereas associations at middle and high elevations were not statistically significant; this raw association should be interpreted cautiously because both variables may contain concurrent temporal trends. These results describe seasonal and elevational greenness patterns within the 2024 forest mask, highlighting the distinct spring temperature association and elevation-dependent relationships between NTL intensity and forest greenness.
Daily precipitation estimates over the Haihe River Basin show strong terrain-related error contrasts. We developed a regional two-stage post-processing fusion workflow for five gridded precipitation products. Physically constrained XGBoost-Tweedie regression uses non-negativity and an archive-derived MaxPrec reference to generate a bias-corrected candidate, ENS_Reg. Dynamic Classification Probability Weighting (DCPW) then uses classifier softmax outputs to weight ENS_Reg and the five original products, producing ENS_RegCla. Models were developed using 2000–2018 data and evaluated on 357,134 station-day records from 2019 to 2023. With the original 1961–2022 MaxPrec reference, ENS_RegCla achieved MAE = 1.064 mm/day, RMSE = 4.807 mm/day, R2 = 0.666, and KGE = 0.746. MAE was 12.6–15.3% lower than for the evaluated equal-weight, inverse-error, and neighborhood error-variance weighting baselines. At a 0.1 mm/day threshold, CSI = 0.653 and HSS = 0.720 exceeded the two specified ablations. Grouped RMSE favored the ablations, whereas grouped MAE differences were not robust. Restricting MaxPrec to 1961–2018 retained better MAE, RMSE, and KGE than all five original products. However, CSI/HSS fell to 0.633/0.698, approximately matching GSMaP-Gauge in CSI but falling below its HSS. The event-skill advantage over the ablation without MaxPrec nearly disappeared, indicating strong dependence on the reference period. Full-sample evaluation included both training and temporal-evaluation periods and yielded pooled MAE = 0.677 mm/day and KGE = 0.783. Of the 2,004,598 full-sample records, 1,647,464 (82.2%) were from model development. GTCH provided an assumption-limited relative-uncertainty diagnostic; the lower minimum statistic after candidate-set expansion was interpreted descriptively, without establishing true uncertainty reduction. Candidate errors may be correlated because ENS_Reg is derived from the original products. The framework supports condition-aware regional precipitation reconstruction, while moderate and heavy rainfall remain priorities for refinement.
Monitoring the dynamics of gullies in Mediterranean agrosystems is critical but severely limited by agricultural practices that generate continuous machinery compaction and tillage of the soil, camouflaging topographic features and disturbing erosion analyses. This study discusses strategies to quantify erosion by distinguishing natural erosion from agricultural disturbance, integrating multitemporal LiDAR data (2014, 2020, 2024) with gully inventories using orthophoto maps (2011–2022). The volumetric changes were assessed using DoD for the periods 2014–2020 and 2020–2024, and the morphometric performance of the TNO index was further validated using ROC curves in R. Both approaches performed consistently higher in detecting permanent gullies. The TNO index has proven its usefulness for semi-automated delimitation in large or low-data areas. By intersecting DoD surfaces with the gully inventory, localized erosion processes were successfully isolated from the surrounding agricultural landscape matrix. The quantified rate of erosion in the gullies refers strictly to their internal dynamics, which have been close to the natural threshold for soil formation, indicating a risk of soil degradation.
Seabed pockmarks are important geomorphological indicators of shallow fluid migration, shallow gas activity, and gas hydrate-related processes. However, automatic pockmark detection from shaded-relief bathymetric imagery remains challenging because many pockmarks have small image-space footprints, occur densely, have weak boundaries, and are embedded in complex seabed backgrounds. To address these issues, this study proposes FSLA-RTDETR, an improved four-scale RT-DETR network for dense small-object seabed pockmark detection. The model introduces SAC-C2f to enhance high-level feature representation, the LPEA Encoder to improve global contextual modeling, and a DGCST-based bidirectional feature fusion network to integrate shallow spatial details and deep semantic information. In addition, the high-resolution P2 feature is incorporated into the decoder to improve small-object localization. A pockmark detection dataset was constructed from publicly available BOEM bathymetric data from the northern Gulf of Mexico, containing 880 images and 8013 annotated pockmark instances. Experimental results show that FSLA-RTDETR achieves mAP50, mAP75, and mAP50–95 values of 0.9218, 0.6221, and 0.5485, respectively. Compared with the baseline RT-DETR, mAP75 and mAP50–95 increase by 6.01 and 2.22 percentage points, respectively. Under the present no-pretraining setting, comparative experiments further demonstrate that FSLA-RTDETR achieves superior high-IoU localization performance compared with the representative object detection models evaluated in this study, providing an effective method for automatic seabed pockmark detection and geomorphological mapping.
Lake water color provides an integrated indicator of aquatic environmental change, yet its long-term phenology and resilience remain poorly understood in arid regions. In this study, Landsat-derived hue angle (α) records were reconstructed for 350 lakes larger than 1 km2 across arid Northwest China from 1984 to 2025. Regional mean α declined significantly over the past 42 years (R2 = 0.51), accompanied by pronounced spatial heterogeneity. Three lake water color phenological types were identified: Aseasonal (75.0%), Summer Greening (13.7%), and Spring Greening (11.3%). Aseasonal lakes were the most stable, whereas seasonal lakes showed greater temporal variability and a tendency to transition toward aseasonality. Lake water color resilience increased in 43% of lakes, declined in 21%, and remained unchanged in 36%, with resilience-loss lakes showing distinct spatial clustering. Lake morphology was most strongly associated with water color change, topography and thermal conditions with its phenology, and watershed population dynamics with its resilience. In situ observations further showed significant relationships between α and optically active constituents and their derived parameters, reflecting the combined optical effects of absorption and scattering and suggesting its potential as a proxy for broader water-quality assessment with more extensive field sampling and validation. These findings highlight contrasting controls on lake-color dynamics, phenology, and resilience, and demonstrate the value of hue angle for monitoring environmental change in vulnerable arid-zone lakes. Overall, these results advance understanding of the long-term environmental responses of arid-zone lakes and provide a scientific basis for targeted monitoring and adaptive management in arid regions.
