According to previous studies, the Lixiahe area in the east of the Jianghuai River was an alternate environment of land and sea in the middle Holocene, and it was not until the late Holocene that the eastern Jianghuai completely became a terrestrial environment. However, recent archaeological studies have found that the extensive Neolithic sites in the Lixiahe area have recorded the rich human activities and cultural connotations of the prehistoric civilization in the Middle Holocene. In this paper, the Gangxi section of Jianhu Lake, Jiangsu Province (GX2) was selected and pollen analysis was fulfilled, then the geomorphic evolution process of the study area from sea to land was investigated according to the palynological assemblages and algae fossils of brackish water, semi-saline water, fresh water and terrestrial in the section strata. During the period of 8500–3800 cal. BP, GX2 was affected by multiple factors such as sea surface fluctuation, ocean flow and sediment deposition carried by seagoing rivers. Since 5800 cal. BP, the area east of the Grand Canal between the Yangtze River and the Huaihe River, and the west of the Yangzhai town, Funing–Longgang town, Yancheng–Dagang town and Yancheng–west of the Dongtai–Hai’an line, have become a terrestrial environment. After the eastern Jianghuai became a land, the Liangzhu culture (5300–4300 cal. BP) in the Taihu Lake basin in the south wing of the Yangtze River delta expanded to the eastern Jianghuai area, and the Longshan culture and Yueshi culture in the Haidai area in the north also migrated south to the eastern Jianghuai area. Admittedly, the main reason in the process of Neolithic cultural development is the internal motivation that the ancient ancestors struggled with nature and pushed forward the continuous development of civilizations. However, our study explains the palaeogeographical origin of Neolithic culture in the eastern and coastal areas of Jianghuai in the middle Holocene, and meanwhile, provides an example for the man–land relationship research on Neolithic culture.

Landslide susceptibility assessment constitutes a pivotal method of preventing and reducing losses caused by geological disasters. However, traditional models are often influenced by subjective grading factors, which can result in unscientific and inaccurate assessment outcomes. In this study, we thoroughly analyze various landslide causative factors, including geological, topographical, hydrological, and environmental components. A quantitative continuous model was employed, with methods such as frequency ratio (FR), cosine amplitude (CA), information value (IV), and certainty factor (CF) being applied in order to assess the landslide susceptibility of the Wanzhou coastline in the Three Gorges Reservoir area. The results were then compared with methods such as Bias-Standardised Information Value (BSIV), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT). This process led to the following key conclusions: (1) Most landslide susceptibility zones are predominantly banded and clustered on both sides of the Dewuidu River, particularly along the left bank of the Yangtze River from Dewuidu Town to Wanzhou City, as well as in the main urban area of Wanzhou. Clusters of the Yangtze River mainstem and surrounding towns characterize these areas. (2) The enhanced statistical analysis model shows a notable increase in sensitivity to landslides, achieving an Area Under the Curve (AUC) of 0.8878 for the IV model—an improvement of 0.0639 over the traditional BSIV model. This enhancement aligns closely with machine learning capabilities, and the spatial results obtained are more continuous. (3) By substituting manual grading with a quantitative continuous model, we achieve a balance between interpretability and computational efficiency. These findings lay a scientific foundation for the prevention and management of geological disasters in Wanzhou and provide valuable insights for comparable regions undertaking landslide susceptibility assessments.

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands.

Accurate landslide susceptibility assessment (LSA) is essential for regional disaster prevention and mitigation planning. Although machine learning and deep learning models have been widely adopted for LSA, their transferability across regions is often compromised by distributional discrepancies between source and target datasets. To address this challenge, we adapted the conditional domain adversarial network (CDAN) framework for cross-regional landslide susceptibility assessment to mitigate domain shift and improve cross-regional robustness. Two cross-regional transfer experiments were conducted to compare CDAN with conventional machine learning, direct-transfer deep learning, and representative domain adaptation methods. The results showed that CDAN achieved the highest mean Accuracy and F1 score among the benchmark models in both target domains, with Accuracy values of 0.77 and 0.80 and F1 scores of 0.78 and 0.80, respectively. The corresponding values of the area under the receiver operating characteristic curve (AUC) were 0.837 and 0.836, respectively. Neighborhood-based spatial information also improved the direct-transfer performance of conventional machine learning models compared with single-pixel representations. These results indicate that the CDAN-based framework can reduce the reliance on labeled target-domain samples and support county-level cross-regional landslide susceptibility assessment under domain shift.

Accurate monitoring of tree recovery in restored open-pit mines is essential for evaluating rehabilitation effectiveness and guiding post-closure management. Using the Huafeng open-pit limestone quarry (Qingyang County, Anhui Province) as a case study, we integrated 2025 unmanned aerial vehicle (UAV)-borne LiDAR with 2024 UAV red, green, and blue (RGB) orthophotos to develop a cross-temporal tree extraction and forest-change assessment workflow. A 0.5 m canopy height model (CHM) was derived from high-density LiDAR point clouds, and individual crown tops and canopy boundaries were delineated by combining local maximum detection with watershed segmentation, yielding 5,863 individual trees under predefined height and crown-size constraints. These LiDAR-derived tree objects were projected onto RGB imagery to generate training/validation labels for a You Only Look Once (YOLO)v9 detector, which was applied to 2024 orthophotos to map tree distribution and forest/non-forest patterns. The net forest increase of approximately 1.34 ha represented a 15.8% relative increase compared with 2024. Non‑forest areas declined by 24.1%, from 5.56 ha to 4.22 ha. Newly established forest was concentrated on the pit‑bottom platform, terraced slopes with topsoil cover, and the former industrial site, whereas persistent non‑forest patches were predominantly associated with steep bare‑rock slopes. These contrasting spatial patterns highlight priority areas for targeted soil conservation and replanting, and demonstrate the value of multi‑source UAV remote sensing for reproducible restoration monitoring.