The Dayingezhuang orogenic gold deposit, located in the northwestern Jiaodong Peninsula, is hosted by the Zhaoping detachment fault, but the paleostress regime during the mineralization period remains poorly understood. In this study, a series of numerical modeling experiments with variable stress conditions were carried out using FLAC3D software to determine the orientation of paleostress and the fluid migration processes during the ore-forming period. The results show that the simple compression or tension stress model led to fluid downward or upward flow along the fault, respectively, accompanying the expansion deformation near the hanging wall or footwall of the Zhaoping fault, which is inconsistent with the known NE oblique mineralization distribution at Dayingezhuang. The reverse and strike-slip model shows that the shear stress was distributed in the gentle dip sites of the fault, and the expansion space occurred in the geometric depression sites of the fault, which is also inconsistent with the known mineralization distribution. The normal and strike-slip model shows that shear stress was distributed in the sites where the fault geometry transforms from steep to gentle. In addition, the expansion deformation zones appeared at sites with dip angles of 35~60° in the footwall and extended along with the NE-trending distribution from shallow to deep levels. The numerical results are quite consistent with the known mineralization, suggesting that the fault movement during the mineralization stage is a combination of the local strike-slip and the NW–SE extension in the Dayingezhuang deposit. Under this stress regime (σ₁ NE–SW, σ₂ vertical, σ₃ NW–SE), the NE dilation zones associated with fault deformation served as channels for the ore-forming fluid migration. Based on the numerical modeling results, the deeper NE levels of the No. 2 orebody in the Dayingezhuang deposit have good prospecting potential. Thus, our study not only highlights that gold mineralization at Dayingezhuang is essentially controlled by the detachment fault geometry associated with certain stress directions but also demonstrates that numerical modeling is a robust tool for identifying potential mineralization.

Satellite remote sensing has become an important means of forest fire monitoring because it has the advantages of wide coverage, few ground constraints and high dynamics. When utilizing satellites for forest fire hotspot monitoring, two types of ground hotspots, agricultural and other fire hotspots can be ruled out through ground object features. False forest fire hotspots within forested areas must be excluded for a more accurate distinction between forest fires and non-forest fires. This study utilizes spatio-temporal data along with time-series classification to excavate false forest fire hotspots exhibiting temporal characteristics within forested areas and construct a dataset of such false forest fire hotspots, thereby achieving a more realistic forest fire dataset. Taking Hunan Province as the research object, this study takes the satellite ground hotspots in the forests of Hunan Province as the suspected forest fire hotspot dataset and excludes the satellite ground hotspots in the forests such as fixed heat sources, periodic heat sources and recurring heat sources which are excavated. The validity of these methods and results was then analyzed. False forest fire hotspots, from satellite ground hotspots extracted from 2019 to 2023 Himawari-8/9 satellite images, closely resemble the official release of actual forest fires data and the accuracy rate in the actual forest fire monitoring is 95.12%. This validates that the method employed in this study can improve the accuracy of satellite-based forest fire monitoring.

With global warming, the incidence of forest fires has risen significantly, negatively affecting the ecological balance and the environment. This paper is aimed at summarizing the current state of research and global development trends regarding monitoring of forest fires with remote sensing. In the present study, a total of 8841 documents from 2009 to 2024 were extracted with the goal of gaining insights into the progress and dynamics of forest fire monitoring based on remote sensing and the core library of the Web of Science (WoS) and visualized and analyzed with CiteSpace bibliometric software. Results show that research on remote sensing to monitor forest fires has mainly gained momentum in the last 16 years and has shown a significant increase after 2018. The United States, as a major research center, leads the global scientific community in terms of national and institutional collaborations. Forests, with 382 publications, was ranked first as the leading published journal in the field. Dr. Sun of the University of Northeast Forestry University, China, became the most prolific author, ranking first with 16 citations. The influence of driving factors on forest fires imposed on properties of soil drivers is revealed through the analysis of trends in research areas. Forest fire spread, index, fire trend will be the next focus of research. By combining biblio-metric methods and systematic reviews, these findings provide forest fire managers and researchers with a deeper comprehension of the development of monitoring forest fires using remote sensing.

Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to refine predictions. Using historical wildfire records from Hunan Province, China, we first derived wildfire occurrence probabilities via ST-DBSCAN, avoiding the need for artificial non-fire samples. We then benchmarked GTWR-RFR against seven models, finding that our approach achieved the highest accuracy (R² = 0.969; RMSE = 0.1743). The framework effectively captures spatiotemporal heterogeneity and quantifies dynamic impacts of environmental drivers. Key contributing drivers include DEM, GDP, population density, and distance to roads and water bodies. Risk maps reveal that central and southern Hunan are at high risk during winter and early spring. Our approach enhances both predictive performance and interpretability, offering a replicable methodology for data-driven wildfire risk assessment.