The dragon fruit industry holds significant market potential and is crucial for rural economic development. However, a comprehensive understanding and precise technical approach for analyzing the spatiotemporal dynamics of dragon fruit agriculture remain lacking. This study utilizes Nighttime Light (NTL) remote sensing data and proposes the Vegetation and Impervious area Adjusted Nighttime light Dragon fruit Index (VIANDI) to extract artificial light sources associated with dragon fruit cultivation. Furthermore, a regression model is constructed to estimate production based on light intensity. By integrating geospatial analysis methods, this study reveals the spatiotemporal evolution of dragon fruit cultivation area and production in Guangxi, China, from 2017 to 2022. The results demonstrate that the proposed method effectively monitors the dynamics of dragon fruit agriculture, achieving a Kappa Coefficient of 0.72 for area extraction and a Mean Relative Error (MRE) of 8.90% for production estimation. The spatial pattern of dragon fruit production follows a northwest–southeast distribution, with its centroid located in Nanning. The spatial expansion of cultivation areas exhibited an initial growth phase followed by stabilization, whereas production distribution transitioned from expansion to aggregation, maintaining an overall upward trend. Notably, 2019 marks a key turning point in these trends. Additionally, the rapid increase in light pollution intensity within cultivation areas warrants further attention. The study results have advanced precise monitoring of dragon fruit agriculture and enhanced understanding of its spatiotemporal evolution patterns.

Thermal anomalies serve as potential earthquake precursors and are crucial for understanding the mechanisms underlying seismogenic mechanisms and geodynamic perturbations. To address the limited understanding of the polarity evolution of thermal anomalies, we developed a dynamic spatiotemporal adaptive framework to quantify global thermal anomaly responses. Four parameters—the coefficient of determination (R²), spatiotemporal uncertainty (SU), temporal–spatial uncertainty ratio (TSUR), and spatiotemporal correlation coefficient (SCC)—were established to characterize the spatiotemporal patterns of thermal anomaly responses. Additionally, the Anomaly Emphasis Proximity (AEP) was introduced to identify statistically significant thermal anomaly events. The results indicate that the spatiotemporal evolution of thermal anomalies exhibits a transition from pre-earthquake mixed anomalies (both positive and negative) to post-earthquake unipolar anomalies (TIB decreased from 92% to 49%), accompanied by pronounced sea–land differentiation (SST increased from 0.3% to 98.7%). The AEP reveals significant thermal anomaly clustering highly consistent with earthquake activity (e.g., the 2008 Mw 8.0 Wenchuan earthquake in the Qinghai–Tibet Plateau), showing strong correlations in structurally active regions (e.g., SCA and SWS; FDR < 18.5%, STCW > 3.7%) but weaker ones in stable regions (e.g., CNA and ECA). Overall, this framework significantly enhances the robustness and reliability of seismic thermal anomaly detection.