Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning.

Water can contain pathogenic viruses. Many studies on RNA virus sources have shown that water can transmit them. However, there are few reports on pathogenic DNA virus transmission through water, such as adenovirus, which pose a widespread public health risk. Therefore, this study aimed to show waterborne viral transmission by detecting viruses in pooled human whole blood samples, tap water, and natural water from Mueang District, Phayao Province, Thailand, using a metagenomic approach. Viral prevalence in whole blood samples was measured by polymerase chain reaction (PCR) and quantitative PCR (qPCR), and environmental factors that affect viral infection were assessed. Metagenomics results showed that Epstein–Barr virus (EBV) members were among the prominent cancer-associated oncogenic DNA viruses detected in human blood and all water types similar to the EBV reference sequence (NC_007605). There were 59 out of 813 (7.26%) human whole blood samples that were positive for EBV DNA based on PCR and qPCR for the EBNA-1 and EBNA-2 genes. Water- and blood-borne human oncogenic EBV should be a concern in tap water treatment and blood transfusion in patients, respectively. Therefore, the detection of EBV in water suggests that transmission via water is possible and should be investigated further.

Traditional methods for assessing sunflower yield across large agricultural fields are typically labor-intensive and time-consuming. This study explores the integration of unmanned aerial vehicle (UAV) imagery and the YOLOv11 deep learning model for automated sunflower head detection and yield estimation. Aerial imagery was collected from sunflower fields using UAVs, and a YOLOv11-based detection model was developed to identify sunflower heads efficiently. Model performance was optimized by tuning the Confidence Threshold and Intersection over Union (IoU) parameters. A total of 1290 image tiles derived from 215 UAV images were used for model training and evaluation. The dataset was divided into training and testing subsets with an 80:20 ratio. The optimal configuration, achieved at a Confidence Threshold of 0.50 and an IoU Threshold of 0.40, yielded balanced and accurate results, including a Precision of 0.84, Recall of 0.95, mAP@0.5 of 0.95, and an F1-score of 0.90. The findings demonstrate that parameter adjustment directly influences model detection accuracy and reliability. Overall, this study confirms that combining UAV remote sensing with YOLOv11 offers a robust and scalable approach for automated sunflower yield estimation, significantly reducing manual effort and processing time. Moreover, the proposed framework can be adapted for other high-value crops, contributing to the advancement of intelligent and data-driven agricultural management systems.

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R² = 0.9744 for GEE and R² = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making.

Freshwater crabs are important biological resources in northern Thailand, yet their diversity remains poorly documented in many areas. This study investigated freshwater crab diversity in Ban Tham Subdistrict, Dok Khamtai District, Phayao Province, using morphological examination and mitochondrial cytochrome c oxidase subunit I (COI) DNA barcoding. A total of 69 specimens were assigned to four taxa. Indochinamon bhumibol and Sayamia bangkokensis were identified to species level, whereas two unidentified taxa were provisionally designated Potamidae sp. 1 and Potamidae sp. 2. Genetic diversity varied among taxa, with Potamidae sp. 2 showing the highest haplotype and nucleotide diversity, whereas S. bangkokensis showed no haplotype variation. Pairwise K2P distances revealed substantially greater divergence among taxa than within taxa. Phylogenetic analysis resolved the four taxa into distinct clades, while Automatic Barcode Gap Discovery (ABGD) recovered four molecular operational taxonomic units in the initial partition, corresponding to the four taxa. These results support the genetic distinctiveness of Potamidae sp. 1 and Potamidae sp. 2, although their species identities remain unresolved. Further taxonomic and molecular studies are required to clarify their identities. These findings provide baseline information for the conservation and sustainable management of freshwater crab resources in the study area.

The shallow groundwater recharge at an area scale in Fai Kwang Subdistrict, Chiang Kham District, Phayao Province, Thailand aim to apply of the groundwater replenishment system to solve the problem of drought and insufficient water for consumption using by shallow groundwater recharge system through the concrete circle in pond integrating with using technology to manage surface water with the GIS system and geophysical surveys for evaluating the potential of groundwater resources to access information on land use, soil conditions, water resources. The groundwater recharge system at depth 12 m and water was replenishment occurs flow from pubic pool collected water converted to water filtration and pumping average water volume of 3.54 m3/hr to solve the problem of surface water quality for water supply in tap water production of Siwilai Village, Fai Kwang Subdistrict, Phayao Province, which enabled people in the area to used water for 200 homes. The social return on investment (SROI) was 59.23%, reducing water purchase and medical expenses due to water quality impact. The study results have led to the creation of water management policies and the application of information and innovations for drought resolution. Measures and requirements for using public water sources for consumption collecting income for the maintenance of the village's water supply system.