Implementing forest protection measures such as afforestation, can be an effective approach towards slowing down the increase of C02 concentration and attaining carbon neutrality. The estimation of forest parameters is of great significance in understanding regional and global climate cbange pattems, aod the forest leaf area lndex(LAI) is a crucial parameter. Current LAI products are mosdy generated by moderate-resolution remote sensing data which doesn't meet the precision requirements for mountain forest ecosystems. To overcome this issue, there is an urgent need for higber resolution LAI data. This paper proposes a data fusion method to map LAI in Wolong Nature Reserve that utilizes Sentinel-2 reftectance data, solar sensor geometry parameters, and vegetation indices(VI) extracted from the Google Earth Engine (GEE) platform, along with canopy beigbt data derived from canopy beigbt estimation models in previous studies, combined with GLASS LAI V6 to estimate LAI using the random forest algorithm. Tbe resulting LAI distribution map was plotted at a resolution of 20 m. The study demonstrated that incorporating canopy heights Into the estimation modelled to an R2 model accnracy of greater thao 0.83. Tbe 20-m resolution LAI map increased spatial details compared to the moderateresolution LAI map, making it more suitable for mountain forrst ecosystems that exhibit significant spatial heterogeneity.

Forest fires in Yanyuan County, a typical dry-hot valley region, pose serious threats to ecological security and public safety. Conventional fire warning methods rely heavily on manual surveys, making them time-consuming, labor-intensive, and prone to missing the critical window for effective intervention. This paper presents a 3D visualization decision support system for fire prevention, developed on a Web-GIS platform using the Cesium engine. The system integrates multi-source data, including a 12.5 m DEM, remote sensing imagery, and real-time video streams. It employs a YOLO11 model for automated fire and smoke detection, achieving a precision of 82.4%. Compared to conventional 2D systems, the platform enhances emergency response speed by 37% while significantly improving spatial awareness and operational coordination. This cross-platform tool facilitates sustainable forest management through efficient resource allocation and real-time monitoring, offering a scalable and practical solution for fire prevention in complex terrains.