Integrated Measurements for Precision Forestry

As dynamic ecosystems, forests are facing unprecedented challenges under the pressures of climate change, land use transitions, and ecological degradation. Precision forestry—which integrates multi-source measurement tools, computational modeling, remote sensing, and machine learning—has empowered researchers with enhanced capabilities to monitor, predict, and assess forest dynamics across spatial and temporal scales. This Special Issue, “Integrated Measurement Technologies in Precision Forestry,” brings together 16 studies across four central themes: (1) forest growth and structure monitoring and modeling; (2) forest disturbances and risk management; (3) climate-driven phenology and productivity dynamics; and (4) ecological restoration effectiveness and ecosystem service accounting.

Understanding the dynamics of forest growth and structural attributes is fundamental to forest inventory, carbon accounting, and informed management decision-making. The work of [1] constructed a multi-model ensemble framework to estimate the growth rate of Populus spp., which not only improved estimation accuracy but also highlighted the critical regulatory roles of precipitation and temperature across different forest stand structures. The work of [2] enhanced the prediction of the site index and stand basal area for Picea asperata Mast. by incorporating climate variables into a nonlinear mixed-effects model, achieving high predictive accuracy and providing a reliable reference for site productivity assessment. At the landscape scale, the work of [3] simulated forest type transitions in the Ganjiang River Basin from 2000 to 2040 and analyzed the characteristics of such changes. The work of [4] investigated the rapid expansion of areca palm plantations in Hainan Island from 1987 to 2022, revealing a strong positive correlation between plantation area and GDP growth. The work of [5] compared the application of LiDAR SLAM and visual SLAM systems in forest inventories, emphasizing the superior accuracy of LiDAR SLAM in measuring tree-level parameters. The work of [6] proposed a new virtual water displacement scenario for simulating tree volume, which significantly reduced computational costs while maintaining accuracy, advancing virtual modeling of forest structure.

Forest ecosystems are increasingly affected by both natural and anthropogenic disturbances, such as wildfires, pests, and disease outbreaks. Timely detection and effective management are essential to enhancing forest resilience. The work of [7] proposed an integrated fire detection system that combines attention mechanisms and loss optimization, achieving outstanding accuracy in detecting both small and large fire targets. The work of [8] developed a wildfire susceptibility map for Northeast China using a random forest framework, identifying key spatiotemporal patterns and climatic drivers. The work of [9] constructed a deep learning model based on gridded socioeconomic and meteorological data to assess the forest fire risk in Ningxia, identifying multiple high-risk areas. The work of [10] developed a dynamic fire risk model using LightGBM and GIS zoning, uncovering strong seasonal cycles and spatial clustering of fire events. The work of [11] explored small-target detection techniques by applying transfer learning and attention modules to enhance the detection accuracy of pests and diseases in tea plantations, offering a feasible solution for forest health monitoring under small-sample conditions.

Climate change exerts significant and regionally heterogeneous effects on vegetation phenology and carbon fluxes. The work of [12] quantified the actual and potential NPP in subtropical China using the EC-LUE and Thornthwaite models, finding that both human activity and climate variability jointly influence vegetation productivity. The work of [13] conducted a novel analysis of preseason daytime and nighttime warming effects on spring phenology in northern deciduous forests, revealing that daytime maximum temperature has a stronger influence on leaf-out timing than nighttime minimum temperature. The work of [14] assessed the spatiotemporal dynamics of forest vegetation in northern China and its response to climate change, finding a consistent greening trend over the past two decades and significant correlations with climate and latitude. The work of [15] carried out a dendrochronological study on Sabina chinensis in the Yangtze River Delta, providing insights into changing climate-growth correlations before and after recent warming transitions.

As the scale of forest restoration projects expands, there is an urgent need to evaluate their ecological effectiveness. The work of [16] used gross ecosystem product (GEP) data from 2000 to 2020 to assess the Three-North Shelterbelt Project. Their study revealed significant ecological benefits in water conservation, carbon sequestration, and erosion control, especially in mountainous areas, establishing a new benchmark for ecological project evaluation based on ecosystem service accounting.

Collectively, these sixteen studies reflect China’s latest advances in the application of key precision forestry technologies, including artificial intelligence, remote sensing, and geospatial modeling. From virtual modeling at the individual tree level to ecosystem-scale assessments of national ecological programs, these studies provide crucial support for accurately understanding forest conditions and informing decision-making in forest management.