森林資源資產核查是林業管理中的核心環節，其目的在于通過系統化手段摸清森林資源的數量、質量、分布及經濟價值，為生態保護、資源利用及政策制定提供數據支撐。核查過程需兼顧科學性、可操作性與生態可持續性，通常涵蓋地面實測、遙感監測、模型模擬及檔案分析等多維度技術手段。

　　Forest resource asset verification is a core link in forestry management, aimed at systematically understanding the quantity, quality, distribution, and economic value of forest resources, providing data support for ecological protection, resource utilization, and policy formulation. The verification process needs to balance scientificity, operability, and ecological sustainability, usually covering multidimensional technical means such as ground measurement, remote sensing monitoring, model simulation, and archive analysis.

　　地面實測法是森林資源核查的基礎。專業人員通過設置樣地或樣線，對林木的胸徑、樹高、株數、蓄積量等指標進行直接測量，并結合土壤類型、坡度、坡向等立地條件記錄數據。此方法數據精度高，但受地形復雜度、人力成本及時間投入限制，通常用于小范圍精準核查或抽樣驗證。為提升效率，現代實測常結合激光測距儀、樹冠分析儀等便攜設備，減少人為誤差。

　　Ground measurement method is the foundation of forest resource verification. Professional personnel directly measure indicators such as tree diameter at breast height, tree height, number of trees, and stock volume by setting up sample plots or lines, and record data based on site conditions such as soil type, slope, and aspect. This method has high data accuracy, but is limited by terrain complexity, labor costs, and time investment, and is usually used for small-scale accurate verification or sampling validation. To improve efficiency, modern measurement often combines portable devices such as laser rangefinders and canopy analyzers to reduce human errors.

　　遙感監測法依托衛星、無人機或航空影像，通過光譜分析識別森林覆蓋類型、郁閉度及變化趨勢。多光譜與雷達數據可穿透云層，實現全季節、大范圍監測，尤其適用于林火后評估、非法砍伐追蹤及生態修復效果評價。然而，遙感數據需與地面實測結果校準，以解決空間分辨率不足及混合像元問題。

　　Remote sensing monitoring method relies on satellite, drone or aerial images to identify forest cover types, canopy density and change trends through spectral analysis. Multispectral and radar data can penetrate cloud layers, enabling full season and large-scale monitoring, especially suitable for post forest fire assessment, illegal logging tracking, and ecological restoration effect evaluation. However, remote sensing data needs to be calibrated with ground measurement results to address issues of insufficient spatial resolution and mixed pixels.

　　模型模擬法通過構建數學模型，整合氣候、土壤、樹種特性等參數，預測森林生長量、碳儲量及潛在經濟價值。例如，生長收獲模型可模擬不同經營模式下林木蓄積變化，為可持續采伐規劃提供依據。模型精度依賴于輸入數據的完整性，需定期結合實測數據修正參數。

　　The model simulation method constructs mathematical models that integrate parameters such as climate, soil, and tree species characteristics to predict forest growth, carbon storage, and potential economic value. For example, the growth and harvest model can simulate changes in forest stock under different management modes, providing a basis for sustainable logging planning. The accuracy of the model depends on the integrity of the input data, and parameters need to be regularly adjusted based on measured data.

　　檔案分析法則是對歷史林權登記、采伐記錄、造林設計等文檔的梳理，用于追溯資源權屬、驗證數據一致性及識別潛在管理漏洞。此方法常與其他技術結合，例如通過對比遙感影像與林班圖，可快速定位權屬爭議區域或違規開發地塊。

　　The archive analysis rule is the sorting of historical forest rights registration, logging records, afforestation design and other documents, used to trace resource ownership, verify data consistency and identify potential management loopholes. This method is often combined with other technologies, such as comparing remote sensing images with forest maps, to quickly locate areas of ownership disputes or illegally developed land.

　　在實踐應用中，核查需根據目標區域特征選擇組合策略。例如，在交通不便的原始林區，遙感與模型模擬可大幅降低作業難度；在人工林經營區，地面實測與檔案分析更能精準反映經濟效益。同時，核查結果需納入動態管理系統，通過定期復核與數據更新，確保資源評估的時效性。

　　In practical applications, verification requires selecting a combination strategy based on the characteristics of the target area. For example, in primitive forest areas with inconvenient transportation, remote sensing and model simulation can significantly reduce the difficulty of operations; In artificial forest management areas, ground measurement and archive analysis can more accurately reflect economic benefits. At the same time, the verification results need to be incorporated into the dynamic management system, ensuring the timeliness of resource assessment through regular reviews and data updates.

　　隨著技術進步，森林資源資產核查正從單一數據收集向智能化、多源融合方向發展。激光雷達點云數據可生成三維林分結構圖，區塊鏈技術可保障數據溯源與共享安全，人工智能算法則能自動識別影像中的樹種與健康狀態。這些創新不僅提升了核查效率，更推動了森林資源管理從“經驗驅動”向“證據驅動”的轉型，為生態文明建設提供堅實保障。

　　With the advancement of technology, forest resource asset verification is evolving from single data collection to intelligent and multi-source integration. Lidar point cloud data can generate three-dimensional forest structure maps, blockchain technology can ensure data traceability and sharing security, and artificial intelligence algorithms can automatically identify tree species and health status in images. These innovations not only improve verification efficiency, but also promote the transformation of forest resource management from "experience driven" to "evidence driven", providing solid guarantees for the construction of ecological civilization.

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