Traditional forest inventory systems, originally designed to quantify merchantable timber volume, often lack the spatial resolution and structural detail required for modern multi-resource ecosystem management. In this manuscript, we present an Enhanced Forest Inventory (EFI) and demonstrate its utility for high-resolution wildlife habitat mapping. The project area covers 270,000 acres of the Eldorado National Forest in California's Sierra Nevada. By integrating 118 ground-truth Forest Inventory and Analysis (FIA) plots with multi-modal remote sensing data (LiDAR, aerial photography, and Sentinel-2 satellite imagery), we developed predictive models for key forest attributes. Our methodology employed a two-tier segmentation approach, partitioning the landscape into approximately 575,000 reporting units with an average size of 0.5 acre to capture forest heterogeneity. We utilized an Elastic-Net Regression framework and automated feature selection to relate remote sensing metrics to ground-measured variables such as basal area, stems per acre, and canopy cover. These physical metrics were translated into functional habitat attributes to evaluate suitability for two focal species: the California Spotted Owl (Strix occidentalis occidentalis) and the Pacific Fisher (Pekania pennanti). Our analysis identified 25,630 acres of nesting and 26,622 acres of foraging habitat for the owl, and 25,636 acres of likely habitat for the fisher based on structural requirements like large-diameter trees and high canopy closure. The results demonstrate that EFIs provide a critical bridge between forestry and conservation ecology, offering forest managers a spatially explicit tool to monitor ecosystem health and manage vulnerable species in complex environments.

翻译：传统的森林资源清查系统最初旨在量化商用木材蓄积量，其空间分辨率和结构细节往往无法满足现代多资源生态系统管理的需求。本文提出了一种增强型森林资源清查方法，并论证了其在高分辨率野生动物栖息地制图中的应用价值。研究区域覆盖加利福尼亚州内华达山脉埃尔多拉多国家森林的27万英亩林地。通过整合118个地面验证的森林资源清查与分析样地数据与多模态遥感数据（激光雷达、航空摄影及哨兵-2号卫星影像），我们建立了关键森林属性的预测模型。研究方法采用双层分割策略，将景观划分为约57.5万个平均面积为0.5英亩的制图单元，以捕捉森林异质性。运用弹性网络回归框架与自动化特征选择技术，建立了遥感指标与地面实测变量（如胸高断面积、每英亩株数、冠层郁闭度）的关联模型。这些物理指标被转化为功能性栖息地属性，用于评估两种重点物种的适宜性：加州斑点鸮（Strix occidentalis occidentalis）与太平洋渔貂（Pekania pennanti）。分析结果显示：基于大径级树木和高郁闭度等结构需求，共识别出25,630英亩的猫头鹰巢址栖息地、26,622英亩的觅食栖息地，以及25,636英亩的渔貂潜在栖息地。研究结果表明，增强型森林资源清查架起了林业与保护生态学之间的关键桥梁，为森林管理者提供了空间显式的工具，以监测复杂环境中的生态系统健康并管理脆弱物种。