RCCZO -- Land Cover, LiDAR, Vegetation -- Biomass Estimate of Sagebrush -- Reynolds Creek Experimental Watershed -- (2012-2012)

Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5 m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2 = 0.87, p < 0.001, RMSE = 3.59 kg). The ALS prediction model was applied to the study watershed and evaluated with independent TLS plots (adjusted R2 = 0.55, RMSE = 4.01 kg, normalized RMSE = 35%). The biomass estimates at the scale of 5 m is sufficient for capturing the variability of biomass needed to initialize models to estimate ecosystem fluxes, and the contiguous estimates across the watershed support analyzing patterns and connectivity of these dynamics. Our model is currently optimized for the sagebrush-steppe environment at the watershed scale and may be readily applied to other shrub-dominated drylands, and especially the Great Basin, U.S., which extends across five western states. Improved derived metrics from ALS data and collection of additional TLS data to refine the relationship between TLS-derived biomass estimates and ALS-derived models of vegetation structure, will strengthen the predictive power of our model and extend its range to similar shrubland ecosystems.区域尺度旱地植被生物量估算对生态建模至关重要，但这类生态系统以低矮、稀疏的植物群落为特征，基于光谱的航空与卫星遥感技术难以精准量化该类群落的生物量并测定其变异特征。为解决上述难题，本研究将多尺度数据——包括野外实测生物量、地面激光扫描（Terrestrial Laser Scanning, TLS）与机载激光扫描（Airborne Laser Scanning, ALS）数据——整合至分层建模框架中。各尺度获取的数据均用于验证范围逐步扩大的三齿蒿（Artemisia tridentata）地上生物量估算模型：首先，采用两种自动冠层勾勒（Crown Delineation）方法对TLS样地内的单株灌木进行轮廓提取；其次，基于TLS数据构建3种灌木体积提取模型，并将模型结果与破坏性采样获取的单株灌木生物量实测数据进行回归分析；第三，以5m分辨率下的TLS反演生物量估算值为基准，通过以ALS反演植被覆盖度为自变量的线性回归校准生物量预测模型（调整后决定系数（Adjusted R-squared）$R^2=0.87$，$p<0.001$，均方根误差（Root Mean Square Error, RMSE）=3.59 kg）。将该ALS生物量预测模型应用于研究流域，并通过独立TLS样地进行验证（调整后决定系数$R^2=0.55$，均方根误差RMSE=4.01 kg，归一化均方根误差（Normalized Root Mean Square Error, NRMSE）=35%）。5m分辨率的生物量估算结果足以捕捉初始化生态系统通量（Ecosystem Fluxes）估算模型所需的生物量变异特征，而流域内连续分布的生物量估算值则可为分析该类动态的空间格局与连通性提供支撑。本模型目前针对流域尺度的三齿蒿草原（Sagebrush-Steppe）环境进行了优化，可直接推广应用于其他以灌木为主的旱地生态系统，尤其是横跨美国西部5个州的大盆地（Great Basin）区域。通过优化ALS数据的反演指标，并补充采集TLS数据以细化TLS反演生物量估算值与ALS植被结构模型之间的关联关系，可进一步提升本模型的预测能力，并将其应用范围拓展至同类灌丛生态系统。