Enterkine Josh, Hojatimalekshah Ahmad, Vermillion Monica, Van Der Weide Thomas, Arispe Sergio A, Price William J, Hulet April, Glenn Nancy F
Boise State University Department of Geosciences Boise Idaho USA.
USDA US Forest Service, Forest Health Protection Region 4 Boise Idaho USA.
Ecol Evol. 2025 Jan 23;15(1):e70883. doi: 10.1002/ece3.70883. eCollection 2025 Jan.
In much of the northern Great Basin of the western United States, rangelands, and semi-arid ecosystems invaded by exotic annual grasses such as cheatgrass () and medusahead () are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring the above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes and better harmonize with other remote sensing techniques. Developments and increased adoption of unoccupied aerial systems (UAS) and instrumentation for vegetation monitoring enable greater understanding of vegetation in many ecosystems. Research to understand the relationship of traditional field measurements with remotely sensed data in rangeland environments is growing rapidly, and there is increasing interest in the use of aerial platforms to quantify AGB and fine-fuel load at pasture and landscape scales. Our study uses relatively inexpensive handheld photography with custom quadrat sampling frames to collect and automatically reconstruct 3D models of the vegetation within 0.2 m quadrats ( = 288). Next, we examine the relationship between volumetric estimates of vegetation with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared with validation ground surface reconstructions. This finding is significant given that our study site is characterized by a dense litter layer covering the ground surface, making reconstruction challenging. Overall, our best reconstruction workflow had an R of 0.42, further emphasizing the importance of high-resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine-fuel litter.
在美国西部大盆地北部的大部分地区，被诸如黑麦草（）和蛇头草（）等外来一年生草本植物入侵的牧场和半干旱生态系统，正经历着越来越短的火灾周期，且这种情况日益严重且持续存在。改进和扩展用于测量地上生物量（AGB）的地面实地方法，可能会使在一个景观区域以及不同演替阶段能够进行更多的样本采集，并更好地与其他遥感技术相协调。无人航空系统（UAS）以及用于植被监测的仪器的发展和更多应用，使人们能够更深入地了解许多生态系统中的植被。旨在理解牧场环境中传统实地测量与遥感数据之间关系的研究正在迅速发展，并且人们越来越有兴趣利用航空平台来量化牧场和景观尺度上的地上生物量和细燃料载量。我们的研究使用相对廉价的手持摄影设备以及定制的样方采样框架，来收集并自动重建0.2米×0.2米样方（=288个）内植被的三维模型。接下来，我们研究植被体积估计值与生物量之间的关系。我们发现，以0.5厘米体素大小（0.125立方厘米）计算的体积最能准确代表生物量重量范围。我们进一步开发了地面点分类方法，发现与验证地面重建相比，预测能力降低了2%。鉴于我们的研究地点地面覆盖着一层厚厚的枯枝落叶层，使得重建具有挑战性，这一发现具有重要意义。总体而言，我们最佳的重建工作流程的R值为0.42，进一步强调了高分辨率图像和重建技术的重要性。最终我们得出结论，需要开展更多范围更广（例如来自无人航空系统的数据）的工作，以更好地理解和限制在具有大量入侵一年生草本植物和细燃料枯枝落叶的生态系统中生物量体积估计的不确定性。
