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Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.rse.2020.112223
Joel B. Sankey , Temuulen T. Sankey , Junran Li , Sujith Ravi , Guan Wang , Joshua Caster , Alan Kasprak

Abstract Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.

中文翻译:

量化牧场中的植物-土壤-养分动态:无人机高光谱激光雷达、无人机多光谱摄影测量和地面激光雷达数字摄影在灌木侵蚀的沙漠草原中的融合

摘要 牧场占世界陆地面积的 70%,为初级生产、土壤碳储存和养分循环提供重要的生态系统服务。这些生态系统服务由厘米到米尺度的土壤碳、养分和植物物种的非常精细的空间格局控制,这种现象被称为“肥力岛”。大多数卫星和载人机载平台很难检测到这种精细尺度的动态。无人驾驶飞行器 (UAV) 的遥感提供了一种替代选择,可在较小范围内检测牧场中的精细土壤养分和植物物种变化。我们证明了一个融合无人机多光谱和结构运动摄影测量的模型对植物功能类型和裸土覆盖进行分类,在因灌木侵占和火灾干扰而退化的牧场中,总体准确度为 95%。我们进一步证明,通过对 9 种不同的植物物种和土壤肥力微型站点类型 (SFMT) 进行分类,以 87% 的总体准确率,采用无人机高光谱和 LiDAR 融合极大地改善了这些结果。其中,牧区最重要的本地物种杂酚油和黑格玛的生产者准确率最高,分别为 98% 和 94%。无人机 LiDAR 衍生的植物高度差异的整合在这些改进中至关重要。最后,我们使用无人机数据集与基于地面的 LiDAR 调查和土壤实验室表征的合成来估计,由于土壤侵蚀过程,被烧毁的牧场可能会损失 1474 公斤/公顷的碳和 113 公斤/公顷的氮。规定的火。然而,在火灾后的第二年,草和植物空间 SFMT 作为沉积物和养分的净汇,增加了大约 175 公斤/公顷的碳和 14 公斤/公顷的氮。这些结果提供了重要的特定地点洞察力,与全球每年被烧毁的 423 Mha 草原和灌木林相关。虽然火灾,特别是火灾后的侵蚀,可以使一些牧场退化,但火灾后的植物-土壤-养分动态可能为因灌木侵蚀而退化的牧场中的草提供竞争优势。
更新日期:2021-02-01
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