Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics.

入侵杂草正对全球草地的生物多样性与牧草生产力构成严重威胁。然而，此类杂草的防控工作却面临两大瓶颈：一是难以在大范围景观中精准侦测小规模的杂草侵染区域，二是对景观尺度的入侵动态（包括杂草斑块扩张、收缩或维持稳定的机制）认知不足。尽管高端高光谱遥感（hyperspectral remote sensing）系统可有效绘制植被覆盖图，但对多数土地管理者而言，这类系统成本高昂且获取渠道有限。本研究展示了一种基于简易航空影像的、更易获取且成本可控的遥感方法，用于量化杂草覆盖度随时间的动态变化。在加利福尼亚一年生草地中，本研究关注的目标群落包括入侵性杂草禾草（山羊草Aegilops triuncialis和水母麦草Elymus caput-medusae）以及优质牧草禾草类群（主要为燕麦属Avena spp.和雀麦属Bromus spp.）。在以一年生植物为主的群落中侦测入侵性一年生禾草的侵染本就极具挑战性，但本研究通过对每年两期（生长中期与季末）获取的影像开展最大似然监督分类，基于二者在生长峰值与衰老阶段的物候差异，实现了对这两类群落的稳定区分。该方法可实现1米分辨率下的杂草主导覆盖区制图（可准确侦测景观中93%的杂草斑块），并能评估杂草覆盖度的时间变化趋势。研究发现，连续数年未进行有效放牧的管理单元内，杂草覆盖度显著高于放牧单元，且侵染更为持久；而放牧单元的牧草覆盖度则更高且更稳定。本应用案例证实了该方法在异质景观中评估精细尺度植被动态变化的有效性，可为入侵物种的小规模早期侦测以及景观动态相关基础问题的验证提供可行途径。