Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring
Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring
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DOI：
10.1016/j.rse.2020.112243
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发表时间：
2021-03-01
影响因子：
13.5
通讯作者：
Kudela, Raphael M.
中科院分区：
文献类型：
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作者：
Finger, Dennis J. I.;McPherson, Meredith L.;Kudela, Raphael M.
从加利福尼亚州中部延伸到阿拉斯加，牛海带(Nerecystis Luetkeana)形成了季节性海藻森林，是东太平洋大部分地区标志性的沿海生态系统。以往和正在进行的实地调查和航空图像被用来提供关于海藻树冠覆盖率和健康的生物数据，但卫星遥感提供了在大空间尺度上产生一致的长期数据集的机会。加州大部分海岸线(例如，圣巴巴拉到圣克鲁斯)都有对巨藻(巨藻)的基于卫星的时间序列测量，但还没有关于牛海带的类似出版物。加利福尼亚州北部最近出现的牛海带损失突出了对树冠趋势进行更广泛的长期监测的必要性。我们使用多终端光谱混合分析(MESMA)对Landsat图像进行了各种海带分类方法的测试，该方法允许在牛海带的狭窄季节最大值期间收集足够的时间和空间数据，并与加州鱼类和野生动物部(CDFW)的航空调查冠层面积产品进行了比较。我们讨论了使陆地卫星能够长期监测加州北部牛海带冠层覆盖的五个主要主题：(1)应用于陆地卫星图像的中尺度配置的影响，包括软件依赖和终端成员配置，(2)陆地卫星与传统调查的比较，(3)陆地卫星传感器之间的差异，(4)潮汐对树冠面积的影响，以及(5)十年时间序列的趋势。我们发现，MESMA平台之间没有统计学差异(p=0.53)(基于IDL的方法和基于PYTHON脚本的方法；按CDFW的范围归一化后的RMSE为1.5 KM(2)或17.6%)，并且7端成员的MESMA模型提供的RMSE最低(1.4 KM(2)或16.9%)。此外，虽然潮汐相可能会淹没或浮现海藻树冠，因此可能会影响海藻的检测，但我们发现潮汐与我们对海藻树冠面积的远程估计的表现之间存在微弱且在统计学上不显著的相关性。从Landsat-8图像估计的冠层比Landsat-4和-5图像产生了更高的NRMSE，但缺乏匹配限制了比较。初秋的图像产生了最大的覆盖率估计。总体而言，我们的结果表明，陆地卫星能够对牛海带树冠覆盖率进行广泛的远程测量，以补充现有的调查方法，并增加监测长期趋势的时间序列的连续性。
Extending from central California to Alaska, bull kelp (Nereocystis luetkeana) forms seasonal kelp forests that are iconic coastal ecosystems in much of the eastern Pacific. Historical and ongoing field surveys and aerial imagery are used to provide biological data on kelp canopy cover and health, but satellite remote sensing provides the opportunity to generate consistent, long-term datasets over a large spatial scale. Robust satellite-based timeseries measurements of giant kelp (Macrocystis pyrifera) are available for much of the California coastline (e.g., Santa Barbara to Santa Cruz), but there have been no equivalent publications for bull kelp. Recent loss of bull kelp in northern California emphasized the need for more expansive long-term monitoring of canopy trends. We tested various kelp classification approaches using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to Landsat imagery, which allowed sufficient temporal and spatial data collection during bull kelp's narrow seasonal maximum, and compare with the California Department of Fish and Wildlife (CDFW) aerial survey canopy area product. We addressed five main topics that have relevance to enabling Landsat in long-term monitoring of bull kelp canopy coverage in northern California: (1) the effect of MESMA configurations applied to Landsat imagery, including software dependencies and endmember configurations, (2) comparison of Landsat to traditional surveys, (3) differences across Landsat sensors, (4) tidal influence on canopy area, and (5) trends in the decadal timeseries. We found that there was no statistical difference (p = 0.53) between MESMA platforms (IDL-based and a Python-scripted method; RMSE 1.5 km(2) or 17.6% when normalized by the range in CDFW), and that a 7-endmember MESMA model provided the lowest RMSE (1.4 km(2) or 16.9%). Furthermore, while tidal phases can submerge or emerge kelp canopy and thus potentially affect kelp detection, we found a weak and statistically insignificant correlation between tides and performance of our remote estimate of kelp canopy area. Canopy estimations from Landsat-8 images yielded a higher NRMSE than Landsat-4 and -5 images, but the lack of matchup limits comparison. Imagery from early fall yielded the largest coverage estimates. Overall, our results show that Landsat enables broad remote measurement of bull kelp canopy coverage to supplement existing survey methods and increase continuity of timeseries for monitoring long-term trends.