Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?
Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?
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DOI：
10.3390/rs15081972
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Hana L. Sellers;S. V. Zesati;S. Elmendorf;Alexandra B. Locher;S. Oberbauer;C. Tweedie;C. Witharana;R. Hollister
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文献类型：
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作者：
Hana L. Sellers;S. V. Zesati;S. Elmendorf;Alexandra B. Locher;S. Oberbauer;C. Tweedie;C. Witharana;R. Hollister
对于植被监测来说，地块水平摄影是一种有吸引力的节省时间的现场测量替代方案。然而，这项技术的广泛采用依赖于图像后处理的高效工作流程和最终产品的准确性。在这里，我们使用传统的野外采样方法(点框)和半自动照片分类方法(地块水平摄影)估算了2012年至2021年阿拉斯加乌特基亚ġvik附近31平方米地块的相对植被覆盖率。基于地理对象的图像分析(GEOBIA)被应用于基于图像的三个光谱波段(红、绿、蓝)的对象生成。然后应用五种机器学习算法将对象分类为植被组，其中随机森林的分类效果最好(总体准确率为60.5%)。物体被可靠地分为以下类别：苔藓植物、杂草、禾本科植物、凋落物、阴影和站立死亡。落叶灌木和地衣的分类不可靠。多项回归模型被用来衡量来自地块水平摄影的盖度估计是否能够准确地预测跨越空间或时间的点帧的盖度估计。地块水平的摄影为禾本科植物的植被覆盖提供了有用的估计。然而，预测性能因植被类别不同而不同，无论是用于预测新地点的覆盖还是预测先前样地随时间的变化。这些结果表明，地块水平的摄影可以最大限度地有效利用时间、资金和现有技术来监测北极的植被覆盖，但目前的半自动图像分析的精度不足以检测覆盖的微小变化。
Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.