This research assesses a survey method for measuring the change in density of intertidal estuarine seagrass (Zostera muelleri) at Wharekawa Harbour, New Zealand, using an autonomous remotely piloted aircraft fitted with a narrowband multispectral camera. Image classification was modelled using the random forest classifier trained with ground observation data sourced from 63 photographed quadrat stations upon three parallel transect lines. Seagrass coverage in the georeferenced and rectified ground photography was estimated by visual interpretation on a three-tier (low, medium and high) density scale, then exact leaf area additionally calculated from digitised seagrass leaf coverage visible in rectified ground photography. Three replicate aerial surveys across four months were conducted to compare predicted change in seagrass density with actual measured change, during austral summer growth and autumn decline. Classification of the resulting image mosaic (2.5 cm pixel size) achieved up to 90–93% overall accuracy across multiple surveys when attributing density class, 93–96% accuracy for prediction of seagrass presence, and 81–91% in terms of detection of seagrass on the ground. Change-maps allow regions of growth and decline to be visualised. Correlation (r) between actual and predicted change for 48 independent test grid squares was 0.89 and 0.61 for the summer and autumn change periods respectively. Rapid visual interpretation of classification end-member classes yielded change measurement equivalent to that of accurately measured seagrass leaf area. The research demonstrates that RPA survey using a multispectral camera is viable for monitoring change in seagrass condition.

这项研究评估了一种测量潮间带河口海草密度变化的调查方法（Zostera muelleri）在新西兰Wharekawa港使用配备了窄带多光谱摄像头的自动遥控飞机。使用随机森林分类器对图像分类进行建模，该随机森林分类器使用来自地面观测数据的训练，地面观测数据来自三个平行样带线上的63个拍照的正交方站。通过三层（低，中，高）密度等级的视觉解释，估算了地理定位和校正后的地面摄影中的海草覆盖率，然后根据校正后的地面摄影中可见的数字化海草叶覆盖率，计算了精确的叶面积。进行了四个月的三个重复航空测量，以比较夏季夏季和秋季秋季期间海草密度的预测变化与实际测量的变化。所得图像镶嵌的分类（2。5像素大小的像素）在归因于密度等级的多个调查中，整体精度高达90–93％，海藻存在的预测精度为93–96％，地面上海藻的检测精度为81–91％。变更图使增长和下降的区域可视化。对于48个独立的测试网格正方形，实际变化与预测变化之间的相关性（r）在夏季和秋季变化时期分别为0.89和0.61。通过快速直观地解释分类最终成员类别，可以得出与精确测量的海草叶面积相当的变化测量值。研究表明，使用多光谱相机进行RPA测量对于监测海草状况的变化是可行的。预测海草存在的准确度为93–96％，而对地面海草的检测准确度为81–91％。变更图使增长和下降的区域可视化。对于48个独立的测试网格正方形，实际变化与预测变化之间的相关性（r）在夏季和秋季变化时期分别为0.89和0.61。通过快速直观地解释分类最终成员类别，可以得出与精确测量的海草叶面积相当的变化测量值。研究表明，使用多光谱相机进行RPA测量对于监测海草状况的变化是可行的。预测海草存在的准确度为93–96％，而对地面海草的检测准确度为81–91％。变更图使增长和下降的区域可视化。对于48个独立的测试网格正方形，实际变化与预测变化之间的相关性（r）在夏季和秋季变化时期分别为0.89和0.61。快速直观地解释分类最终成员类别产生的变化测量值与精确测量的海草叶面积的变化测量值相同。研究表明，使用多光谱相机进行RPA测量对于监测海草状况的变化是可行的。对于48个独立的测试网格正方形，实际变化与预测变化之间的相关性（r）在夏季和秋季变化时期分别为0.89和0.61。快速直观地解释分类最终成员类别产生的变化测量值与精确测量的海草叶面积的变化测量值相同。研究表明，使用多光谱相机进行RPA测量对于监测海草状况的变化是可行的。对于48个独立的测试网格正方形，实际变化与预测变化之间的相关性（r）在夏季和秋季的变化时期分别为0.89和0.61。通过快速直观地解释分类最终成员类别，可以得出与精确测量的海草叶面积相当的变化测量值。研究表明，使用多光谱相机进行RPA测量对于监测海草状况的变化是可行的。