Billions of dollars are projected to be spent on restoration projects along the northern Gulf Coast which will require efficient monitoring at both landscape and project-specific scales. Recent developments in unmanned aircraft systems (UAS) have sparked interest in the ability of these “drones” to capture hyperspatial imagery (pixel resolution < 10 cm) that resolves individual species and produces accurate data for monitoring programs in coastal landscapes. We present a case study conducted at Coastwide Reference Monitoring System (CRMS) station 0392, a Spartina patens–dominated, oligohaline coastal marsh in Terrebonne Parish, Louisiana. Results demonstrate the ability of UAS technology to collect hyperspatial, multispectral aerial images in a coastal wetland, and to produce very-high-resolution orthomosaics and digital elevation models. We then used object-based image analysis (OBIA) techniques to (1) delineate the land–water interface, (2) classify composition by dominant species, and (3) quantify average plant height by species. Model results were validated with traditional on-the-ground CRMS vegetation surveys. Results suggest that OBIA methods can overcome the spectral variability of hyperspatial datasets, quantify uncertainties in conventional techniques, and provide improved estimates of wetland vegetation cover and species composition. These methods scale conventional plot-level coverage values to data-rich landscape-level models and provide useful tools to monitor restoration performance, landscape changes, and ecosystem services in coastal wetland systems.

预计将花费数十亿美元用于墨西哥湾沿岸北部的修复项目，这将需要对景观和项目特定规模进行有效监控。无人机系统（UAS）的最新发展引起了人们对这些“无人机”捕获超空间图像（像素分辨率<10厘米）的能力的关注，该图像可解析单个物种并为海岸景观监测程序提供准确的数据。我们介绍了一个在Spartina patens海岸参考基准监视系统（CRMS）站0392进行的案例研究–位于路易斯安那州Terrebonne Parish的低盐沿海沼泽地。结果表明，UAS技术具有在沿海湿地中收集高空间，多光谱航拍图像并产生超高分辨率的正镶嵌和数字高程模型的能力。然后，我们使用基于对象的图像分析（OBIA）技术来（1）描绘土地与水的界面，（2）按优势种对成分进行分类，以及（3）按物种对平均植物高度进行量化。模型结果已通过传统的地面CRMS植被调查进行了验证。结果表明，OBIA方法可以克服超空间数据集的光谱变异性，量化传统技术中的不确定性，并提供对湿地植被覆盖率和物种组成的改进估计。