The wave attenuation of vegetation plays an increasingly important role in flood control in coastal areas. Past studies have found that the interaction of waves with vegetation mainly depends on hydraulic conditions and vegetation characteristics. Therefore, it is necessary to build vegetation models and quantify vegetation characteristics related to wave attenuation. Mangroves are one of the typical tropical intertidal vegetation. This study aimed to test the potential of using a low-cost, convenient smartphone-based structure-from-motion with multi-view stereo-photogrammetry (SfM-MVS) to accurately measure mangrove parameters related to wave attenuation. SfM-MVS is a computer vision technique in which the point cloud coordinates of an object can be calculated from a series of 2D photos to generate a 3D point cloud model. This study first tested the optimal photography distance （about 25cm） and optimal weather conditions （cloudy and no sunlight） using the smartphone-based SfM-MVS method. Then, based on the optiaml usage conditions, 3Dpoint cloud models of 10 individual mangrove samples were reconstructed using this method, then the mangrove parameters related to wave attenuation were estimated according to the model: linear parameters (tree height, crown diameter, stem diameter, branches diameter) and the frontal area at a certain height. The true values of these parameters were measured using traditional hand measurements to evaluate smartphone-based SfM-MVS-derived parameter estimates. The results show that the estimation of the linear parameters of the mangroves generally achieves high accuracy (RMSE of tree height, canopy diameter, stem diameter and thick branch diameter are all lower than 15%). There were significant negative biases in the estimates of tree height and crown diameter, and no significant biases in the estimates of stems and branches. The results of linear regression show that there is a strong positive correlation between the estimated values of all parameters and the true values. There are large errors and negative biases in the estimates of the frontal area at different heights. Overall, RMSE of frontal area at a certain height is 45.58% with a bias of -38.83%. This result showed that the SfM-MVS model resulted in a large underestimation of the frontal area at each height. A series of analyses showed that the main reason for the large error in the frontal area at a certain height was that the terminal branches with the lowest branch order (smallest diameter) were difficult to visualize in the SfM-MVS model. And this part of the branches actually occupies a large proportion of the frontal area. This study demonstrates that smartphone-based SfM-MVS is capable of estimating mangrove tree height, canopy diameter, stem diameter, and thick branch diameter. Its accuracy is comparable to other existing methods such as TLS, ALS, camera-based SfM-MVS. However, the resolution is insufficient for very thin branches (<=5mm), which makes the method inaccurate in estimating the frontal area at a certain height. Factors such as photography distance and ambient lighting can affect the accuracy of the model. Compared with the currently used remote sensing technologies such as TLS, the smartphone-based SfM-MVS has the advantages of low cost, high flexibility, and low difficulty for those who lack professional knowledge or training, which makes it an alternative with great potential.