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首页> 外文会议>Earth Resources and Environmental Remote Sensing/GIS Applications >Mangrove classification through the use of object oriented classification and support vector machine of lidar datasets: a case study in Naawan and Manticao, Misamis Oriental, Philippines
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Mangrove classification through the use of object oriented classification and support vector machine of lidar datasets: a case study in Naawan and Manticao, Misamis Oriental, Philippines

机译：通过使用面向对象的分类和LIDAR Datasets的对象分类和支持传染媒介机的红树林分类：以菲律宾Misamis东方的Naawan和Manticao的案例研究

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Mangroves are trees or shrubs that grows at the surface between the land and the sea in tropical and sub-tropical latitudes. Mangroves are essential in supporting various marine life, thus, it is important to preserve and manage these areas. There are many approaches in creating Mangroves maps, one of which is through the use of Light Detection and Ranging (LiDAR). It is a remote sensing technique which uses light pulses to measure distances and to generate three-dimensional point clouds of the Earth's surface. In this study, the topographic LiDAR Data will be used to analyze the geophysical features of the terrain and create a Mangrove map. The dataset that we have were first pre-processed using the LAStools software. It is a software that is used to process LiDAR data sets and create different layers such as DSM, DTM, nDSM, Slope, LiDAR Intensity, LiDAR number of first returns, and CHM. All the aforementioned layers together was used to derive the Mangrove class. Then, an Object-based Image Analysis (OBIA) was performed using eCognition. OBIA analyzes a group of pixels with similar properties called objects, as compared to the traditional pixel-based which only examines a single pixel. Multi-threshold and multiresolution segmentation were used to delineate the different classes and split the image into objects. There are four levels of classification, first is the separation of the Land from the Water. Then the Land class was further dived into Ground and Non-ground objects. Furthermore classification of Nonvegetation, Mangroves, and Other Vegetation was done from the Non-ground objects. Lastly Separation of the mangrove class was done through the Use of field verified training points which was then run into a Support Vector Machine (SVM) classification. Different classes were separated using the different layer feature properties, such as mean, mode, standard deviation, geometrical properties, neighbor-related properties, and textural properties. Accuracy assessment was done using a different set of field validation points. This workflow was applied in the classification of Mangroves to a LiDAR dataset of Naawan and Manticao, Misamis Oriental, Philippines. The process presented in this study shows that LiDAR data and its derivatives can be used in extracting and creating Mangrove maps, which can be helpful in managing coastal environment.
机译：红树林是树木或灌木，在土地和热带和亚热带纬度的土地和海洋之间生长。西红柿在支持各种海洋生物方面是必不可少的，因此，保留和管理这些领域很重要。创建红树林地图有许多方法，其中一个是通过使用光检测和测距（LIDAR）。它是一种遥感技术，它使用光脉冲来测量距离并产生地球表面的三维点云。在这项研究中，地形激光雷达数据将用于分析地形的地球物理特征，并创建红树林地图。我们首先使用Lastools软件预处理的数据集。它是一种用于处理LIDAR数据集的软件，并创建不同的图层，如DSM，DTM，NDSM，斜率，激光雷达强度​​，LIDAR的第一个返回和CHM。所有上述层一起用于派生红树林。然后，使用Ecognition执行基于对象的图像分析（OBIA）。 OBIA与类似的像素的基于像素相比，分析了一组具有类似的属性的像素，只有仅检查单个像素。使用多阈值和多分辨率分割来描绘不同的类并将图像分成对象。有四个级别的分类，首先是从水中分离土地。然后将陆级阶级进一步划分为地面和非接地物体。此外，从非接地物体完成了非缠结，红树林和其他植被的分类。通过使用现场验证的培训点来完成红树林类的分离，然后将其运行到支持向量机（SVM）分类中。使用不同的层特征属性分离不同的类，例如平均值，模式，标准偏差，几何特性，邻居相关的属性和纹理属性。使用不同的现场验证点进行准确性评估。这个工作流程应用于红宝酒的分类到Naawan和Manticao，Misamis东方，菲律宾的Lidar DataSet。本研究中呈现的过程表明，LIDAR数据及其衍生物可用于提取和创建红树林地图，这可能有助于管理沿海环境。

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