Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam
Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam
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DOI：
10.3390/rs12142289
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
N. Quang;C. Quinn;L. Stringer;Rachael H. Carrie;C. Hackney;L. Hue;D. Tan;P. Nga
中科院分区：
文献类型：
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作者：
N. Quang;C. Quinn;L. Stringer;Rachael H. Carrie;C. Hackney;L. Hue;D. Tan;P. Nga
本研究调查了四种不同的机器学习监督图像分类器的性能：人工神经网络（ANN）、决策树（DT）、随机森林（RF）和支持向量机（SVM），使用SPOT-7和Sentinel-1图像对红河河口2019年的红树林年龄和物种进行了分类，这是越南北部其他红树林的典型特征。选择这四种分类器是因为它们被认为具有很高的准确性，然而，它们在红树林年龄和物种分类中的应用迄今为止受到限制。利用1975 - 2019年的Landsat时间序列图像，采用迭代自组织数据分析技术（ISODATA）的无监督分类方法，并与K-means分类的精度进行比较，绘制了红树林范围的变化图，结果发现，尽管在20世纪80年代红树林面积有所下降，但红树林面积有所增加，这表明红树林种植和当地人民的森林保护工作取得了成功。为了评估监督图像分类器，评估了183个原位训练地块，其中70%用于训练监督算法，30%用于验证结果。为了改善红树林的物种分离，采用Gram-Schmidt和主成分分析图像融合技术，得到了质量更好的图像。绘制了所有监督和非监督（2019年）红树林年龄、物种和范围的结果，并对其准确性进行了评估。混淆矩阵的计算表明，分类层与地面真实数据一致，大多数生产者和用户的准确性大于80%。总体精度和Kappa系数（约0.9）表明图像分类效果很好。结果表明，SVM在本案例中准确率最高，DT、ANN、RF次之。本研究确定的红树林面积变化和利用遥感数据测试的方法将对红树林人工林项目的监测和评价评估有价值。
This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects.