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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 70-79     DOI: 10.6046/zrzyyg.2022207
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Classification and change analysis of the substrate of the Yongle Atoll in the Xisha Islands based on Landsat8 remote sensing data
LI Tianchi1(), WANG Daoru2, ZHAO Liang1(), FAN Renfu2
1. College of Marine and Environmental Sciences, Tianjin University of Science and Technology, Tianjin 300457, China
2. Hainan Academy of Ocean and Fisheries Sciences, Haikou 571126, China
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Abstract  

In view of the drastic changes in the ocean-atmosphere environment, the accurate and efficient identification of coral reef substrate information is essential for the dynamic monitoring of coral reefs. Based on the Landsat8 satellite data of the Yongle Atoll in the Xisha Islands of four periods during 2013—2021, this study proposed a decision tree classification model using spectral and texture indices according to the spectral and texture differences between different substrates. Then, the coral information was extracted using object-oriented and pixel-based classification methods. In addition, the changes in the substrate of the Yongle Atoll were quantitatively analyzed. The results are as follows: ① The results of the object-oriented classification are superior to those of pixel-based classification overall. Moreover, the decision tree classification results yielded Kappa coefficients of 0.63~0.68, with classification accuracy about 7~10 percentage points higher than that of conventional supervised classification; ② Coral thickets are mostly distributed in the central, weakly-hydrodynamic parts of islands and reefs. The corals in the Yinyu Reef and the Jinyin Island exhibit a planar distribution pattern, while those in other islands and reefs mostly show a zonal distribution pattern; ③ The areas of coral thickets and sandbanks in the Yongle Atoll changed significantly overall. Although the total area of coral thickets increased by 1.689 km2, the coral thickets in the Shiyu, Jinqing, Quanfu, and Shanhu islands and the Lingyang reef were severely degraded, with areas decreasing by 0.107~0.892 km2. This study verified that the substrate index established using medium spatial resolution images is reliable and can be applied to remote sensing information extraction of corals. Therefore, this study will provide technical support for the investigation and scientific management of coral reef resources.
Keywords remote sensing      Yongle Atoll      spectral characteristics      decision tree      object-oriented classification     
ZTFLH:  TP79  
Issue Date: 07 July 2023
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Tianchi LI
Daoru WANG
Liang ZHAO
Renfu FAN
Cite this article:   
Tianchi LI,Daoru WANG,Liang ZHAO, et al. Classification and change analysis of the substrate of the Yongle Atoll in the Xisha Islands based on Landsat8 remote sensing data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 70-79.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022207     OR     https://www.gtzyyg.com/EN/Y2023/V35/I2/70
Fig.1  Location of study area
底质类型 描述 遥感影像 底质类型 描述 遥感影像
珊瑚丛生带 大多位于岛礁中部,活珊瑚覆盖度较高,颜色呈棕褐色,面状或条带状分布 点礁 蓝绿色斑块,相互独立且呈点状分布
礁坪 礁体大部分区域,覆盖大量生物碎屑,同时还有不同种类的沉积物 礁前斜坡 位于礁体四周,是礁体边缘向外海延伸的水下斜坡,因其中有珊瑚生长呈现较亮的浅蓝色
沙洲 由沙质碎屑组成,一般还可能包含白化死亡的珊瑚,色调明亮 建筑和植被 与珊瑚礁其他底质区别明显,建筑以沙质为主呈黄白色,植被呈绿色
Tab.1  Classification system of coral reef sediment types in the study area
Fig.2  Spectral characteristic curves of coral reef sediments
Fig.3  Textural values for green band of different sediments
Fig.4  Mean textural feature curves of coral reef sediments
Fig.5  Decision tree for coral reef sediment classification
Fig.6  ROCLV variation curve at different scales
Fig.7  Comparison of between object-oriented classification and pixel based classification
底质类型 面向对象 基于像元
决策树分类 SVM KNN 决策树分类 SVM MDC
UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/%
礁坪 83.79 75.17 87.83 70.46 85.42 71.01 77.56 76.75 87.47 72.60 84.41 61.78
礁前斜坡 86.69 83.56 84.97 72.95 81.92 74.70 86.43 79.39 72.92 85.92 85.72 72.97
点礁 74.34 75.02 44.08 73.04 42.98 57.32 71.27 67.51 38.61 70.04 45.37 51.93
建筑和植被 91.35 71.84 84.32 75.76 83.47 75.97 90.34 69.28 85.06 78.06 83.96 78.87
沙洲 71.84 69.16 67.75 68.01 66.36 67.34 56.22 62.09 60.23 78.42 43.46 77.72
珊瑚丛生带 47.15 74.43 40.53 71.75 38.11 67.65 42.57 59.24 39.58 64.14 28.54 66.22
OA/% 77.33 71.69 70.64 74.25 71.14 66.07
Kappa系数 0.681 0.614 0.595 0.631 0.594 0.541
Tab.2  Comparison of accuracy of different classification methods
Fig.8  Classification results of coral reefs in different periods
底质类型 2013年 2015年 2018年 2021年
礁坪 40.295 39.842 38.531 37.284
礁前斜坡 29.909 29.541 28.891 28.378
点礁 6.415 6.647 6.786 6.617
建筑和植被 2.890 3.165 2.900 2.913
沙洲 2.413 3.206 2.399 2.736
珊瑚丛生带 5.775 4.073 6.993 7.464
Tab.3  Variation of sediment area in each period of the study area(km2)
Fig.9  Changes of coral clusters in some islands
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