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自然资源遥感  2024, Vol. 36 Issue (2): 60-69    DOI: 10.6046/zrzyyg.2023032
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
五种典型遥感时空信息融合算法在湿地区域植被指数重建中的适用性比较
罗佳欢1(), 严翼1(), 肖飞2, 刘欢1, 胡铮铮2,3, 王宙2,3
1.资源转化与污染控制国家民委重点实验室中南民族大学资源与环境学院,武汉 430074
2.中国科学院精密测量科学与技术创新研究院,武汉 430071
3.中国科学院大学,北京 100049
Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas
LUO Jiahuan1(), YAN Yi1(), XIAO Fei2, LIU Huan1, HU Zhengzheng2,3, WANG Zhou2,3
1. Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan 430074, China
2. Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

为探讨不同遥感时空信息融合算法在水陆转换频繁、地物类型多样的湿地区域的适用性问题,该文以鄱阳湖样区为研究区,选取5种典型的时空信息融合算法(STARFM,ESTARFM,FSDAF,Fit-FC和STNLFFM)。根据不同时期地物差异状况,选取Landsat和MODIS遥感数据分别开展枯水期、平水期2个时段的归一化植被指数(normalized difference vegetation index,NDVI)影像融合实验,并在空间和光谱2个维度进行算法精度评估。结果表明,仅一对粗细分辨率影像输入时,FSDAF算法在枯水期的融合预测效果最好,总体误差为0.433 5; STNLFFM算法在平水期的融合预测效果最好,总体误差为0.514 7; 同时应用枯水期、平水期2对粗细分辨率影像时,ESTARFM算法融合预测效果最好,总体误差为0.467 0。不同时空信息融合算法在湿地地区的适用性与研究区域内水体面积的占比情况有关,STNLFFM算法在水体区域的融合预测效果最好。
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罗佳欢
严翼
肖飞
刘欢
胡铮铮
王宙
关键词 ： 时空信息融合,  鄱阳湖湿地,  FSDAF模型,  STNLFFM模型,  ESTARFM模型    
Abstract：

This study aims to explore the applicability of various spatio-temporal information fusion algorithms based on remote sensing data to wetland areas characterized by frequent land-water conversion and diverse surface features. With the Poyang Lake sample area as the study area, this study examined five typical spatio-temporal information fusion algorithms (STARFM, ESTARFM, FSDAF, Fit-FC, and STNLFFM). Considering the differences in surface features among different periods, Landsat and MODIS remote sensing data were selected to conduct image fusion experiments for normalized difference vegetation indices (NDVIs) during low- and normal-water periods. Moreover, the accuracy of these algorithms was evaluated in spatial and spectral dimensions. The results of this study are as follows: ① In the case of only one pair of coarse- and fine-resolution images as input, the FSDAF exhibited the optimal fusion prediction effect for the low-water period, with an overall error of 0.433 5, whereas the STNLFFM manifested the optimal fusion prediction effect for the normal-water period, with an overall error of 0.514 7; ② In the case of two pairs of coarse- and fine-resolution images of low- and normal-water periods as input, the ESTARFM demonstrated the optimal fusion prediction effect, with an overall error of 0.467 0; ③ The applicability of different algorithms to a wetland area is associated with the proportion of water bodies in the study area. The STNLFFM displayed the optimal fusion prediction effect for water bodies.
Key words： spatio-temporal information fusion    Poyang Lake wetland    FSDAF    STNLFFM    ESTARFM
收稿日期: 2023-02-22      出版日期: 2024-06-14
ZTFLH:  TP79  
基金资助:中国科学院战略性先导科技专项(A类)“长江经济带干流典型湖泊水生态修复与综合调控”(XDA23040201);湖北省重点研发计划项目“长江中游流域生态环境监测关键技术与装备研发”(2020BCA074);国家自然科学基金项目“汉江中下游河谷平原土地沙化空间格局动态监测及防治区划研究”(41901235)
通讯作者: 严 翼(1986-),女,讲师,主要从事“3S”技术在资源与环境中的应用研究。Email: yanyi@mail.scuec.edu.cn。
作者简介: 罗佳欢(2000-),男,硕士研究生,主要从事生态环境遥感方面的研究。Email: 2021120858@mail.scuec.edu.cn。
引用本文:   
罗佳欢, 严翼, 肖飞, 刘欢, 胡铮铮, 王宙. 五种典型遥感时空信息融合算法在湿地区域植被指数重建中的适用性比较[J]. 自然资源遥感, 2024, 36(2): 60-69.
LUO Jiahuan, YAN Yi, XIAO Fei, LIU Huan, HU Zhengzheng, WANG Zhou. Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas. Remote Sensing for Natural Resources, 2024, 36(2): 60-69.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023032      或      https://www.gtzyyg.com/CN/Y2024/V36/I2/60
Fig.1  研究区域位置
Landsat8/9 OLI MODIS
2021年12月06日 2021年12月06日
2021年01月12日 2021年01月11日
2020年04月15日 2020年04月16日
2020年03月14日 2020年03月15日
Tab.1  Landsat8/9 OLI和MODIS影像获取时间
Fig.2  影像融合实验方案
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 50 0.011 4 0.136 6 -0.289 6 -0.077 1 0.514 7
FSDAF 15 0.022 1 0.149 7 -0.241 8 0.019 9 0.433 5
Fit-FC 50 0.017 7 0.149 5 -0.279 6 0.055 1 0.501 9
STNLFFM 51 0.017 1 0.141 0 -0.275 3 -0.050 3 0.483 7
Tab.2  实验01to12 NDVI影像融合结果精度
Fig.3  实验01to12 NDVI影像融合结果
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 15 0.034 8 0.173 9 -0.359 5 -0.048 1 0.616 3
FSDAF 20 0.043 8 0.191 0 -0.233 6 0.037 7 0.506 1
Fit-FC 50 0.038 7 0.199 5 -0.302 6 0.058 8 0.599 6
STNLFFM 51 0.035 2 0.185 6 -0.294 5 -0.042 4 0.557 7
ESTARFM 50 -0.014 9 0.175 2 -0.214 4 0.062 5 0.467 0
STNLFFM2 51 -0.005 0 0.163 7 -0.291 9 0.009 2 0.469 8
Tab.3  实验01to03 NDVI影像融合结果精度
Fig.4  实验01to03 NDVI影像融合结果
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 15 -0.041 2 0.196 3 -0.326 7 -0.037 2 0.601 4
FSDAF 30 -0.045 9 0.211 3 -0.253 1 0.046 2 0.556 5
Fit-FC 50 0.002 0 0.234 4 -0.311 0 0.037 7 0.585 1
STNLFFM 15 -0.040 4 0.212 5 -0.251 2 -0.010 6 0.514 7
ESTARFM 50 -0.014 9 0.175 2 -0.214 4 0.062 5 0.467 0
STNLFFM2 51 -0.005 0 0.163 7 -0.291 9 0.009 2 0.469 8
Tab.4  实验04to03 NDVI影像融合结果精度
Fig.5  实验04to03 NDVI影像融合结果
算法_窗口 区域 AD RMSE Edge LBP 总体误差
STARFM_15 水体 0.031 0 0.136 9 -0.110 1 -0.003 3 0.281 3
非水体 0.003 8 0.107 3 -0.061 6 -0.043 0 0.215 7
FSDAF_20 水体 0.033 3 0.137 4 -0.072 0 0.041 6 0.284 3
非水体 0.010 5 0.132 6 -0.031 2 -0.001 5 0.175 9
Fit-FC_50 水体 0.039 1 0.160 0 -0.056 5 0.055 9 0.311 5
非水体 -0.000 4 0.119 2 -0.060 9 0.009 9 0.190 5
STNLFFM_51 水体 0.034 9 0.146 9 -0.082 3 -0.016 0 0.280 2
非水体 0.000 3 0.113 4 -0.050 8 -0.024 8 0.189 4
ESTARFM_50 水体 -0.012 2 0.138 7 -0.042 9 0.048 8 0.242 5
非水体 -0.002 7 0.107 0 -0.045 7 0.015 0 0.170 4
STNLFFM2_51 水体 0.000 5 0.131 0 -0.067 3 0.018 9 0.217 7
非水体 -0.005 4 0.098 1 -0.060 3 -0.008 7 0.172 5
Tab.5  水体和非水体区域NDVI影像融合结果精度
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