定量遥感尺度转换方法研究进展

doi:10.13249/j.cnki.sgs.2019.03.002

地理科学 ›› 2019, Vol. 39 ›› Issue (3): 367-376.doi: 10.13249/j.cnki.sgs.2019.03.002

定量遥感尺度转换方法研究进展

姚远^1,^3,⁴(), 陈曦^1,⁴, 钱静^1,²()

1.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室,新疆乌鲁木齐 830011
2. 中国科学院深圳先进技术研究院,广东深圳 518055
3. 中国科学院大学,北京 100049
4. 中国科学院中亚生态与环境研究中心,新疆乌鲁木齐 830011

收稿日期:2018-03-05 修回日期:2018-07-18 出版日期:2019-03-10 发布日期:2019-03-10
作者简介:
作者简介：姚远（1987-）,男,新疆乌鲁木齐人,博士研究生,主要从事干旱区资源遥感研究。E-mail: xinjiangyaoyuan@sina.com
基金资助:
中国科学院A类战略性先导科技专项（XDA20060303）、中国科学院“西部之光”人才培养引进计划青年学者A类项目（2016-QNXZ-A-5）、国家自然科学基金项目（41761144079）、深圳国际合作研究项目（GJHZ20160229194322570）和广东省省级科技计划项目（2017A050501027）资助

A Review on the Methodology of Scale Issues in Quantitative Remote Sensing

Yuan Yao^1,^3,⁴(), Xi Chen^1,⁴, Jing Qian^1,²()

1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
3. University of Chinese Academy of Sciences, Beijing 100049, Beijing, China
4. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received:2018-03-05 Revised:2018-07-18 Online:2019-03-10 Published:2019-03-10
Supported by:
The Strategic Priority Research Program of Chinese Academy of Sciencess (XDA20060303), Chinese Academy of Sciences “Light of West China” Program (2016-QNXZ-A-5), National Natural Science Foundation of China (41761144079) , Shenzhen International S&T Cooperation Project (GJHZ20160229194322570) and Science and Technology Planning Project of Guangdong Province (2017A050501027)

摘要/Abstract

摘要：

首先对遥感科学和气象学、水文学、生态学、地理学和的尺度概念及其转换方法进行了区分。其次对遥感数据空间尺度转换方法的国内外研究进展情况进行了系统梳理,重点比对了目前空间尺度转换常用6种转换方法：统计转换法、分类转换法、数据融合转换方法、分形分析法、基于局域动态模型的转换方法和基于物理意义尺度转换方法及其各自所属方法的优点和缺点。再次以遥感时间尺度转换应用最为广泛的地表蒸散发和农业旱情监测等2个领域为例,对遥感时间尺度转换方法进行了总结。最后预测了今后定量遥感尺度转换研究可能的研究重点,以期为今后更好地开展尺度效应和尺度转换研究工作提供参考。

关键词: 定量遥感, 尺度效应, 尺度转换, 时间尺度, 空间尺度

Abstract:

The advance of remote sensing technology provides an effective way for disaster prediction, environmental monitoring, resource surveys. Remote sensing technology has become a effective tools to get geo-information comprehensively, accurately, and quickly. Undoubtedly, remote sensing technology in the 21th century has entered an era of quantitative analysis. Thus, the scale issues have always attracted increasing the attention in quantitative remote sensing owing to scale effects limite the accuracy of retrieval the development of its applications. It is a significant problem in remote sensing research, and the methods of scale transformation have been proved is useful to resolve this problem. In ordert to study the scale issues of remote sensing, the advances in methodology of scale issues in quantitative remote sensing must be discussed. Firstly, as we know, the concept of scale is now suffering from a discrepancy in different research area, such as remote sensing, meteorology, hydrology, ecology and geography. Therefore, the definition of scale and relevant methodology in above mentioned research area are introduced in the first of this paper. Secondly, in reviewing the scale issues in remote sensing, the approaches for spatial and temporal scale transformation are reviewed. We summarize the advanges and disadvantages of the mainly spatial scale transformation methods, such as statistical transformation method, classification transformation method, data fusion transformation method, fractal analysis method, local dynamic model based scaling approach and scale transformation method based on physical meaning. Thirdly, the application of measured vapotranspiration and agricultural drought are restricted by remotely sensed data owing to the direct result are instantaneous value estimated at the passing time of satellite. Consequently, different time scales values have practiacal significance. We have summarized many remote sensing data time scale extrapolation methods. The results obtained show that each method has its own advantages and disadvantages. Finally, the future research directions were equally proposed in order to provide a reference for future researchers interested in quantitative remote sensing.

Key words: quantitative remote sensing, scale effect, scale transformation, spatial scale, temporal scale

中图分类号:

P237

姚远, 陈曦, 钱静. 定量遥感尺度转换方法研究进展[J]. 地理科学, 2019, 39(3): 367-376.

Yuan Yao, Xi Chen, Jing Qian. A Review on the Methodology of Scale Issues in Quantitative Remote Sensing[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(3): 367-376.

图/表 4

图1

表1

基于统计的遥感尺度转换方法"

方法名称	定义	优点	缺点
（1）局部平均法^[23]	将高分辨率遥感影像中一定大小窗口内的像元合并,以其平均值作为合并后像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	够较好地保持影像均值信息,且运算简单	易丢失部分细节信息
（2）中心像元法^[24]	将高分辨率遥感影像中一定大小窗口内中心像元的值作为新像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	方法简单易行,能够在一定程度上保持原影像的纹理特征	转换后的图像质量较差,且在窗口较大的情况下接近于随机图像
（3）中值采样法^[25]	将高分辨率遥感影像中一定大小窗口内所有像元值的中位数值作为新像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	该方法运算量小,能够较好地保持原图像的均值	尺度转换过程中信息损失量大,像元值不连续的问题
（4）重采样法^[26] ① 最近邻法	将高分辨率遥感影像中一定大小窗口内离采样点最近的像元值赋予转换后低分辨率影像所对应的像元值	算法简单、易实现	没有考虑除最近像元点以外的其它点,因而图像质量易受损失,像元不连续,且转换后的图像人为加工痕迹较明显
② 双线性内插法	将高分辨率遥感影像中2个正交方向上的像元值,按照先水平方向后垂直方向的顺序进行一阶线性插值,通过加权平均作为转换后较低分辨率影像中对应点的新像元值。	能够克服最近邻法的不足,避免转换后图像像元的不连续	运算量较大,转换精度不高、图像边缘信息易丢失
③ 无重叠平均法	将高分辨率遥感影像中无重叠方形窗口内的像元平均值赋予转换后低分辨率影像所对应的像元值	Hay等^[27]认为该方法的转换效果较好,而最近邻法、双线性内插法和立方卷积内插法在转换尺度超过5个像元时,不适用于遥感影像的尺度上推,转换可信度低	当影像中涉及大量复杂地物时,尺度转换精度不高
④ 立方卷积内插法	与双线性内插法原理相似,将高分辨率影像中的某像元周边的4个像元值改为16个进行加权平均,通过转换得到较低分辨率影像中对应点的新像元值	计算精度较高,考虑了相邻像元间的灰度值和其间的变化率,影像的边缘信息和纹理得到较好地保留	计算量大

表1

表2

表3

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方法名称	定义	优点	缺点
简单聚合法	通过同时增加不同遥感影像的空间分辨率,将较高分辨率影像的参数或变量的平均值转换为较低分辨率影像	方法简单,容易实现	忽略了变量或参数在遥感影像尺度变化时发生的非线性变化,误差较大
显示积分法	要求高分辨率影像模型在空间上能够积分,是空间显示的数学函数,通过显示积分进行尺度转换,已连续函数表示空间异质性	计算复杂度较期望外推法和直接外推法有所降低,且在理论上是一种有效和精确的方法	以连续函数形式表达空间异质性的难度较大,方法尚待进一步完善,因而限制了该方法的推广应用
期望外推法	通过高分辨率影像模型按照地物类别的不同进行模拟,并根据结果计算较低空间分辨率影像的空间结构特征期望值与影像面积相乘,以此实现尺度转换	是当前较为通用的一种方法。通过蒙特卡罗模拟法估算得出的期望值,经得起不确定性分析	转换结果的准确性易受类别现象描述的准确性或空间变异性的影响
直接外推法	将局部分析模型从高分辨率影像应用到适合该模型的所有区域,通过计算不同地物的像素输出综合,实现尺度转换	计算方法简单直接,概念明晰,应用推广度最好	尺度转换的准确性易受到影像尺度转换过程中的误差积累影响

方法名称	定义	优点	缺点
蒸发比法^[53,54]	在云量保持恒定或晴朗的白天,由于正午瞬时蒸发比近似日均蒸发比,以此建立基于蒸发比的尺度转换模型,实现区域ET的估算	方法简易,且较为常用	在实际应用中误差较大,需要进一步改进,并校正偏差,提高精度
作物系数法^[55,56]	由于实际ET与作物ET比值（作物系数）在日内变化较小,因而主要由联合国粮农组织提供的基于作物系数的时间尺度扩展方法,将遥感影像所获取的瞬时作物系数代替日作物系数,实现由瞬时 ET 到日的时间尺度扩展	在地表植被均匀的情况下,所得到的结果精度较高	在作物生育中后期,该方法所确定的作物系数与实测值有较大误差,必须进行参数调校,才能提高精度
冠层阻力法^[57,58]	以蒸散发估算最为有效,应用最为广泛的Penman-Monteith模型为基础,采用遥感瞬时数据估算模型中的下垫面、通量数据等相关参数,进而推导出卫星过境时刻的冠层阻力。据此,估算不同时间尺度区域ET值	大量研究结果表明,该方法可以有效实现大区域尺度,长时间序列ET数据的扩展	易受遥感数据及模型局限性的限制,估算结果的变异性较大
正弦关系法^[59]	太阳辐射在日尺度范围内呈现周期性变化。在晴朗的天气条件下,卫星过境时获取的瞬时辐射与日内辐射间存在正弦曲线关系,其比值可用于瞬时ET与日ET比值的替换,以此实现ET的时间尺度扩展	简单易用,便于理解,在天气晴朗的条件下,具有较好的可行性,结果精度较高	扩展后的日ET数值易受复杂天气和地面条件的影响,与实测数值日ET数值相比有所偏离

定量遥感尺度转换方法研究进展

A Review on the Methodology of Scale Issues in Quantitative Remote Sensing

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