CN115909080A - A multi-source nocturnal light remote sensing image integration method based on land use data - Google Patents
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Abstract
Description
技术领域Technical Field
本发明属于遥感图像处理技术领域，尤其涉及一种基于土地利用数据的多源夜光遥感影像整合方法。The invention belongs to the technical field of remote sensing image processing, and in particular relates to a multi-source night light remote sensing image integration method based on land use data.
背景技术Background Art
夜间灯光(Nighttime light,NTL)与人类活动密切相关，照明设施的密度和强度在一定程度上可以反映该区域的人类活动强度和经济繁荣程度。夜光遥感影像可以反映夜间城镇灯光，还可以捕捉到森林火灾、天然气燃烧、夜间渔船的发光等；并被广泛地应用于社会经济参数估算、人口分析、区域发展研究、城市空间分布格局研究、能源消耗、渔业监测、重大事件评估等诸多研究领域。Nighttime light (NTL) is closely related to human activities. The density and intensity of lighting facilities can reflect the intensity of human activities and economic prosperity in the region to a certain extent. Nighttime light remote sensing images can reflect the lights of cities and towns at night, and can also capture forest fires, natural gas combustion, and the light of fishing boats at night. They are widely used in many research fields such as socio-economic parameter estimation, population analysis, regional development research, urban spatial distribution pattern research, energy consumption, fishery monitoring, and major event assessment.
目前夜光遥感进行时空分析的主要数据源是是DMSP/OLS遥感影像与NPP/VIIRS遥感影像，前者的时间覆盖为1992年-2013年，后者的时间覆盖是2013年至今(2022年)。两者充足的数据量与30年的时间覆盖为时空分析提供了数据基础。但遗憾的是，DMSP/OLS与NPP/VIIRS数据之间存在着极大的差异，两套数据无法被同时使用，从而限制了夜间灯光数据的可用时间序列长度。两套数据的差异主要体现在如下方面:1)遥感平台不一致；2)获取数据的传感器不一致；3)影像空间分辨率不一致；4)成像时间不一致；5)DMSP像元值为无量纲的DN值，NPP为实际探测的辐射强度值；6)同一像元(或地区)一段时间内，DMSP-OLS和VIIRS呈现的夜光强度变化趋势和变化模式不同；7)基于DMSP-OLS和VIIRS的应用(如城市提取、GDP估计)结果不同。At present, the main data sources for spatiotemporal analysis of night light remote sensing are DMSP/OLS remote sensing images and NPP/VIIRS remote sensing images. The former covers the period from 1992 to 2013, while the latter covers the period from 2013 to the present (2022). The sufficient data volume and 30-year time coverage of both provide a data basis for spatiotemporal analysis. Unfortunately, there are great differences between DMSP/OLS and NPP/VIIRS data, and the two sets of data cannot be used simultaneously, thus limiting the length of the available time series of night light data. The differences between the two sets of data are mainly reflected in the following aspects: 1) inconsistent remote sensing platforms; 2) inconsistent sensors for acquiring data; 3) inconsistent image spatial resolution; 4) inconsistent imaging time; 5) DMSP pixel values are dimensionless DN values, while NPP is the actual detected radiation intensity value; 6) for the same pixel (or region) over a period of time, the night light intensity change trends and change patterns presented by DMSP-OLS and VIIRS are different; 7) the results of applications based on DMSP-OLS and VIIRS (such as urban extraction and GDP estimation) are different.
同时由于夜光遥感的应用仍缺乏连续一致的全球数据集，整合20世纪90年代至今的夜间灯光数据将为监测全球及区域尺度长时间人类活动提供可能。现有一些研究己经开始尝试使用DMSP/OLS和NPP/VIIRS进行时间序列的分析:如Shao尝试基于NPP/VIIRS的DNB波段，将南极弯顶C上空的每日DMSP/OLS影像校准为类似NPP/VIIRS的数据，但这一方法只适用于DMSP/OLS有限的DN值范围。Li提出了一种基于幂函数和高斯低通滤波的相互校准模型，使用订购的月度DMSP/OLS数据与NPP/VIIRS数据进行整合，探究2011-2017年叙利亚人类居住区灯光亮度的变化，这一方法不具有普适的数据可获取性。董鹤松等基于最优幂函数模型构建1992-2017年中国三大城市群长时间序列夜间灯光数据集，探究三大城市群发育的空间扩张和时空动态。近年来Ma和Zhao等提出了新的整合模型，基于逻辑函数模型以NPP/VIIRS数据模拟2013年后的DMSP/OLS数据，这一类模型的精度可以达到95％以上。Yu等基于此模型整合2001-2019年长三角城市群夜间灯光数据集，探索长三角城市群城市化过程中建成区扩张和灯光亮度变化的时空异质性。但这些研究都是基于更早时间的DMSP/OLS影像，将数据质量更好的NPP/VIIRS改变去模拟DMSP/OLS影像，这反而降低了夜光影像的质量，并且，往往只能在都是在一些总体层次上进行建模分析，只在大区域大范围且总体性的评价尺度上精度较好，而细化到城市甚至像元尺度精度很低，并不具备太多的实际意义。并且很少结合土地利用现状栅格数据进行DMSP/OLS遥感影像与NPP/VIIRS遥感影像的整合。At the same time, since the application of night light remote sensing still lacks a continuous and consistent global dataset, integrating night light data from the 1990s to the present will provide the possibility for monitoring long-term human activities at global and regional scales. Some existing studies have begun to try to use DMSP/OLS and NPP/VIIRS for time series analysis: For example, Shao tried to calibrate the daily DMSP/OLS images over the Antarctic Curve C to data similar to NPP/VIIRS based on the DNB band of NPP/VIIRS, but this method is only applicable to the limited DN value range of DMSP/OLS. Li proposed a mutual calibration model based on power function and Gaussian low-pass filtering, using the monthly DMSP/OLS data ordered and NPP/VIIRS data to integrate and explore the changes in light brightness in human settlements in Syria from 2011 to 2017. This method does not have universal data accessibility. Dong Hesong et al. constructed a long-term night light dataset of China's three major urban agglomerations from 1992 to 2017 based on the optimal power function model to explore the spatial expansion and spatiotemporal dynamics of the development of the three major urban agglomerations. In recent years, Ma and Zhao et al. proposed a new integration model based on the logistic function model to simulate the DMSP/OLS data after 2013 with NPP/VIIRS data. The accuracy of this type of model can reach more than 95%. Based on this model, Yu et al. integrated the night light data set of the Yangtze River Delta urban agglomeration from 2001 to 2019 to explore the spatiotemporal heterogeneity of built-up area expansion and light brightness changes during the urbanization process of the Yangtze River Delta urban agglomeration. However, these studies are based on DMSP/OLS images from an earlier time, and the NPP/VIIRS with better data quality is changed to simulate the DMSP/OLS images, which reduces the quality of the night light images. In addition, modeling and analysis can only be performed at some general levels, and the accuracy is good only at the large-scale and overall evaluation scale of large areas, while the accuracy is very low when it is refined to the city or even pixel scale, which does not have much practical significance. And rarely combine the current land use status raster data to integrate DMSP/OLS remote sensing images with NPP/VIIRS remote sensing images.
通过上述分析，多源夜光遥感影像的不兼容问题严重的制约了其应用范围和应用效果，现有的整合技术多采用全体像元共用一个模型去整合的办法，只在大区域大范围且总体性的评价尺度上精度较好，而细化到城市甚至像元尺度精度很低，并不具备太多的实际意义。Through the above analysis, the incompatibility problem of multi-source night light remote sensing images has seriously restricted its application scope and application effect. The existing integration technology mostly adopts the method of integrating all pixels using one model. It has good accuracy only in large-scale and overall evaluation scales, but the accuracy is very low when it is refined to the city or even pixel scale, and it does not have much practical significance.
发明内容Summary of the invention
针对现有技术存在的问题，本发明对DMSP/OLS图像进行饱和度分析，对月度NPP/VIIRS图像进行连续校正和平均，得到年生图像；然后，针对不同土地利用区的各种DMSP/OLS数据和NPP/VIIRS数据，建立了各种常规拟合模型。对比分析不同的夜间光照数据预处理方法、各种土地利用区域和拟合模型。In view of the problems existing in the prior art, the present invention performs saturation analysis on DMSP/OLS images, continuously corrects and averages monthly NPP/VIIRS images, and obtains annual images; then, various conventional fitting models are established for various DMSP/OLS data and NPP/VIIRS data in different land use areas. Different nighttime illumination data preprocessing methods, various land use areas, and fitting models are compared and analyzed.
本发明是这样实现的，本发明提供一种基于土地利用数据的多源夜光遥感影像整合方法，具体如图1所示，所述方法包括以下步骤：The present invention is implemented in this way. The present invention provides a multi-source night light remote sensing image integration method based on land use data, as shown in Figure 1, the method comprises the following steps:
步骤a，选择2013年覆盖研究区域的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据；Step a, select DMSP/OLS night light remote sensing images, NPP/VIIRS night light remote sensing images and land use status raster data covering the study area in 2013;
步骤b，对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像、土地利用现状栅格数据进行投影变换，转换为WGS_1984_Albers投影坐标系；Step b, projecting and transforming the DMSP/OLS night light remote sensing images, NPP/VIIRS night light remote sensing images, and land use status raster data into the WGS_1984_Albers projection coordinate system;
步骤c，对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据进行空间重采样，使DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据空间分辨率均为500米；Step c, spatially resampling the DMSP/OLS night light remote sensing images, the NPP/VIIRS night light remote sensing images, and the land use status raster data, so that the spatial resolution of the DMSP/OLS night light remote sensing images, the NPP/VIIRS night light remote sensing images, and the land use status raster data is 500 meters;
步骤d，以土地利用现状栅格数据为基准分别对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行地理配准；Step d, georeferencing the DMSP/OLS night light remote sensing images and the NPP/VIIRS night light remote sensing images respectively based on the land use status raster data;
步骤e，使用研究区域的边界矢量数据，对土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像，进行镶嵌、裁剪得到研究区域的土地利用现状栅格数据、DMSP/OLS夜光遥感影像和NPP/VIIRS夜光遥感影像；Step e, using the boundary vector data of the study area, mosaicking and cropping the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images to obtain the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images of the study area;
步骤f，根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS夜光遥感影像和NPP/VIIRS夜光遥感影像；Step f, extracting DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of various categories according to the land use category information of the land use status raster data;
步骤g，对每一类土地利用现状类型的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行线性与非线性回归分析，将各类别模型组合得到回归模型组；Step g, performing linear and nonlinear regression analysis on the DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of each type of land use status, and combining the models of each category to obtain a regression model group;
步骤h，基于得到的回归模型组，利用1992年至2012年间的DMSP/OLS影像与对应年的土地利用现状栅格数据模拟生成该年份的NPP/VIIRS影像，完成多源夜光遥感影像的整合。Step h, based on the obtained regression model group, the DMSP/OLS images from 1992 to 2012 and the land use status raster data of the corresponding year are used to simulate and generate the NPP/VIIRS images of the year, completing the integration of multi-source night light remote sensing images.
进一步地，步骤a中选择版本4的2013年DMSP/OLS年稳定灯光影像。Furthermore, in step a, version 4 of the 2013 DMSP/OLS annual stable light image is selected.
进一步地，步骤a中选择V2版2013年的NPP/VIIRS年均值灯光影像。Furthermore, in step a, the V2 version of the 2013 NPP/VIIRS annual mean light image is selected.
进一步地，所述V2版NPP/VIIRS年均值灯光影像需要通过2013年的灯光掩膜栅格数据对2013年的NPP/VIIRS年均值数据进行掩膜，从而得到2013年的V2版NPP/VIIRS年均值灯光影像,其提取过程计算公式为：Furthermore, the V2 version of the NPP/VIIRS annual average light image needs to be masked by the 2013 NPP/VIIRS annual average data using the 2013 light mask grid data, thereby obtaining the 2013 V2 version of the NPP/VIIRS annual average light image. The calculation formula for the extraction process is:
其中，
进一步，本发明实施例中的步骤a中土地利用现状栅格数据的选择方法为：Further, the method for selecting the land use status grid data in step a of the embodiment of the present invention is:
若为栅格数据，选择空间分辨率与NPP/VIIRS相近，分类尽可能详细，精度尽可能更高的土地利用现状栅格数据；If it is raster data, select land use status raster data with a spatial resolution similar to NPP/VIIRS, as detailed classification as possible, and as high accuracy as possible;
若为矢量数据，需使用相关专业软件，如Arcgis等，将其转换为栅格数据。If it is vector data, you need to use relevant professional software, such as ArcGIS, to convert it into raster data.
进一步，本发明实施例的步骤e中所述研究区的土地利用现状栅格数据需根据所述研究区域大小以及所选土地利用现状栅格数据的实际情况判断是否需要多景土地利用现状进行镶嵌得到一幅覆盖研究区域的影像，再利用研究区域的边界矢量数据对其进行裁剪得到研究区域的土地利用现状栅格数据。Furthermore, the land use status raster data of the study area in step e of the embodiment of the present invention needs to be judged based on the size of the study area and the actual situation of the selected land use status raster data whether it is necessary to mosaic multiple land use status scenes to obtain an image covering the study area, and then use the boundary vector data of the study area to crop it to obtain the land use status raster data of the study area.
进一步，所述步骤f中根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS和NPP/VIIRS夜光遥感影像，则按以下公式提取：Furthermore, in step f, DMSP/OLS and NPP/VIIRS night light remote sensing images of various categories are extracted according to the land use category information of the land use status raster data, and the extraction is performed according to the following formula:
式中，(Landuseij＝＝Valuen)为逻辑语句，Landuseij等于Valuen，则该部分为1，反之为0；其中n为土地利用现状栅格数据的类别数目，
进一步，所述步骤g中对每一类土地利用现状类型的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行线性与非线性回归分析，将各类别模型组合得到回归模型组的方法具体为：Furthermore, in step g, linear and nonlinear regression analysis is performed on the DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of each type of land use status, and the method of combining each category model to obtain a regression model group is specifically as follows:
对每一类别的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像建立回归模型，建立模型的方式为以每一个类别的同一空间位置的DMSP/OLS影像像元值为自变量X，NPP/VIIRS影像像元值为因变量Y，通过线性与非线性函数模型分别进行回归分析；A regression model was established for each category of DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images. The model was established by taking the pixel value of DMSP/OLS images at the same spatial position of each category as the independent variable X and the pixel value of NPP/VIIRS images as the dependent variable Y, and performing regression analysis using linear and nonlinear function models respectively.
以相关系数R2的大小为依据寻找R2最大的拟合函数作为回归模型函数，计fi(x)为第i类别的回归模型函数；Based on the size of the correlation coefficient R 2 , find the fitting function with the largest R 2 as the regression model function, and count fi (x) as the regression model function of the i-th category;
由各类别回归模型函数结合成回归模型组，以函数组的形式直观表现，如下：The regression model functions of each category are combined into a regression model group, which is intuitively expressed in the form of a function group, as follows:
fi(x)为第i类的回归模型。 fi (x) is the regression model of the i-th class.
进一步，所述步骤h中利用DMSP/OLS灯光影像和土地利用数据模拟对应年份的NPP/VIIRS影像的方法为：首先，通过步骤b、c、d、e，得到研究区的坐标系、空间分辨率、投影方式一致且经过地理配准的1992年至2012年的DMSP/OLS灯光影像与同年的土地利用现状栅格数据，以1992-2013年，每一年的DMSP/OLS年稳定灯光影像与对应年的土地利用现状栅格数据为输入数据，根据步骤g中回归模型组F(x)逐像元计算得到模拟的对应年份的NPP/VIIRS影像像元的像素值。Furthermore, the method of using DMSP/OLS light images and land use data to simulate NPP/VIIRS images of corresponding years in step h is as follows: first, through steps b, c, d, and e, the DMSP/OLS light images from 1992 to 2012 and the land use status raster data of the same years with consistent coordinate system, spatial resolution, and projection mode of the study area and after georeferencing are obtained; the DMSP/OLS annual stable light images of each year from 1992 to 2013 and the land use status raster data of the corresponding year are used as input data; the pixel values of the simulated NPP/VIIRS image pixels of the corresponding years are calculated pixel by pixel according to the regression model group F(x) in step g.
结合上述的所有技术方案，本发明所具备的优点及积极效果为：Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows:
本发明利用土地利用现状的数据，对DMSP/OLS与NPP/VIIRS赋予地类属性。对各个地类属性的灯光建立其回归模型，最终形成将DMSP/OLS转换成类NPP/VIIRS的整合模型。经实验表明，本发明有效地进行了DMSP/OLS与NPP/VIIRS夜光遥感影像的整合，较好的削弱了两种夜间灯光遥感影像之间的灯光强度差异。The present invention uses the data of the current land use status to assign land attributes to DMSP/OLS and NPP/VIIRS. A regression model is established for the lights of each land attribute, and finally an integrated model is formed to convert DMSP/OLS into NPP/VIIRS-like images. Experiments show that the present invention effectively integrates the night light remote sensing images of DMSP/OLS and NPP/VIIRS, and effectively weakens the light intensity difference between the two night light remote sensing images.
本发明充分地分析了各种土地利用类别的DMSP/OLS与NPP/VIIRS的灯光强度之间的关系，得到了不同地类的DMSP/OLS与NPP/VIIRS的灯光转换模型，建立了一种基于土地利用数据的多源夜光遥感影像整合方法。本发明方法解决多源夜光遥感影像数据不兼容问题，为构建具有广泛应用价值的高质量、长时间夜光遥感时间序列数据集打下了基础。The present invention fully analyzes the relationship between the light intensity of DMSP/OLS and NPP/VIIRS of various land use categories, obtains the light conversion model of DMSP/OLS and NPP/VIIRS of different land types, and establishes a multi-source night light remote sensing image integration method based on land use data. The method of the present invention solves the incompatibility problem of multi-source night light remote sensing image data, and lays the foundation for constructing a high-quality, long-term night light remote sensing time series data set with wide application value.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明实施例提供的一种基于土地利用数据的多源夜光遥感影像整合方法流程图；FIG1 is a flow chart of a method for integrating multi-source night light remote sensing images based on land use data provided by an embodiment of the present invention;
图2是本发明实施例提供的一种基于土地利用数据的多源夜光遥感影像整合方法影像整合实验流程图；2 is an image integration experiment flow chart of a multi-source night light remote sensing image integration method based on land use data provided by an embodiment of the present invention;
图3是本发明实施例提供的2013年江西省区域的土地利用影像示意图；FIG3 is a schematic diagram of land use images of Jiangxi Province in 2013 provided by an embodiment of the present invention;
图4是本发明实施例提供的2013年江西省区域的DMSP/OLS年稳定夜光遥感影像示意图；4 is a schematic diagram of a DMSP/OLS annual stable night light remote sensing image of Jiangxi Province in 2013 provided by an embodiment of the present invention;
图5是本发明实施例提供的2013年江西省区域的NPP/VIIRS第V2版年夜光遥感影像示意图；5 is a schematic diagram of the NPP/VIIRS Version V2 high-light remote sensing image of Jiangxi Province in 2013 provided by an embodiment of the present invention;
图6是本发明实施例提供的进行整合的2010年江西省DMSP/OLS年稳定夜光遥感影像示意图；6 is a schematic diagram of an integrated DMSP/OLS annual stable night light remote sensing image of Jiangxi Province in 2010 provided by an embodiment of the present invention;
图7是本发明实施例提供的进行整合的2010年江西省区域的土地利用影像示意图；FIG7 is a schematic diagram of integrated land use images of Jiangxi Province in 2010 provided by an embodiment of the present invention;
图8是本发明实施例提供的进行整合后的2010年江西省模拟NPP/VIIRS夜光遥感影像示意图；FIG8 is a schematic diagram of an integrated simulated NPP/VIIRS night light remote sensing image of Jiangxi Province in 2010 provided by an embodiment of the present invention;
图9是用于与本发明方法对比的使用陈佐旗的方法进行整合2010年江西省的模拟NPP/VIIRS夜光遥感影像示意图。FIG. 9 is a schematic diagram of integrating simulated NPP/VIIRS night light remote sensing images of Jiangxi Province in 2010 using Chen Zuoqi's method for comparison with the method of the present invention.
具体实施方式DETAILED DESCRIPTION
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
针对现有技术中NPP/VIIRS灯光影像与DMSP/OLS灯光影像的差异由多种因素复合导致，差异巨大。不同土地利用类型的DMSP/OLS灯光影像与NPP/VIIRS灯光影像的相互关系不同。根据DMSP/OLS与NPP/VIIRS的灯光分布规律不同，通过实验发现不同地类属性的DMSP/OLS影像与NPP/VIIRS之间的强度特征也有所不同。本发明提供了一种基于土地利用数据的多源夜光遥感影像整合方法，下面结合附图对本发明作详细的描述。如图1所示，所述方法包括：The difference between NPP/VIIRS light images and DMSP/OLS light images in the prior art is caused by a combination of multiple factors, and the difference is huge. The relationship between DMSP/OLS light images and NPP/VIIRS light images of different land use types is different. According to the different light distribution patterns of DMSP/OLS and NPP/VIIRS, it is found through experiments that the intensity characteristics between DMSP/OLS images and NPP/VIIRS of different land attributes are also different. The present invention provides a multi-source night light remote sensing image integration method based on land use data, and the present invention is described in detail below in conjunction with the accompanying drawings. As shown in Figure 1, the method comprises:
步骤a，选择2013年覆盖研究区域的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据；Step a, select DMSP/OLS night light remote sensing images, NPP/VIIRS night light remote sensing images and land use status raster data covering the study area in 2013;
步骤b，对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像、土地利用现状栅格数据进行投影变换，转换为WGS_1984_Albers投影坐标系；Step b, projecting and transforming the DMSP/OLS night light remote sensing images, NPP/VIIRS night light remote sensing images, and land use status raster data into the WGS_1984_Albers projection coordinate system;
步骤c，对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据进行空间重采样，使DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像和土地利用现状栅格数据空间分辨率均为500米；Step c, spatially resampling the DMSP/OLS night light remote sensing images, the NPP/VIIRS night light remote sensing images, and the land use status raster data, so that the spatial resolution of the DMSP/OLS night light remote sensing images, the NPP/VIIRS night light remote sensing images, and the land use status raster data is 500 meters;
步骤d，以土地利用现状栅格数据为基准分别对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行地理配准；Step d, georeferencing the DMSP/OLS night light remote sensing images and the NPP/VIIRS night light remote sensing images respectively based on the land use status raster data;
步骤e，使用研究区域的边界矢量数据，对土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像，进行镶嵌、裁剪得到研究区域的土地利用现状栅格数据、DMSP/OLS夜光遥感影像和NPP/VIIRS夜光遥感影像；Step e, using the boundary vector data of the study area, mosaicking and cropping the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images to obtain the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images of the study area;
步骤f，根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS夜光遥感影像和NPP/VIIRS夜光遥感影像；Step f, extracting DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of various categories according to the land use category information of the land use status raster data;
步骤g，对每一类土地利用现状类型的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行线性与非线性回归分析，将各类别模型组合得到回归模型组；Step g, performing linear and nonlinear regression analysis on the DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of each type of land use status, and combining the models of each category to obtain a regression model group;
步骤h，基于得到的回归模型组，利用1992年至2012年间的DMSP/OLS影像与对应年的土地利用现状栅格数据模拟生成该年份的NPP/VIIRS影像，完成多源夜光遥感影像的整合。Step h, based on the obtained regression model group, the DMSP/OLS images from 1992 to 2012 and the land use status raster data of the corresponding year are used to simulate and generate the NPP/VIIRS images of the year, completing the integration of multi-source night light remote sensing images.
本发明实施例的步骤a中选择版本4的2013年DMSP/OLS年稳定灯光影像。In step a of the embodiment of the present invention, version 4 of the 2013 DMSP/OLS annual stable light image is selected.
本发明实施例的步骤a中选择V2版2013年的NPP/VIIRS年均值灯光影像。In step a of the embodiment of the present invention, the V2 version of the 2013 NPP/VIIRS annual average light image is selected.
进一步地，V2版NPP/VIIRS年均值灯光影像需要通过2013年的灯光掩膜栅格数据对2013年的NPP/VIIRS年均值数据进行掩膜，从而得到2013年的V2版NPP/VIIRS年均值灯光影像，其提取过程计算公式为：Furthermore, the V2 version of the NPP/VIIRS annual mean light image needs to be masked with the 2013 NPP/VIIRS annual mean light data using the 2013 light mask raster data, thereby obtaining the 2013 V2 version of the NPP/VIIRS annual mean light image. The calculation formula for the extraction process is:
其中
本发明实施例中的步骤a中土地利用现状栅格数据的选择方法为：The method for selecting the land use status grid data in step a of the embodiment of the present invention is:
若为栅格数据，选择空间分辨率与NPP/VIIRS相近，分类尽可能详细，精度尽可能更高的土地利用现状栅格数据；If it is raster data, select land use status raster data with a spatial resolution similar to NPP/VIIRS, as detailed classification as possible, and as high accuracy as possible;
若为矢量数据，需使用相关专业软件，如Arcgis等，将其转换为栅格数据。If it is vector data, you need to use relevant professional software, such as ArcGIS, to convert it into raster data.
进一步，本发明实施例的步骤e中所述研究区的土地利用现状栅格数据需根据所述研究区域大小以及所选土地利用现状栅格数据的实际情况判断是否需要多景土地利用现状进行镶嵌得到一幅覆盖研究区域的影像，再利用研究区域的边界矢量数据对其进行裁剪得到研究区域的土地利用现状栅格数据。Furthermore, the land use status raster data of the study area in step e of the embodiment of the present invention needs to be judged based on the size of the study area and the actual situation of the selected land use status raster data whether it is necessary to mosaic multiple land use status scenes to obtain an image covering the study area, and then use the boundary vector data of the study area to crop it to obtain the land use status raster data of the study area.
进一步，所述步骤f中根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS和NPP/VIIRS夜光遥感影像，则按以下公式提取：Furthermore, in step f, DMSP/OLS and NPP/VIIRS night light remote sensing images of various categories are extracted according to the land use category information of the land use status raster data, and the extraction is performed according to the following formula:
式中，(Landuseij＝＝Valuen)为逻辑语句，Landuseij等于Valuen，则该部分为1，反之为0；其中n为土地利用现状栅格数据的类别数目，
进一步，所述步骤g中对每一类土地利用现状类型的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行线性与非线性回归分析，将各类别模型组合得到回归模型组的方法具体为：Furthermore, in step g, linear and nonlinear regression analysis is performed on the DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of each type of land use status, and the method of combining each category model to obtain a regression model group is specifically as follows:
对每一类别的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像建立回归模型，建立模型的方式为以每一个类别的同一空间位置的DMSP/OLS影像像元值为自变量X，NPP/VIIRS影像像元值为因变量Y，通过线性与非线性函数模型分别进行回归分析；A regression model was established for each category of DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images. The model was established by taking the pixel value of DMSP/OLS images at the same spatial position of each category as the independent variable X and the pixel value of NPP/VIIRS images as the dependent variable Y, and performing regression analysis using linear and nonlinear function models respectively.
以相关系数R2的大小为依据寻找R2最大的拟合函数作为回归模型函数，计fi(x)为第i类别的回归模型函数；Based on the size of the correlation coefficient R 2 , find the fitting function with the largest R 2 as the regression model function, and count fi (x) as the regression model function of the i-th category;
由各类别回归模型函数结合成回归模型组，以函数组的形式直观表现，如下：The regression model functions of each category are combined into a regression model group, which is intuitively expressed in the form of a function group, as follows:
fi(x)为第i类的回归模型。 fi (x) is the regression model of the i-th class.
所述步骤h中利用DMSP/OLS灯光影像和土地利用数据模拟对应年份的NPP/VIIRS影像的方法为：首先，通过步骤b、c、d、e，得到研究区的坐标系、空间分辨率、投影方式一致且经过地理配准的1992年至2012年的DMSP/OLS灯光影像与同年的土地利用现状栅格数据，以1992-2013年，每一年的DMSP/OLS年稳定灯光影像与对应年的土地利用现状栅格数据为输入数据，根据步骤g中回归模型组F(x)逐像元计算得到模拟的对应年份的NPP/VIIRS影像像元的像素值。The method of using DMSP/OLS light images and land use data to simulate NPP/VIIRS images of corresponding years in step h is as follows: first, through steps b, c, d, and e, the DMSP/OLS light images from 1992 to 2012 and the land use status raster data of the same years with consistent coordinate system, spatial resolution, and projection mode of the study area and after georeferencing are obtained; the DMSP/OLS annual stable light images of each year from 1992 to 2013 and the land use status raster data of the corresponding year are used as input data; the pixel values of the simulated NPP/VIIRS image pixels of the corresponding years are calculated pixel by pixel according to the regression model group F(x) in step g.
下面结合实验对本发明的技术方案做进一步的描述。The technical solution of the present invention is further described below in conjunction with experiments.
为了更好地理解本发明的技术方案，结合图2所示，下面以江西省2013年的DMSP/OLS年稳定夜光遥感影像与NPP/VIIRS整合实验来说明具体实施方式。In order to better understand the technical solution of the present invention, in conjunction with FIG2 , the specific implementation method is described below using the DMSP/OLS annual stable night light remote sensing image and NPP/VIIRS integration experiment in Jiangxi Province in 2013.
步骤a，选择2013年江西省的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像、土地利用现状栅格数据的通过以下步骤实施：Step a, select the DMSP/OLS night light remote sensing image, NPP/VIIRS night light remote sensing image, and land use status raster data of Jiangxi Province in 2013 and implement it through the following steps:
1.值得说明的是，研究区域选择以中国江西省为例，从EOG数据网站(https://eogdata.mines.edu/products/vnl/#annual_v2)下载2013年V2版NPP/VIIRS年影像与DMSP/OLS年稳定灯光影像，其中NPP/VIIRS影像包括年均值影像与灯光区域掩膜文件；1. It is worth noting that the study area is selected from Jiangxi Province, China as an example. The 2013 V2 version of NPP/VIIRS annual images and DMSP/OLS annual stable light images were downloaded from the EOG data website (https://eogdata.mines.edu/products/vnl/#annual_v2). The NPP/VIIRS images include annual mean images and light area mask files.
2.利用MATLAB软件，通过2013年的灯光掩膜栅格数据与2013年的NPP/VIIRS年均值数据，合成2013年的V2版NPP/VIIRS年均值灯光影像,其计算公式为：2. Using MATLAB software, the 2013 V2 version of the NPP/VIIRS annual average light image was synthesized through the 2013 light mask grid data and the 2013 NPP/VIIRS annual average data. The calculation formula is:
其中
3.从中国科学院资源环境科学与数据中心中下载覆盖江西省的2013年土地利用现状影像，如图3所示，其土地利用类别信息如表1所示；3. Download the 2013 land use status image covering Jiangxi Province from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, as shown in Figure 3, and its land use category information is shown in Table 1;
表1 2013年江西省区域的土地利用影像土地利用类别信息表与对应类别的整合模型与模型精度Table 1 Land use category information table of land use images in Jiangxi Province in 2013 and the integration model and model accuracy of the corresponding categories
根据结果表明，(1)power函数和多项式函数模型在各种拟合模型中的相关系数s最高。(2)D饱和DMSP/OLS数据在幂函数模型中的精度低于原始DMSP/OLS数据，但在多项式函数模型中则相反。(3)NPP/VIIRS数据的对数变换处理可有效提高拟合精度。(4)人工要素各土地利用面积的拟合精度高于整个区域，非人工要素的拟合精度最低。The results show that (1) the power function and polynomial function models have the highest correlation coefficient s among various fitting models. (2) The accuracy of D-saturated DMSP/OLS data in the power function model is lower than that of the original DMSP/OLS data, but the opposite is true in the polynomial function model. (3) The logarithmic transformation of NPP/VIIRS data can effectively improve the fitting accuracy. (4) The fitting accuracy of each land use area of artificial elements is higher than that of the entire region, and the fitting accuracy of non-artificial elements is the lowest.
步骤b，对土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行投影变换，转换为WGS_1984_Albers投影坐标系的具体实施方式为：Step b, projecting and transforming the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images into the WGS_1984_Albers projection coordinate system is as follows:
利用ENVI软件中的投影变换功能，将土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行投影变换，使土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像地理坐标系与投影一致，转换为Albers_WGS84投影坐标系；The projection transformation function in ENVI software is used to transform the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images so that their geographic coordinate systems are consistent with the projection and converted into the Albers_WGS84 projection coordinate system.
步骤c，对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像、土地利用现状栅格数据进行空间重采样，使其空间分辨率均为500米的具体实施方式为：Step c, spatially resampling the DMSP/OLS night light remote sensing images, NPP/VIIRS night light remote sensing images, and land use status raster data so that their spatial resolution is 500 meters is as follows:
利用ENVI软件中的栅格重采样功能，使土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像空间分辨率一致，为500米；The raster resampling function in ENVI software was used to make the spatial resolution of land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images consistent at 500 meters.
步骤d，以土地利用数据为基准分别对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行地理配准：Step d: Geo-reference the DMSP/OLS night light remote sensing images and the NPP/VIIRS night light remote sensing images based on the land use data:
本实施例中所选择遥感影像并未有明显的地理位置差异，故并未进行地理配准；The remote sensing images selected in this embodiment do not have obvious geographical location differences, so geo-registration is not performed;
步骤e，使用研究区域的边界矢量数据，对土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像，进行镶嵌、裁剪得到研究区域的土地利用现状栅格数据、DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像的具体实施方式为：Step e, using the boundary vector data of the study area, mosaicking and cropping the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images to obtain the land use status raster data, DMSP/OLS night light remote sensing images, and NPP/VIIRS night light remote sensing images of the study area. The specific implementation method is:
利用ENVI软件中的栅格影像裁剪功能，结合研究区域的边界矢量数据对DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像裁剪得到中国江西省的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像，具体如图4、图5所示；需要说明的是，在本实施例中所使用的土地利用现状栅格数据可直接下载江西省的土地利用现状栅格数据，故不需要进一步的裁剪与镶嵌。By using the raster image cropping function in the ENVI software and combining the boundary vector data of the study area, the DMSP/OLS night light remote sensing images and the NPP/VIIRS night light remote sensing images are cropped to obtain the DMSP/OLS night light remote sensing images and the NPP/VIIRS night light remote sensing images of Jiangxi Province, China, as shown in Figures 4 and 5. It should be noted that the land use status raster data used in this embodiment can directly download the land use status raster data of Jiangxi Province, so no further cropping and mosaicking are required.
步骤f，根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS夜光遥感影像和NPP/VIIRS夜光遥感影像的具体实施方式为：Step f, the specific implementation method of extracting various categories of DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images according to the land use category information of the land use status raster data is as follows:
利用MATLAB软件，根据土地利用现状栅格数据的土地利用类别信息提取各类别的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像，则按以下公式提取：Using MATLAB software, we extract various categories of DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images based on the land use category information of the land use status raster data, and use the following formula to extract:
式中，(Landuseij＝＝Valuen)为逻辑语句，Landuseij等于Valuen，则该部分为1，反之为0；其中n为土地利用现状栅格数据的类别数目，
步骤g，对每一类土地利用现状类型的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像进行线性与非线性回归分析，将各类别模型组合得到回归模型组的具体实施方式为：Step g, linear and nonlinear regression analysis is performed on the DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images of each type of land use status, and the specific implementation method of combining each category model to obtain a regression model group is as follows:
利用MATLAB软件，对每一类别的DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像分别进行线性与非线性回归分析，以同一空间位置的DMSP/OLS影像像元值为横坐标，NPP/VIIRS影像像元值为纵坐标进行线性与非线性回归分析；Using MATLAB software, linear and nonlinear regression analysis was performed on each category of DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images, respectively. The pixel values of DMSP/OLS images at the same spatial position were used as the horizontal coordinates, and the pixel values of NPP/VIIRS images were used as the vertical coordinates.
并以相关系数R2的大小为依据，寻找R2最大的拟合曲线函数f(x)，为该类别的整合模型，各函数模型及其精度如表1所示；由各类别回归模型函数结合成回归模型组，以函数组的形式直观表现具体参考表2所示。Based on the size of the correlation coefficient R2 , we find the fitting curve function f(x) with the largest R2 , which is the integrated model of this category. The function models and their accuracy are shown in Table 1. The regression model functions of each category are combined into a regression model group, which is intuitively expressed in the form of a function group. The specific reference is shown in Table 2.
表2各类别回归模型函数结合成回归模型组，以函数组的形式直观表现Table 2: The regression model functions of each category are combined into regression model groups, which are intuitively expressed in the form of function groups.
步骤h,基于步骤g得到的回归模型组，利用1992-2012年的DMSP/OLS影像和土地利用数据模拟对应年份的NPP/VIIRS影像的具体实施方式为：Step h, based on the regression model group obtained in step g, the specific implementation method of simulating the NPP/VIIRS images of the corresponding years using the DMSP/OLS images and land use data from 1992 to 2012 is as follows:
1.根据步骤a、b下载1992-2012年的DMSP/OLS影像和对应年份的土地利用现状栅格数据，根据步骤c、d、e、f得到1992-2012年的研究区域的投影坐标系、空间分辨率一致的并进行地理配准的DMSP/OLS影像与土地利用现状栅格数据，如图6、图7所示分别显示了2010年的研究区DMSP/OLS年稳定夜光遥感影像和土地利用现状栅格数据；1. According to steps a and b, download the DMSP/OLS images from 1992 to 2012 and the land use status raster data of the corresponding years. According to steps c, d, e, and f, obtain the DMSP/OLS images and land use status raster data of the study area from 1992 to 2012 with consistent projection coordinate system and spatial resolution and georeferenced. As shown in Figures 6 and 7, the DMSP/OLS stable night light remote sensing images and land use status raster data of the study area in 2010 are shown respectively.
2.利用MATLAB软件，以每一年DMSP/OLS影像与对应年的土地利用现状栅格数据为输入数据，通过函数组F(x)模拟每一年的NPP/VIIRS影像，如图8所示显示了2010年的模拟NPP/VIIRS影像。2. Using MATLAB software, the DMSP/OLS image of each year and the land use status grid data of the corresponding year are used as input data, and the NPP/VIIRS image of each year is simulated through the function group F(x). The simulated NPP/VIIRS image of 2010 is shown in Figure 8.
为了验证本发明方法的有效性，将本发明方法与另一种利用DMSP/OLS影像模拟对应年份的NPP/VIIRS影像的方法进行比较，该方法是Chen等提出的利用增强型植被指数与自动编码器模型的跨传感器校正方法；In order to verify the effectiveness of the method of the present invention, the method of the present invention is compared with another method of using DMSP/OLS images to simulate NPP/VIIRS images of the corresponding year. This method is a cross-sensor correction method using enhanced vegetation index and autoencoder model proposed by Chen et al.
图9为Chen等在该省的2010年模拟NPP/VIIRS结果影像；从图8与图9的对比来看，本发明建立所有地类上DMSP/OLS灯光与NPP/VIIRS灯光的复杂关系，相对而言，更有效地进行了DMSP/OLS夜光遥感影像、NPP/VIIRS夜光遥感影像的整合，较好的削弱了两种夜间灯光遥感影像之间的灯光强度差异。FIG9 is a simulated NPP/VIIRS result image of the province in 2010 by Chen et al.; from the comparison between FIG8 and FIG9 , it can be seen that the present invention establishes a complex relationship between DMSP/OLS lights and NPP/VIIRS lights on all land types, and relatively speaking, more effectively integrates DMSP/OLS night light remote sensing images and NPP/VIIRS night light remote sensing images, and better weakens the light intensity difference between the two night light remote sensing images.
在本发明中，也可以用其它合理方法合成的NPP/VIIRS年影像代替V2版NPP/VIIRS年影像，只要其将NPP/VIIRS的灯光影像存在的背景噪声、异常值噪声消除。In the present invention, the NPP/VIIRS annual images synthesized by other reasonable methods may be used to replace the V2 version of the NPP/VIIRS annual images, as long as the background noise and outlier noise existing in the NPP/VIIRS light images are eliminated.
综上所述，本发明缓和了DMSP/OLS与NPP/VIIRS多源夜光遥感影像的不兼容问题，体现了多源夜光遥感数据的一致性，为构建具有广泛应用价值的高质量、长时间夜光遥感时间序列数据集打下了基础。在基于长时间序列影像的夜光遥感应用领域里具有一定的实用价值和应用前景。In summary, the present invention alleviates the incompatibility problem between DMSP/OLS and NPP/VIIRS multi-source night light remote sensing images, reflects the consistency of multi-source night light remote sensing data, and lays the foundation for building a high-quality, long-term night light remote sensing time series dataset with wide application value. It has certain practical value and application prospects in the field of night light remote sensing applications based on long-term series images.
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，都应涵盖在本发明的保护范围之内。The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.
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