Improved remote sensing ecological index calculation method based on spatio-temporal data fusion

Abstract

The invention provides an improved remote sensing ecological index calculation method based on spatio-temporal data fusion. The method comprises the following steps: collecting remote sensing data of a research area; preprocessing the collected remote sensing data; performing mask processing on the water body in the processed remote sensing data; performing space-time fusion on the preprocessed remote sensing data; according to the remote sensing data after fusion processing, index data used for representing improved remote sensing ecological indexes are calculated respectively; the method comprises the following steps: acquiring aerosol optical thickness for representing air quality and night light data for representing human activity intensity on a gee platform, and resampling the two index data; performing parameter standardization processing on the index data to obtain standardized data; principal component analysis is adopted, principal component extraction is carried out according to the standardized data, and an evaluation index result is obtained. Through the spatio-temporal data fusion technology, the spatio-temporal resolution of the data can be improved, and the problem that index selection of the remote sensing ecological indexes is lack of comprehensiveness is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for calculating an improved remote sensing ecological index based on spatiotemporal data fusion. Background Art

[0002] With the increasing awareness of environmental protection, ecological environment quality evaluation has been a hot topic in recent years. There are more and more methods for ecological environment quality evaluation. The comprehensive index method is the most commonly used method for ecological environment quality evaluation. How to quickly and accurately evaluate the ecological environment quality is a problem that should be solved at present. In 2006, the Ministry of Environmental Protection of the People's Republic of China issued the "Environmental Protection Industry Standard of the People's Republic of China (Trial)" (hereinafter referred to as the "Specification") in the form of an industry standard. The specification contains the calculation method of the ecological environment index (Ecological Index, EI), but the EI calculation in the specification has the problems of cumbersome calculation, some data may not be available, and the results can only be presented in administrative areas (such as a certain county) as a unit. Therefore, researchers cannot fully follow the specification to evaluate the ecological environment quality. Since satellite remote sensing earth observation systems have the advantages of being macroscopic, fast and real-time, Xu Hanqiu proposed the Remote Sensing Ecological Index (RSEI) in 2013, which is completely based on remote sensing image calculations (Reference: Xu Hanqiu. Remote Sensing Evaluation Index of Regional Ecological Environment Changes [J]. China Environmental Science, 2013, 33(05): 889-97.). This index evaluates the ecological environment quality using four indicators: greenness, humidity, heat and dryness. The main information of the four indicators is concentrated through principal component analysis to reflect the quality of the ecological environment. After the remote sensing ecological index was proposed, the ecological environment quality evaluation has developed rapidly due to its convenient calculation and visualization of results.

[0003] The current remote sensing data fusion technology has significant deficiencies in spatiotemporal resolution and ecological index calculation. Landsat data has a high spatial resolution, but it is limited by the temporal resolution and it is difficult to achieve frequent dynamic monitoring of the surface; while MODIS data has a high temporal resolution, but its spatial resolution is low and it is difficult to meet the needs of refined monitoring. In addition, the remote sensing ecological index calculation method given in the prior art selects four indexes for evaluation: greenness, dryness, humidity and heat. Although the vegetation index representing greenness is similar to the vegetation coverage index in the "Specifications" and is highly correlated with the biological abundance index; the index representing humidity is consistent with the water network index; the bare soil index representing dryness is closely related to the land degradation index, three of these ecological indices are closely related to the four indicators in the "Specifications", but there are still problems in the selection of indices. It is mainly reflected in the lack of comprehensiveness: the environmental quality index in the "Specifications" includes the pollution load index, while the four indicators in the remote sensing ecological index (greenness, dryness, humidity, and heat) are not related to the pollution load index. In addition, the intensity of human activities, as an important factor of environmental quality, is not involved in the remote sensing ecological index. Therefore, the lack of comprehensiveness in index selection remains the main problem that current technology needs to solve.

[0004] The innovation of this patent is that it proposes a method for calculating an improved remote sensing ecological index based on spatiotemporal data fusion, which not only effectively combines the advantages of Landsat and MODIS, improves the spatiotemporal resolution, but also improves the remote sensing ecological index. Through multi-source data matching and the introduction of key environmental factors such as pollution load and human activities, the patented method not only improves the spatiotemporal accuracy of the data, but also expands the calculation dimension of the ecological index, enabling it to fully reflect the environmental quality. In addition, this innovative technology significantly improves the ability to monitor dynamic changes in the environment, and can be widely used in agriculture, climate change, urban expansion and other fields, providing more refined and timely spatiotemporal data support for related industries. Summary of the invention

[0005] The purpose of this application is to provide a method for calculating an improved remote sensing ecological index based on spatiotemporal data fusion, with the aim of improving the spatiotemporal resolution of data and solving the problem of lack of comprehensiveness in index selection of remote sensing ecological index.

[0006] To achieve the above-mentioned purpose, the present application provides a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, the method comprising the following steps: collecting remote sensing data of the study area; preprocessing the collected remote sensing data; masking the water bodies in the processed remote sensing data; spatiotemporally fusing the preprocessed remote sensing data; calculating the index data for characterizing the improved remote sensing ecological index based on the fused remote sensing data; wherein the index data include: greenness index, humidity index, dryness index, and heat index; obtaining aerosol optical thickness for characterizing air quality and night light data for characterizing the intensity of human activities on the gee platform, and resampling these two index data; performing parameter standardization on the index data to obtain standardized data; using principal component analysis, and performing principal component extraction based on the standardized data to obtain an evaluation index result.

[0007] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion is as described above, wherein the evaluation index result is the improved remote sensing ecological index, and the improved remote sensing ecological index is synthesized by the greenness index, humidity index, heat index and air quality index through principal component analysis transformation.

[0008] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion as described above, wherein the method also includes: dividing the ecological quality of the study area according to the evaluation index results, and obtaining the spatial distribution data of the ecological quality of the study area.

[0009] As described above, the calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion includes: selecting remote sensing data of the study area according to a preset time range to obtain remote sensing images to be processed; de-clouding the remote sensing images to be processed; and resampling and defining projections of the remote sensing images to be processed after de-clouding.

[0010] As described above, the method for calculating the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, wherein the method for masking the water bodies in the pre-processed remote sensing data includes: calculating the improved normalized difference water index; identifying the water bodies in the remote sensing data according to the normalized difference water index, and masking the identified water bodies.

[0011] As described above, the calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, wherein the remote sensing data of the study area include: Landsat series remote sensing images and MODIS series remote sensing images.

[0012] As described above, the calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion is used, wherein the greenness index, humidity index, dryness index and heat index are calculated based on the fused high spatiotemporal resolution remote sensing images; the air quality index is obtained based on the MODIS series remote sensing images; and the night light data is obtained based on the NOAA NPP light data to represent the human activity intensity index.

[0013] As described above, the calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, wherein the parameter standardization processing of the index data adopts the following formula:

[0014]

[0015] Among them, NIi is the normalized index value, and its value range is 0 to 1; Ii is the index data at pixel i; Imin is the minimum value of the index data; and Imax is the maximum value of the index data.

[0016] As described above, the calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, wherein the calculation formula of the improved normalized difference water index is:

[0017]

[0018] Among them, ρ G , ρ swir1 Represent the reflectance of the green band and shortwave infrared 1 respectively.

[0019] As described above, the improved remote sensing ecological index calculation method for ecological environment quality evaluation under spatiotemporal data fusion is used, in which areas with normalized difference water index (MNDWI) greater than 0.2 are regarded as water bodies, and the water bodies in the remote sensing data in the study area are removed by masking.

[0020] The beneficial effects achieved by this application are as follows:

[0021] (1) The improved remote sensing ecological index of the present invention has a high consistency with the ecological environment index (ecological index, EI) released by the Ministry of Environmental Protection, and is supplemented with aerosol optical depth (AOD) that can represent the air pollution index and night light data that can represent the intensity of human activities. Therefore, compared with the remote sensing ecological index (RSEI), the improved remote sensing ecological index (MRSEI) is more suitable for the evaluation of remote sensing ecological quality. Since the indicators are all derived from remote sensing data, the improved remote sensing ecological index (MRSEI) also has the advantages of easy data acquisition, simple calculation, detailed results to pixels, and visualization of results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For those skilled in the art, other drawings can also be obtained based on these drawings.

[0023] Figure 1 This is a flowchart of a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion according to an embodiment of the present application.

[0024] Figure 2 This is a framework diagram of a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion in an embodiment of the present application.

[0025] Figure 3 This is the ecological environment quality evaluation result of Liaoyuan City in October 2020 according to the embodiment of this application. DETAILED DESCRIPTION

[0026] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] like Figure 1 and 2 As shown, the present application provides a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatiotemporal data fusion, the method comprising the following steps:

[0028] Step S1, collecting remote sensing data of the study area.

[0029] Among them, remote sensing data mainly include Landsat series remote sensing images and MODIS series remote sensing images. Landsat series remote sensing images can capture data in visible light, infrared and thermal infrared bands. MODIS series remote sensing images can capture images of the earth's surface and atmosphere.

[0030] Step S2, preprocessing the collected remote sensing data.

[0031] Step S2 includes the following sub-steps:

[0032] Step S210, selecting remote sensing data of the study area according to a preset time range to obtain a remote sensing image to be processed.

[0033] Step S220, performing cloud removal processing on the remote sensing image to be processed.

[0034] Step S230, resampling and defining projection of the remote sensing image to be processed after the cloud removal process.

[0035] Step S3, masking the water bodies in the remote sensing data.

[0036] Specifically, by masking the water body, the water body index (MNDWI) can be used to enhance the water body information and weaken the information of other land features, making it easier to extract the water body range in the study area.

[0037] Step S3 includes the following sub-steps:

[0038] Step S310, calculating the improved normalized difference water index (MNDWI).

[0039] Specifically, the calculation formula of the improved normalized difference water index (MNDWI) is:

[0040]

[0041] Among them, ρ green , ρ swir1 Represent the reflectance of the green band and shortwave infrared 1 respectively.

[0042] Step S320: identifying water bodies in the remote sensing data according to the normalized difference water body index, and performing mask processing on the identified water bodies.

[0043] Specifically, 0.2 was used as the threshold, and the areas with the normalized difference water index (MNDWI) greater than 0.2 were regarded as water bodies, and the water bodies in the remote sensing data of the study area were removed by masking.

[0044] Step S4, using the OL-STARSM model to perform spatiotemporal fusion on the processed remote sensing data.

[0045] Step S5, respectively calculating the greenness index, wetness index, dryness index and heat index used to characterize the improved remote sensing ecological index (MRSEI) based on the fused remote sensing data.

[0046] Specifically, the masked remote sensing data is the remote sensing image after the water body is removed. According to the remote sensing image after the water body is removed, four index data for characterizing the improved remote sensing ecological index (MRSEI) are calculated: green index, humidity index, heat index and air quality index. The green index, humidity index, heat index and air quality index of the present invention are related to the pollution load index, and there is no high correlation between the four index data, which will not lead to duplication of ecological significance of factors (or layers).

[0047] As a specific embodiment of the present invention, the enhanced vegetation index (EVI) is used to characterize the greenness index, and the greenness index calculation formula is as follows:

[0048]

[0049] Among them, EVI represents the greenness index; ρ nir , red , blue Represent the reflectivity of near-infrared, red and blue light bands respectively.

[0050] As a specific embodiment of the present invention, the humidity component obtained by using the tasseled cap transformation is used as the humidity index, and the calculation formula is as follows:

[0051] TM:WET OLI =0.0315ρ B +0.2021ρ G +0.3102ρ R +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 ;

[0052] ETM+: WET OLI =0.2626ρ B +0.2141ρ G +0.0926ρ R +0.0656ρ NIR -0.7629ρ SWIR1 -0.5388ρ SWIR2 ;

[0053] OLI: WET OLI =0.1511ρ B +0.1973ρ G +0.3283ρ R +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ;

[0054] Among them, WET OLI Represents the humidity component of Landsat8 OLI. ρ B , G , R , NIR , SWIR1 , SWIR2 Represents the reflectivity of the image in the blue, green, red, near infrared, shortwave infrared 1 and shortwave infrared 2 bands respectively; TM: WET OLI 、ETM+:WET OLI、OLI:WET OLI The component representing the humidity index.

[0055] It needs to be explained that the tasseled cap transform (KT transform) is a type of image enhancement achieved through matrix operations.

[0056] As a specific embodiment of the present invention, the normalized difference soil index (NDBSI) is used to represent the dryness index, and the formula is as follows:

[0057]

[0058] Among them, ρblue, ρgreen, ρred, ρnir, and ρswir1 represent the reflectivity of the blue light band, green light band, red light band, near-infrared band, and mid-infrared band, respectively.

[0059] As a specific embodiment of the present invention, the land surface temperature index (LST) is used to represent the heat index, and the formula is as follows:

[0060] LST=T b / {1+[(λT b / ρ) / lnε]}-273.15;

[0061] Among them, LST represents the heat index; T b represents brightness temperature; ε represents emissivity; ρ is a constant with a value of 1.438×10-2mK; λ represents the central wavelength of band 10.

[0062] Step S6, obtain air quality index and human activity intensity index on the gee platform, and resample the air quality index and human activity intensity index. As a specific embodiment of the present invention, aerosol optical thickness (AOD) is used to characterize the air quality index. The air quality index AOD is obtained from MODIS data (i.e., MODIS series remote sensing images).

[0063] As a specific embodiment of the present invention, night light data is used to characterize the human activity intensity index, and the night light data is obtained based on NOAA NPP light data. NOAA NPP light data provides high-resolution remote sensing data by observing night lights and fires on the earth's surface through satellites.

[0064] Specifically, since the air quality index and human activity intensity index are inconsistent with the spatial resolution of the fused remote sensing data, these two indicators need to be resampled, and the spatial resolution is resampled to 30m.

[0065] Step S7, performing parameter standardization processing on the greenness index, humidity index, dryness index, heat index, air quality index and human activity intensity index respectively to obtain standardized data.

[0066] Specifically, the green index, humidity index, dryness index, heat index, air quality index and human activity intensity index resampled in step S6 are respectively subjected to parameter standardization processing by the maximum and minimum normalization model. The parameter standardization processing method is as follows:

[0067]

[0068] Among them, NIi is the normalized index value (i.e., standardized data), and its value range is 0 to 1; Ii is the index value (indicator data) at pixel i; Imin is the minimum value of the index value (indicator data); and Imax is the maximum value of the index value (indicator data).

[0069] Step S8, using principal component analysis and extracting principal components based on standardized data to obtain evaluation index results.

[0070] Specifically, principal component analysis (PAC) is used, and principal component extraction is performed based on the standardized data of step S8, and the first principal component MRSEI0 in the principal component analysis result is used as the result of the evaluation index MRSEI (remote sensing ecological index).

[0071] It can be understood that: Principal Component Analysis (PCA) is a multivariate statistical method. It is one of the most commonly used dimensionality reduction methods. It converts a set of variable data that may be correlated into a set of linearly unrelated variables through orthogonal transformation. The converted variables are called principal components. Principal component analysis is mainly used to discover the basic structure in the data and the relationship between variables in the data. It is also used for data preprocessing for other machine learning. There are two main algorithms for principal component analysis: eigenvalue decomposition of the covariance matrix and singular value decomposition of the data matrix.

[0072] Step S9, according to the evaluation index results, the ecological quality of the study area is divided to obtain the spatial distribution data of the ecological quality of the study area.

[0073] Specifically, MRSEI0 was standardized using a normalization model to obtain the improved remote sensing ecological index MRSEI of the study area. The improved remote sensing ecological index MRSEI was divided into five levels according to the numerical interval of 0.2: excellent (0.8, 1], good (0.6, 0.8], medium (0.4, 0.6], poor (0.2, 0.4] and very poor [0.0, 0.2], and the spatial distribution data of the ecological quality of the study area were obtained.

[0074] It can be understood that the improved remote sensing ecological index MRSEI is synthesized by principal component analysis (PCA) transformation through six component layers: green index, humidity index, dryness index, heat index, air quality index and human activity intensity index.

[0075] As a specific embodiment of the present invention, the implementation of the present invention is implemented on the GEE platform. The GEE (Google Earth Engine) platform is a comprehensive platform that integrates scientific analysis and geographic information data visualization. The platform provides a wealth of APIs and tools to facilitate viewing, calculating, processing and analyzing a wide range of GIS data such as various images. The specific implementation method of the present invention is as follows:

[0076] Step T1, import the collected remote sensing data of the study area into the gee platform.

[0077] Specifically, the remote sensing data of the study area is imported into the GEE platform. The remote sensing data is a shp file named roi. The collected remote sensing data of the study area is imported into the GEE platform for subsequent cropping of the study area.

[0078] Step T2, import the cloud-based function on the gee platform.

[0079] Specifically, the declouding function is used to perform declouding on the remote sensing image to be processed.

[0080] Step T3, select a data set from the remote sensing data of the study area.

[0081] As a specific embodiment of the present invention, the selected data set is the data of October 20, and the images with cloud cover less than 15% are selected. After de-clouding, resampling and unified projection are defined.

[0082] Step T4, input the Landsat and MODIS remote sensing images into the OL-STARFM model to generate a high-speed spatiotemporal fusion image.

[0083] Among them, the OL-STARFM model is an existing spatiotemporal fusion model.

[0084] Step T5, remove the water bodies in the study area of ​​the dataset through masking.

[0085] Specifically, the MNDWI was calculated, with 0.2 as the threshold, and values ​​greater than 0.2 were considered water bodies, and the water bodies in the study area were removed by masking.

[0086] Step T6, import the normalization function on the gee platform.

[0087] Specifically, the normalization function is used to perform normalization calculation using the maximum and minimum normalization model. The maximum and minimum normalization model is used to perform parameter standardization processing on the green index, humidity index, dryness index, heat index, air quality index and human activity intensity index respectively.

[0088] Step T7, executing the calculation step of the heat index LST, and calculating and obtaining the heat index LST.

[0089] Step T8, obtaining the aerosol optical depth AOD used to represent the air quality index in the MODIS data (ie, the MODIS series remote sensing images).

[0090] Step T9, executing the calculation step of the greenness index NDVI and the humidity index WET, and calculating and obtaining the greenness index NDVI and the humidity index WET.

[0091] Step T10, enter basic information on the gee platform.

[0092] Specifically, the basic information includes the study area, image band, and spatial resolution.

[0093] Step T11, renaming the image bands in the basic information according to the image band renaming function.

[0094] Step T12, calculating the average value of each indicator data.

[0095] Specifically, the mean value of the improved remote sensing ecological index MRSEI in the study area was calculated.

[0096] Step T13, perform principal component analysis to obtain analysis results.

[0097] Specifically, principal component analysis (PAC) is used, and principal components are extracted based on the standardized data of each indicator data, and the first principal component in the principal component analysis result is used as the result of the evaluation index MRSEI0 (remote sensing ecological index).

[0098] Step T14, exporting analysis result data.

[0099] Specifically, the analysis result data is exported on the gee platform.

[0100] The improved remote sensing ecological index of the present invention has a high consistency with the ecological environment index (ecological index, EI) released by the Ministry of Environmental Protection, removes the duplication between remote sensing ecological indexes and supplements the aerosol optical thickness (AOD) that can represent the air pollution index. Therefore, compared with the remote sensing ecological index (RSEI), the improved remote sensing ecological index (MRSEI) is more appropriate to the ecological environment index released by the Ministry of Environmental Protection and is more suitable for the evaluation of remote sensing ecological quality. Since the indicators are all derived from remote sensing data, the improved remote sensing ecological index (MRSEI) has the characteristics of easy data acquisition, simple calculation, detailed results to pixels, and visualization of results.

[0101] In order to verify the applicability of the improved remote sensing ecological index, the urban area of ​​Liaoyuan City, Jilin Province was used as the research area for verification, and October 2020 was used as the verification year. The improved remote sensing ecological index (MRSEI) of the urban area of ​​Liaoyuan City, Jilin Province was calculated ( Figure 3 ).

[0102] The present application also provides a computer storage medium, the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the address mapping method of the large-capacity solid-state hard disk. The computer storage medium contains one or more program instructions, and the one or more program instructions are used by the processor to execute a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation.

[0103] The embodiment disclosed in the present invention provides a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned method for calculating an improved remote sensing ecological index for ecological environment quality evaluation.

[0104] An embodiment of the present invention provides a processor for processing the above-mentioned calculation method of an improved remote sensing ecological index for ecological environment quality evaluation.

[0105] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0106] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0107] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0108] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct RAM bus random access memory (DRRAM).

[0109] The beneficial effects achieved by this application are as follows:

[0110] (1) The improved remote sensing ecological index of the present invention has a high consistency with the ecological environment index (ecological index, EI) released by the Ministry of Environmental Protection, and is supplemented with aerosol optical depth (AOD) that can represent the air pollution index and night light data that can represent the intensity of human activities. Therefore, compared with the remote sensing ecological index (RSEI), the improved remote sensing ecological index (MRSEI) is more consistent with the ecological environment index released by the Ministry of Environmental Protection and is more suitable for the evaluation of remote sensing ecological quality. Since the indicators are all derived from remote sensing data, the improved remote sensing ecological index (MRSEI) has the advantages of easy data acquisition, simple calculation, detailed results to pixels, and visualization of results.

[0111] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0112] In the description of the present application, the word "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0113] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for calculating an improved remote sensing ecological index based on spatiotemporal data fusion, characterized in that: The method comprises the following steps: Collect remote sensing data of the study area; Preprocess the collected remote sensing data; The water bodies in the pre-processed remote sensing data are masked using the improved normalized difference water index; Perform spatiotemporal fusion on the preprocessed remote sensing data; According to the fused remote sensing data, the index data used to characterize the improved remote sensing ecological index are calculated respectively; wherein the index data include: green index, humidity index, dryness index, and heat index; Obtain aerosol optical depth data, which is used to characterize air quality, and night light data, which is used to characterize the intensity of human activities, on the GEE platform, and resample these two indicator data; Perform parameter standardization on the indicator data to obtain standardized data; Principal component analysis was used and principal component extraction was performed based on standardized data to obtain evaluation index results; The remote sensing data of the study area include: Landsat series remote sensing images and MODIS series remote sensing images; Based on Landsat and MODIS remote sensing images, the OL-STARFM model is used to obtain high spatiotemporal fusion data; Greenness index, humidity index, heat index, and dryness index are calculated based on the fused high-temporal and spatial resolution images; air quality index is obtained based on the MODIS series remote sensing images; nighttime light data is obtained based on NOAA NPP light data to represent the intensity index of human activities; The following formula is used to standardize the parameters of the indicator data: Among them, NIi is the normalized index value, and its value range is 0 to 1; Ii is the index data at pixel i; Imin is the minimum value of the index data; Imax is the maximum value of the index data; The calculation formula of the improved normalized difference water index is: Where MNDWI stands for the modified normalized difference water index; ρ G , ρ swir1 Represent the reflectance of green band and shortwave infrared 1 respectively; The areas with improved normalized difference water index greater than 0.2 were regarded as water bodies, and the water bodies in the remote sensing data of the study area were removed by masking.

2. The method for calculating the improved remote sensing ecological index based on spatiotemporal data fusion according to claim 1 is characterized in that: The evaluation index result is an improved remote sensing ecological index, which is synthesized by principal component analysis transformation of greenness index, humidity index, dryness index, heat index, air quality index and human activity intensity index.

3. The calculation method of the improved remote sensing ecological index based on spatiotemporal data fusion according to claim 1 or 2 is characterized in that: The method also includes: dividing the ecological quality of the study area according to the evaluation index results, and obtaining the spatial distribution data of the ecological quality of the study area.

4. The calculation method of the improved remote sensing ecological index based on spatiotemporal data fusion according to claim 1 or 2 is characterized in that: The methods for preprocessing the collected remote sensing data include: According to the preset time range, the remote sensing data of the study area is selected to obtain the remote sensing images to be processed; Perform cloud removal on the remote sensing images to be processed; Image resampling and projection definition are performed on the remote sensing image to be processed after cloud removal.

5. The method for calculating the improved remote sensing ecological index based on spatiotemporal data fusion according to claim 1 or 2, characterized in that: Methods for masking water bodies in preprocessed remote sensing data include: Calculate the improved normalized difference water index; According to the normalized difference water index, the water bodies in the remote sensing data are identified and mask processing is performed on the identified water bodies.

Images