Calculation method of improved remote sensing ecological index based on spatio-temporal data fusion

Abstract

The application provides a kind of improved remote sensing ecological index calculation method based on space-time data fusion, the method comprises the following steps: collecting remote sensing data of the study area;The collected remote sensing data is pretreated;The water in the processed remote sensing data is masked and treated;The remote sensing data after pretreatment is spatiotemporally fused;According to the remote sensing data after fusion processing, the index data for representing the improved remote sensing ecological index is calculated respectively;Get aerosol optical depth to represent air quality and nighttime light data to represent human activity intensity on the gee platform, and resample the two index data;The index data is parameter standardized, and the standardized data is obtained;Principal component analysis is used, and principal component extraction is carried out according to the standardized data, to obtain the evaluation index result.The application can improve the spatiotemporal resolution of data through space-time data fusion technology, and solve the problem of lack of comprehensiveness in index selection of remote sensing ecological index.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a calculation method of an improved remote sensing ecological index based on spatio-temporal data fusion. BACKGROUND

[0002] With the gradual improvement of environmental protection consciousness, ecological environment quality evaluation has been a hot topic in recent years, and the method of ecological environment quality evaluation is also more and more, and 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 the problem to be solved at present. In 2006, the State Environmental Protection Department issued the "People's Republic of China Environmental Protection Industry Standard (Trial)" (hereinafter referred to as "the standard") in the form of industry standard, and the standard has the calculation method of ecological environment index (Ecological Index, EI), but the EI calculation in the standard has the problems of complicated calculation, part of the data may not be obtained, and the result can only be presented in the unit of administrative area (such as a certain county), so the researchers cannot completely evaluate the ecological environment quality according to the standard. Since the satellite remote sensing observation system has the advantages of macro, rapid and real-time, then Xu Hanqiu proposed the remote sensing ecological index (Remote Sensing Ecological Index, RSEI) completely dependent on remote sensing image calculation in 2013 (reference: Xu Hanqiu. Remote sensing evaluation index of regional ecological environment change [J]. China Environmental Science, 2013, 33 (05): 889-97.), the index evaluates the ecological environment quality with four indexes of greenness, wetness, heat and dryness, and the main information of the four indexes is concentrated by the principal component analysis method to reflect the good and bad of the ecological environment quality, since the remote sensing ecological index is proposed, due to the characteristics of convenient calculation and result visualization, the ecological environment quality evaluation has been rapidly developed.

[0003] Current remote sensing data fusion techniques have significant shortcomings in terms of spatial and temporal resolution and ecological index calculation. Landsat data has high spatial resolution, but is limited by temporal resolution, making it difficult to achieve frequent ground dynamic monitoring. MODIS data has high temporal resolution, but its spatial resolution is low, making it difficult to meet the needs of fine monitoring. In addition, the existing remote sensing ecological index calculation method selects four indexes of greenness, aridity, humidity and heat for evaluation. Although the vegetation index representing greenness is similar to the vegetation cover index in the specification 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 aridity is closely related to the land degradation index, three of these ecological indexes are closely related to the four indexes in the specification, but there are still problems in index selection. The main problem is the lack of comprehensiveness: the environmental quality index in the specification includes the pollution load index, while the four indexes (greenness, aridity, humidity, heat) in the remote sensing ecological index are unrelated to the pollution load index, in addition, human activity intensity, an important factor of environmental quality, is not involved in the remote sensing ecological index. Therefore, the lack of comprehensiveness in index selection is still the main problem that needs to be solved in the current technology.

[0004] The innovation of this patent lies in proposing an improved remote sensing ecological index calculation method based on spatio-temporal data fusion, which not only effectively combines the advantages of Landsat and MODIS, improves the spatio-temporal 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 activity, the patent method not only improves the spatio-temporal accuracy of the data, but also expands the calculation dimension of the ecological index, making it can fully reflect the environmental quality. In addition, this innovative technology significantly improves the monitoring ability of environmental dynamic changes, and can be widely applied in the fields of agriculture, climate change, urban expansion, etc., providing more fine and timely spatio-temporal data support for related industries. SUMMARY

[0005] The purpose of this application is to provide an improved remote sensing ecological index calculation method based on spatio-temporal data fusion, aiming to improve the spatio-temporal resolution of the data and solve the problem of lack of comprehensiveness in index selection of remote sensing ecological index.

[0006] In order to achieve the above object, the application provides a calculation method of an improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion, which comprises the following steps: collecting remote sensing data of a study area; pre-processing the collected remote sensing data; performing mask processing on water bodies in the pre-processed remote sensing data; performing spatio-temporal fusion on the pre-processed remote sensing data; calculating index data for representing the improved remote sensing ecological index according to the fused remote sensing data; wherein the index data comprises a greenness index, a wetness index, a dryness index and a heat index; obtaining aerosol optical depth for representing air quality and nighttime 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; performing principal component analysis and extracting principal components according to the standardized data to obtain evaluation index results.

[0007] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein the evaluation index results are the improved remote sensing ecological index, and the improved remote sensing ecological index is obtained by principal component analysis transformation of the greenness index, the wetness index, the heat index and the air quality index.

[0008] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein the method further comprises: dividing the study area according to the evaluation index results to obtain spatial distribution data of ecological quality of the study area.

[0009] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein the method of pre-processing the collected remote sensing data comprises: selecting remote sensing data of the study area according to a preset time range to obtain a to-be-processed remote sensing image; performing cloud removal processing on the to-be-processed remote sensing image; and resampling and defining projection on the to-be-processed remote sensing image after the cloud removal processing.

[0010] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein the method of performing mask processing on the water bodies in the pre-processed remote sensing data comprises: calculating an improved normalized difference water index; and identifying water bodies in the remote sensing data according to the normalized difference water index, and performing mask processing on the identified water bodies.

[0011] The calculation method of the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein the remote sensing data of the study area comprises Landsat series remote sensing images and MODIS series remote sensing images.

[0012] The method for calculating the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein, based on the fused high spatio-temporal resolution remote sensing image, a greenness index, a wetness index, a dryness index and a heat index are calculated; based on the MODIS series remote sensing image, an air quality index is obtained; based on the NOAA NPP light data, night light data is obtained to represent a human activity intensity index.

[0013] The method for calculating the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein, the parameter standardization processing of the index data adopts the following formula:

[0014]

[0015] wherein, NI i is a normalized index value, and the value range is 0-1; I i is the index data at pixel i; I min is the minimum value of the index data; I max is the maximum value of the index data.

[0016] The method for calculating the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein, the calculation formula of the improved normalized difference water index is:

[0017]

[0018] wherein, ρ G , ρ swir1 respectively represent the reflectivity of green band and short wave infrared 1.

[0019] The method for calculating the improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion as described above, wherein, the region with the normalized difference water index (MNDWI) greater than 0.2 is regarded as water body, and the water body in the remote sensing data in the study area is removed by mask method.

[0020] The application achieves the following beneficial effects:

[0021] (1) The improved remote sensing ecological index has high consistency with the ecological index (EI) released by the Ministry of Environmental Protection, and supplements the aerosol optical depth (AOD) which can represent the air pollution index and the night light data which 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 from remote sensing data, the improved remote sensing ecological index (MRSEI) also has the advantages of easy data acquisition, simple calculation, detailed results to the pixel, and result visualization. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0023] Figure 1 A flow chart of a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion according to an embodiment of the present application.

[0024] Figure 2 A framework diagram of a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion according to an embodiment of the present application.

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

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0027] As shown in Figure 1 and 2 The present application provides a method for calculating an improved remote sensing ecological index for ecological environment quality evaluation under spatio-temporal data fusion, which comprises the following steps:

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

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

[0030] In step S2, the collected remote sensing data is preprocessed.

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

[0032] In step S210, remote sensing data of the study area within a preset time range is selected to obtain a to-be-processed remote sensing image.

[0033] In step S220, the to-be-processed remote sensing image is cloud-removed.

[0034] In step S230, the cloud-removed to-be-processed remote sensing image is resampled and projected.

[0035] In step S3, the water body in the remote sensing data is masked.

[0036] Specifically, the water body can be masked by using a water body index (MNDWI) to enhance water body information and weaken information of other ground objects, so as to facilitate extraction of the water body range of the study area.

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

[0038] In step S310, an improved normalized difference water body index (MNDWI) is calculated.

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

[0040]

[0041] wherein, ρ green , ρ swir1 respectively represent reflectivity of green band and short wave infrared 1.

[0042] In step S320, the water body in the remote sensing data is identified according to the normalized difference water body index, and the identified water body is masked.

[0043] Specifically, 0.2 is taken as a threshold, the area with the normalized difference water body index (MNDWI) greater than 0.2 is taken as the water body, and the water body in the remote sensing data in the study area is removed by masking.

[0044] In step S4, the processed remote sensing data is spatiotemporally fused by using an OL-STARSM model.

[0045] Step S5, according to the remote sensing data after fusion processing, respectively calculate the greenness index, humidity index, aridity index and heat index for representing the improved remote sensing ecological index (MRSEI).

[0046] Specifically, the remote sensing data after mask processing, i.e. the remote sensing image after removing water, according to the remote sensing image after removing water, four index data for representing the improved remote sensing ecological index (MRSEI) are calculated: greenness index, humidity index, heat index and air quality index. The greenness index, humidity index, heat index and air quality index of the application are related to the pollution load index, and there is no high correlation between the four index data, which will not cause the ecological meaning of factor (or layer) to be repeated.

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

[0048]

[0049] Wherein, EVI represents the greenness index; ρ nir , ρ red , ρ blue Respectively represent the reflectivity of near-infrared, red and blue light bands.

[0050] As a specific embodiment of the application, the humidity component obtained by using the Canny transform 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-0.7117p SWIR1 -0.4559p SWIR2 ;

[0054] wherein WET OLI represents the moisture component of Landsat8 OLI. p B , p G , p R , p NIR , p SWIR1 , p SWIR2 respectively represent the reflectivity of the image in the blue, green, red, near-infrared, short-wave infrared 1 and short-wave infrared 2 bands; TM: WET OLI , ETM+: WET OLI , OLI: WET OLI represent the components of the moisture index.

[0055] It needs to be explained that the Tasseled Cap Transformation (K-T Transformation) is an image enhancement realized by matrix operation.

[0056] As a specific embodiment of the present application, the normalized difference built-up and soil index (NDBSI) is used to represent the aridity index, and the formula is as follows:

[0057]

[0058] wherein p blue , p green , p red , p nir , p swir1 respectively represent the reflectivity of the blue band, the green band, the red band, the near-infrared band and the mid-infrared band.

[0059] As a specific embodiment of the present application, the Land Surface Temperature (LST) is used to represent the heat index, and the formula is as follows:

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

[0061] wherein LST represents the heat index; T b represents the brightness temperature; ε represents the radiation coefficient; p is a constant value of 1.438×10-2 mK; λ represents the center wavelength of the band 10.

[0062] Step S6, obtaining the air quality index and the human activity intensity index on the gee platform, and resampling the air quality index and the human activity intensity index. As a specific embodiment of the present application, aerosol optical depth (AOD) is used to represent 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 application, night light data is used to represent the human activity intensity index, and the night light data is based on NOAA NPP light data. The NOAA NPP light data provides high-resolution remote sensing data by observing the information of night light and fire light on the earth's surface through satellites.

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

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

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

[0067]

[0068] Wherein, NI i is the normalized index value (i.e. standardized data), and its value range is 0-1; I i is the index value (index data) at pixel i; I min is the minimum value of the index value (index data); I max is the maximum value of the index value (index data).

[0069] Step S8, performing principal component analysis, and extracting principal components according to the standardized data to obtain evaluation index results.

[0070] Specifically, principal component analysis (PAC) is adopted, and the first principal component MRSEI0 in the principal component analysis result is taken as the result of the evaluation index MRSEI (remote sensing ecological index) according to the standardized data of step S8.

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

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

[0073] Specifically, the MRSEI0 is standardized by using a normalization model to obtain an improved remote sensing ecological index MRSEI of the study area. The improved remote sensing ecological index MRSEI is divided into five levels according to a 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 is obtained.

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

[0075] As a specific embodiment of the present application, the implementation of the present application is implemented on the gee platform. The GEE (Google Earth Engine) platform is a comprehensive platform for scientific analysis and geographic information data visualization. The platform provides rich APIs and tools to facilitate the viewing, calculation, processing and analysis of large-scale various images and other GIS data. The specific implementation method of the present application is as follows:

[0076] Step T1, importing 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, and the remote sensing data is named as shp file roi. The collected remote sensing data of the study area is imported into the gee platform for the subsequent cutting of the study area.

[0078] Step T2, importing the cloud removal function into the gee platform.

[0079] Specifically, the cloud removal function is used for cloud removal processing of the to-be-processed remote sensing image.

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

[0081] As a specific embodiment of the present application, the data set is selected as the data of October in the past 20 years, the image with cloud cover less than 15% is selected, and resampling and uniform projection are performed after cloud removal.

[0082] Step T4, inputting Landsat and MODIS remote sensing images into the OL-STARFM model to generate high spatio-temporal fusion images.

[0083] The OL-STARFM model is an existing spatio-temporal fusion model.

[0084] Step T5, removing water bodies in the study area in the data set by a mask.

[0085] Specifically, the MNDWI is calculated, and a threshold of 0.2 is set, and water bodies greater than 0.2 are removed by a mask to remove water bodies in the study area.

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

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

[0088] Step T7, performing a calculation step of the heat index LST to obtain the heat index LST.

[0089] Step T8, obtaining the aerosol optical depth AOD representing the air quality index in the MODIS data (i.e. the MODIS series remote sensing images).

[0090] Step T9, performing a calculation step of the greenness index NDVI and the humidity index WET to obtain the greenness index NDVI and the humidity index WET.

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

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

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

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

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

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

[0097] Specifically, principal component analysis (PAC) is adopted, and the first principal component in the principal component analysis result is taken as the result of the evaluation index MRSEI0 (remote sensing ecological index) according to the standardized data of each index data.

[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 has high consistency with the ecological environment status index (EI) published by the Ministry of Environmental Protection, removes the repetition between the remote sensing ecological index, and supplements the aerosol optical depth (AOD) which can represent the air pollution index. 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 all the indexes come from remote sensing data, the improved remote sensing ecological index (MRSEI) has the characteristics of easy data acquisition, simple calculation, detailed results to the pixel, and result visualization.

[0101] To verify the applicability of the improved remote sensing ecological index, the urban area of Liaoyuan City in Jilin Province is taken as the verification research area, and October 2020 is taken as the verification year. The improved remote sensing ecological index (MRSEI) of the urban area of Liaoyuan City in Jilin Province is calculated. Figure 3 )。

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

[0103] The disclosed embodiment provides a computer readable storage medium, which stores computer program instructions, and when the computer program instructions run on a computer, the computer program instructions make the computer execute the above-mentioned calculation method of an improved remote sensing ecological index for ecological environment quality evaluation.

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

[0105] In an embodiment of the present application, the processor can be an integrated circuit chip with the processing capability of signals. The processor can 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 device, discrete gate or transistor logic device, or a discrete hardware component.

[0106] The disclosed methods, steps, and logic block diagrams in the embodiments of the present application can be implemented or executed by using 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 device, discrete gate or transistor logic device, discrete hardware component, or any combination thereof. The steps of the methods disclosed in the embodiments of the present application can be directly embodied as one or more hardware coding processing procedures executed by a general purpose processor, or one or more hardware coding processing procedures combined with software modules in the processor. The software modules can be located in a storage medium such as random access memory (RAM), flash memory, read only memory (ROM), programmable read only memory (PROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), registers, or other mature storage mediums in the field. The processor reads information in the storage medium and combines the hardware to complete the steps of the above methods.

[0107] The storage medium can be a memory such as a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0108] The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM).

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

[0110] (1) The improved remote sensing ecological index has high consistency with the ecological environment status index (EI) published by the Ministry of Environmental Protection, and supplements the aerosol optical depth (AOD) representing the air pollution index and the night light data representing the intensity of human activities. Therefore, compared with the remote sensing ecological index (RSEI), the improved remote sensing ecological index (MRSEI) is more appropriate for the evaluation of remote sensing ecological quality. Since all the indexes come from remote sensing data, the improved remote sensing ecological index (MRSEI) has the advantages of easy data acquisition, simple calculation, detailed results to the pixel, and result visualization.

[0111] In the description of the present application, the terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0112] In the description of the present application, the word "exemplary" is used to mean "serving as an example, instance, or illustration." Any implementation described as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. The following description is presented to enable any person skilled in the art to make and use the application. Details are set forth in the following description for purpose of explanation. It should be appreciated that one of ordinary skill in the art would realize that the application can be practiced without the use of these specific details. In other instances, well-known structures and processes are not elaborated as they would be understood by one of ordinary skill in the art. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded with the widest scope consistent with the principles and features disclosed.

[0113] The above description is merely illustrative of the embodiments of the present application and is not intended to limit the scope of the application. Variations and modifications as can be obvious to those skilled in the art do not, however, part from the spirit and scope of the application. Any application related measures not described but obvious in light of the above teachings should be within the spirit and scope of the present application.

Claims

1. An improved method for calculating remote sensing ecological indices based on spatiotemporal data fusion, characterized in that, The method includes the following steps: Collect remote sensing data of the study area; Preprocess the collected remote sensing data; The water bodies in the preprocessed remote sensing data were masked using an improved normalized differential water index. Spatiotemporal fusion of preprocessed remote sensing data; Based on the fused remote sensing data, the indicator data used to characterize the improved remote sensing ecological index are calculated respectively; among them, the indicator data include: greenness index, humidity index, dryness index, and heat index. Acquire aerosol optical thickness, which characterizes air quality, and nighttime light data, which characterizes the intensity of human activity, on the GEE platform, and resample these two data points. Standardize the parameters of the indicator data to obtain standardized data; Principal component analysis was used, and principal components were extracted based on standardized data to obtain the evaluation index results. Remote sensing data for the study area includes: Landsat series remote sensing images and MODIS series remote sensing images; High spatiotemporal fusion data were obtained using the OL-STARFM model based on Landsat and MODIS remote sensing images; Greenness index, humidity index, heat index, and dryness index are calculated based on the fused high spatiotemporal resolution images; air quality index is obtained based on MODIS series remote sensing images; and nighttime light data is obtained based on NOAA NPP light data to represent the intensity of human activities. The following formula is used to standardize the parameters of the indicator data: Among them, NI i This is a normalized index value, ranging from 0 to 1; I i I represents the index data at pixel i; min The minimum value of the indicator data; I max The maximum value of the indicator data; The improved formula for calculating the normalized difference water index is as follows: Where MNDWI represents the improved normalized differential water index; ρ G , ρ swir1 These represent the reflectance of the green band and shortwave infrared 1, respectively; Regions with an improved normalized difference water index greater than 0.2 were considered as water bodies, and 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, 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 method for calculating the improved remote sensing ecological index based on spatiotemporal data fusion according to claim 1 or 2, characterized in that, The method also includes: classifying the study area into ecological quality zones based on the evaluation index results, and obtaining spatial distribution data of the ecological quality of the study area.

4. 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 preprocessing acquired remote sensing data include: Based on a preset time range, remote sensing data of the study area is selected to obtain remote sensing images to be processed; Perform cloud removal processing on the remote sensing image to be processed; Image resampling and definition projection are performed on the remote sensing images 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 differential water index; Based on the normalized difference water body index, water bodies in remote sensing data are identified, and the identified water bodies are then masked.

Images