CN110544236A - Coral reef whitening remote sensing monitoring method based on time series satellite images - Google Patents
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Abstract
the invention discloses a coral reef whitening remote sensing monitoring method based on a time sequence satellite image, which utilizes the time sequence satellite image to establish an optimal time-space change characteristic combination set for coral reef whitening remote sensing monitoring; combining with a sea temperature remote sensing monitoring product, carrying out coral reef whitening satellite remote sensing monitoring based on a change detection method; and (4) evaluating coral reef whitening change detection precision based on field verification data. The coral reef whitening monitoring system has the beneficial effect that the coral reef whitening condition can be accurately and efficiently monitored and judged.
Description
Technical Field
The invention belongs to the technical field of satellite remote sensing monitoring, and relates to a coral reef whitening remote sensing monitoring method based on time series satellite images.
background
coral reefs are one of the most productive and abundant ecosystems on earth, are ecological key areas with special value for maintaining the productivity of marine resources, and are also important indicators of marine environmental health (Zhao Meixia et al, 2006; Zhao Huan ting et al, 2009; exercise and health, 2010). However, in the past decades, coral reefs are changing throughout the world due to global climate change effects and human activity disturbances (Wilkinson, 2008; yellow Hui et al, 2013; Suchana et al, 2015). Studies have shown that 75% of coral reefs worldwide are threatened to varying degrees, with the southeast asia coral reefs being the most threatened (Burke et al, 2011; zhao, et al, 2016). Facing to a plurality of environmental and human activity threats, coral reef ecosystems are continuously degraded all over the world, wherein 'whitening' is a coral reef degradation phenomenon with the largest influence degree and the widest spread range.
The traditional coral reef whitening monitoring mainly depends on underwater on-site investigation data until the appearance of a remote sensing technology provides a new possibility for large-area and rapid monitoring. The most successful of these are the Hotspot (Hotspot) and weekly (Degree Heating Weeks) products developed by NOAA to monitor and warn of coral reef whitening. However, the indirect judgment method only considers the influence of the sea temperature on the whitening of the coral reef, has strong regionality and has certain limitation in application. Therefore, a considerable number of scholars are dedicated to the remote sensing classification research of coral reef substrate, namely, the remote sensing data is used for directly obtaining the coral reef substrate information, and although a large number of research results exist, the remote sensing technology still faces a lot of challenges when facing an ecosystem which is a coral reef and has extremely strong spatial heterogeneity and is located at a certain depth underwater. For example, one pixel in the remote sensing image is often mixed with different coral reef substrate type information, even in a clear water body, the currently widely applied optical remote sensing technology can only detect ground objects of 20 meters underwater at most, and the problem of peeling of the coral reef different substrate remote sensing information is more complicated due to the strong absorption attenuation of the water body in a visible light band.
disclosure of Invention
the invention aims to provide a coral reef whitening remote sensing monitoring method based on a time series satellite image.
The technical scheme adopted by the invention is carried out according to the following steps:
Firstly, establishing an optimal time-space change characteristic combination set for coral reef whitening remote sensing monitoring by using time sequence satellite images;
Secondly, combining a sea temperature remote sensing monitoring product to carry out coral reef whitening satellite remote sensing monitoring based on a change detection method;
and thirdly, evaluating coral reef whitening change detection precision based on field verification data.
Further, the first step comprises
1. image radiometric calibration, atmospheric correction, geometric correction
selecting satellite data, and sequentially performing geometric correction, radiometric calibration and atmospheric correction on the image data, wherein the geometric correction is completed in Arcgis software by combining ground control point coordinates, the relative position error between each time phase image after geometric correction is less than 0.5 pixel, the radiometric calibration is mainly calculated according to a calibration formula and a calibration coefficient in various satellite data header files, and the atmospheric correction is completed by using a FlaSH module in ENVI software;
2. Sea surface wave correction
Estimating the solar flare size of a visible light waveband by using near-infrared waveband data, namely firstly assuming that a water body signal of the near-infrared waveband is approximately zero, and the visible light waveband is similar to solar flare distribution in the near-infrared waveband, then determining the flare size distribution of each waveband by using the difference value of the brightest pixel and the darkest pixel on the near-infrared waveband, and enabling the darkest pixel value on the near-infrared waveband to be approximately 0 after correction;
3. Water correction
Establishing an index which does not change along with the water depth by utilizing two wave band data with better water body penetrating capacity, firstly constructing a coordinate system consisting of two wave band reflectivity logarithm values, wherein the coral reef substrate plaques of the same kind are linearly distributed in the coordinate system, the slope of the straight line is the ratio of the propagation attenuation coefficients of light of the two wave bands in water, and the intercept of the straight line is irrelevant to the attenuation coefficient of the water body, so that the coral reef substrate plaques of different substrate types can be represented to be used as substrate classification factors;
4. Relative radiation correction
normalizing the radiation value of an image to be processed to a reference image by adopting a relative radiation correction method based on pseudo-invariant features, so that the radiation values of front and rear images are at the same level, thereby reducing the pseudo-variation of ground objects, wherein the normalization coefficient is determined by selecting a certain number of feature points on the front and rear images, in a coral reef area, a dark image element selects an undisturbed deep water area, and a bright image element can select bare rock or seabed sand;
5. time-space change characteristic analysis of coral reef whitening remote sensing signal
the method comprises the steps of performing time spectrum analysis and spatial characteristic analysis on coral reef whitening remote sensing signals; the spatial feature analysis is based on time sequence satellite remote sensing images and compares spatial scale features, texture features, semantic features and shape features of the coral reef at different whitening stages; the coral reef whitening time spectrum analysis is combined with an indoor simulation experiment and time series satellite remote sensing image analysis, namely, in an indoor control experiment, coral whitening time and coral whitening degree are observed by controlling different temperatures, and meanwhile, reflection spectrum curves of coral, chlorophyll content in vivo, zooxanthellae density and other physiological parameters are measured.
Further, combining a sea temperature remote sensing monitoring product and the spectral feature analysis of the coral reef whitening in front, firstly determining time series satellite remote sensing data capable of reflecting the coral reef whitening process, and then selecting a change detection method, mainly adopting a method of comparison analysis before classification to carry out the remote sensing identification of the coral reef whitening area, wherein the specific method comprises the following steps:
DN values of the coral reef areas in the two stages are sequentially expressed as follows:
DN＝G(L+L+L)+B (1)
DN＝G(L+L+L)+B (2)
Wherein subscript 1 represents the early stage image and subscript 2 represents the late stage image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, a deep water area and coral; li is the radiance of different substrates; la is the atmospheric path radiation; lr is a reflected signal of the sea surface to the downward radiation of the atmosphere and the direct radiation of the sun; g is offset, B is gain, and radiance of different substrate types indicates:
L＝TECρexp(-2kZ) (3)
L＝TECρexp[-2k(Z+ΔZ)] (4)
Wherein T is the atmospheric penetration rate; ed is the downwash irradiance above the water surface; c is a coefficient indicating the radiation loss at the sea-air interface due to reflection; ρ i is the reflectivity of different substrates, for the three substrate types, the reflectivity of sand and deep water areas is considered to be constant in the early and late stage images, ρ s1 ═ ρ s2 ═ ρ s, ρ d1 ═ ρ d2 ═ ρ d, and the coral reflectivity of the two stages of images changes due to whitening; k is the water attenuation coefficient, and k1 is k2 is k; z is the water depth, and Δ Z is the change in water depth caused by the tidal difference between the previous and the next two images, and at this time, the difference between the DN values of sand and coral in the previous image is expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)] (5)
the difference between the DN values of sand and coral at the same location in the later image can be expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)]exp(-2kΔZ) (6)
In order to eliminate the systematic radiation difference between the images, a normalization coefficient a is established, namely two obvious distribution areas with sediment types of sand and deep water are selected in a research area, and then alpha is calculated according to the following formula:
and (3) carrying out normalization processing on DN value differences of corresponding pixels of the later period image:
α(DN-DN)＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)] (8)
at this time, the formula (5) and the formula (8) are compared, and if the calculation results of the two exceed a certain threshold, the coral whitening phenomenon is shown to occur.
Detailed Description
the present invention will be described in detail with reference to the following embodiments.
firstly, establishing an optimal time-space change characteristic combination set for coral reef whitening remote sensing monitoring by using time sequence satellite images
1. Image radiometric calibration, atmospheric correction, geometric correction
selecting a terrestrial satellite of NASA of Landsat USA, a Sentine1-2 (sentinel-2 satellite) and domestic high-resolution satellite series data, and sequentially performing geometric correction, radiometric calibration and atmospheric correction on the image data. The geometric correction is completed in Arcgis software by combining ground control point coordinates, and the relative position error between each time phase image after geometric correction is less than 0.5 pixel. The radiation calibration is mainly calculated and completed according to calibration formulas and calibration coefficients in various satellite data header files. Atmospheric correction is mainly completed by using a FlaASH module in ENVI software, wherein relevant parameters are set according to field measured data or relevant empirical values.
2. sea surface wave correction
the coral reef distribution area is often very strong in the local reflected signal (Sun glare) of the water surface caused by the sea surface wave, so that it is necessary to correct the wave of the image. The project estimates the solar flare size of a visible light waveband by using near-infrared waveband data, namely, firstly, supposing that a water body signal of the near-infrared waveband is approximately zero, and the solar flare distribution of the visible light waveband is similar to that of the near-infrared waveband, then, determining the flare size distribution of each waveband by using the difference value of the brightest pixel and the darkest pixel on the near-infrared waveband, and enabling the darkest pixel value on the near-infrared waveband to be approximately 0 after correction.
3. Water correction
in order to effectively enhance the reflection signals of the underwater coral reef area, the absorption attenuation effect of the water body on light needs to be removed, namely, the water body correction is carried out on the image. The method comprises the steps of establishing an index (DII) which does not change along with the Depth of water by utilizing two wave band data with good water penetration capacity, firstly constructing a coordinate system consisting of two wave band reflectivity logarithm values, distributing coral reef substrate patches of the same kind in a straight line in the coordinate system, wherein the slope of the straight line is the ratio of the propagation attenuation coefficients of light of the two wave bands in water, and the intercept of the straight line is irrelevant to the attenuation coefficients of the water, so that the patches with different substrate types of the coral reef can be represented and used as substrate classification factors.
4. Relative radiation correction
When the coral reef whitening change detection is carried out by using the time series remote sensing images, the radiation values of the images in the front and the rear stages have system errors due to the reasons of different acquisition time, sensor calibration, meteorological conditions and the like, so that the relative radiation correction is necessary to be carried out on the images to eliminate the radiation difference caused by non-ground object change. A relative radiation correction method based on a pseudo-invariant feature (PIF) will be employed herein. The method is often applied to multi-temporal remote sensing image data processing, and particularly has a better effect when ground objects such as deep water bodies or bare rocks and the like are used as pseudo-invariant feature points in a research area. The main principle is to normalize the radiation value of the image to be processed to a reference image, so that the radiation values of the images in the two stages before and after the image is in the same level, thereby reducing the 'pseudo-variation' of the ground features. The normalization coefficient is determined by selecting a certain number of characteristic points on the images in the two stages, for example, in a coral reef area, a dark image element can select an undisturbed deep water area, and a bright image element can select bare rock or seabed sand.
5. time-space change characteristic analysis of coral reef whitening remote sensing signal
The method comprises the following two aspects: and (3) time spectrum analysis and spatial characteristic analysis of the coral reef whitening remote sensing signal. The spatial feature analysis is mainly based on time sequence satellite remote sensing images and compares spatial scale features, texture features, semantic features, shape features and the like of the coral reef at different whitening stages. The coral reef whitening time spectrum analysis mainly combines an indoor simulation experiment and time series satellite remote sensing image analysis, namely, the coral whitening time and degree are observed by controlling different temperatures in an indoor control experiment, and meanwhile, the reflection spectrum curve of coral, the chlorophyll content in vivo, the zooxanthellae density and other physiological parameters are measured. On the basis, aiming at several important global coral reef whitening events in history, such as whitening events in 1998, 2002 and 2006, the south China sea, west Sand island coral reef cluster is taken as a research object, and by combining the conclusion of MODIS sea temperature remote sensing monitoring products and indoor control experiments, the time interval points and the change characteristics of the spectral change of the coral reef whitening at different temperatures are analyzed by using different remote sensing data such as Landsat, Sentinel-2, national high-score satellite series and the like.
Secondly, combining with a sea temperature remote sensing monitoring product, developing coral reef whitening satellite remote sensing monitoring based on a change detection method
the time-series satellite remote sensing data capable of reflecting the coral reef whitening process needs to be determined firstly by combining a sea temperature remote sensing monitoring product and the spectral feature analysis of the coral reef whitening process in front. Then, the selection of the change detection method mainly adopts a method of comparison and analysis before classification to carry out remote sensing identification on the coral reef whitening area. The method comprises three steps of feature extraction, feature analysis and decision judgment. In the feature extraction, an important feature index is to consider the change of reflection spectrum difference values of the coral before and after albedo and the sand and other substrate types. The specific principle is as follows:
DN values of the coral reef areas in the first and second stages can be sequentially expressed as:
DN＝G(L+L+L)+B (1)
DN＝G(L+L+L)+B (2)
Wherein subscript 1 represents the early stage image and subscript 2 represents the late stage image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, a deep water area and coral; li is the radiance of different substrates; la is the atmospheric path radiation; lr is a reflected signal of the sea surface to the downward radiation of the atmosphere and the direct radiation of the sun; g is offset and B is gain. While radiance for different substrate types may represent:
L＝TECρexp(-2kZ) (3)
L＝TECρexp[-2k(Z+ΔZ)] (4)
Wherein subscripts 1, 2, i have the same meaning as above; t is the atmospheric penetration rate; ed is the downwash irradiance above the water surface; c is a coefficient indicating the radiation loss at the sea-air interface due to reflection; pi is the reflectivity of different substrates, for the three substrate types, the reflectivity of the sand and the deep water areas is considered to be constant in the front and the rear stages of images, rho 1 is rho s2 is rho s, and rho d1 is rho d2 is rho d, and the coral reflectivity of the front and the rear stages of images changes due to whitening; k is a water body attenuation coefficient, and k1 ═ k2 ═ k can be considered; z is the water depth. Δ Z is the change in water depth due to the tidal difference of the two preceding and following phases of the image. At this time, in the previous image, the difference between the DN values of sand and coral can be expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)] (5)
the difference between the DN values of sand and coral at the same location in the later image can be expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)]exp(-2kΔZ) (6)
in order to eliminate systematic radiation differences between images, it is necessary to establish a normalization factor α, i.e. to select two distinct distribution regions of the sediment type sand and deep water in the study region, and then calculate α according to the following formula:
And (3) carrying out normalization processing on DN value differences of corresponding pixels of the later period image:
α(DN-DN)＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)] (8)
at this time, the formula (5) and the formula (8) are compared, and if the calculation results of the two exceed a certain threshold, the coral whitening phenomenon is shown to occur. Wherein, the selection of the threshold value can be determined according to the result of the temporal-spatial remote sensing characteristic analysis of the coral reef whitening.
and thirdly, evaluating coral reef whitening change detection precision based on field verification data.
selecting the time close to the crossing of the remote sensing image to carry out coral reef field investigation, acquiring ecological parameters such as coral coverage rate, whitening rate and the like, recording DPS point positions, comparing with coral reef whitening remote sensing monitoring results, and calculating a confusion matrix to evaluate the change detection precision.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiments according to the technical spirit of the present invention are within the scope of the present invention.
Claims (3)
1. The coral reef whitening remote sensing monitoring method based on the time series satellite images is characterized by comprising the following steps of:
firstly, establishing an optimal time-space change characteristic combination set for coral reef whitening remote sensing monitoring by using time sequence satellite images;
Secondly, combining a sea temperature remote sensing monitoring product to carry out coral reef whitening satellite remote sensing monitoring based on a change detection method;
And thirdly, evaluating coral reef whitening change detection precision based on field verification data.
2. The coral reef whitening remote sensing monitoring method based on the time series satellite images as set forth in claim 1, wherein: the first step comprises
1. Image radiometric calibration, atmospheric correction, geometric correction
Selecting satellite data, and sequentially performing geometric correction, radiometric calibration and atmospheric correction on the image data, wherein the geometric correction is completed in Arcgis software by combining ground control point coordinates, the relative position error between each time phase image after geometric correction is less than 0.5 pixel, the radiometric calibration is mainly calculated according to a calibration formula and a calibration coefficient in various satellite data header files, and the atmospheric correction is completed by using a FlaSH module in ENVI software;
2. Sea surface wave correction
Estimating the solar flare size of a visible light waveband by using near-infrared waveband data, namely firstly assuming that a water body signal of the near-infrared waveband is approximately zero, and the visible light waveband is similar to solar flare distribution in the near-infrared waveband, then determining the flare size distribution of each waveband by using the difference value of the brightest pixel and the darkest pixel on the near-infrared waveband, and enabling the darkest pixel value on the near-infrared waveband to be approximately 0 after correction;
3. Water correction
establishing an index which does not change along with the water depth by utilizing two wave band data with better water body penetrating capacity, firstly constructing a coordinate system consisting of two wave band reflectivity logarithm values, wherein the coral reef substrate plaques of the same kind are linearly distributed in the coordinate system, the slope of the straight line is the ratio of the propagation attenuation coefficients of light of the two wave bands in water, and the intercept of the straight line is irrelevant to the attenuation coefficient of the water body, so that the coral reef substrate plaques of different substrate types can be represented to be used as substrate classification factors;
4. Relative radiation correction
normalizing the radiation value of an image to be processed to a reference image by adopting a relative radiation correction method based on pseudo-invariant features, so that the radiation values of front and rear images are at the same level, thereby reducing the pseudo-variation of ground objects, wherein the normalization coefficient is determined by selecting a certain number of feature points on the front and rear images, in a coral reef area, a dark image element selects an undisturbed deep water area, and a bright image element can select bare rock or seabed sand;
5. Time-space change characteristic analysis of coral reef whitening remote sensing signal
the method comprises the steps of performing time spectrum analysis and spatial characteristic analysis on coral reef whitening remote sensing signals; the spatial feature analysis is based on time sequence satellite remote sensing images and compares spatial scale features, texture features, semantic features and shape features of the coral reef at different whitening stages; the coral reef whitening time spectrum analysis is combined with an indoor simulation experiment and time series satellite remote sensing image analysis, namely, in an indoor control experiment, coral whitening time and coral whitening degree are observed by controlling different temperatures, and meanwhile, reflection spectrum curves of coral, chlorophyll content in vivo, zooxanthellae density and other physiological parameters are measured.
3. The coral reef whitening remote sensing monitoring method based on the time series satellite images as set forth in claim 1, wherein: secondly, combining a sea temperature remote sensing monitoring product and the spectral feature analysis of the coral reef whitening in front, firstly determining time series satellite remote sensing data capable of reflecting the coral reef whitening process, and then selecting a change detection method, wherein the method mainly adopts a comparison analysis method before classification to carry out the remote sensing identification of the coral reef whitening area, and the specific method is as follows:
DN values of the coral reef areas in the two stages are sequentially expressed as follows:
DN＝G(L+L+L)+B (1)
DN＝G(L+L+L)+B (2)
Wherein subscript 1 represents the early stage image and subscript 2 represents the late stage image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, a deep water area and coral; li is the radiance of different substrates; la is the atmospheric path radiation; lr is a reflected signal of the sea surface to the downward radiation of the atmosphere and the direct radiation of the sun; g is offset, B is gain, and radiance of different substrate types indicates:
L＝TECρexp(-2kZ) (3)
L＝TECρexp[-2k(Z+ΔZ)] (4)
Wherein T is the atmospheric penetration rate; ed is the downwash irradiance above the water surface; c is a coefficient indicating the radiation loss at the sea-air interface due to reflection; ρ i is the reflectivity of different substrates, for the three substrate types, the reflectivity of sand and deep water areas is considered to be constant in the early and late stage images, ρ s1 ═ ρ s2 ═ ρ s, ρ d1 ═ ρ d2 ═ ρ d, and the coral reflectivity of the two stages of images changes due to whitening; k is the water attenuation coefficient, and k1 is k2 is k; z is the water depth, and Δ Z is the change in water depth caused by the tidal difference between the previous and the next two images, and at this time, the difference between the DN values of sand and coral in the previous image is expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2ρZ)] (5)
the difference between the DN values of sand and coral at the same location in the later image can be expressed as:
DN-DN＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)]exp(-2kΔZ) (6)
in order to eliminate the systematic radiation difference between the images, a normalization coefficient alpha is established, namely two obvious distribution areas with sediment types of sand and deep water are selected in a research area, and then the alpha is calculated according to the following formula:
And (3) carrying out normalization processing on DN value differences of corresponding pixels of the later period image:
α(DN-DN)＝GTEC[ρexp(-2kZ)-ρexp(-2kZ)] (8)
At this time, the formula (5) and the formula (8) are compared, and if the calculation results of the two exceed a certain threshold, the coral whitening phenomenon is shown to occur.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274938A (en) * | 2020-01-19 | 2020-06-12 | 四川省自然资源科学研究院 | Web-oriented dynamic monitoring method and system for high-resolution remote sensing river water quality |
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| CN111861934A (en) * | 2020-07-29 | 2020-10-30 | 贵阳欧比特宇航科技有限公司 | Hyperspectral satellite image data production, mosaic and metadata manufacturing method |
| CN111983609A (en) * | 2020-07-30 | 2020-11-24 | 中国科学院空天信息创新研究院 | Wet reed extraction method based on radar remote sensing image |
| CN115186203A (en) * | 2022-07-15 | 2022-10-14 | 广东海洋大学 | Coral ecology visualization analysis method, system, equipment, medium and terminal |
| CN115186203B (en) * | 2022-07-15 | 2024-02-02 | 广东海洋大学 | Coral ecological visualization analysis method, system, equipment, medium and terminal |
| CN116822710A (en) * | 2023-05-24 | 2023-09-29 | 国家海洋环境预报中心 | Coral reef whitening hot spot prediction method, calcification rate prediction method and electronic equipment |
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