CN110544236B - Coral reef whitening remote sensing monitoring method based on time sequence satellite images - Google Patents
Coral reef whitening remote sensing monitoring method based on time sequence satellite images Download PDFInfo
- Publication number
- CN110544236B CN110544236B CN201910714278.8A CN201910714278A CN110544236B CN 110544236 B CN110544236 B CN 110544236B CN 201910714278 A CN201910714278 A CN 201910714278A CN 110544236 B CN110544236 B CN 110544236B
- Authority
- CN
- China
- Prior art keywords
- whitening
- coral
- remote sensing
- coral reef
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 235000014653 Carica parviflora Nutrition 0.000 title claims abstract description 104
- 230000002087 whitening effect Effects 0.000 title claims abstract description 62
- 238000012544 monitoring process Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 27
- 244000132059 Carica parviflora Species 0.000 title description 3
- 241000243321 Cnidaria Species 0.000 claims abstract description 101
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000012795 verification Methods 0.000 claims abstract description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 42
- 238000012937 correction Methods 0.000 claims description 32
- 230000005855 radiation Effects 0.000 claims description 30
- 239000000758 substrate Substances 0.000 claims description 28
- 239000004576 sand Substances 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 9
- 238000002310 reflectometry Methods 0.000 claims description 9
- 238000001228 spectrum Methods 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 6
- 238000010183 spectrum analysis Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 4
- 230000035515 penetration Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 239000011435 rock Substances 0.000 claims description 4
- 239000005886 Chlorantraniliprole Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- PSOVNZZNOMJUBI-UHFFFAOYSA-N chlorantraniliprole Chemical compound CNC(=O)C1=CC(Cl)=CC(C)=C1NC(=O)C1=CC(Br)=NN1C1=NC=CC=C1Cl PSOVNZZNOMJUBI-UHFFFAOYSA-N 0.000 claims description 3
- 229930002875 chlorophyll Natural products 0.000 claims description 3
- 235000019804 chlorophyll Nutrition 0.000 claims description 3
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 claims description 3
- 238000010191 image analysis Methods 0.000 claims description 3
- 238000001727 in vivo Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 241000894007 species Species 0.000 claims description 2
- 230000000149 penetrating effect Effects 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000012806 monitoring device Methods 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 230000007850 degeneration Effects 0.000 description 1
- 230000005183 environmental health Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000007261 regionalization Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/80—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
- Y02A40/81—Aquaculture, e.g. of fish
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
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 the coral reef whitening remote sensing monitoring; carrying out coral reef whitening satellite remote sensing monitoring based on a change detection method by combining with a sea temperature remote sensing monitoring product; and (5) evaluating the detection precision of the coral reef whitening change based on field verification data. The coral reef whitening monitoring device has the beneficial effects that the whitening situation of the coral reef 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 sequence satellite images.
Background
Coral reefs are one of the most productive and most abundant biological systems on the earth, are ecologically critical areas of special value for maintaining marine resource productivity, and are also important indicators of marine environmental health (Zhao Meixia, etc., 2006; zhao Huanting, etc., 2009; practice and health, etc., 2010). However, over the past few decades, coral reefs are undergoing profound changes worldwide due to global climate change effects and human activity interference (Wilkinson, 2008; huang 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 Asian coral reefs being the most serious threat (Burke et al 2011; zhao Huanting, et al 2016). The coral reef ecological system is continuously degenerated in the world, which faces the threat of numerous environments and human activities, wherein whitening is a coral reef degeneration phenomenon with the greatest influence degree and the widest scope of spread.
Traditional coral reef whitening monitoring mainly depends on underwater field investigation data until the appearance of remote sensing technology provides new possibilities for large-area and rapid monitoring. Among the most successful are hot spot (Hotspot) and Zhou Redu (Degree Heating Weeks) products developed by NOAA to monitor and pre-warn of coral reef whitening. However, the indirect judgment method only considers the influence of sea temperature on the whitening of the coral reef, has strong regionalization and has certain limitation in application. Therefore, a significant part of students are devoted to the remote sensing classification research of the coral reef substrate, namely, the remote sensing data is utilized to directly acquire the coral reef substrate information, and although a great deal of research results exist, the remote sensing technology still faces a plurality of challenges when facing the ecological system with extremely strong spatial heterogeneity of the coral reef and a certain depth under water. For example, one pixel in a remote sensing image is often mixed with information of different coral reef substrate types, even in a clear water body, at most, the optical remote sensing technology widely applied at present can only detect ground objects 20 meters underwater, and the problem of stripping of the remote sensing information of different coral reef substrates is more complicated due to the strong absorption and attenuation effects of the water body in a visible light wave band.
Disclosure of Invention
The invention aims to provide a coral reef whitening remote sensing monitoring method based on time sequence satellite images, which has the beneficial effects of accurately and efficiently monitoring and judging the coral reef whitening condition.
The technical scheme adopted by the invention is carried out according to the following steps:
1. establishing an optimal space-time variation characteristic combination set for coral reef whitening remote sensing monitoring by utilizing time sequence satellite images;
2. carrying out coral reef whitening satellite remote sensing monitoring based on a change detection method by combining with a sea temperature remote sensing monitoring product;
3. and (5) evaluating the detection precision of the coral reef whitening change based on field verification data.
Further, step one includes
1. Image radiometric calibration, atmospheric correction, geometric correction
The method comprises the steps of selecting satellite data, sequentially carrying out geometric correction, radiometric calibration and atmospheric correction on image data, wherein the geometric correction is completed in Arcgis software by combining ground control point coordinates, the relative position error between images in each time phase after geometric correction is smaller than 0.5 pixel, the radiometric calibration is mainly completed by calculating according to a calibration formula and a calibration coefficient in various satellite data head files, and the atmospheric correction is completed by using a FlAASH module in ENVI software;
2. sea surface wave correction
Estimating the solar flare size of the visible light wave band by utilizing near infrared wave band data, namely firstly, assuming that a water body signal of the near infrared wave band is approximately zero and the solar flare distribution of the visible light wave band is similar to that of the solar flare in the near infrared wave band, then determining the flare size distribution of each wave band by utilizing the difference value of the brightest pixel and the darkest pixel on the near infrared wave band, and enabling the darkest pixel value on the near infrared wave band to be approximately 0 after correction;
3. water body correction
Establishing an index which does not change along with the depth of water by utilizing two wave band data with better water penetration capacity, firstly establishing a coordinate system consisting of the logarithmic values of the reflectivities of the two wave bands, wherein the substrate plaques of the coral reefs of the same species are distributed in a straight line in the coordinate system, the slope of the straight line is the ratio of the propagation attenuation coefficients of the light of the two wave bands in water, the intercept of the straight line is irrelevant to the attenuation coefficients of the water, and the different substrate type plaques of the coral reefs can be characterized and used as substrate classification factors;
4. relative radiation correction
The method comprises the steps of adopting a relative radiation correction method based on pseudo-invariant features to normalize the radiation value of an image to be processed to a reference image, enabling the radiation values of a front image and a rear image to be at the same level, reducing pseudo-change of ground objects, determining a normalization coefficient by selecting a certain number of feature points on the front image and the rear image, selecting undisturbed deepwater areas in coral reef areas and dark pixels, and selecting bare rocks or seabed sand in bright pixels;
5. time-space variation characteristic analysis of coral reef whitening remote sensing signals
The method comprises time spectrum analysis and space feature analysis of coral reef whitening remote sensing signals; the space feature analysis is based on a time sequence satellite remote sensing image, and the space scale features, texture features, semantic features and shape features of coral reef whitening at different stages are compared; the coral reef whitening time spectrum analysis is combined with an indoor simulation experiment and a time sequence satellite remote sensing image analysis, namely, in an indoor control experiment, the time and the degree of coral whitening are observed by controlling different temperatures, and meanwhile, physiological parameters such as a reflection spectrum curve of the coral, the in-vivo chlorophyll content, the density of the chlorantraniliprole and the like are measured.
Further, combining a sea temperature remote sensing monitoring product with the previous coral reef whitening time spectrum characteristic analysis, firstly determining time sequence satellite remote sensing data capable of reflecting the coral reef whitening process, and then selecting a change detection method mainly by adopting a pre-classification comparison analysis method to carry out remote sensing identification on a coral reef whitening area, wherein the specific method is as follows:
DN values of the front and back image coral reef areas are expressed as follows:
DN i1 ＝G 1 (L i1 +L a1 +L r1 )+B 1 (1)
DN i2 ＝G 2 (L i2 +L a2 +L r2 )+B 2 (2)
wherein the subscript 1 represents the pre-period image and the subscript 2 represents the post-period image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, deep water area and coral; l (L) i The radiance of different substrates; l (L) a Is large gas path radiation; l (L) r The reflection signal of the sea surface to the atmospheric downlink radiation and the direct solar radiation; g is offset, B is gain, and radiance for different substrate types represents:
L i1 ＝T 1 E d1 C 1 ρ i1 exp(-2k 1 Z i1 ) (3)
L i2 ＝T 2 E d2 C 2 ρ i2 exp[-2k 2 (Z i1 +ΔZ)] (4)
wherein T is the atmospheric penetration rate; e (E) d Is the downlink irradiance above the water surface; c is a coefficient indicating the loss of radiation at the sea interface due to reflection; ρ i For the three substrate types mentioned above, sand andthe reflectivity of the deepwater zone is unchanged in the front and back images, and ρ s1 ＝ρ s2 ＝ρ s ，ρ d1 ＝ρ d2 ＝ρ d The coral reflectivity of the front and rear images changes due to the whitening; k is the attenuation coefficient of the water body, k 1 ＝k 2 =k; z is the water depth, and DeltaZ is the water depth change due to the tidal range of the images in the front and rear stages, at this time, the difference between the sand and coral DN values in the images in the front stage is expressed as:
DN s1 -DN c1 ＝G 1 T 1 E d1 C 1 [ρ s exp(-2kZ s1 )-ρ c1 exp(-2kZ c1 )] (5)
the difference between the sand and coral DN values at the same location in the later image can be expressed as:
DN s2 -DN c2 ＝G 2 T 2 E d2 C 2 [ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )]exp(-2kΔZ) (6)
in order to eliminate the system radiation difference between images, a normalization coefficient a is established, namely, two obvious distribution areas with the substrate types of sand and deep water are selected in a research area, and then alpha is calculated according to the following formula:
carrying out normalization processing on DN value differences of pixels corresponding to the images in the later period:
α(DN s2 -DN c2 )＝G 1 T 1 E d1 C 1 [ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )] (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 occurrence of coral whitening phenomenon is indicated.
Detailed Description
The present invention will be described in detail with reference to the following embodiments.
1. Establishing an optimal space-time variation characteristic combination set for coral reef whitening remote sensing monitoring by utilizing time sequence satellite images
1. Image radiometric calibration, atmospheric correction, geometric correction
And (3) selecting land satellite, sentine1-2 (sentry-2 satellite) and domestic high-resolution satellite series data of NASA in Landsat U.S. to sequentially perform geometric correction, radiometric calibration and atmospheric correction on the image data. The geometric correction is to combine the ground control point coordinates at Arc g And the relative position error between the images of each time phase after geometric correction is smaller than 0.5 pixel after the completion in the is software. The radiometric calibration is mainly calculated according to calibration formulas and calibration coefficients in various satellite data head files. The atmospheric correction is mainly accomplished by using a flash module in ENVI software, wherein the setting of relevant parameters is based on field measured data or relevant empirical values.
2. Sea surface wave correction
The coral reef distribution area often has strong water surface local reflection signals (Sun Glint) caused by sea surface waves, so that wave correction is necessary for images. The method comprises the steps of estimating the solar flare size of a visible light wave band by utilizing near infrared wave band data, namely, firstly, assuming that a water body signal of the near infrared wave band is approximately zero, and the visible light wave band is similar to the solar flare distribution in the near infrared wave band, then, determining the flare size distribution of each wave band by utilizing the difference value between the brightest pixel and the darkest pixel on the near infrared wave band, and enabling the darkest pixel value on the near infrared wave band to be approximately 0 after correction.
3. Water body correction
In order to effectively enhance the reflection signals of the underwater coral reef areas, the absorption and attenuation effects of the water body on light are eliminated, namely, the image is subjected to water body correction. An index (Depth invariant indices, DII) which does not change along with the depth of water is established by utilizing two wave band data with better water penetration capacity, a coordinate system consisting of the numerical values of the reflectivities of the two wave bands is firstly established, the substrate plaques of the same coral reef are distributed in a straight line in the coordinate system, the slope of the straight line is the ratio of the propagation attenuation coefficients of the light of the two wave bands in water, the intercept of the straight line is irrelevant to the attenuation coefficient of the water, and the different substrate type plaques of the coral reef can be characterized and used as substrate classification factors.
4. Relative radiation correction
When coral reef whitening change detection is performed by using time series remote sensing images, systematic errors exist in radiation values of images at the front and rear stages due to different acquisition time, sensor calibration, meteorological conditions and the like, so that the images are required to be subjected to relative radiation correction to eliminate radiation differences caused by non-ground object changes. A relative radiation correction method based on a pseudo-invariant feature (PIF) will be used herein. The method is often applied to multi-time-phase remote sensing image data processing, and particularly has better effect when ground features such as deep water bodies or bare rock and the like are used as pseudo-unchanged characteristic points in a research area. The main principle is that the radiation values of the images to be processed are normalized to the reference image, so that the radiation values of the images in the front period and the rear period are at the same level, thereby reducing the false change of the ground object. The normalization coefficient is determined by selecting a certain number of characteristic points on the images of the front period and the back period, for example, in a coral reef area, a dark pixel can select an undisturbed deepwater area, and a bright pixel can select bare rock or submarine sand.
5. Time-space variation characteristic analysis of coral reef whitening remote sensing signals
The method comprises the following two aspects: and (5) performing time spectrum analysis and spatial feature analysis on the coral reef whitened remote sensing signals. The space feature analysis is mainly based on time sequence satellite remote sensing images, and compares space scale features, texture features, semantic features, shape features and the like of coral reef whitening at different stages. The coral reef whitening time spectrum analysis is mainly combined with indoor simulation experiments and time sequence satellite remote sensing image analysis, namely, in indoor control experiments, the time and the degree of coral whitening are observed by controlling different temperatures, and meanwhile, physiological parameters such as a reflection spectrum curve of coral, in-vivo chlorophyll content, chlorantraniliprole density and the like are measured. On the basis, aiming at several times of serious global coral reef whitening events in history, such as whitening events in 1998, 2002 and 2006, etc., taking the island group of the coral reef in the cissama of the south China sea as a research object, combining with the conclusions of MODIS sea temperature remote sensing monitoring products and indoor control experiments, analyzing the time interval points and the change characteristics of spectrum change when the coral reef whitens at different temperatures by utilizing different remote sensing data such as Landsat, sentinel-2, domestic high-molecular satellite series and the like.
2. Combining with sea temperature remote sensing monitoring products, carrying out coral reef whitening satellite remote sensing monitoring based on change detection method
The time sequence satellite remote sensing data capable of reflecting the coral reef whitening process is determined firstly by combining the sea temperature remote sensing monitoring product and the front coral reef whitening time spectrum characteristic analysis. Then, the selection of the change detection method mainly adopts a method of comparison analysis before classification to carry out remote sensing identification of the coral reef whitening area. The method comprises three steps of feature extraction, feature analysis and decision judgment. In the process of feature extraction, an important feature index is to consider the change of the difference value of the reflection spectrum of the substrate types such as coral whitening and sand. The specific principle is as follows:
DN values of the front and back image coral reef areas can be expressed as follows in sequence:
DN i1 ＝G 1 (L i1 +L a1 +L r1 )+B 1 (1)
DN i2 ＝G 2 (L i2 +L a2 +L r2 )+B 2 (2)
wherein the subscript 1 represents the pre-period image and the subscript 2 represents the post-period image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, deep water area and coral; l (L) i The radiance of different substrates; l (L) a Is large gas path radiation; l (L) r The reflection signal of the sea surface to the atmospheric downlink radiation and the direct solar radiation; g is offset and B is gain. While the radiance of different substrate types may represent:
L i1 ＝T 1 E d1 C 1 ρ i1 exp(-2k 1 Z i1 ) (3)
L i2 ＝T 2 E d2 C 2 ρ i2 exp[-2k 2 (Z i1 +ΔZ)] (4)
wherein, subscripts 1, 2, i have the same meaning as defined above; t is the atmospheric punctureTransmittance; e (E) d Is the downlink irradiance above the water surface; c is a coefficient indicating the loss of radiation at the sea interface due to reflection; p is p i For the three substrate types, the reflectivity of the sand and deepwater zones is considered to be unchanged in the front and back images, and ρ s1 ＝ρ s2 ＝ρ s ，ρ d1 ＝ρ d2 ＝ρ d The coral reflectivity of the front and rear images changes due to the whitening; k is the attenuation coefficient of the water body, and can be considered as k 1 ＝k 2 =k; z is the depth of water. Δz is the change in water depth due to the difference in the front and rear images. At this time, in the earlier image, the difference between sand and coral DN values can be expressed as:
DN s1 -DN c1 ＝G 1 T 1 E d1 C 1 [ρ s exp(-2kZ s1 )-ρ c1 exp(-2kZ c1 )] (5)
the difference between the sand and coral DN values at the same location in the later image can be expressed as:
DN s2 -DN c2 ＝G 2 T 2 E d2 C 2 [ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )]exp(-2kΔZ) (6)
in order to eliminate the systematic radiation differences between images, it is necessary to establish a normalization factor α, i.e. to select two distinct distribution areas of the matrix type sand and deepwater in the investigation region, and then calculate α according to the following formula:
carrying out normalization processing on DN value differences of pixels corresponding to the images in the later period:
α(DN s2 -DN c2 )＝G 1 T 1 E d1 C 1 [ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )] (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 occurrence of coral whitening phenomenon is indicated. The selection of the threshold value can be determined according to the analysis result of the space-time remote sensing characteristics of the coral reef whitening.
3. And (5) evaluating the detection precision of the coral reef whitening change based on field verification data.
And (3) carrying out coral reef field investigation work in a time similar to the passing of the remote sensing image, acquiring ecological parameters such as coral coverage rate, whitening rate and the like, recording DPS points, comparing with the coral reef whitening remote sensing monitoring result, and calculating an confusion matrix to evaluate the change detection precision.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the invention in any way, and any simple modification, equivalent variation and modification made to the above embodiments according to the technical substance of the present invention falls within the scope of the technical solution of the present invention.
Claims (2)
1. The coral reef whitening remote sensing monitoring method based on the time sequence satellite images is characterized by comprising the following steps of: step one, utilizing a time sequence satellite image to establish an optimal space-time variation characteristic combination set for coral reef whitening remote sensing monitoring;
secondly, carrying out coral reef whitening satellite remote sensing monitoring based on a change detection method by combining with a sea temperature remote sensing monitoring product;
and thirdly, evaluating the detection precision of the coral reef whitening change based on field verification data.
The second step is to combine the sea temperature remote sensing monitoring product and the previous coral reef whitening time spectrum feature analysis, firstly determine the time sequence satellite remote sensing data capable of reflecting the coral reef whitening process, then select the change detection method mainly to adopt the method of comparing and analyzing before classification to carry out remote sensing identification of the coral reef whitening area, and the specific method is as follows: DN values of the front and back image coral reef areas are expressed as follows:
DN i1 ＝G 1 (L i1 +L a1 +L r1 )+B 1 (1)
DN i2 ＝G 2 (L i2 +L a2 +L r2 )+B 2 (2)
wherein the subscript 1 represents the pre-period image and the subscript 2 represents the post-period image; i can be identifiers s, d and c which are respectively used for indicating three substrate types of the coral reef, namely sand, deep water area and coral; l (L) i The radiance of different substrates; l (L) a Is large gas path radiation; l (L) r The reflection signal of the sea surface to the atmospheric downlink radiation and the direct solar radiation; g is offset, B is gain, and radiance for different substrate types represents:
L i1 ＝T 1 E d1 C 1 ρ i1 exp(-2k 1 Z i1 )(3)
L i2 ＝T 2 E d2 C2ρ i2 exp[-2k 2 (Z i1 +ΔZ)](4)
wherein T is the atmospheric penetration rate; e (E) d Is the downlink irradiance above the water surface; c is a coefficient indicating the loss of radiation at the sea interface due to reflection; ρ i For the three substrate types, the reflectivity of the sand and deepwater zones is considered to be unchanged in the front and back images, and ρ s1 ＝ρ s2 ＝ρ s ，ρ d1 ＝ρ d2 ρd, but due to whitening, the coral reflectivity of the front and rear images changes; k is the attenuation coefficient of the water body, k 1 ＝k 2 =k; z is the water depth, and DeltaZ is the water depth change due to the tidal range of the images in the front and rear stages, at this time, the difference between the sand and coral DN values in the images in the front stage is expressed as:
DN s1 -DN c1 ＝G 1 T 1 E d1 C 1 [ρ s exp(-2kZ s1 )-ρ c1 exp(-2ρZ c1 )](5)
the difference between the sand and coral DN values at the same location in the later image can be expressed as:
DN s2 -DN c2 ＝G 2 T 2 E d2 C 2 [ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )]exp(-2kΔZ)(6)
in order to eliminate the system radiation difference between images, a normalization coefficient alpha is established, namely, two obvious distribution areas with the substrate types of sand and deep water are selected in a research area, and then alpha is calculated according to the following formula:
carrying out normalization processing on DN value differences of pixels corresponding to the images in the later period:
α(DN s2 -DN c2 )＝G 1 T 1 E d1 C1[ρ s exp(-2kZ s1 )-ρ c2 exp(-2kZ c1 )](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 occurrence of coral whitening phenomenon is indicated.
2. The coral reef whitening remote sensing monitoring method based on time series satellite images according to claim 1, wherein the method comprises the following steps of: firstly, selecting satellite data by image radiometric calibration, atmospheric calibration and geometric calibration, and sequentially carrying out geometric calibration, radiometric calibration and atmospheric calibration on the image data, wherein the geometric calibration is completed in Arcgis software by combining ground control point coordinates, the relative position error between phase images after geometric calibration is smaller than 0.5 pixel, the radiometric calibration is mainly completed by calculating according to a calibration formula and a calibration coefficient in various satellite data head files, and the atmospheric calibration is completed by using a FlAASH module in ENVI software;
2. sea surface wave correction utilizes near infrared band data to estimate the solar flare size of a visible light band, namely, firstly, a water body signal of the near infrared band is assumed to be approximately zero, the visible light band is similar to the solar flare distribution in the near infrared band, then, the difference value of the brightest pixel and the darkest pixel on the near infrared band is utilized to determine the flare size distribution of each band, and the darkest pixel value on the near infrared band is approximately 0 after correction;
3. the water body correction utilizes two wave band data with better water body penetrating capacity to establish an index which does not change along with water depth, firstly, a coordinate system consisting of the logarithmic values of the reflectivities of the two wave bands is established, the substrate plaques of the coral reefs of the same species are distributed in a straight line in the coordinate system, the slope of the straight line is the ratio of the propagation attenuation coefficients of the light of the two wave bands in water, the intercept of the straight line is irrelevant to the attenuation coefficients of the water body, and the plaque of different substrate types of the coral reefs can be characterized and used as a substrate classification factor;
4. the relative radiation correction adopts a relative radiation correction method based on pseudo-invariant features, the radiation value of an image to be processed is normalized to a reference image, so that the radiation values of a front image and a rear image are at the same level, thereby reducing the pseudo-variation of ground objects, the normalization coefficient is determined by selecting a certain number of feature points on the front image and the rear image, a dark pixel is selected from undisturbed deepwater areas in coral reef areas, and a bright pixel can be selected from bare rock or submarine sand;
5. the time-space variation characteristic analysis of the coral reef whitening remote sensing signals comprises time spectrum analysis and space characteristic analysis of the coral reef whitening remote sensing signals; the space feature analysis is based on a time sequence satellite remote sensing image, and the space scale features, texture features, semantic features and shape features of coral reef whitening at different stages are compared; the coral reef whitening time spectrum analysis is combined with an indoor simulation experiment and a time sequence satellite remote sensing image analysis, namely, in an indoor control experiment, the time and the degree of coral whitening are observed by controlling different temperatures, and meanwhile, physiological parameters such as a reflection spectrum curve of the coral, the in-vivo chlorophyll content, the density of the chlorantraniliprole and the like are measured.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910714278.8A CN110544236B (en) | 2019-08-03 | 2019-08-03 | Coral reef whitening remote sensing monitoring method based on time sequence satellite images |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910714278.8A CN110544236B (en) | 2019-08-03 | 2019-08-03 | Coral reef whitening remote sensing monitoring method based on time sequence satellite images |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110544236A CN110544236A (en) | 2019-12-06 |
| CN110544236B true CN110544236B (en) | 2023-06-16 |
Family
ID=68710020
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910714278.8A Active CN110544236B (en) | 2019-08-03 | 2019-08-03 | Coral reef whitening remote sensing monitoring method based on time sequence satellite images |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110544236B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274938B (en) * | 2020-01-19 | 2023-07-21 | 四川省自然资源科学研究院 | Web-oriented high-resolution remote sensing river water quality dynamic monitoring method and system |
| 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 |
| CN111983609B (en) * | 2020-07-30 | 2022-02-08 | 中国科学院空天信息创新研究院 | Wet reed extraction method based on radar remote sensing image |
| 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 |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102032903A (en) * | 2010-12-01 | 2011-04-27 | 南京大学 | Coral island reef remote sensing information automatic extraction method based on Landsat data source |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10262205B2 (en) * | 2015-07-28 | 2019-04-16 | Chiman KWAN | Method and system for collaborative multi-satellite remote sensing |
-
2019
- 2019-08-03 CN CN201910714278.8A patent/CN110544236B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102032903A (en) * | 2010-12-01 | 2011-04-27 | 南京大学 | Coral island reef remote sensing information automatic extraction method based on Landsat data source |
Non-Patent Citations (4)
| Title |
|---|
| Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection;John Hedley等;《ELSEVIER》;145–155 * |
| Detection of Coral Bleaching on Sekisei Lagoon Using Multispectral Sensor Data;Emiko Ariyasu等;《Remote Sensing of the Marine Environment II》;全文 * |
| 基于SST的珊瑚礁白化监测技术综述;孙旋;蔡玉林;索琳琳;夹尚丰;;国土资源遥感(第02期);全文 * |
| 珊瑚礁遥感监测方法研究;徐兵;《万方数据》;第3、5、6章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110544236A (en) | 2019-12-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110544236B (en) | Coral reef whitening remote sensing monitoring method based on time sequence satellite images | |
| Lohrenz et al. | Satellite estimation of coastal pCO2 and air-sea flux of carbon dioxide in the northern Gulf of Mexico | |
| Singh et al. | A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters | |
| Eren et al. | Bottom characterization by using airborne lidar bathymetry (ALB) waveform features obtained from bottom return residual analysis | |
| Navarro et al. | Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach | |
| Shanmugam et al. | OSABT: An innovative algorithm to detect and characterize ocean surface algal blooms | |
| CN103984862A (en) | Multielement remote sensing information coordinated snow cover parameter inversion method | |
| CN112014331A (en) | Method, device and equipment for detecting water body pollution and storage medium | |
| CN109635765B (en) | Automatic extraction method for remote sensing information of shallow sea coral reef | |
| CN115797760B (en) | Active and passive fusion type water quality three-dimensional remote sensing inversion method, system and storage medium | |
| CN114819150B (en) | Remote sensing inversion method for primary productivity of polar region ocean in winter | |
| Shaoqing et al. | The comparative study of three methods of remote sensing image change detection | |
| CN107036968A (en) | A kind of soil moisture method of real-time | |
| CN113221813B (en) | Coastline remote sensing extraction method | |
| CN109300133B (en) | Urban river network area water body extraction method | |
| Rahnemoonfar et al. | Automatic seagrass disturbance pattern identification on sonar images | |
| CN103456011A (en) | Improved hyperspectral RX abnormal detection method by utilization of complementary information | |
| Yang et al. | Evaluation and mitigation of rain effect on wave direction and period estimation from X-band marine radar images | |
| CN113673385B (en) | Sea surface ship detection method based on infrared image | |
| CN116817869B (en) | Submarine photon signal determination method using laser radar data | |
| CN108198178A (en) | The determining method and apparatus of atmospheric path radiation value | |
| CN104268831B (en) | Infrared image compensation method under marine sunlight bright band interference | |
| Serouart et al. | Analyzing changes in maize leaves orientation due to GxExM using an automatic method from RGB images | |
| CN109738372A (en) | A kind of Airborne Hyperspectral rock mine detection multi-data processing method | |
| CN113255240B (en) | Ocean internal wave amplitude remote sensing inversion method based on dynamic condition constraint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |