CN115689327A - Remote sensing comprehensive evaluation method for ecological environment quality of karst watershed - Google Patents
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Abstract
The invention belongs to the technical field of ecological environment evaluation, and particularly relates to a remote sensing comprehensive evaluation method for ecological environment quality of a karst watershed, which specifically comprises the steps of collecting Landsat time sequence image data of the karst watershed and preprocessing the Landsat time sequence image data; respectively calculating and researching 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed; carrying out normalization processing on the 5 evaluation indexes; water body mask processing; and calculating the CEIKB index, wherein the larger the value of the CEIKB index is, the better the ecological environment quality of the researched karst watershed is, and otherwise, the worse the ecological environment quality of the researched karst watershed is. According to the invention, on the basis of fully considering vegetation coverage index (greenness), humidity index, heat index and dryness index, a stony desertification characterization index is introduced according to the characteristics of karst landform, a remote sensing comprehensive ecological model suitable for a karst watershed is constructed, and the defect that the non-applicability of the conventional single evaluation index or different regions adopting the same index is overcome.
Description
Technical Field
The invention belongs to the technical field of ecological environment evaluation, and particularly relates to a karst watershed ecological environment quality remote sensing comprehensive evaluation method.
Background
The comprehensive, objective and scientific ecological environment quality evaluation method can provide important decision basis for environmental management. The karst watershed is special in geology, and how to accurately monitor the quality of the ecological environment of the karst watershed faces huge challenges. The current karst watershed ecological environment quality monitoring method mainly comprises the following steps: an artificial field investigation method, a ground instrument observation method and a remote sensing monitoring method. Although the traditional manual field investigation method is high in accuracy, field information acquisition is time-consuming and labor-consuming, and the field information acquisition needs to face environmental conditions such as complex terrain and bad weather, so that large-area real-time monitoring cannot be achieved. In addition, human intervention in vulnerable ecosystems can also cause unexpected damage. The satellite remote sensing is an important remote sensing means for monitoring the quality of the current ecological environment by virtue of the advantages of large scale, rapidness, high space-time resolution, low cost, strong operability and the like.
Most methods refer to EI indexes for monitoring the ecological environment quality at present, but some defects still exist in the aspect of evaluating the ecological environment quality of the karst basin, and a remote sensing comprehensive evaluation method special for the ecological environment quality of the karst landform area is lacked.
Disclosure of Invention
In order to solve the problems, the invention provides a karst watershed ecological environment quality remote sensing comprehensive evaluation method, which comprises the following specific technical scheme:
a karst basin ecological environment quality remote sensing comprehensive evaluation method comprises the following steps:
s1, firstly, collecting Landsat image data of a karst watershed, and preprocessing the image data;
s2, respectively calculating and researching 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed according to the preprocessed Landsat data;
s3, carrying out normalization processing on the 5 evaluation indexes to enable the values to be between 0 and 1;
s4, performing mask processing on the water body by using the improved normalized water body index to obtain 5 indexes after the mask processing;
and S5, performing band synthesis on the 5 evaluation indexes obtained after the mask processing in the step S4, then performing principal component analysis, coupling a single variable with most information of the 5 evaluation index variables, and using the calculated CEIKB index as an ecological environment remote sensing comprehensive evaluation index of the researched karst watershed, wherein the value is between 0 and 1, the larger the value is, the better the ecological environment quality of the researched karst watershed is, and otherwise, the worse the ecological environment quality of the researched karst watershed is.
Preferably, the preprocessing in step S1 includes performing radiometric calibration, atmospheric correction, image stitching, and cropping on the Landsat image data.
Preferably, the evaluation index of greenness of karst watershed studied in step S2 is represented by normalized vegetation index NDVI, and the calculation method is as follows:
NDVI＝(ρ NIR -ρ R )/(ρ NIR +ρ R )； (1)
in the formula, ρ NIR 、ρ R Respectively representing the reflectivity of the near infrared band and the red band of the Landsat image.
Preferably, the calculation formula for studying the humidity evaluation index of the karst watershed in step S2 is as follows: for the Landsat image data of the TM sensor, the humidity evaluation index is calculated as follows:
WET TM ＝0.0315ρ B +0.2021ρ G +0.3102ρ R +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 ； (2)
for the Landsat image data of the OLI sensor, the humidity evaluation index is calculated as follows:
WET OLI ＝0.1511ρ B +0.1972ρ G +0.3283ρ R +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ； (3)
where ρ is B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 、ρ SWIR2 Respectively represent LandsaAnd the reflectivities of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a short wave infrared 1 wave band and a short wave infrared 2 wave band corresponding to the t image.
Preferably, the calculation formula for studying the evaluation index of the heat degree of the karst watershed in the step S2 is as follows: for the Landsat image data of the TM sensor, the heat evaluation index was calculated as follows:
L 6 ＝gain×DN+bias； (4)
T＝K 2 /ln(K 1 /L 6 +1)； (5)
LST＝T/[1+(λT/ρ)lnε 6 ]； (6)
for the Landsat image data of the OLT sensor, the heat evaluation index is calculated as follows:
LST＝K 2 /ln[K 1 /B 10 (T S )+1]； (9)
wherein L is 6 And L 10 The radiation value of a thermal infrared band pixel at a sensor is shown, and DN is a pixel gray value; gain and bias values of 6 wave bands are gain and bias; t is a temperature value at the TM sensor; k 1 And K 2 For calibration parameters, LST represents the surface temperature, λ represents the center wavelength of the thermal infrared band, ρ =1.438 × 10 -2 mK，ε 6 And epsilon 10 Is the surface emissivity, tau 10 Is the transmittance of the atmosphere in the thermal infrared band,
and
respectively the atmospheric downward and upward radiation brightness, B 10 (T S ) Is and T S Black body thermal radiation brightness at the same temperature.
Preferably, the dryness evaluation index NDSI of the karst watershed studied in step S2 is represented by a normalized difference building index and a bare soil index that are comprehensively represented by a bare soil index SI and a building index IBI, and the calculation method is as follows:
NDSI＝(SI+IBI)/2； (10)
SI＝[(ρ SWIR1 +ρ R )-(ρ NIR +ρ B )]/[(ρ SWIR1 +ρ R )+(ρ NIR +ρ B )]； (11)
wherein ρ B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 Respectively represents the reflectivity of blue wave band, green wave band, red wave band, near infrared wave band and short wave infrared 1 wave band corresponding to the Landsat time sequence image.
Preferably, the rock-desertification evaluation index RDI of the karst watershed studied in the step S2 is comprehensively characterized by vegetation coverage VC and rock exposure rate RER, and the calculation method is as follows:
the vegetation coverage VC is calculated based on the normalized vegetation index NDVI, specifically as follows:
wherein NDVI min Represents the minimum value of the normalized vegetation index NDVI, NDVI max Represents the maximum value of the normalized vegetation index NDVI;
the rock exposure RER is calculated based on the normalized rock index NDRI, and the normalized rock index NDRI is calculated in the following mode:
wherein,ρ NIR 、ρ SWIR2 respectively representing the reflectivities of near-infrared wave bands and short-wave infrared 2 of Landsat time sequence images;
the rock exposure RER is calculated as follows:
wherein NDRI min Represents the minimum value of the normalized rock index NDRI; NDRI max Represents the maximum value of the normalized rock index NDRI;
the calculation mode for researching the stony desertification evaluation index RDI of the karst watershed is as follows:
RDI＝a*RER-b*VC； (16)
in the formula, a and b respectively represent the weight of the rock uncovering rate and the vegetation coverage.
Preferably, the weight a of the rock uncovering rate and the weight b of the vegetation coverage are calculated as follows: calculating contribution values of the rock exposure rate and the vegetation coverage by using a geographic detector, and calculating weights a and b of the contribution values based on the contribution values, wherein the calculation method comprises the following steps:
wherein y represents the dependent variable stony desertification grade, and x is the independent variable rock uncovering rate RER and vegetation coverage VC, sigma 2 The variance of the grid values representing y, n the total number of grids, σ i 2 M respectively represents the ith type grid value variance and the type total number of x; and calculating contribution rates PRER and PVC of the rock bare rate RER and the vegetation coverage VC respectively, and corresponding to the weights a and b.
Preferably, the step S4 specifically includes the following steps:
extracting the range of the water body by using the MNDWI index, and leading out the water body into a shp format;
manufacturing a water Mask area, opening an shp file, and manufacturing the water Mask by using a (Build Mask) tool;
and (3) performing water body mask processing on the data according to the manufactured mask area, setting the research value of the water area which does not participate in calculation to be NaN, and performing mask processing on 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed.
Preferably, the CEIKB index in step S5 is calculated as follows:
most of the information in the selection set containing 5 evaluation indexes according to the principal component analysis is used for representing the original CEIKB index CEIKB 0 The calculation method is as follows:
CEIKB 0 ＝PCA[f(NDVI,Wet,LST,NDSI,RDI)]； (18)
for CEIKB 0 Normalization to obtain the CEIKB index:
CEIKB＝(CEIKB 0 -CEIKB 0-min )/(CEIKB 0-max -CEIKB 0-min )； (19)
wherein, CEIKB 0-min Representing the original CEIKB index CEIKB 0 Minimum of (C), CEIKB 0-max Representing the original CEIKB index CEIKB 0 Is measured.
The beneficial effects of the invention are as follows: according to the invention, on the basis of fully considering vegetation cover index (greenness), humidity index, heat index and dryness index, a stony desertification characterization index is introduced aiming at the characteristics of karst landform, a remote sensing Comprehensive ecological model suitable for a karst watershed is constructed, the construction of a karst watershed ecological environment remote sensing Comprehensive Evaluation Index (CEIKB) is realized, and the defect that the existing single evaluation index or different areas adopt the same index to evaluate the non-applicability is overcome.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings used in the detailed description or the prior art description will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic diagram of the area of the karst watershed study selected in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the specific embodiment of the present invention provides a karst watershed ecological environment quality remote sensing comprehensive evaluation method, which includes the following steps:
step S1, selecting a Lijiang river basin shown in FIG. 2 as a karst basin for researching the invention, firstly, collecting Landsat image data of the karst basin, and preprocessing the image data; the preprocessing comprises the steps of carrying out radiometric calibration, atmospheric correction, image splicing and cutting processing on the Landsat image data. And S2, respectively calculating and researching 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed according to the preprocessed Landsat data.
The evaluation index of the greenness of the karst watershed is expressed by a normalized vegetation index NDVI, and the calculation method is as follows:
NDVI＝(ρ NIR -ρ R )/(ρ NIR +ρ R )； (1)
in the formula, ρ NIR 、ρ R Respectively representing the reflectivity of the near infrared band and the red band of the Landsat image.
The calculation formula for researching the humidity evaluation index of the karst watershed is as follows:
for the Landsat time series image data of the TM sensor, the humidity evaluation index is calculated as follows:
WET TM ＝0.0315ρ B +0.2021ρ G +0.3102ρ R +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 ； (2)
for the Landsat image data of the OLI sensor, the humidity evaluation index is calculated as follows:
WET OLI ＝0.1511ρ B +0.1972ρ G +0.3283ρ R +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ； (3)
where ρ is B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 、ρ SWIR2 Respectively shows the reflectivity of blue band (TM band 1, OLI band 2), green band (TM band 2, OLI band 3), red band (TM band 3, OLI band 4), near infrared band (TM band 4, OLI band 5), short wave infrared 1 band (TM band 5, OLI band 6) and short wave infrared 2 band (TM band 7, OLI band 7) corresponding to the Landsat image.
The calculation formula for researching the heat evaluation index of the karst watershed is as follows:
for Landsat image data of a TM sensor, the heat evaluation index is calculated as follows:
L 6 ＝gain×DN+bias； (4)
T＝K 2 /ln(K 1 /L 6 +1)； (5)
LST＝T/[1+(λT/ρ)lnε 6 ]； (6)
for the Landsat image data of the OLT sensor, the heat evaluation index is calculated as follows:
LST＝K 2 /ln[K 1 /B 10 (T S )+1]； (9)
wherein L is 6 And L 10 The radiation value of a thermal infrared band pixel at a sensor is shown, and DN is a pixel gray value; gain and bias values of 6 wave bands are gain and bias; t is a temperature value at the TM sensor; k 1 And K 2 For calibration parameters, LST denotes the surface temperature, λ denotes the center wavelength of the thermal infrared band, ρ =1.438 × 10 -2 mK，ε 6 And ε 10 Is the surface emissivity, tau 10 Is the transmittance of the atmosphere in the thermal infrared band,
and
respectively the atmospheric downward and upward radiation brightness, B 10 (T S ) Is and T S Black body thermal radiation brightness at the same temperature. Where the subscript 6 denotes the 6 bands acquired by the TM sensor and the subscript 10 denotes the 10 bands acquired by the OLT sensor.
The dryness evaluation index NDSI for researching the karst basin is represented by a normalized difference building index and a bare soil index which are comprehensively represented by a bare soil index SI and a building index IBI, and the calculation method is as follows:
NDSI＝(SI+IBI)/2； (10)
SI＝[(ρ SWIR1 +ρ R )-(ρ NIR +ρ B )]/[(ρ SWIR1 +ρ R )+(ρ NIR +ρ B )]； (11)
where ρ is B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 Respectively representing the reflectivity of a blue wave band, a green wave band, a red wave band, a near infrared wave band and a short wave infrared 1 wave band corresponding to the Landsat time sequence image.
The rock desertification evaluation index RDI of the karst watershed is comprehensively characterized by vegetation coverage VC and rock bare rate RER, and the calculation method is as follows:
the vegetation coverage VC is calculated based on the normalized vegetation index NDVI, specifically as follows:
wherein NDVI min Represents the minimum value of the normalized vegetation index NDVI, NDVI max Represents the maximum value of the normalized vegetation index NDVI;
the rock exposure RER is calculated based on the normalized rock index NDRI, and the normalized rock index NDRI is calculated in the following mode:
wherein ρ NIR 、ρ SWIR2 Respectively representing the reflectivity of a near infrared band and a short wave infrared 2 (TM 7/OLI 7) of the Landsat time series image;
the rock exposure RER is calculated as follows:
wherein NDRI min Represents the minimum value of the normalized rock index NDRI; NDRI max Represents the maximum value of the normalized rock index NDRI;
the calculation mode for researching the stony desertification evaluation index RDI of the karst watershed is as follows:
RDI＝a*RER-b*VC； (16)
in the formula, a and b represent the weight of the rock exposure rate and the vegetation coverage respectively. The calculation method of the weight a of the rock uncovering rate and the weight b of the vegetation coverage degree is as follows:
and calculating contribution values of the rock exposure rate and the vegetation coverage by using a geographic detector, and calculating weights a and b of the contribution values based on the contribution values. The calculation method is as follows:
wherein y represents the dependent variable stony desertification grade, and x is the independent variable rock uncovering rate RER and vegetation coverage VC, sigma 2 The variance of the grid values representing y, n the total number of grids, σ i 2 And m respectively represent the ith grid value variance and the type total number of x. The method calculates and obtains the contribution rates PRER and PVC of the rock exposure rate RER and the vegetation coverage VC respectively, and the corresponding weights a and b.
S3, because the unit and the dimension of 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed are different, before the principal component analysis is carried out, normalization processing needs to be carried out on the 5 evaluation indexes, and the value is enabled to be between 0 and 1; the normalization method is as follows:
NI i ＝(Indicator i –Indicator min )/(Indicator max -Indicator min )； (18)
wherein NI i Is a certain index value after standardization, indicator i Is the value of the index in the pixel i, indicator min And Indicator max The minimum and maximum values of the index are respectively.
S4, in order to avoid influencing the load distribution of the PCA, masking the water body by using the improved normalized water body index before performing principal component analysis, and obtaining 5 indexes after masking; the method specifically comprises the following steps:
extracting the range of the water body by using the MNDWI index, and leading out the water body into a shp format;
making a water Mask area, opening the shp file, and making the water Mask by using a Build Mask tool;
and (3) performing water body mask processing on the data according to the manufactured mask area, setting the research value of the water area which does not participate in calculation to be NaN, and performing mask processing on 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed.
And S5, performing band synthesis on the 5 evaluation indexes obtained after the mask processing in the step S4, then performing principal component analysis, integrating the information of the 5 evaluation indexes by adopting a CEIKB index, coupling a single variable with 5 evaluation index variables, integrating most information of the 5 evaluation index variables, and taking the CEIKB index obtained by calculation as an ecological environment remote sensing comprehensive evaluation index of the researched karst watershed, wherein the value is between 0 and 1, the larger the value is, the better the ecological environment quality of the researched karst watershed is, and otherwise, the worse the ecological environment quality of the researched karst watershed is. The CEIKB index is calculated as follows:
most of the information in the selection set containing 5 evaluation indexes is used for representing the original CEIKB index CEIKB 0 The calculation method is as follows:
CEIKB 0 ＝PCA[f(NDVI,Wet,LST,NDSI,RDI)]； (19)
for CEIKB 0 Normalization was performed to obtain the CEIKB index:
CEIKB＝(CEIKB 0 -CEIKB 0-min )/(CEIKB 0-max -CEIKB 0-min )； (20)
wherein, CEIKB 0-min Representing the original CEIKB index CEIKB 0 Minimum value of (3), CEIKB 0-max Representing the original CEIKB index CEIKB 0 Of (2)A large value.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations thereof, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being covered by the appended claims and their equivalents.
Claims (10)
1. A karst watershed ecological environment quality remote sensing comprehensive evaluation method is characterized by comprising the following steps:
s1, firstly, collecting Landsat time sequence image data of a karst watershed, and preprocessing the image data;
s2, respectively calculating 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed according to the preprocessed Landsat time sequence data;
s3, carrying out normalization processing on the 5 evaluation indexes to enable the values to be between 0 and 1;
s4, performing mask processing on the water body by using the improved normalized water body index to obtain 5 indexes after the mask processing;
and S5, performing band synthesis on the 5 evaluation indexes obtained after the mask processing in the step S4, then performing principal component analysis, coupling a single variable with most information of the 5 evaluation index variables, and using the calculated CEIKB index as an ecological environment remote sensing comprehensive evaluation index of the researched karst watershed, wherein the value is between 0 and 1, the larger the value is, the better the ecological environment quality of the researched karst watershed is, and otherwise, the worse the ecological environment quality of the researched karst watershed is.
2. The method for comprehensively evaluating the ecological environment quality of the karst watershed in a remote sensing manner according to claim 1, wherein the preprocessing in the step S1 comprises radiometric calibration, atmospheric correction, image splicing and cutting processing of Landsat image data.
3. The method for comprehensively evaluating the ecological environment quality of the karst watershed by remote sensing according to claim 1, wherein the evaluation index of the greenness of the karst watershed studied in the step S2 is represented by a normalized vegetation index NDVI, and the calculation method comprises the following steps:
NDVI＝(ρ NIR -ρ R )/(ρ NIR +ρ R )； (1)
in the formula, ρ NIR 、ρ R Respectively representing the reflectivity of the near infrared band and the red band of the Landsat image.
4. The method for comprehensively evaluating the ecological environment quality of the karst watershed by remote sensing according to claim 1, wherein a calculation formula for researching the humidity evaluation index of the karst watershed in the step S2 is as follows:
for the Landsat image data of the TM sensor, the humidity evaluation index was calculated as follows:
WET TM ＝0.0315ρ B +0.2021ρ G +0.3102ρ R +0.1594ρ NIR -0.6806ρ SWIR1 -0.6109ρ SWIR2 ； (2)
for the Landsat image data of the OLI sensor, the humidity evaluation index is calculated as follows:
WET OLI ＝0.1511ρ B +0.1972ρ G +0.3283ρ R +0.3407ρ NIR -0.7117ρ SWIR1 -0.4559ρ SWIR2 ； (3)
where ρ is B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 、ρ SWIR2 Respectively represents the reflectivity of a blue wave band, a green wave band, a red wave band, a near infrared wave band, a short wave infrared 1 wave band and a short wave infrared 2 wave band corresponding to the Landsat image.
5. The remote sensing comprehensive evaluation method for the ecological environment quality of the karst watershed as claimed in claim 1, wherein the calculation formula for researching the heat evaluation index of the karst watershed in the step S2 is as follows: for the Landsat image data of the TM sensor, the heat evaluation index was calculated as follows:
L 6 ＝gain×DN+bias； (4)
T＝K 2 /ln(K 1 /L 6 +1)； (5)
LST＝T/[1+(λT/ρ)lnε 6 ]； (6)
for the Landsat image data of the OLT sensor, the heat evaluation index is calculated as follows:
LST＝K 2 /ln[K 1 /B 10 (T S )+1]； (9)
wherein L is 6 And L 10 The radiation value of a thermal infrared band pixel at a sensor is shown, and DN is a pixel gray value; gain and bias values of 6 wave bands are gain and bias values; t is a temperature value at the TM sensor; k 1 And K 2 For calibration parameters, LST represents the surface temperature, λ represents the center wavelength of the thermal infrared band, ρ =1.438 × 10 -2 mK，ε 6 And ε 10 Is the surface emissivity, tau 10 Is the transmittance of the atmosphere in the thermal infrared band,
and
respectively the atmospheric downward and upward radiation brightness, B 10 (T S ) Is and T S Black body thermal radiation brightness at the same temperature.
6. The remote sensing comprehensive evaluation method for the ecological environment quality of the karst watershed as claimed in claim 1, wherein the dryness evaluation index NDSI for studying the karst watershed in the step S2 is represented by a normalized difference building index and a bare soil index which are comprehensively represented by a bare soil index SI and a building index IBI, and the calculation method is as follows:
NDSI＝(SI+IBI)/2； (10)
SI＝[(ρ SWIR1 +ρ R )-(ρ NIR +ρ B )]/[(ρ SWIR1 +ρ R )+(ρ NIR +ρ B )]； (11)
where ρ is B 、ρ G 、ρ R 、ρ NIR 、ρ SWIR1 Respectively representing blue wave band, green wave band, red wave band and near infrared wave corresponding to Landsat imageReflectivity of band, short wave infrared 1 band.
7. The method for comprehensively evaluating the ecological environment quality of the karst watershed with remote sensing according to claim 1, wherein the rocky desertification evaluation index RDI of the karst watershed researched in the step S2 is comprehensively characterized by vegetation coverage VC and rock bare rate RER, and the calculation method is as follows:
the vegetation coverage VC is calculated based on the normalized vegetation index NDVI, specifically as follows:
wherein NDVI min Represents the minimum value of the normalized vegetation index NDVI, NDVI max Represents the maximum value of the normalized vegetation index NDVI;
the rock exposure RER is calculated based on the normalized rock index NDRI, and the normalized rock index NDRI is calculated in the following mode:
where ρ is NIR 、ρ SWIR2 Respectively representing the reflectivity of a Landsat image near-infrared band and the reflectivity of short-wave infrared 2; the rock exposure RER is calculated as follows:
wherein NDRI min Represents the minimum value of the normalized rock index NDRI; NDRI max Represents the maximum value of the normalized rock index NDRI;
the calculation mode for researching the stony desertification evaluation index RDI of the karst watershed is as follows:
RDI＝a*RER-b*VC； (16)
in the formula, a and b represent the weight of the rock exposure rate and the vegetation coverage respectively.
8. The method for comprehensively evaluating the ecological environment quality of the karst watershed through remote sensing according to claim 7, wherein the calculation mode of the weight a of the rock exposure rate and the weight b of the vegetation coverage is as follows:
calculating contribution values of the rock exposure rate and the vegetation coverage by using a geographic detector, and calculating weights a and b of the contribution values based on the contribution values, wherein the calculation method comprises the following steps:
wherein y represents the dependent variable stony desertification grade, and x is the independent variable rock uncovering rate RER and vegetation coverage VC, sigma 2 The grid value variance of y, n the total number of grids,
m respectively represents the ith grid value variance and the type total number of x; calculating to respectively obtain the contribution rate P of the rock bare rate RER and the vegetation coverage VC RER And P VC Corresponding to weights a and b.
9. The method for comprehensively evaluating the ecological environment quality of the karst watershed by remote sensing according to claim 1, wherein the step S4 specifically comprises the following steps:
extracting the range of the water body by using the MNDWI index, and leading out the water body into a shp format;
manufacturing a water Mask area, opening an shp file, and manufacturing the water Mask by using a (Build Mask) tool;
and (3) performing water body mask processing on the data according to the manufactured mask area, setting the research value of the water area which does not participate in calculation to be NaN, and performing mask processing on 5 evaluation indexes of greenness, humidity, heat, dryness and stony desertification of the karst watershed.
10. The remote sensing comprehensive evaluation method for the ecological environment quality of the karst watershed as claimed in claim 1, wherein the CEIKB index in the step S5 is calculated in the following manner:
most of the information in the selection set containing 5 evaluation indexes is used for representing the original CEIKB index CEIKB 0 The calculation method is as follows:
CEIKB 0 ＝PCA[f(NDVI,Wet,LST,NDSI,RDI)]； (18)
for CEIKB 0 Normalization was performed to obtain the CEIKB index:
CEIKB＝(CEIKB 0 -CEIKB 0-min )/(CEIKB 0-max -CEIKB 0-min )； (19)
wherein, CEIKB 0-min Representing the original CEIKB index CEIKB 0 Minimum value of (3), CEIKB 0-max Representing the original CEIKB index CEIKB 0 Of (c) is calculated.
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| CN116359137A (en) * | 2023-05-31 | 2023-06-30 | 武汉大学 | Multi-water-area urban ecological environment remote sensing monitoring method |
| CN116359137B (en) * | 2023-05-31 | 2023-08-15 | 武汉大学 | Multi-water-area urban ecological environment remote sensing monitoring method |
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