CN115424136A - Forest canopy health evaluation method and system combining remote sensing and forest map - Google Patents
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Abstract
The invention relates to a method and a system for evaluating canopy health by combining remote sensing and a forest map, wherein the method comprises the following steps: determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image; obtaining a forest area image according to the forest phase image and the multispectral reflectivity image; determining a spectral index according to the forest area image; carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes; constructing a canopy health evaluation index according to the weight value and the spectral index; and evaluating the canopy of the forest region according to the calculated canopy health evaluation index. The method integrates satellite images and spectral index information, carries out quantitative evaluation on the health condition of the canopy, improves the objectivity of the evaluation result, is easy to operate in the evaluation process, and improves the reliability of the evaluation process.
Description
Technical Field
The invention relates to the technical field of quantitative evaluation of canopy health, in particular to a canopy health evaluation method and system combining remote sensing and a forest facies map.
Background
The method for evaluating the health of the forest canopy at home and abroad is mainly based on the evaluation of ground sampling survey or by means of man-machine aerial survey and visual interpretation, the methods provide valuable information for forest managers, but the evaluation of the health of the forest canopy through manual survey not only needs to invest a large amount of manpower, material resources and financial resources and is difficult to ensure the time consistency in a large area, but also has complicated evaluation indexes and subjectivity; the manned and airborne survey has high taxi fee, is often limited by weather and airspace control and the like, and is difficult to acquire all-area data in time; the satellite remote sensing can effectively monitor the health state of the forest vegetation canopy by virtue of the advantages of rapidness and macroscopicity.
Compared with ground sampling survey and manned flight survey methods, the method for monitoring and evaluating the health condition of the canopy by using the satellite remote sensing technology has the advantages of wide monitoring range, capability of monitoring at intervals of time of year, month and day, and more detailed and more spatial results. However, the existing research mostly focuses on evaluating the overall health level of the regional ecological system or the forest, has multiple and complex indexes, the analysis on the forest growth condition is usually only aimed at single conditions such as drought stress or certain pest control, the selection of index factors is also different from person to person, and a simple, convenient and easy-to-operate quantitative method for monitoring and evaluating the health condition of the canopy is still lacking at present.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a canopy health evaluation method and a canopy health evaluation system combining remote sensing and a forest phase diagram.
In order to achieve the purpose, the invention provides the following scheme:
a canopy health evaluation method combining remote sensing and a forest map comprises the following steps:
determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image;
obtaining a forest area image according to the forest phase image and the multispectral reflectivity image;
determining a spectral index according to the forest area image;
carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
constructing a canopy health evaluation index according to the weight value and the spectral index;
and evaluating the canopy of the forest region according to the calculated canopy health evaluation index.
Preferably, the determining the multispectral reflectivity image of the region to be detected according to the high-resolution six-number satellite wide image includes:
acquiring an L1A-level multispectral image of the wide image of the high-resolution six-number satellite in clear sky in the forest vegetation growing season in the area to be detected;
carrying out absolute radiometric calibration and atmospheric correction on the L1A-level multispectral image to obtain a first corrected image;
performing geometric correction on the first correction image based on the RPC of the product data and the reference image to obtain a second correction image;
and splicing, cutting and cloud mask processing are carried out on the second corrected image to obtain the multispectral reflectivity image.
Preferably, the obtaining a forest area image according to a forest map and the multispectral reflectivity image comprises:
merging the image spots of the forest map to generate a forest area and a non-forest area;
and performing mask processing on the multispectral reflectivity image, reserving original reflectivity values of all wave band values of the image covering the forest area, and setting the reflectivity values of all wave band values of the non-forest area to be 0 to obtain the forest area image.
Preferably, the determining a spectral index from the forest area image comprises:
carrying out statistical analysis on spectral indexes of the selected healthy and sub-healthy samples in the forest region image by adopting a random forest regression method to obtain an analysis result;
and selecting the spectral index which reflects that the correlation of the greenness, the moisture and the disturbed state of the tree crown leaves is greater than a preset threshold value according to the analysis result.
Preferably, the spectral indices include greenness normalized vegetation index, greenness ratio index, red-edge normalized vegetation index, yellow-edge normalized vegetation index, normalized water body index, and fire passing index.
Preferably, the formula for calculating the spectral index is:
GNDVI is the greenness normalized vegetation index, GVI is the greenness ratio index, RENDVI is the red-edge normalized vegetation index, YENDVI is the yellow-edge normalized vegetation index, NDWI is the normalized water body index, BAI is the fire passing index, and rho 1, rho 2, rho 3, rho 4, rho 5, rho 6 and rho 8 are the reflectivities of wave band 1, wave band 2, wave band 3, wave band 4, wave band 5, wave band 6 and wave band 8 of the high-resolution six-satellite wide-width image in sequence.
Preferably, the performing data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes includes:
using a formula
Normalizing each spectral index to obtain normalized data; wherein, X ni The normalized value of a certain spectral index in a pixel i; x i The value of the spectral index in a pixel i is shown; x max And X min Respectively the maximum value and the minimum value of the statistics of the spectral index;
and performing linear transformation on the normalized data based on a principal component analysis method to obtain the weight value.
Preferably, the formula of the canopy health evaluation index is as follows:
FCHI＝a×G+b×R+c×Y+d×V+e×N+f×B；
FCHI is the health evaluation index of the canopy, and a, B, c, d, e and f are weight values of G, R, Y, V, N and B in sequence; g, R, Y, V, N and B are values after GNDVI, RENDVI, GVI, NDWI and BAI normalization processing in sequence.
Preferably, evaluating the canopy of the forest region according to the calculated canopy health evaluation index includes:
using a formula
Standardizing the canopy health evaluation index to obtain a standardized index; wherein, FCHI · For the normalized index, FCHI max 、FCHI min Maximum and minimum health index values within the region, respectively;
and classifying the result obtained by the standardized index into healthy, mild damage, moderate damage and severe damage types according to a natural breakpoint method, and giving different colors to the result and then outputting the drawing to obtain an evaluation result.
A canopy health evaluation system combining remote sensing and a forest map comprises:
the first image determining unit is used for determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image;
the second image determining unit is used for obtaining a forest area image according to the forest phase image and the multispectral reflectivity image;
the index determining unit is used for determining a spectral index according to the forest area image;
the weight determining unit is used for carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
the index construction unit is used for constructing a canopy health evaluation index according to the weight value and the spectral index;
and the evaluation unit is used for evaluating the forest crowns in the forest area according to the calculated forest crown health evaluation index.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a canopy health evaluation method and system combining remote sensing and a forest map, wherein the method comprises the following steps: determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image; obtaining a forest area image according to the forest phase image and the multispectral reflectivity image; determining a spectral index according to the forest area image; carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes; constructing a canopy health evaluation index according to the weight value and the spectral index; and evaluating the canopy of the forest region according to the calculated canopy health evaluation index. The method integrates satellite images and spectral index information, carries out quantitative evaluation on the health condition of the canopy, improves the objectivity of the evaluation result, is easy to operate in the evaluation process, and improves the reliability of the evaluation process.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method in an embodiment provided by the present invention;
fig. 2 is a schematic diagram of an evaluation process in an embodiment provided by 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide a method and a system for evaluating the health of a canopy by combining remote sensing and a forest phase diagram, which integrate satellite images and spectral index information, carry out quantitative evaluation on the health condition of the canopy, improve the objectivity of an evaluation result, facilitate the operation of an evaluation process and improve the reliability of the evaluation process.
The high-resolution six-satellite 16m wide data (GF-6 WFV) has the width of 800km, and a 4d repetition period obtains 8 spectral band image data of the same region, which can be used for distinguishing vegetation physiological characteristics, so that the method can be used for forest canopy health monitoring and evaluation in a large region. The invention provides a forest canopy health quantitative evaluation method integrating spectral index information of satellite images by combining GF-6WFV images and forest map data, and aims to provide an objective, reliable and easy-to-operate quantitative evaluation method for forest canopy health monitoring and managers.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a method in an embodiment provided by the present invention, and as shown in fig. 1, the present invention provides a method for evaluating canopy health by combining remote sensing and a forest map, including:
step 100: determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image;
step 200: obtaining a forest area image according to the forest map and the multispectral reflectivity image;
step 300: determining a spectral index according to the forest area image;
step 400: carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
step 500: constructing a canopy health evaluation index according to the weight value and the spectral index;
step 600: and evaluating the canopy of the forest region according to the calculated canopy health evaluation index.
Preferably, the step 100 specifically includes:
acquiring an L1A-level multispectral image of the wide image of the high-resolution six-number satellite in clear sky in the forest vegetation growing season in the area to be detected;
carrying out absolute radiometric calibration and atmospheric correction on the L1A-level multispectral image to obtain a first corrected image;
performing geometric correction on the first corrected image based on the RPC of the product data and the reference image to obtain a second corrected image;
and splicing, cutting and cloud mask processing are carried out on the second corrected image to obtain the multispectral reflectivity image.
Fig. 2 is a schematic diagram of an evaluation process in an embodiment provided by the present invention, and as shown in fig. 2, the first step in this embodiment is image selection and processing, namely, selecting a clear sky high-resolution six-size satellite wide-width image (GF-6 WFV) L1A-level multispectral image in a forest vegetation growing season of a test area, then performing absolute radiometric calibration and atmospheric correction on the selected image, performing geometric correction on the multispectral image based on RPC and a reference image carried by product data, and finally performing splicing or clipping, cloud mask processing, and the like to obtain a cloud-free multispectral reflectance image with a spatial resolution of the test area of 16 m.
Preferably, the step 200 specifically includes:
merging the pattern spots of the forest map to generate a forest area and a non-forest area;
and performing mask processing on the multispectral reflectivity image, reserving original reflectivity values of all wave band values of the image covering the forest area, and setting the reflectivity values of all wave band values of the non-forest area to be 0 to obtain the forest area image.
Specifically, in this embodiment, the second step is to select a forest region image, merge forest map patches to generate a forest region and a non-forest region, perform mask processing on the processed GF-6WFV reflectance image, retain original reflectance values of each waveband of the image covering the forest region, and set reflectance values of each waveband of the rest of the non-forest region to 0.
Preferably, the step 300 specifically includes:
carrying out statistical analysis on spectral indexes of the selected healthy and sub-healthy samples in the forest region image by adopting a random forest regression method to obtain an analysis result;
and selecting the spectral index which reflects that the correlation of the greenness, the moisture and the disturbed state of the tree crown leaves is greater than a preset threshold value according to the analysis result.
Further, in the third step of this embodiment, spectral index screening is performed, a random forest regression method is adopted to statistically analyze spectral indexes of selected healthy and sub-healthy (mild damage, moderate damage, and severe damage) samples for GF-6WFV images of covered forest regions generated by processing, and then spectral indexes with high correlation of states such as greenness, moisture, disturbance and the like of forest canopy leaves are selected and used for constructing forest canopy health quantitative evaluation indexes.
Preferably, the spectral indices include greenness normalized vegetation index, greenness ratio index, red-edge normalized vegetation index, yellow-edge normalized vegetation index, normalized water body index, and fire passing index.
Preferably, the formula for calculating the spectral index is:
GNDVI is the greenness normalized vegetation index, GVI is the greenness ratio index, RENDVI is the red-edge normalized vegetation index, YENDVI is the yellow-edge normalized vegetation index, NDWI is the normalized water body index, BAI is the fire passing index, and rho 1, rho 2, rho 3, rho 4, rho 5, rho 6 and rho 8 are the reflectivities of wave band 1, wave band 2, wave band 3, wave band 4, wave band 5, wave band 6 and wave band 8 of the high-resolution six-satellite wide-width image in sequence.
Preferably, the step 400 specifically includes:
using a formula
Normalizing each spectral index to obtain normalized data; wherein, X ni The normalized value of a certain spectral index in the pixel i; x i The value of the spectral index in a pixel i is shown; x max And X min Respectively the maximum value and the minimum value of the statistics of the spectral index;
and performing linear transformation on the normalized data based on a principal component analysis method to obtain the weight value.
Optionally, the fourth step in this embodiment is data normalization processing, and since the selected and calculated exponential factor dimensions are not completely uniform, direct use for modeling may cause imbalance of each index. Therefore, the linear function normalization method is adopted to unify the dimensions of the exponential factors to [0,1], and the normalization processing is carried out on the exponential factors. The normalized formula is:
in the formula, X ni The value of a certain factor in the pixel i after normalization; x i The value of the factor in the pixel i; x max And X min Respectively the maximum and minimum of the factor statistic.
Further, in the fifth step of this embodiment, the weight of each factor is determined, and Principal Component Analysis (PCA) is used to convert the multidimensional remote sensing index information into a few variables that are easy to interpret and are not related to each other for 6 index factors after data normalization, where the first principal component (PC 1) of the variables contains most of the information in the original data set. The 6 variables can be transformed by linear transformation by utilizing the PC1 to construct the canopy health index, so that the result deviation caused by the conventional artificial subjective weight setting is avoided.
Preferably, the formula of the canopy health evaluation index is as follows:
FCHI＝a×G+b×R+c×Y+d×V+e×N+f×B；
FCHI is the health evaluation index of the canopy, and a, B, c, d, e and f are weight values of G, R, Y, V, N and B in sequence; g, R, Y, V, N and B are values after GNDVI, RENDVI, GVI, NDWI and BAI normalization processing in sequence.
Specifically, in this embodiment, the sixth step is to construct a canopy health evaluation index, and calculate a canopy health evaluation index (FCHI) according to equation (8) by using weight values of 6 index factors in PC 1.
FCHI＝a×G+b×R+c×Y+d×V+e×N+f×B
In the formula, a, B, c, d, e and f are weight values of G, R, Y, V, N and B in sequence; g, R, Y, V, N and B are values after GNDVI, RENDVI, GVI, NDWI and BAI normalization processing in sequence.
Preferably, the step 600 specifically includes:
using the formula
Standardizing the canopy health evaluation index to obtain a standardized index; wherein, FCHI · For the normalized index, FCHI max 、FCHI min Maximum and minimum health index values within the region, respectively;
and classifying the result obtained by the standardized index into healthy, mild damage, moderate damage and severe damage types according to a natural breakpoint method, and giving different colors to the result and then outputting the drawing to obtain an evaluation result.
Specifically, in this embodiment, the last step is the classification of canopy health, in order to facilitate the measurement and comparison of evaluation indexes, the FCHI result is normalized according to the following formula, the values are unified to [0, 100], and the types of health, mild damage, moderate damage and severe damage are classified according to the natural breakpoint method, and different colors are given for chart output.
In the formula: FCHI max 、FCHI min Are respectively asMaximum and minimum health index values within a region. The closer the FCHI value is to 100, the healthier the forest canopy leaves are; otherwise, the less healthy it is.
Corresponding to the method, the embodiment provides a canopy health evaluation system combining remote sensing and a forest map, which includes:
the first image determining unit is used for determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image;
the second image determining unit is used for obtaining a forest area image according to the forest phase image and the multispectral reflectivity image;
the index determining unit is used for determining a spectral index according to the forest area image;
the weight determining unit is used for carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
the index construction unit is used for constructing a canopy health evaluation index according to the weight value and the spectral index;
and the evaluation unit is used for evaluating the forest crowns in the forest area according to the calculated forest crown health evaluation index.
The invention has the following beneficial effects:
the method integrates satellite images and spectral index information, carries out quantitative evaluation on the health condition of the canopy, improves the objectivity of the evaluation result, is easy to operate in the evaluation process, and improves the reliability of the evaluation process.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the foregoing, the description is not to be taken in a limiting sense.
Claims (10)
1. A canopy health evaluation method combining remote sensing and a forest map is characterized by comprising the following steps:
determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-number satellite wide image;
obtaining a forest area image according to the forest map and the multispectral reflectivity image;
determining a spectral index according to the forest area image;
carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
constructing a canopy health evaluation index according to the weight value and the spectral index;
and evaluating the canopy of the forest region according to the calculated canopy health evaluation index.
2. The method for canopy health assessment by remote sensing in combination with a forest map of claim 1, wherein determining a multispectral reflectance image of the area to be assessed from the high-resolution six-satellite wide-width image comprises:
acquiring an L1A-level multispectral image of the wide image of the high-resolution six-number satellite in clear sky in the forest vegetation growing season in the area to be detected;
carrying out absolute radiometric calibration and atmospheric correction on the L1A-level multispectral image to obtain a first corrected image;
performing geometric correction on the first correction image based on the RPC of the product data and the reference image to obtain a second correction image;
and splicing, cutting and cloud mask processing are carried out on the second corrected image to obtain the multispectral reflectivity image.
3. The method for canopy health assessment using remote sensing and forest map together of claim 1, wherein said obtaining a forest area image from a forest map and said multispectral reflectance image comprises:
merging the pattern spots of the forest map to generate a forest area and a non-forest area;
and performing mask processing on the multispectral reflectivity image, reserving original reflectivity values of all wave band values of the image covering the forest area, and setting the reflectivity values of all wave band values of the non-forest area to be 0 to obtain the forest area image.
4. The method for evaluating canopy health by combining remote sensing with a forest map of claim 1, wherein determining a spectral index from the forest area image comprises:
carrying out statistical analysis on spectral indexes of the health and sub-health samples in the selected forest region image by adopting a random forest regression method to obtain an analysis result;
and selecting the spectral index which reflects that the greenness, the moisture and the disturbed state of the tree leaves are more than a preset threshold value according to the analysis result.
5. The method of claim 4, wherein the spectral indices comprise greenness normalized vegetation index, greenness ratio index, red-edge normalized vegetation index, yellow-edge normalized vegetation index, normalized water body index, and fire index.
6. The method for forest crown health assessment by combining remote sensing and forest map according to claim 5, wherein the formula for calculating the spectral index is as follows:
GNDVI is the greenness normalized vegetation index, GVI is the greenness ratio index, RENDVI is the red-edge normalized vegetation index, YENDVI is the yellow-edge normalized vegetation index, NDWI is the normalized water body index, BAI is the fire passing index, and rho 1, rho 2, rho 3, rho 4, rho 5, rho 6 and rho 8 are the reflectivities of wave band 1, wave band 2, wave band 3, wave band 4, wave band 5, wave band 6 and wave band 8 of the high-resolution six-satellite wide-width image in sequence.
7. The method for canopy health assessment with combined remote sensing and forest map of claim 6, wherein the performing data normalization and weight determination on the spectral indices to obtain weight values corresponding to each of the spectral indices comprises:
using a formula
Normalizing each spectral index to obtain normalized data; wherein, X ni The normalized value of a certain spectral index in a pixel i; x i The value of the spectral index in a pixel i is obtained; x max And X min Are respectively provided withAre the statistical maximum and minimum values of the spectral index;
and performing linear transformation on the normalized data based on a principal component analysis method to obtain the weight value.
8. The method for forest crown health assessment by combining remote sensing and forest map according to claim 7, wherein the formula of the forest crown health assessment index is as follows:
FCHI＝a×G+b×R+c×Y+d×V+e×N+f×B；
FCHI is the health evaluation index of the canopy, and a, B, c, d, e and f are weight values of G, R, Y, V, N and B in sequence; g, R, Y, V, N and B are values after GNDVI, RENDVI, GVI, NDWI and BAI normalization processing in sequence.
9. The method for evaluating the health of a forest crown by combining remote sensing and a forest map according to claim 8, wherein evaluating the forest crown of a forest area according to the calculated forest crown health evaluation index comprises:
using a formula
Standardizing the canopy health evaluation index to obtain a standardized index; wherein, FCHI · Is the normalized index, FCHI max 、FCHI min Maximum and minimum health index values within the region, respectively;
and classifying the result obtained by the standardized index into healthy, mild victim, moderate victim and severe victim types according to a natural breakpoint method, and giving different colors for drawing output to obtain an evaluation result.
10. A canopy health evaluation system combining remote sensing and a forest map is characterized by comprising:
the first image determining unit is used for determining a multispectral reflectivity image of the area to be detected according to the high-resolution six-satellite wide image;
the second image determining unit is used for obtaining a forest area image according to the forest phase image and the multispectral reflectivity image;
the index determining unit is used for determining a spectral index according to the forest area image;
the weight determining unit is used for carrying out data normalization processing and weight determination on the spectral indexes to obtain weight values corresponding to the spectral indexes;
the index construction unit is used for constructing a canopy health evaluation index according to the weight value and the spectral index;
and the evaluation unit is used for evaluating the canopy of the forest area according to the calculated canopy health evaluation index.
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