CN114252405A - Forest surface combustible load capacity estimation method and device based on vegetation index - Google Patents
Forest surface combustible load capacity estimation method and device based on vegetation index Download PDFInfo
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Abstract
The invention discloses a forest surface combustible load estimation method and device based on vegetation indexes, which can reduce the manual field investigation cost and improve the accuracy of estimating the surface combustible load, and comprises the following steps: obtaining remote sensing image data of an area to be estimated, and obtaining a grid image of a vegetation index based on the remote sensing image data; acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample; if the statistical analysis determines that the surface combustible load of any one sample party and the vegetation index have a linear correlation relationship, establishing a linear regression model between the vegetation index and the surface combustible load; calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
Description
Technical Field
The invention relates to the technical field of forest fire prevention in forest conservation science, in particular to a method and a device for estimating the combustible load on the forest surface based on a vegetation index.
Background
Forest combustibles are the material basis for the occurrence of forest fires. The combustible loading capacity mainly refers to the combustible loading capacity of drying, and the size of the combustible loading capacity directly influences forest fire behaviors such as forest fire combustion intensity, flame height and spreading speed. Forest fires in south China mostly take surface fire as the main fire, and the main combustible is surface combustible. The survey of the combustible material loading on the forest ground surface is generally carried out by adopting a sample plot survey method, a sample plot is required to be set on site, shrubs, herbs and dried wastes in the sample plot are respectively collected, the fresh weight is measured, and a sample is brought back to measure the water content and the dry weight, so that the combustible material loading on the ground surface is obtained. However, the collection and calculation of the ground surface combustible substance loading capacity have strong specialization, and are easily influenced by the geographical environment, the field collection workload is large, the manual work efficiency is low, and the collected samples are easily influenced by sampling errors.
Multispectral remote sensing data such as a satellite film and a navigation film can obtain forest images in a large range, and possibility is provided for reducing field investigation work. With the continuous improvement of the remote sensing technology theory, the remote sensing data is more accurate and clear, and the investigation of forest combustible by utilizing various remote measuring means becomes a new method and trend. However, current remote sensing studies using combustible aspects are focused primarily on classifying combustibles, and there is little research on combustible loadings.
Therefore, there is a need for a highly accurate estimation scheme for combustible material loading on ground that reduces the cost of manual field investigation.
Disclosure of Invention
Based on the above, the invention aims to provide a forest surface combustible load estimation method and device based on vegetation indexes, which are used for reducing the manual field investigation cost and improving the accuracy of the estimation of the surface combustible load.
In a first aspect, an embodiment of the present invention provides a forest surface combustible load estimation method based on a vegetation index, where the method includes:
obtaining remote sensing image data of an area to be estimated, and obtaining a grid image of a vegetation index based on the remote sensing image data;
acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample;
if the statistical analysis determines that the surface combustible load of any one sample party and the vegetation index have a linear correlation relationship, establishing a linear regression model between the vegetation index and the surface combustible load;
calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
In one possible design, obtaining remote sensing image data of an area to be estimated includes:
and acquiring and processing image data of the area to be estimated by adopting an orthographic projection mode through an imaging device to obtain the remote sensing image data.
In one possible design, the remote sensing image data is hyperspectral image data or multispectral image data.
In one possible design, obtaining a raster image of a vegetation index based on the remote sensing image data includes:
and acquiring the raster image by using ENVI software based on the remote sensing image data.
In one possible design, the statistical analysis determines that there is a linear correlation between the surface combustible load and the vegetation index for any of the parties, including:
carrying out statistical analysis based on the ground surface combustible load and the vegetation index of any one of the parties, and determining a correlation coefficient between the ground surface combustible load and the vegetation index;
and if the absolute value of the correlation coefficient is larger than a preset threshold value, determining that a linear correlation exists between the ground surface combustible load data and the vegetation index.
In one possible design, a linear regression model between the vegetation index and surface combustible loading is established, comprising:
and fitting the ground surface combustible load of any one sample and the vegetation index by adopting a linear function, and establishing the linear regression model.
In one possible design, calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image comprises:
based on the linear regression model and a preset assignment formula, assigning a corresponding load value to each pixel in the raster image by using ArcGIS;
and calculating the sum of the corresponding load values of all pixel elements in the raster image to obtain the total surface combustible load.
In one possible design, the preset assignment formula is expressed as:
wherein, TiExpressing the surface combustible load quantity of the ith pixel needing to be assigned in the raster image, expressing Y as the linear regression model, and expressing S as each pixel in the raster imageThe corresponding ground area.
In a second aspect, an embodiment of the present invention further provides an estimation apparatus, including:
the system comprises a receiving unit, a calculating unit and a calculating unit, wherein the receiving unit is used for acquiring remote sensing image data of an area to be estimated and acquiring a raster image of a vegetation index based on the remote sensing image data; acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample;
the processing unit is used for establishing a linear regression model between the vegetation index and the ground surface combustible load if the linear correlation relationship exists between the ground surface combustible load and the vegetation index of any one party through statistical analysis; calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
In one possible design, the receiving unit is specifically configured to:
and acquiring and processing image data of the area to be estimated by adopting an orthographic projection mode through an imaging device to obtain the remote sensing image data.
In one possible design, the remote sensing image data is hyperspectral image data or multispectral image data.
In one possible design, the receiving unit is specifically configured to:
and acquiring the raster image by using ENVI software based on the remote sensing image data.
In one possible design, the processing unit is specifically configured to:
carrying out statistical analysis based on the ground surface combustible load and the vegetation index of any one of the parties, and determining a correlation coefficient between the ground surface combustible load and the vegetation index;
and if the absolute value of the correlation coefficient is larger than a preset threshold value, determining that a linear correlation exists between the ground surface combustible load data and the vegetation index.
In one possible design, the processing unit is specifically configured to:
and fitting the ground surface combustible load of any one sample and the vegetation index by adopting a linear function, and establishing the linear regression model.
In one possible design, the processing unit is specifically configured to:
based on the linear regression model and a preset assignment formula, assigning a corresponding load value to each pixel in the raster image by using ArcGIS;
and calculating the sum of the corresponding load values of all pixel elements in the raster image to obtain the total surface combustible load.
In one possible design, the preset assignment formula is expressed as:
wherein, TiAnd expressing the surface combustible load quantity of the ith pixel needing to be assigned in the raster image, expressing Y as the linear regression model, and expressing S as the corresponding surface area of each pixel in the raster image.
In a third aspect, an embodiment of the present invention further provides an estimation apparatus, where the estimation apparatus includes: at least one memory and at least one processor;
the at least one memory is for storing one or more programs;
the one or more programs, when executed by the at least one processor, implement the method as recited in any one of the possible designs of the first aspect above.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where at least one program is stored in the computer-readable storage medium; the at least one program, when executed by a processor, performs the method of any one of the possible designs of the first aspect.
The invention has the following beneficial technical effects:
in the technical scheme provided by the embodiment of the invention, because the remote sensing image can be used for obtaining the forest image in a large range, various vegetation indexes can be obtained after being processed by professional software (such as ENVI software), and the vegetation indexes have certain relevance with the ground surface combustible load, in the embodiment of the invention, a linear regression model of a certain vegetation index and the surface combustible load corresponding to the area to be estimated can be obtained by utilizing a small amount of ground investigation, used for estimating the surface combustible load, compared with the manual collection and the surface combustible load calculation of the traditional sample-plot investigation method, in addition, the ground surface combustible substance loading capacity in the embodiment of the invention can be refined to each grid pattern spot, so that the accuracy of the total ground surface combustible substance loading capacity of the area to be estimated can be improved.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flow chart of a forest surface combustible load estimation method based on a vegetation index according to an embodiment of the present invention;
FIG. 2 is a hyperspectral image of an area to be estimated according to an embodiment of the invention;
fig. 3 is a raster image of a normalized vegetation index according to an embodiment of the present invention;
fig. 4 is a raster image of an enhanced vegetation index according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a distribution of a plurality of samples in a region to be estimated according to an embodiment of the present invention;
FIG. 6 is a schematic representation of the surface combustible loading and normalized vegetation index distribution of each of a plurality of parties provided by an embodiment of the present invention;
FIG. 7 is a schematic representation of the surface combustible loading and enhanced vegetation index distribution of each of a plurality of parties provided by an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an estimation apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of another estimation apparatus according to an embodiment of the present invention.
Detailed Description
The terms of orientation of up, down, left, right, front, back, top, bottom, and the like, referred to or may be referred to in this specification, are defined relative to their configuration, and are relative concepts. Therefore, it may be changed according to different positions and different use states. Therefore, these and other directional terms should not be construed as limiting terms.
The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of implementations consistent with certain aspects of the present disclosure.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Unless stated to the contrary, the embodiments of the present invention refer to the ordinal numbers "first", "second", etc., for distinguishing a plurality of objects, and do not limit the sequence, timing, priority, or importance of the plurality of objects.
The shapes and sizes of the various elements in the drawings are not to be considered true scale, but are merely illustrative of the implementations described in the exemplary embodiments below.
The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a forest surface combustible load estimation method based on vegetation indexes according to an embodiment of the present invention. As shown in fig. 1, the method flow may include the following steps:
s101, obtaining remote sensing image data of an area to be estimated, and obtaining a grid image of a vegetation index based on the remote sensing image data.
In some embodiments, the remote sensing image data may be obtained by acquiring and processing image data of the region to be estimated by an imaging device in an orthographic projection manner. Wherein, this imaging device can be for the imaging lens that the spaceborne, manned machine or unmanned aerial vehicle machine carried. The remote sensing image data may be hyperspectral image data or multispectral image data, for example, if the imaging device is a hyperspectral and high-definition imaging lens, the remote sensing image data is hyperspectral image data.
For example, the area to be estimated is located in the district of Heming town, Henshan, Guangdong province, where the northern latitude is 22 ° 57'23 "to 22 ° 57'45", the east longitude is 112 ° 46'45 "to 112 ° 47'10", and the area is 34 hectare. The land distance is less than 500 meters from the burning land of the cloud mountain major forest fire in the alpine region of 5 Rizhan city of 12 months and 5 days in 2019, the land belongs to the tropical monsoon climate region of south Asia, and the main vegetation comprises masson pine, slash pine, eucalyptus, camphor tree, schima superba, Japanese glory, very pale tannin extract, red cone, banyan tree, litsea cubeba, various miscellaneous shrubs and bamboos, and basically covers the main vegetation type in the alpine "12.5" major forest fire. Therefore, the estimation of the ground surface combustible material loading capacity on the ground has positive significance for forest fire prevention of the ground.
For example, taking the area to be estimated as the area located in the mountains, the guangdong province, the high-brightness area, the town, the pit and the village, the hyperspectral and high-definition imaging lens carried by the unmanned aerial vehicle (e.g., a rotorcraft) can be used to collect the image data of the area in an orthographic projection manner, for example, the flight height of the flight path is set to about 200 meters, and the image data of the area is collected for 3 times. And performing radiation correction, geometric correction and reflection correction on the acquired hyperspectral image, and then performing band cutting, for example, controlling the band interval at 400-900nm for band cutting. Afterwards, single-track splicing can be performed on the cut data segment by using ENVI software, then, geometric fine correction is performed on the single-track data according to an orthographic base map, corrected images are output, global splicing is performed, namely, uniform color splicing is performed on single-frame secondary data, uniform color splicing is performed on multiple-frame secondary data, a research area (ROI) is generated, and a final hyperspectral image, such as the hyperspectral image shown in FIG. 2, can be generated after redundant data in the research area is cut out.
In a specific implementation process, the vegetation index can be used as a simple, effective and empirical measure of the vegetation condition on the ground, and plays an important role in reflecting vegetation growth, vegetation classification, vegetation health condition and the like. Thus, in multispectral or hyperspectral remote sensing, vegetation indices have been widely used to qualitatively and quantitatively evaluate vegetation coverage and its growth vigor. Because the vegetation spectrum shows complex mixed reaction of vegetation, soil brightness, environmental influence, shadow, soil color and humidity and is influenced by atmospheric space-time phase change, the vegetation index does not have a universal value, and researches thereof often show different results. However, a correlation relationship can exist between the vegetation index and the combustible load of the forest land surface, so that a model between the vegetation index and the combustible load of the forest land surface can be established, and the combustible load of the forest land surface with a larger area can be estimated.
Exemplary, commonly used vegetation indices may include, but are not limited to, normalized vegetation index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Greenness Vegetation Index (GVI), vertical vegetation index (PVI), vegetation index to adjust soil brightness (SAVI), differential environmental vegetation index (DVI).
In some embodiments, a raster image of the corresponding vegetation index may be obtained using the ENVI software based on the remote sensing image data.
For example, taking the region to be estimated as the high-brightness area, the commercial town, the pit-side village, in the mountain of foshan, guangdong, as an example, based on the remote sensing image data, the raster image of the normalized vegetation index shown in fig. 3 or the raster image of the enhanced vegetation index shown in fig. 4 may be obtained by using the ENVI software.
S102, surface combustible load data of a plurality of samples arranged in an area to be estimated are obtained, and the surface combustible load data comprise surface combustible load of any sample.
In some embodiments, the surface combustible load data may also include an average surface combustible load for a plurality of parties.
In a specific implementation process, in order to make the plurality of parties conform to the sample statistical rule, so as to further improve the accuracy of estimating the total surface combustible load of the area to be estimated, the number of the plurality of parties is set to be greater than 30.
In a specific implementation process, in order to obtain the relationship between the vegetation index and the actual ground surface combustible load, 1 m × 1 m of samples can be arranged in an area to be estimated, more than 30 samples can be arranged in a grid manner, and more samples can be supplemented if necessary. For example, still taking the area to be estimated as the area located in the high-brightness area of the township town, foe, guangdong province as an example, 36 samples can be set according to a grid of 100 meters, 14 random samples can be supplemented according to different forest stand types, and 50 samples can be set in total, and the distribution can be as shown in fig. 5.
For example, taking the area to be estimated as the area located in the high-brightness area of the township town, fogshan, guangdong province, as an example, all the surface combustibles in 50 samples can be collected, and then all the surface combustibles collected in each sample are dried and then the loading capacity is measured. In a certain collection, the surface combustible load was measured to be between 1.085 kg and 7.893 kg for any of the 50 panelists, with an average surface combustible load of 3.933 kg for the 50 panelists.
The surface combustible matter in the embodiment of the present invention may be a narrow-sense near-surface shrub, herbal, humus combustible matter (for example, humus collection bags, etc.), and the like, and does not include trees. In which living objects such as shrubs, herbs, etc. near the surface of the earth may be living or dead.
S103, if the linear relation between the ground surface combustible load and the vegetation index of any sample party is determined through statistical analysis, establishing a linear regression model between the vegetation index and the ground surface combustible load.
In some embodiments, a statistical analysis may be performed based on the surface combustible load and the vegetation index for either party, determining a correlation coefficient between the surface combustible load and the vegetation index. And if the absolute value of the correlation coefficient is larger than a preset threshold value, determining that a linear correlation exists between the ground surface combustible load data and the vegetation index.
In a specific implementation process, the preset threshold may be set according to a type of the correlation coefficient, which is not limited in the embodiment of the present invention. For example, to further improve the accuracy of estimating the total surface combustible load of the area to be estimated, the preset threshold value may be set to a value that can be used to characterize the surface combustible load data as being related to the vegetation index, for example, if the correlation coefficient is the pearson correlation coefficient, the preset threshold value may be set to be greater than or equal to 0.3.
Illustratively, the pearson correlation coefficient r is a common statistical measure used to measure the degree of linear correlation between two sets of data. In general, an absolute value of r between 0 and 0.09 indicates no correlation between the two sets of data, between 0.1 and 0.3 indicates a weak correlation between the two sets of data, between 0.3 and 0.5 indicates a moderate correlation between the two sets of data, and between 0.5 and 1 indicates a strong correlation between the two sets of data. When the r value is more than 0.3, the vegetation index and the ground surface combustible load quantity are considered to have a linear correlation, and when the r value is less than 0.3, the vegetation index and the ground surface combustible load quantity are considered to have no linear correlation, at this time, the vegetation index is not suitable for estimating the ground surface combustible load quantity of the area where the sample is located.
Illustratively, taking the area to be estimated as the high-brightness area, namely, the mountain, in the east of the Guangdong province, the town pit-side village, the area to be estimated is located in the Fushan city, the correlation coefficient is the Pearson correlation coefficient, and the preset threshold is set to 0.3, for example, the vegetation index can be represented by the X axis, the surface combustible load can be represented by the Y axis, after statistical analysis is performed based on the surface combustible load and the normalized vegetation index of each of the collected 50 sample parties, the distribution of the surface combustible load and the normalized vegetation index can be shown in FIG. 6, and the Pearson correlation coefficient r between the surface combustible load and the normalized vegetation index can be calculated to be equal to-0.303, and the absolute value thereof is greater than 0.3. Alternatively, the vegetation index may be represented by an X axis, the surface combustible load may be represented by a Y axis, and after statistical analysis is performed based on the surface combustible load and the enhanced vegetation index of each of the collected 50 samples, a pearson correlation coefficient r between the surface combustible load and the enhanced vegetation index may be calculated to be equal to-0.434, and an absolute value thereof is greater than 0.3, as shown in fig. 7. Namely, the normalized vegetation index and the enhanced vegetation index have linear correlation with the ground combustible loading.
In some embodiments, after determining that there is a linear correlation between the surface combustible load of any one of the parties and the vegetation index, fitting the surface combustible load of any one of the parties and the vegetation index by using a linear function, and establishing a linear regression model between the vegetation index and the surface combustible load.
For example, for two sets of data with medium or strong correlation, the vegetation index can be represented by X, and the ground surface combustible load of 1 square meter can be represented by Y, and after the ground surface combustible load and the vegetation index of any one sample are fitted by using a linear function, the established linear regression model can be represented as the following formula (1).
Y＝AX+B (1)
Wherein A and B are constants.
For example, taking the area to be estimated as the high-brightness area, the town, and the town, the hill, and the village, and the vegetation index as the normalized vegetation index, the linear regression model established by fitting the surface combustible loading and the vegetation index of each of the 50 samples with the linear function can be expressed as the following formula (2).
Y＝-5.9354X+8.4663 (2)
For example, taking the area to be estimated as the high-brightness area, the town, the hill, and the village, and the vegetation index as the enhanced vegetation index, the linear regression model established by fitting the surface combustible loading and the vegetation index of the 50 samples with the linear function can be expressed as the following formula (3).
Y＝-5.8485X+6.7271 (3)
And S104, calculating the total ground surface combustible load of the area to be estimated based on the linear regression model and the raster image.
In some embodiments, each pixel in the raster image may be assigned a corresponding load value using ArcGIS based on a linear regression model and a preset assignment formula. In a specific implementation process, the preset assignment formula can be expressed as the following formula (4):
wherein, TiThe surface combustible load of the ith pixel needing to be assigned in the raster image is expressed, Y is expressed as a linear regression model, and S is expressed as the corresponding surface area of each pixel in the raster image.
In a specific implementation, the above formula (1) and formula (4) are combined, and X is usediRepresenting the vegetation index of any pixel in the raster image, the assignment of any pixel in the raster image can be calculated from the following equation (5).
For example, taking the area to be estimated as the high-brightness area located in the mountain of foshan, Guangdong province, the town of the lotus, and the vegetation index as the normalized vegetation index, if the ground area corresponding to each pixel in the raster image is 0.092 square meters, the assignment of any pixel in the raster image can be calculated by the following formula (6).
In a specific implementation process, because some vegetation index values can distinguish forest lands from non-forest lands, the grid pixels of the non-forest lands can be directly assigned to be 0. For example, in the normalized vegetation index, when the value of the normalized vegetation index corresponding to a certain pixel is less than 0.3, it can be determined that the pixel basically belongs to non-vegetation-covered non-forest land, and X can be setiLess than 0.3, TiEqual to 0.
In some embodiments, after all the pixels in the raster image are assigned with the corresponding load values, the total ground surface combustible load of the area to be estimated can be obtained by calculating the sum of the load values corresponding to all the pixel elements in the raster image according to the following formula (7).
T＝∑Ti (7)
Where T represents the total surface combustible load for the area to be estimated.
As can be seen from the above description, in the technical solution provided in the embodiment of the present invention, since a large-scale forest image can be obtained by using a remote sensing image, and various vegetation indexes can be obtained after being processed by professional software (e.g., ENVI software), and the vegetation indexes have a certain correlation with the amount of surface combustibles, in the embodiment of the present invention, a linear regression model of a certain vegetation index and the surface combustible load corresponding to the area to be estimated can be obtained by utilizing a small amount of ground investigation, used for estimating the surface combustible load, compared with the manual collection and the surface combustible load calculation of the traditional sample-plot investigation method, in addition, the ground surface combustible substance loading capacity in the embodiment of the invention can be refined to each grid pattern spot, so that the accuracy of the total ground surface combustible substance loading capacity of the area to be estimated can be improved.
Based on the same inventive concept, an estimation apparatus is further provided in the embodiments of the present invention, as shown in fig. 8, the estimation apparatus 200 may include:
the system comprises a receiving unit 201, a calculating unit and a calculating unit, wherein the receiving unit 201 is used for obtaining remote sensing image data of an area to be estimated and obtaining a grid image of a vegetation index based on the remote sensing image data; acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample;
the processing unit 202 is configured to, if it is determined through statistical analysis that a linear correlation exists between the ground surface combustible load amount of any one of the parties and the vegetation index, establish a linear regression model between the vegetation index and the ground surface combustible load amount; calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
In one possible design, the receiving unit 201 is specifically configured to:
and acquiring and processing image data of the area to be estimated by adopting an orthographic projection mode through an imaging device to obtain the remote sensing image data.
In one possible design, the remote sensing image data is hyperspectral image data or multispectral image data.
In one possible design, the receiving unit 201 is specifically configured to:
and acquiring the raster image by using ENVI software based on the remote sensing image data.
In one possible design, the processing unit 202 is specifically configured to:
carrying out statistical analysis based on the ground surface combustible load and the vegetation index of any one of the parties, and determining a correlation coefficient between the ground surface combustible load and the vegetation index;
and if the absolute value of the correlation coefficient is larger than a preset threshold value, determining that a linear correlation exists between the ground surface combustible load data and the vegetation index.
In one possible design, the processing unit 202 is specifically configured to:
and fitting the ground surface combustible load of any one sample and the vegetation index by adopting a linear function, and establishing the linear regression model.
In one possible design, the processing unit 202 is specifically configured to:
based on the linear regression model and a preset assignment formula, assigning a corresponding load value to each pixel in the raster image by using ArcGIS;
and calculating the sum of the corresponding load values of all pixel elements in the raster image to obtain the total surface combustible load.
In one possible design, the preset assignment formula is expressed as:
wherein, TiAnd expressing the surface combustible load quantity of the ith pixel needing to be assigned in the raster image, expressing Y as the linear regression model, and expressing S as the corresponding surface area of each pixel in the raster image.
The estimation device 200 in the embodiment of the present invention is based on the same concept as the estimation method for combustible material on forest surface based on vegetation index shown in fig. 1, and through the foregoing detailed description of the estimation method for combustible material on forest surface based on vegetation index, those skilled in the art can clearly understand the implementation process of the estimation device 200 in the embodiment, so that for the sake of brevity of the description, no further description is provided here.
Based on the same inventive concept, an estimation apparatus is further provided in the embodiments of the present invention, as shown in fig. 9, the estimation apparatus 300 may include: at least one memory 301 and at least one processor 302. Wherein:
the at least one memory 301 is used to store one or more programs.
The method of estimating forest surface combustible load based on vegetation index as described above in fig. 1 is implemented when one or more programs are executed by the at least one processor 302.
The estimation apparatus 300 may further optionally include a communication interface for communication and data interactive transmission with an external device.
It should be noted that the memory 301 may include a high-speed RAM memory, and may also include a nonvolatile memory (nonvolatile memory), such as at least one disk memory.
In a specific implementation process, if the memory 301, the processor 302 and the communication interface are integrated on a chip, the memory 301, the processor 302 and the communication interface may complete communication with each other through an internal interface. If the memory 301, the processor 302 and the communication interface are implemented independently, the memory 301, the processor 302 and the communication interface may be connected to each other through a bus and perform communication with each other.
Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, which may store at least one program, and when the at least one program is executed by a processor, the method for estimating combustible load on forest surface based on vegetation index shown in fig. 1 is implemented.
It should be understood that the computer-readable storage medium is any data storage device that can store data or programs which can thereafter be read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, CD-ROM, HDD, DVD, magnetic tape, optical data storage devices, and the like.
The computer readable storage medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A forest surface combustible load estimation method based on vegetation indexes is characterized by comprising the following steps:
obtaining remote sensing image data of an area to be estimated, and obtaining a grid image of a vegetation index based on the remote sensing image data;
acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample;
if the statistical analysis determines that the surface combustible load of any one sample party and the vegetation index have a linear correlation relationship, establishing a linear regression model between the vegetation index and the surface combustible load;
calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
2. The method of claim 1, wherein obtaining remote sensing image data of the area to be estimated comprises:
and acquiring and processing image data of the area to be estimated by adopting an orthographic projection mode through an imaging device to obtain the remote sensing image data.
3. The method of claim 2, wherein the remote sensing image data is hyperspectral image data or multispectral image data.
4. The method of claim 1, wherein obtaining a raster image of vegetation indices based on the remotely sensed image data comprises:
and acquiring the raster image by using ENVI software based on the remote sensing image data.
5. The method of claim 1, wherein the statistical analysis determines that there is a linear correlation between the surface combustible load and the vegetation index for any of the parties, comprising:
carrying out statistical analysis based on the ground surface combustible load and the vegetation index of any one of the parties, and determining a correlation coefficient between the ground surface combustible load and the vegetation index;
and if the absolute value of the correlation coefficient is larger than a preset threshold value, determining that a linear correlation exists between the ground surface combustible load data and the vegetation index.
6. The method of claim 1, wherein establishing a linear regression model between the vegetation index and surface combustible loading comprises:
and fitting the ground surface combustible load of any one sample and the vegetation index by adopting a linear function, and establishing the linear regression model.
7. The method of any one of claims 1-6, wherein calculating the total surface combustible load for the area to be estimated based on the linear regression model and the raster image comprises:
based on the linear regression model and a preset assignment formula, assigning a corresponding load value to each pixel in the raster image by using ArcGIS;
and calculating the sum of the corresponding load values of all pixel elements in the raster image to obtain the total surface combustible load.
8. The method of claim 7, wherein the preset assignment formula is expressed as:
wherein, TiAnd expressing the surface combustible load quantity of the ith pixel needing to be assigned in the raster image, expressing Y as the linear regression model, and expressing S as the corresponding surface area of each pixel in the raster image.
9. A curation device, comprising:
the system comprises a receiving unit, a calculating unit and a calculating unit, wherein the receiving unit is used for acquiring remote sensing image data of an area to be estimated and acquiring a raster image of a vegetation index based on the remote sensing image data; acquiring surface combustible load data of a plurality of samples arranged in the area to be estimated, wherein the surface combustible load data comprises the surface combustible load of any sample;
the processing unit is used for establishing a linear regression model between the vegetation index and the ground surface combustible load if the linear correlation relationship exists between the ground surface combustible load and the vegetation index of any one party through statistical analysis; calculating the total surface combustible load of the area to be estimated based on the linear regression model and the raster image.
10. A computer-readable storage medium characterized in that the computer-readable storage medium stores at least one program; the at least one program, when executed by a processor, performs the method of any of claims 1-8.
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