CN116628405A - Remote sensing method for estimating grassland vegetation height - Google Patents
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Abstract
The application provides a remote sensing method for estimating grassland vegetation height, which comprises the following steps: acquiring a distribution range of grasslands, and sampling the grasslands in the distribution range; constructing a vegetation height estimation model of the grassland; acquiring coefficients and intercept of the vegetation height estimation model based on the sampling data; and acquiring a vegetation index and a gradient of the target grassland, inputting the vegetation index and the gradient into the vegetation height estimation model after determining the coefficient and the intercept, and acquiring the vegetation height of the target grassland. The application establishes the grass height estimation model suitable for different grassland types, makes up the defect of estimating the vegetation height of the grassland by the existing remote sensing technology, and provides important reference and decision basis for the management and protection of the grassland ecological system.
Description
Technical Field
The application belongs to the fields of ecology, environmental science and remote sensing science, and particularly relates to a remote sensing method for estimating grassland vegetation height.
Background
China is a large country of grassland resources, and the main grassland types include warm meadow grassland, warm typical grassland, warm desertification grassland, alpine meadow and alpine meadow, and various natural grasslands account for about 41% of the total land area of the country. As an important component of the land ecological system, the grassland has the functions of retaining water and soil, preventing wind and fixing sand, conserving water sources and the like, and has important significance for maintaining ecological balance and guaranteeing biological diversity. Grassland height, which is a typical biophysical feature, is an important index for evaluating grassland ecological environment and biomass in addition to reflecting the growth condition and degradation degree of grassland vegetation. Therefore, accurate estimation of the grassland height is beneficial to scientific management and reasonable utilization of grassland resources, and has important significance for revealing grassland vegetation change and analyzing the carbon reserves of the grassland ecosystem.
The traditional grassland height measurement method is field investigation, and the method has the advantages of high precision, obvious limitation on time and labor consumption and space-time scale and great subjective influence by people. With the development of laser radar technology, methods for estimating grassland height by using vehicle-mounted or airborne laser radar are becoming mature. The method can quickly acquire the elevation information of the ground and the vegetation and generate a fine three-dimensional vegetation structural model, but has higher cost and relatively smaller coverage range. In recent years, satellite remote sensing technology with high time resolution, high spatial resolution and high spectral resolution is rapidly developed, and a method for estimating the height of grassland vegetation by utilizing a machine learning model based on the remote sensing technology is widely applied. The method has the advantages of wide coverage, long duration, strong timeliness and the like, and has better precision in estimating the height of the grassland in a large area scale.
Different types of grasses vary widely in distribution, growing environment, and the number and types of plants involved. However, current methods of estimating the vegetation height of grasslands by remote sensing technology do not take these differences into account, nor do they lack data processing and modeling methods for different types of grasslands, which makes it difficult for existing methods to accurately estimate the heights of different types of grasslands. Therefore, it is necessary to build a grassland height estimation model suitable for different grassland types to make up for the defects of the existing remote sensing technology in estimating the grassland vegetation height, so as to better manage and protect grassland resources.
In summary, grasslands are an important component of the terrestrial ecosystem, playing a vital role in carbon recycling. The grassland height is one of important indexes of the vegetation growth condition of grasslands, can reflect the ecological system attributes such as grassland productivity, plant community structure, species composition and the like, and can reflect the influence of climate change on the environment. However, the current method for estimating the vegetation height of grasslands by remote sensing technology does not fully consider the differences of different types of grasslands in terms of distribution range, growing environment, plant types and quantity, and the like, so that it is difficult to accurately estimate the heights of the different types of grasslands. Therefore, a grass height estimation model suitable for different grassland types needs to be established to make up for the defect that the existing remote sensing technology estimates the vegetation height of the grassland, and an important reference and decision basis is provided for management and protection of the grassland ecological system.
Disclosure of Invention
In order to solve the technical problems, the application provides a remote sensing method for estimating the height of grassland vegetation, which is used for establishing a grassland height estimation model suitable for different grassland types, makes up the defect of estimating the height of grassland vegetation by the existing remote sensing technology, and provides important reference and decision basis for management and protection of grassland ecological systems.
To achieve the above object, the present application provides a remote sensing method for estimating a height of vegetation on a grassland, comprising:
acquiring a distribution range of grasslands, and sampling the grasslands in the distribution range;
constructing a vegetation height estimation model of the grassland;
acquiring coefficients and intercept of the vegetation height estimation model based on the sampling data;
and acquiring a vegetation index and a gradient of the target grassland, inputting the vegetation index and the gradient into the vegetation height estimation model after determining the coefficient and the intercept, and acquiring the vegetation height of the target grassland.
Optionally, the types of grasslands include: warm meadow, warm classical meadow, warm desertification meadow, alpine meadow and alpine meadow.
Optionally, the vegetation height estimation model comprises: a vegetation height estimation model of a warm meadow, a vegetation height estimation model of a warm typical meadow, a vegetation height estimation model of a warm desertification meadow, a vegetation height estimation model of a alpine meadow and a vegetation height estimation model of a alpine meadow.
Optionally, the vegetation height estimation model of the warm meadow grassland is:
H＝a×GLI+b×NDVI+c×NDVI×GCC+intercept
wherein H is the vegetation height of the warm meadow grassland, a, b and c are coefficients, the intercept is the intercept, the GLI is the green leaf index, the NDVI is the normalized vegetation index, and the GCC is the relative greenness index;
the vegetation height estimation model of the warm typical grassland is as follows:
H＝a×NDVI+b×ARVI+c×NDVI×ARVI+d×ARVI 2 +e×ARVI×GNDVI
+intercept
wherein, H is the vegetation height of a warm typical grassland, a, b, c, d, e is a coefficient, intermittent is intercept, NDVI is a normalized vegetation index, ARVI is an atmospheric resistance vegetation index, and GNDVI is a green normalized vegetation index;
the vegetation height estimation model of the warm desertification grassland comprises the following steps:
H＝a×GLI+b×MCARI2+c×GCC+intercept
wherein, H is the vegetation height of the warm desertification grassland, a, b and c are coefficients, the intercept is intercept, the GLI is a green leaf index, the MCARI2 is an improved chlorophyll absorption index, and the GCC is a relative greenness index;
the vegetation height estimation model of the alpine meadow is as follows:
H＝a×RVI×aspect+b×RVI×GVMI+c×aspect×Red+d×aspect×Blue
+e×aspect×Green+intercept
wherein, H is the vegetation height of the alpine meadow, a, b, c, d, e is a coefficient, the interval is the intercept, aspect is the gradient, RVI is the ratio vegetation index, GVGI is the global vegetation humidity index, red, green, blue is the reflectivity of red, green and blue wave bands respectively;
the vegetation height estimation model of the alpine grassland is as follows:
H＝a×GLI+b×GCC+c×MSR+d×WDRVI+intercept
wherein, H is the vegetation height of the alpine grassland, a, b, c, d is the coefficient, the intercept is the intercept, the GLI is the green leaf index, the GCC is the relative greenness index, the MSR is the corrected ratio vegetation index, and the WDRVI is the wide dynamic vegetation index.
Optionally, sampling the grass comprises:
selecting a first preset number of small sample sides in a selected large sample area for measurement, respectively collecting the canopy height of two preset numbers of vegetation of each small sample Fang Nadi, obtaining an average value of the canopy heights, and taking the average value as the vegetation height of a sampling point; and simultaneously recording the sampling time and longitude and latitude of the sampling point.
Optionally, sampling the grass further comprises:
for warm meadow grassland, warm typical grassland and warm desertification grassland, sampling is carried out at a flat terrain area;
for alpine meadow and alpine grassland, sampling is selected at different terrains and slopes.
Optionally, obtaining the coefficients and intercept of the vegetation height estimation model comprises:
acquiring remote sensing images and digital elevation data of the sampling points based on the sampling time and longitude and latitude of the sampling points;
acquiring a vegetation index and a gradient required by the vegetation height estimation model based on the remote sensing image and the digital elevation data;
and acquiring coefficients and intercept of the vegetation height estimation model based on the vegetation height, the vegetation index and the gradient of the sampling points.
Optionally, based on the vegetation index, obtaining coefficients and intercept of the vegetation height estimation model comprises:
acquiring a green leaf index, a normalized vegetation index and a relative greenness index of a ground sample point in the warm meadow, taking the vegetation index and the vegetation height of the warm meadow as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm meadow by a least square method;
acquiring a normalized vegetation index, an atmospheric resistance vegetation index and a green normalized vegetation index of ground sample points in a warm typical grassland, taking the vegetation index and vegetation height of the warm typical grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm typical grassland by a least square method;
acquiring a green leaf index, an improved chlorophyll absorption index and a relative greenness index of a ground sample point in the warm desertification grassland, taking a vegetation index and a vegetation height of the warm desertification grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm desertification grassland by a least square method;
acquiring a ratio vegetation index, a global vegetation humidity index, a gradient and red, green and blue wave band reflectivities of ground sample points in the alpine meadow, taking the vegetation index, the gradient, the red, green and blue wave band reflectivities and vegetation heights of the alpine meadow as known parameters, and calculating coefficients and intercept in a vegetation height model of the alpine meadow by a least square method;
the method comprises the steps of obtaining a green leaf index, a relative greenness index, a corrected ratio vegetation index and a wide dynamic vegetation index of ground sample points in the alpine grassland, taking the vegetation index and the vegetation height of the alpine grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the alpine grassland by a least square method.
Optionally, obtaining the vegetation index of the target grassland comprises:
acquiring remote sensing images and digital elevation data of the target grassland;
and acquiring the vegetation index and the gradient based on the remote sensing image and the digital elevation data.
Compared with the prior art, the application has the following advantages and technical effects:
the application establishes the grass height estimation model suitable for different grassland types to make up for the defect of estimating the vegetation height of the grassland by the existing remote sensing technology, and provides important reference and decision basis for the management and protection of the grassland ecological system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic flow chart of a remote sensing method according to an embodiment of the application;
fig. 2 is a schematic diagram of a vegetation height remote sensing result according to an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
As shown in fig. 1, the present application provides a remote sensing method for estimating a grassland vegetation height, comprising: acquiring a distribution range of grasslands, and sampling the grasslands in the distribution range; constructing a vegetation height estimation model of the grassland; acquiring coefficients and intercept of the vegetation height estimation model based on the sampling data; and acquiring a vegetation index and a gradient of the target grassland, inputting the vegetation index and the gradient into the vegetation height estimation model after determining the coefficient and the intercept, and acquiring the vegetation height of the target grassland.
The method specifically comprises the following steps:
s1, determining distribution ranges of different types of grasslands
Based on ground survey data or a current vegetation map, a distribution range of 5 grassland types including a warm meadow grassland, a warm typical grassland, a warm desertification grassland, a high cold meadow and a high cold meadow is obtained.
S2, constructing a grassland vegetation height estimation model
And respectively constructing a warm meadow, a warm typical meadow, a warm desertification meadow and a vegetation height estimation model of the high cold meadow by combining the spectral reflectivity, the vegetation index and the topography characteristics.
S2.1. Warm meadow plains
H＝a×GLI+b×NDVI+c×NDVI×GCC+intercept
Wherein H is the vegetation height of the warm meadow grassland, a, b and c are coefficients, the intercept is the intercept, the GLI is the green leaf index, the NDVI is the normalized vegetation index, the GCC is the relative greenness index, and the reflectivity of red, green, blue and near infrared bands is Red, green, blue, NIR respectively.
S2.2. temperature typical grassland
H＝a×NDVI+b×ARVI+c×NDVI×ARVI+d×ARVI 2 +e×ARVI×GNDVI
+intercept
Wherein H is the vegetation height of a warm typical grassland, a, b, c, d, e is a coefficient, intermittent is an intercept, NDVI is a normalized vegetation index, ARVI is an atmospheric resistance vegetation index, GNDVI is a green normalized vegetation index, and Red, green, blue, NIR is the band reflectivity of red, green, blue and near infrared respectively.
S2.3. warm desertification grassland
H＝a×GLI+b×MCARI2+c×GCC+intercept
Wherein H is the vegetation height of the warm desertification grassland, a, b and c are coefficients of variables, intermittent is intercept, GLI is green leaf index, MCARI2 is improved chlorophyll absorption index, GCC is relative greenness index, and Red, green, blue, NIR is the reflectivity of red, green, blue and near infrared bands respectively.
MCARI2 is an improved vegetation index which is insensitive to soil background and LAI and more sensitive to chlorophyll content changes, and can be used for extracting chlorophyll content of crop canopy, and the calculation formula is as follows:
s2.4. alpine meadow
H＝a×RVI×aspect+b×RVI×GVMI+c×aspect×Red+d×aspect×Blue
+e×aspect×Green+intercept
Wherein, H is the vegetation height of the alpine meadow, a, b, c, d, e is the coefficient of the variable, the interval is the intercept, aspect is the gradient, RVI is the ratio vegetation index, GVGI is the global vegetation humidity index, and Red, green, blue, NIR is the reflectivity of red, green, blue and near infrared bands respectively.
S2.5. high and cold grasslands
H＝a×GLI+b×GCC+c×MSR+d×WDRVI+intercept
Wherein H is the vegetation height of the alpine grassland, a, b, c, d is a coefficient, intermittent is intercept, GLI is green leaf index, GCC is relative greenness index, MSR is corrected ratio vegetation index, WDRVI is wide dynamic vegetation index, and Red, green, blue, NIR is the reflectivity of red, green, blue and near infrared bands respectively.
S3, obtaining ground sample points through field investigation
Of the 5 grassland-type areas, a typical section, which is spatially distributed more uniformly and can represent a large-scale area, is selected as a large plot. Sampling a warm meadow grassland, a warm typical grassland and a warm desertification grassland as far as possible by selecting a flat terrain area; for alpine meadows and alpine grasslands, sampling is performed at different terrains and slopes as much as possible, so that diversity and representativeness of samples are guaranteed. 3 small sample sides with the size of 1 multiplied by 1m are selected in a large sample area for measurement, the canopy height of 10 plants is measured in the 3 small sample sides by using a tape measure, and the average value is taken as the grassland height of the large sample area, so that the accuracy and precision of data are ensured, and meanwhile, the measurement time, longitude and latitude and other information of the sample area are recorded.
S4, determining model coefficients and intercept items
And acquiring remote sensing images and digital elevation data of longitude and latitude and sampling time of ground sample points of 5 grassland types, calculating vegetation indexes and gradients required by different types of grassland height estimation models, and further determining coefficients and intercept items of each model.
Obtaining coefficients and intercept of the vegetation height estimation model includes:
acquiring remote sensing images and digital elevation data of the sampling points based on the sampling time and longitude and latitude of the sampling points;
acquiring vegetation indexes and gradients required by a vegetation height estimation model based on the remote sensing images and the digital elevation data; calculating vegetation indexes of the sampling points based on different wave band reflectivities and vegetation index formulas in the remote sensing images; calculating the gradient of the sampling point based on the digital elevation data;
and acquiring coefficients and intercept of a vegetation height estimation model based on the vegetation height, the vegetation index and the gradient of the sampling points.
S4.1. Warm meadow prairie
Acquiring GLI, NDVI, GCC vegetation index of ground sample points in the warm meadow grassland, taking GLI, NDVI, GCC vegetation index and vegetation height of the warm meadow grassland as known parameters, a, b, c, intercept in the warm meadow vegetation height model was calculated by least squares.
S4.2. temperature typical grassland
Acquiring NDVI, ARVI, GNDVI vegetation indexes of ground sample points in the warm typical grassland, taking NDVI, ARVI, GNDVI vegetation indexes and vegetation heights of the warm typical grassland as known parameters, and calculating a, b, c, d, e, intercept in a vegetation height model of the warm typical grassland by a least square method.
S4.3. temperature desertification grassland
And acquiring GLI, MCARI2 and GCC vegetation indexes of ground sample points in the warm desertification grassland, taking the GLI, MCARI2 and GCC vegetation indexes and vegetation height of the warm desertification grassland as known parameters, and calculating a, b, c, intercept in a vegetation height model of the warm desertification grassland by a least square method.
S4.4. alpine meadow
RVI, GVGI vegetation index and gradient of ground sample points in the alpine meadow and reflectivity of red, green and blue wave bands are obtained, RVI, GVGI vegetation index, gradient, reflectivity of red, green and blue wave bands and vegetation height of the alpine meadow are used as known parameters, and a, b, c, d, e, intercept in a high-cold meadow vegetation height model is calculated through a least square method.
All models require the estimation of unknown coefficients and intercepts using vegetation height (H) and vegetation index as known parameters. The gradient and the red-green-blue band reflectivity are additionally used only in the alpine meadow model. All vegetation indexes are calculated based on the band reflectivity in the remote sensing image.
S4.5. high and cold grasslands
Acquiring GLI, GCC, MSR, WDRVI vegetation indexes of ground sample points in the alpine grassland, taking GLI, GCC, MSR, WDRVI vegetation indexes and vegetation heights of the alpine grassland as known parameters, and calculating a, b, c, d, intercept in a vegetation height model of the alpine grassland by a least square method.
S5, estimating the grassland vegetation height of the area scale
And acquiring remote sensing images and digital elevation data of the target grassland in the year to be estimated, calculating vegetation indexes and slopes of different types of grasslands based on the remote sensing images and the digital elevation data, and estimating vegetation heights of different types of grasslands under a large area scale by combining 5 models after determining coefficients and intercept items.
Method verification is performed in this embodiment
Take as an example the estimation of 5 types of grassland vegetation heights in Mongolia (39 DEG 23'-53 DEG 33' N,105 DEG 29'-124 DEG 20' E) and Tibet plateau (25 DEG 59 '26' -40 DEG 1'6' N,67 DEG 40 '37' -104 DEG 40 '43' E) areas within 2020. The warm meadow grassland, warm typical grassland and warm desertification grassland are mainly distributed in the inner Mongolian area and account for more than 80 percent of the total area of the whole area; the alpine meadow and the alpine grassland are distributed in the Qinghai-Tibet plateau area, accounting for about 51% of the total area of the Qinghai-Tibet plateau.
1) Determining a distribution range of a grass type
And determining warm meadow grassland, warm typical grassland, warm desertification grassland range and alpine meadow and alpine grassland distribution range of the Tibet plateau in the inner Mongolia region through a space-time variant map of the European large Liu Wenxing grassland type-three-level classification of the inner Mongolia region (2009) in China and a vegetation map of the Tibet plateau of 10 meters resolution (2020).
2) Construction of 5 grassland vegetation height estimation models
And respectively constructing a warm meadow, a warm typical meadow, a warm desertification meadow and a vegetation height estimation model of the high cold meadow by combining the spectral reflectivity, the vegetation index and the topography characteristics.
a) Warm meadow grassland
H＝a×GLI+b×NDVI+c×NDVI×GCC+intercept
b) Warm typical grassland
H＝a×NDVI+b×ARVI+c×NDVI×ARVI+d×ARVI 2 +e×ARVI×GNDVI
+intercept
c) Warm desertification grassland
H＝a×GLI+b×MCARI2+c×GCC+intercept
d) Alpine meadow
H＝a×RVI×aspect+b×RVI×GVMI+c×aspect×Red+d×aspect×Blue
+e×aspect×Green+intercept
e) High and cold grassland
H＝a×GLI+b×GCC+c×MSR+d×WDRVI+intercept
3) Obtaining ground sample points
In order to ensure the accuracy and precision of grassland height sample data, a typical section with flat topography and uniform spatial distribution is selected as a large sample area from a warm meadow grassland, a warm typical grassland and a warm desertification grassland in inner Mongolia; in alpine meadow and alpine grassland of Tibet plateau, a typical section with relatively stable altitude and relatively gentle morphology is selected as much as possible as a large sample area. 3 1X 1m small sample sides are selected in a large sample area for measurement, the canopy height of 10 plants is measured in each small sample side by using a tape measure, the average value is taken as the grassland height of the large sample area, and meanwhile, the measurement time, longitude and latitude and other information of the sample area need to be recorded. Finally, 15, 49, 11, 9 and 32 sample points are respectively measured in the warm meadow, the warm typical meadow, the warm desertification meadow, the alpine meadow and the alpine meadow.
4) Determining coefficients of a model
And obtaining MOD09GA earth surface reflectivity data and SRTM digital elevation data of all the ground sample points at corresponding sampling time and longitude and latitude. The MOD09GA data set has the time resolution of 1 day and the spatial resolution of 500 meters, and is subjected to atmosphere correction, radiation correction, geometric correction and cloud removal; the spatial resolution of the SRTM digital elevation data is 30m. Vegetation indexes of the actually measured samples of different types of grasslands are obtained through MOD09GA ground surface reflectivity data, and gradients corresponding to the actually measured samples of alpine meadows are obtained through SRTM digital elevation data.
a) Warm meadow grassland
Acquiring surface reflectivity data of 15 ground sample points of the warm meadow at corresponding sampling time, calculating GLI, NDVI, GCC vegetation indexes, taking GLI, NDVI, GCC vegetation indexes and vegetation heights of the 15 ground sample points as known parameters, and calculating a, b, c, intercept in a vegetation height model of the warm meadow by a least square method.
H＝53×GLI+129×NDVI-236×NDVI×GCC+0
b) Warm typical grassland
Acquiring surface reflectivity data of 49 ground sample points of the warm typical grassland at corresponding sampling time, calculating NDVI, ARVI, GNDVI vegetation indexes, taking NDVI, ARVI, GNDVI vegetation indexes and vegetation heights of the 49 ground sample points as known parameters, and calculating a, b, c, d, e, intercept in a vegetation height model of the warm typical grassland by a least square method.
H＝37×NDVI+65×ARVI-28×NDVI×ARVI-43×ARVI 2 +7×ARVI
×GNDVI-12
c) Warm desertification grassland
Acquiring surface reflectivity data of 11 ground sample points of the warm desertification grassland at corresponding sampling time, calculating GLI, MCARI2 and GCC vegetation indexes, taking the GLI, MCARI2 and GCC vegetation indexes of the 11 ground sample points and vegetation heights as known parameters, and calculating a, b, c, intercept in the vegetation height model of the warm desertification grassland by a least square method.
H＝3004×GLI-15×MCARI2-6040×GCC+2024
d) Alpine meadow
Gradient values and surface reflectivity data of 9 ground sample points of the alpine meadow at corresponding sampling time are obtained, RVI and GVGI vegetation indexes are calculated, RVI, GVGI vegetation indexes, gradient, red, green and blue wave band reflectivities and vegetation heights of the 9 ground sample points are used as known parameters, and a, b, c, d, e, intercept in the alpine meadow vegetation height model is calculated through a least square method.
H＝1×RVI×aspect+1117×RVI×GVMI-9×aspect×Red-6
×aspect×Blue+8×aspect×Green-97
e) High and cold grassland
The method comprises the steps of obtaining surface reflectivity data of 32 ground sample points of a alpine grassland at corresponding sampling time, calculating GLI, GCC, MSR, WDRVI vegetation indexes, taking GLI, GCC, MSR, WDRVI vegetation indexes and vegetation heights of the 32 ground sample points as known parameters, and calculating a, b, c, d, intercept in a vegetation height model of the alpine grassland through a least square method.
H＝1129×GLI-1941×GCC-176×MSR+463×WDRVI+963
5) Region scale calculation
And 5 kinds of SRTM digital elevation data of the grassland areas and MOD09GA ground surface reflectivity data of 7-8 months in 2020 are obtained, and gradient and vegetation indexes are calculated. Based on the determined coefficients and the 5 grassland type vegetation height estimation models after the intercept, calculating vegetation heights of the warm meadow, the warm typical meadow, the warm desertification meadow, the alpine meadow and the alpine meadow in 2020, wherein the calculation results are shown in figure 2.
In this embodiment, the vegetation heights of the warm meadow, warm classical meadow, warm desertification meadow, alpine meadow and alpine meadow of inner mongolia and Qinghai-Tibet plateau are estimated from MOD09GA ground surface reflectivity data and SRTM digital elevation data. It should be noted that the method of the present embodiment is equally applicable to estimating vegetation height of these 5 types of grasslands from other remote sensing data sets, such as the Landsat data set or the Sentinel-2 data set.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (9)
1. A remote sensing method for estimating the height of grassland vegetation, comprising:
acquiring a distribution range of grasslands, and sampling the grasslands in the distribution range;
constructing a vegetation height estimation model of the grassland;
acquiring coefficients and intercept of the vegetation height estimation model based on the sampling data;
and acquiring a vegetation index and a gradient of the target grassland, inputting the vegetation index and the gradient into the vegetation height estimation model after determining the coefficient and the intercept, and acquiring the vegetation height of the target grassland.
2. The remote sensing method for estimating a height of a grass vegetation according to claim 1, wherein the type of grass comprises: warm meadow, warm classical meadow, warm desertification meadow, alpine meadow and alpine meadow.
3. The remote sensing method for estimating a height of a grassland vegetation according to claim 1, wherein the vegetation height estimation model comprises: a vegetation height estimation model of a warm meadow, a vegetation height estimation model of a warm typical meadow, a vegetation height estimation model of a warm desertification meadow, a vegetation height estimation model of a alpine meadow and a vegetation height estimation model of a alpine meadow.
4. A remote sensing method of estimating a grassland vegetation height according to claim 3, wherein the vegetation height estimation model of the warm meadow grassland is:
H＝a×GLI+b×NDVI+c×NDVI×GCC+intercept
wherein H is the vegetation height of the warm meadow grassland, a, b and c are coefficients, the intercept is the intercept, the GLI is the green leaf index, the NDVI is the normalized vegetation index, and the GCC is the relative greenness index;
the vegetation height estimation model of the warm typical grassland is as follows:
H＝a×NDVI+b×ARVI+c×NDVI×ARVI+d×ARVI 2 +e×ARVI×GNDVI
+intercept
wherein, H is the vegetation height of a warm typical grassland, a, b, c, d, e is a coefficient, intermittent is intercept, NDVI is a normalized vegetation index, ARVI is an atmospheric resistance vegetation index, and GNDVI is a green normalized vegetation index;
the vegetation height estimation model of the warm desertification grassland comprises the following steps:
H＝a×GLI+b×MCARI2+c×GCC+intercept
wherein, H is the vegetation height of the warm desertification grassland, a, b and c are coefficients, the intercept is intercept, the GLI is a green leaf index, the MCARI2 is an improved chlorophyll absorption index, and the GCC is a relative greenness index;
the vegetation height estimation model of the alpine meadow is as follows:
H＝a×RVI×aspect+b×RVI×GVMI+c×aspect×Red+d×aspect×Blue+e×aspect×Green+intercept
wherein, H is the vegetation height of the alpine meadow, a, b, c, d, e is a coefficient, the interval is the intercept, aspect is the gradient, RVI is the ratio vegetation index, GVGI is the global vegetation humidity index, red, green, blue is the reflectivity of red, green and blue wave bands respectively;
the vegetation height estimation model of the alpine grassland is as follows:
H＝a×GLI+b×GCC+c×MSR+d×WDRVI+intercept
wherein, H is the vegetation height of the alpine grassland, a, b, c, d is the coefficient, the intercept is the intercept, the GLI is the green leaf index, the GCC is the relative greenness index, the MSR is the corrected ratio vegetation index, and the WDRVI is the wide dynamic vegetation index.
5. The method of claim 2, wherein sampling the grassland comprises:
selecting a first preset number of small sample sides in a selected large sample area for measurement, respectively collecting the canopy height of two preset numbers of vegetation of each small sample Fang Nadi, obtaining an average value of the canopy heights, and taking the average value as the vegetation height of a sampling point; and simultaneously recording the sampling time and longitude and latitude of the sampling point.
6. The method of claim 2, wherein sampling the grassland further comprises:
for warm meadow grassland, warm typical grassland and warm desertification grassland, sampling is carried out at a flat terrain area;
for alpine meadow and alpine grassland, sampling is selected at different terrains and slopes.
7. The remote sensing method of estimating a height of a grassland vegetation of claim 5 wherein obtaining coefficients and intercept of the vegetation height estimation model comprises:
acquiring remote sensing images and digital elevation data of the sampling points based on the sampling time and longitude and latitude of the sampling points;
acquiring a vegetation index and a gradient required by the vegetation height estimation model based on the remote sensing image and the digital elevation data;
and acquiring coefficients and intercept of the vegetation height estimation model based on the vegetation height, the vegetation index and the gradient of the sampling points.
8. The remote sensing method of estimating a height of a grassland vegetation of claim 7 wherein obtaining coefficients and intercept of the vegetation height estimation model based on the vegetation index comprises:
acquiring a green leaf index, a normalized vegetation index and a relative greenness index of a ground sample point in the warm meadow, taking the vegetation index and the vegetation height of the warm meadow as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm meadow by a least square method;
acquiring a normalized vegetation index, an atmospheric resistance vegetation index and a green normalized vegetation index of ground sample points in a warm typical grassland, taking the vegetation index and vegetation height of the warm typical grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm typical grassland by a least square method;
acquiring a green leaf index, an improved chlorophyll absorption index and a relative greenness index of a ground sample point in the warm desertification grassland, taking a vegetation index and a vegetation height of the warm desertification grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the warm desertification grassland by a least square method;
acquiring a ratio vegetation index, a global vegetation humidity index, a gradient and red, green and blue wave band reflectivities of ground sample points in the alpine meadow, taking the vegetation index, the gradient, the red, green and blue wave band reflectivities and vegetation heights of the alpine meadow as known parameters, and calculating coefficients and intercept in a vegetation height model of the alpine meadow by a least square method;
the method comprises the steps of obtaining a green leaf index, a relative greenness index, a corrected ratio vegetation index and a wide dynamic vegetation index of ground sample points in the alpine grassland, taking the vegetation index and the vegetation height of the alpine grassland as known parameters, and calculating coefficients and intercept in a vegetation height model of the alpine grassland by a least square method.
9. The method of claim 1, wherein obtaining a vegetation index of the target grassland comprises:
acquiring remote sensing images and digital elevation data of the target grassland;
and acquiring the vegetation index and the gradient based on the remote sensing image and the digital elevation data.
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