CN110967300B - Hyperspectral remote sensing method for estimating species abundance of vascular plants - Google Patents
Hyperspectral remote sensing method for estimating species abundance of vascular plants Download PDFInfo
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Abstract
The invention discloses a hyperspectral remote sensing method for estimating species abundance of vascular plants, which comprises the following steps: acquiring remote sensing data, and extracting spectral data after radiation correction, geometric correction and terrain correction are carried out; correcting and checking the acquired spectral data to ensure that no data error exists; smoothing the acquired spectral data, and acquiring a mean value of intermediate data on the basis of 4 adjacent data; calculating a first-order spectral derivative; calculating the ratio of the first derivative of each wave band; and calculating the estimated value of the species diversity index. The method can quickly estimate the diversity of plant species, and can be widely applied to quick evaluation of the species abundance of the vascular plants in grasslands, shrubs, farmlands, nursery lands and the like; different from the large amount of manpower and material resources consumed by field sample investigation or the large amount of cost for purchasing remote sensing images, the method can quickly determine the diversity index of the plant species without large amount of manpower and material resource input, and has the advantages of high efficiency in information processing and investment cost saving.
Description
Technical Field
The invention relates to a method for estimating species abundance of a vascular plant community, in particular to a method for estimating species abundance of a vascular plant community based on hyperspectral remote sensing data.
Background
Remote sensing technology has been widely applied to the aspects of ecological environment monitoring, crop pest and disease damage and crop yield estimation, geological exploration and the like, and plays an increasingly important role. The spectral characteristics of the plants are the changes of absorption, transmission and reflection of light caused by physiological and ecological characteristics and composition structural characteristics, and the physiological and biochemical parameters of vegetation can be quantitatively inverted by utilizing remote sensing data, and mainly relate to vegetation coverage, biomass, leaf area indexes, moisture content, chlorophyll content, mineral nutrition content, cellulose, lignin, starch and protein content of leaves or canopies, photosynthetic physiological parameters and the like.
The grassland area of China is wide, which occupies more than 1/3 of the area of China, and about 50-60% of natural grasslands have degeneration phenomena of different degrees. In order to treat the ecological degeneration, various measures are required for ecological restoration. The vegetation ecological restoration is a long-term process, the vegetation communities have different characteristics in different ecological restoration stages, and the dominant plant species in the communities are important indexes for ecological restoration. The mineral nutrition status, the physiological ecological status and the morphological structure characteristics of the dominant plant species in different ecological restoration stages are different. The performance of the grassland in different ecological restoration stages is monitored in a large area, and for a traditional plant sample survey method, a large amount of manpower and material resources are needed, and the method usually takes several months to complete. The remote sensing can effectively monitor the mineral nutrition status, the physiological ecological status and the growth status of the plants, has diversity resolution and has advantages in large-area ecological restoration monitoring. However, there is no effective remote sensing monitoring method for monitoring the abundance of plant species, so it is urgently needed to develop a set of method for rapidly evaluating the abundance of plant species suitable for grassland, farmland and other objects.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a hyperspectral remote sensing method for estimating the richness of vascular plant species.
In order to solve the technical problems, the invention adopts the technical scheme that: a hyperspectral remote sensing method for estimating the richness of species of vascular plants comprises the following steps:
acquiring remote sensing data, and extracting vegetation reflectivity data between 760 and 920nm as spectral data after radiation correction, geometric correction and terrain correction;
II, correcting and checking the acquired spectral data to ensure that no data error exists; correcting and checking the acquired spectral data according to the type of the ground feature, extracting vegetation data only, and ensuring that the acquired remote sensing data are vegetation data and have no data errors;
III, smoothing the acquired spectral data, and acquiring a mean value of intermediate data on the basis of 4 adjacent data;
IV, calculating the first-order spectral derivative of the data processed in the step III according to a formula I;
wherein FDλ(j)Is the first derivative of the spectral reflectance at each band; rλ(j)Is the reflectivity of band j; rλ(j+1)Is the reflectivity of band j +1, Δ λ is the interval of wavelengths j to j + 1;
v, calculating the ratio of the first derivative of each wave band, wherein the calculation method is shown as a formula II;
wherein Pi represents the ratio of the first derivative of each waveband; FDmean (760-920) represents the first derivative of the average spectral reflectance at 760-920 nm;
VI, calculating the estimated value of the species diversity index model, as shown in a formula (III);
shannon fd760-920 ═ Σ Pi ln Pi, formula c
Wherein Shannon is Shannon wiener index; the Shannon Vera index of the spectral reflectivity first-order derivative at 760-920nm is a species multi-style index; lnPi denotes the natural logarithm of Pi.
Furthermore, the radiation correction method in the step I is shown as a formula (IV);
E0A, formula
Wherein E is the electromagnetic wave energy received by the sensor, E0 is the radiation energy of the ground object, and A is the attenuation coefficient of the atmosphere.
Further, the geometric correction method in the step I is to correct the geometric distortion of the remote sensing image by using a ground control point.
Further, in the step I, the terrain correction is realized by synchronously obtaining the ratio of the gray values of the pixels corresponding to any 2 wave bands in the same area or the ratio of the gray values of the pixels corresponding to the combination of a plurality of wave bands; the influence of shadows on the ratio image is eliminated, and the quantitative analysis and the identification classification precision of the remote sensing image are improved.
The method can quickly estimate the abundance of the plant species, and can be widely applied to the quick estimation of the abundance of the vascular plant species in grasslands, shrubs, farmlands, nursery lands and the like; the method is different from the large amount of manpower and material resources consumed by field sample survey or the large amount of cost for purchasing remote sensing images, can quickly determine the diversity index of the plant species, does not need large amount of manpower and material resource input, and has the advantages of high efficiency in information processing and investment cost saving.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
A hyperspectral remote sensing method for estimating the richness of species of vascular plants comprises the following steps:
i, acquiring Landsat remote sensing image data, performing radiation correction, geometric correction and terrain correction, removing cloud layers, cutting vegetation areas, and extracting vegetation reflectivity data between 760 and 920nm to be spectral data;
the radiation correction method is shown as a formula (IV):
E0A, formula
E is the electromagnetic wave energy received by the sensor, E0 is the radiation energy of the ground object, and A is the attenuation coefficient of the atmosphere.
The geometric correction method is to correct the geometric distortion of the remote sensing image caused by other factors by using a ground control point GCP (a point which is easy to identify and can be accurately positioned on the remote sensing image).
The terrain correction is the ratio of gray values of corresponding pixels of any 2 wave bands of the same area obtained synchronously or the ratio of gray values of corresponding pixels combined by a plurality of wave bands; the influence of shadows on the ratio image is eliminated, and the quantitative analysis and the identification classification precision of the remote sensing image are improved.
II, correcting and checking the acquired spectral data to ensure that no data error exists; correcting and checking the acquired spectral data according to the type of the ground feature, extracting vegetation data only, and ensuring that the acquired remote sensing data are vegetation data and have no data errors;
III, smoothing the acquired spectral data, and acquiring a mean value of intermediate data on the basis of 4 adjacent data;
IV, calculating the first-order spectral derivative of the data processed in the step III according to a formula I;
wherein FDλ(j)Is the first derivative of the spectral reflectance at each band; rλ(j)Is the reflectivity of band j; rλ(j+1)Is the reflectivity of band j +1, Δ λ is the interval of wavelengths j to j + 1;
v, calculating the ratio of the first derivative of each wave band, wherein the calculation method is shown as a formula II;
wherein Pi represents the ratio of the first derivative of each waveband; FDmean (760-920) represents the first derivative of the average spectral reflectance at 760-920 nm;
VI, calculating an estimated value Shannon FD760-920 of the species diversity index model, as shown in a formula (c);
shannon fd760-920 ═ Σ Pi ln Pi, formula c
Wherein Shannon is Shannon wiener index; the Shannon Vera index of the spectral reflectivity first-order derivative at 760-920nm is a species multi-style index; lnPi denotes the natural logarithm of Pi.
Compared with the prior art, the invention has the advantages that:
based on the application, repeated tests and improvement of the existing vegetation parameter model, a new method capable of inverting the diversity index of the plant species is developed according to the generation and decomposition principles of remote sensing spectral information of different plant species and the biological characteristics of spectral reflectances of different wave bands; the diversity of plant species can be rapidly estimated, and the method can be widely applied to rapid evaluation of the species abundance of the vascular plants in grasslands, shrubs, farmlands, nursery lands and the like;
the method is different from the large amount of manpower and material resources consumed by field sample survey or the large amount of cost for purchasing remote sensing images, can quickly determine the diversity index of the plant species, does not need large amount of manpower and material resource input, and has the advantages of high efficiency in information processing and investment cost saving.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (4)
1. A hyperspectral remote sensing method for estimating species abundance of vascular plants is characterized by comprising the following steps: the method comprises the following steps:
acquiring remote sensing data, and extracting vegetation reflectivity data between 760 and 920nm as spectral data after radiation correction, geometric correction and terrain correction;
II, correcting and checking the acquired spectral data to ensure that no data error exists; correcting and checking the acquired spectral data according to the type of the ground feature, extracting vegetation data only, and ensuring that the acquired remote sensing data are vegetation data and have no data errors;
III, smoothing the acquired spectral data, and acquiring a mean value of intermediate data on the basis of 4 adjacent data;
IV, calculating the first-order spectral derivative of the data processed in the step III according to a formula I;
wherein FDλ(j)Is the first derivative of the spectral reflectance at each band; rλ(j)Is the reflectivity of band j; rλ(j+1)Is the reflectivity of band j +1, Δ λ is the interval of wavelengths j to j + 1;
v, calculating the ratio of the first derivative of each wave band, wherein the calculation method is shown as a formula II;
wherein Pi represents the ratio of the first derivative of each waveband; FDmean (760-920) represents the first derivative of the average spectral reflectance at 760-920 nm;
VI, calculating the estimated value of the species diversity index, as shown in a formula (III);
shannon fd760-920 ═ Σ PilnPi, equation c
Wherein Shannon is Shannon wiener index; the Shannon Vera index of the spectral reflectivity first-order derivative at 760-920nm is the species diversity index; lnPi denotes the natural logarithm of Pi.
2. The hyperspectral remote sensing method for estimating richness of species of vascular plants according to claim 1, wherein: the radiation correction method in the step I is shown as a formula (IV);
E0A, formula
Wherein E is the electromagnetic wave energy received by the sensor, E0 is the radiation energy of the ground object, and A is the attenuation coefficient of the atmosphere.
3. The hyperspectral remote sensing method for estimating richness of species of vascular plants according to claim 1, wherein: and the geometric correction method in the step I is to correct the geometric distortion of the remote sensing image by using a ground control point.
4. The hyperspectral remote sensing method for estimating richness of species of vascular plants according to claim 1, wherein: the terrain correction in the step I is the ratio of gray values of corresponding pixels of any 2 wave bands of the same area obtained synchronously or the ratio of gray values of corresponding pixels combined by a plurality of wave bands; the influence of shadows on the ratio image is eliminated, and the quantitative analysis and the identification classification precision of the remote sensing image are improved.
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| CN111507223B (en) * | 2020-04-09 | 2023-07-07 | 中央民族大学 | Estimation method and system for beta diversity of plant species |
| CN112683822B (en) * | 2020-12-17 | 2024-04-05 | 淮阴师范学院 | Vegetation and non-vegetation identification method based on visible light wave band |
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