A forest vegetation information data collection and analysis system for agricultural big data

Abstract

The present invention discloses a forest vegetation information data collection and analysis system for agricultural big data, comprising the following steps: using the forestry fixed sample data of the continuous forest resource inventory, calculating the biomass of the forest vegetation in each sample through the biomass estimation model of each organ of each tree species group, and performing principal component transformation, tassel cap transformation and vegetation index calculation on the remote sensing image to generate its derived data, unifying the grid sample data, remote sensing data and its derived data, grid terrain data and grid meteorological data under the same coordinate system and projection, and interpolating all the data into 30m resolution grid data. The present invention analyzes the correlation between the biomass of young forests, middle-aged forests, near-mature forests and mature and over-mature forests and their corresponding derived data, meteorological data and terrain data, so as to obtain the forest coverage rate of vegetation in different age layers, and uses big data to statistically analyze forest vegetation information, so as to have a good monitoring effect on the entire ecological environment.

Description

Technical Field

[0001] The present invention relates to the technical field of forest vegetation information research, and in particular to a forest vegetation information data collection and analysis system for agricultural big data. Background Art

[0002] Agricultural big data is a collection of data with wide sources, diverse types, complex structures, potential value, and difficult to process and analyze using common methods, which is generated by integrating agricultural regionality, seasonality, diversity, and periodicity. Agricultural big data retains the basic characteristics of big data, such as huge scale, diverse types, low value density, fast processing speed, high accuracy, and high complexity, and extends and deepens the information flow within agriculture. Agricultural big data consists of structured and unstructured data. With the development and construction of agriculture and the application of the Internet of Things, unstructured data is showing a rapid growth momentum, and its amount will greatly exceed that of structured data. The dynamic monitoring of vegetation biomass is of great significance for the decision-making of regional sustainable development and the study of global change. The dynamic monitoring of vegetation biomass using remote sensing technology is both a need for the development of biomass monitoring technology and a need for the development of remote sensing technology applications.

[0003] The current forest vegetation information data collection and analysis system for agricultural big data is unable to quickly estimate and statistically analyze the growth and survival of vegetation in different age groups, and the traditional sampling and estimation method is not accurate enough. Summary of the invention

[0004] The purpose of the present invention is to provide a forest vegetation information data collection and analysis system for agricultural big data, so as to solve the problems raised in the above background technology that the current forest vegetation information data collection and analysis system for agricultural big data cannot quickly estimate and statistically analyze the growth and survival of vegetation in different age groups, and the traditional sampling and estimation method is not accurate enough.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a forest vegetation information data collection and analysis system for agricultural big data, comprising the following steps:

[0006] S1: Using the forestry fixed sample data from the continuous forest resource inventory, the biomass of forest vegetation in each sample plot is calculated through the biomass estimation model of each organ of each tree species group;

[0007] S2: Use topographic maps to perform geometric correction on remote sensing images, and perform principal component transformation, tasseled cap transformation, and vegetation index calculation on remote sensing images to generate their derived data;

[0008] S3: Unify the grid sample data, remote sensing data and its derived data, grid terrain data and grid meteorological data into the same coordinate system and projection, and interpolate all data into 30m resolution grid data;

[0009] S4: Perform grid spatial overlay analysis using the sample plot data, remote sensing data and its derived data, terrain data and meteorological data, so as to obtain the sample plot data, remote sensing data and its derived data, terrain data and meteorological data of each sample plot;

[0010] S5: All data are stratified into sample data of several different age groups, including young forest, middle-aged forest, nearly mature forest and over-mature forest, according to the age group to which the dominant tree species in each plot belong;

[0011] S6: Correlation analysis is performed on the biomass of the young forest, middle-aged forest, near-mature forest and over-mature forest plots with their corresponding remote sensing data and derived data, meteorological data and topographic data, and the growth and survival of vegetation in different age layers is then obtained.

[0012] Preferably, in step S1, a sample plot GIS database may be established according to the coordinates of the sample plot.

[0013] Preferably, a topographic map is used as a base map, and the remote sensing image is geometrically corrected by selecting obvious ground object points with the same name on the map and the remote sensing image. The pixel brightness value is determined using the nearest neighbor method, the TM6 band is resampled to a pixel size of 30m×30m, and the remote sensing image is aligned with the sample site GIS database.

[0014] Preferably, the meteorological data includes temperature data and rainfall data, the temperature data includes annual average temperature data (TA) and accumulated temperature data (T0) greater than 0°C, and the rainfall data is annual average rainfall data (PA).

[0015] Preferably, GIS is used in combination with DEM data and relevant geological and meteorological knowledge to perform spatial interpolation on temperature and rainfall data, thereby obtaining corresponding surface digital data and generating humidity index data (IM) based on temperature and rainfall.

[0016] Preferably, the terrain data is mainly 1:250,000 topographic map digitized data, contour lines are extracted from the topographic map digital data, and digital elevation data and slope aspect data are obtained using GIS software.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention analyzes the correlation between the biomass of young forests, middle-aged forests, near-mature forests and over-mature forests and their corresponding derived data, meteorological data and terrain data, so as to obtain the forest coverage rate of vegetation in different age groups, and uses big data statistical analysis of forest vegetation information to have a good monitoring effect on the entire ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] See also Figure 1 , an embodiment provided by the present invention: a forest vegetation information data collection and analysis system for agricultural big data, comprising the following steps:

[0022] S1: Using the forestry fixed sample data from the continuous forest resource inventory, the biomass of forest vegetation in each sample plot is calculated through the biomass estimation model of each organ of each tree species group;

[0023] S2: Use topographic maps to perform geometric correction on remote sensing images, and perform principal component transformation, tasseled cap transformation, and vegetation index calculation on remote sensing images to generate their derived data;

[0024] S3: Unify the grid sample data, remote sensing data and its derived data, grid terrain data and grid meteorological data into the same coordinate system and projection, and interpolate all data into 30m resolution grid data;

[0025] S4: Perform grid spatial overlay analysis using the sample plot data, remote sensing data and its derived data, terrain data and meteorological data, so as to obtain the sample plot data, remote sensing data and its derived data, terrain data and meteorological data of each sample plot;

[0026] S5: All data are stratified into sample data of several different age groups, including young forest, middle-aged forest, nearly mature forest and over-mature forest, according to the age group to which the dominant tree species in each plot belong;

[0027] S6: Correlation analysis is performed on the biomass of the young forest, middle-aged forest, near-mature forest and over-mature forest plots with their corresponding remote sensing data and derived data, meteorological data and topographic data, and the growth and survival of vegetation in different age layers is then obtained.

[0028] Furthermore, in step S1, a sample plot GIS database may be established according to the coordinates of the sample plot.

[0029] Furthermore, using the topographic map as the base map, the remote sensing image was geometrically corrected by selecting obvious ground object points with the same name on the map and the remote sensing image, the pixel brightness value was determined using the nearest neighbor method, the TM6 band was resampled to a pixel size of 30m×30m, and the remote sensing image was aligned with the sample site GIS database.

[0030] Furthermore, the meteorological data includes temperature data and rainfall data, the temperature data includes annual average temperature data (TA) and accumulated temperature data greater than 0°C (T0), and the rainfall data is annual average rainfall data (PA).

[0031] Furthermore, GIS is used in combination with DEM data and relevant geological and meteorological knowledge to perform spatial interpolation on temperature and rainfall data, thereby obtaining corresponding surface digital data and generating humidity index data (IM) based on temperature and rainfall.

[0032] Furthermore, the terrain data is mainly 1:250,000 topographic map digital data, contour lines are extracted from the topographic map digital data, and digital elevation data and slope data are obtained using GIS software.

[0033] A series of vegetation index derivatives are generated from LANDSAT TM data, such as the difference vegetation index, normalized difference vegetation index, vertical vegetation index, ratio vegetation index, soil-adjusted ratio vegetation index, soil-adjusted vegetation index, deformed soil-adjusted vegetation index and mid-infrared vegetation index. The algorithm is as follows: difference vegetation index: DVI = TM4-A×TM3; normalized difference vegetation index: NDVI = (TM4-TM3) / (TM4+TM3); vertical vegetation index: PVI = (TM4-A×TM3-B) / SQR(1+A2); ratio vegetation index: RVI = TM4 / TM3; soil adjusted ratio vegetation index: SARVI = TM4 / (TM3+B / A); deformed soil adjusted vegetation index: TSAVI = A×(TM4-A×TM3-B) / (TM3+A×TM4-A×B); mid-infrared vegetation index: VI3 = 100×(TM4-TM5) / (TM4+TM5). Where TM3 and TM4 are the red band and near-infrared band of LANDSAT TM. According to relevant references, the values ​​of A and B are 0.96916 and 0.084726, respectively. Finally, the principal component transformation is performed on the LANDSAT TM image, and the components of the principal components generated are PC1, PC2, PC3, PC4 and PC5, which are the first, second, third, fourth and fifth principal components, respectively. According to the age group to which each sample belongs, all samples are divided into young forest, middle-aged forest, nearly mature forest and mature and over-mature forest. Then, the correlation analysis between biomass and remote sensing geoscience data is performed on the sample data in each age group.

[0034] Through the correlation analysis of the biomass of the plots of young forests, middle-aged forests, near-mature forests and over-mature forests and their corresponding remote sensing data and their derived data, meteorological data and terrain data, it was found that the biomass of young forests was significantly correlated with the brightness values ​​of the TM1 and TM6 bands of LANDSAT at the level of 0.05, and the correlation coefficients were all -0.33. However, the correlation with other factors at this level was not significant enough. There was a negative correlation between the biomass of young forests and the TM1 band, which means that the higher the biomass, the stronger the absorption of this band, and the lower the TM1 value. This is mainly because the TM1 band is a blue light band. When vegetation is photosynthesizing, the chlorophyll in the vegetation has a strong absorption effect on blue light, and the amount of chlorophyll is related to the amount of leaves. The proportion of the biomass of leaves in young forests to its total biomass is relatively large. Therefore, the higher the biomass of young forests, the lower the value of its TM1 band. By analyzing the correlation between the biomass of young forests, middle-aged forests, near-mature forests and over-mature forests and their corresponding derived data, meteorological data and terrain data, we can obtain the forest coverage rate of vegetation in different age groups. By using big data statistical analysis of forest vegetation information, we can have a good monitoring effect on the entire ecological environment.

[0035] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for collecting and analyzing forest vegetation information data for agricultural big data, comprising the following steps: S1: Using the forestry fixed sample data from the continuous forest resource inventory, the biomass of forest vegetation in each sample plot is calculated through the biomass estimation model of each organ of each tree species group; S2: Use topographic maps to perform geometric correction on remote sensing images, and perform principal component transformation, tasseled cap transformation, and vegetation index calculation on remote sensing images to generate their derived data; S3: Unify the grid sample data, remote sensing data and its derived data, grid terrain data and grid meteorological data into the same coordinate system and projection, and interpolate all data into 30m resolution grid data; S4: Perform grid spatial overlay analysis using the sample plot data, remote sensing data and its derived data, terrain data and meteorological data, so as to obtain the sample plot data, remote sensing data and its derived data, terrain data and meteorological data of each sample plot; S5: All data are stratified into sample data of young forest, middle-aged forest, nearly mature forest and over-mature forest according to the age group to which the dominant tree species in each plot belong; S6: Correlation analysis is performed on the biomass of the sample plots of young forests, middle-aged forests, nearly mature forests and over-mature forests and their corresponding remote sensing data and derived data, meteorological data and terrain data, and then the growth and survival of vegetation in different age layers are obtained; the characteristics are: Using the topographic map as the base map, the remote sensing image was geometrically corrected by selecting obvious identical ground points on the map and remote sensing image, the pixel brightness value was determined by the nearest neighbor method, the TM6 band was resampled to a pixel size of 30m×30m, and the remote sensing image was aligned with the sample site GIS database. The meteorological data includes temperature data and rainfall data. The temperature data includes annual average temperature data and accumulated temperature data greater than 0°C. The rainfall data is annual average rainfall data. By using GIS and combining DEM data with relevant geological and meteorological knowledge, the temperature and rainfall data are spatially interpolated to obtain the corresponding surface digital data, and the humidity index data is generated based on the temperature and rainfall. The terrain data are mainly 1:250,000 topographic map digitized data. Contour lines are extracted from the topographic map digital data, and digital elevation data and slope data are obtained using GIS software.

2. The method for collecting and analyzing forest vegetation information data for agricultural big data according to claim 1 is characterized by: In the step S1, a sample plot GIS database is established according to the coordinates of the sample plot.

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