CN112396532A - Forest vegetation information data collecting and analyzing system for agricultural big data - Google Patents
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Abstract
The invention discloses a forest vegetation information data collecting and analyzing system for agricultural big data, which comprises the following steps: the method comprises the steps of utilizing forestry fixed sample plot data continuously checked by forest resources, calculating biomass of forest vegetation in various lands through biomass estimation models of organs of various tree groups, carrying out principal component transformation, tassel cap transformation and vegetation index calculation on remote sensing images to generate derivative data of the forest vegetation, unifying raster sample plot data, remote sensing data and derivative data thereof, raster terrain data and raster meteorological data to the same coordinate system and projection, and interpolating all data into grid data with the resolution of 30 m. According to the invention, correlation among biomass of young forests, middle-aged forests, near-aged forests and mature forests and corresponding derivative data, meteorological data and topographic data is analyzed, so that forest coverage rates of vegetation at different ages can be obtained, and forest vegetation information is statistically analyzed by utilizing big data, so that a good monitoring effect is achieved on the whole ecological environment.
Description
Technical Field
The invention relates to the technical field of forest vegetation information research, in particular to a forest vegetation information data collecting and analyzing system for agricultural big data.
Background
The agricultural big data is a data set which is generated after self characteristics such as agricultural regionality, seasonality, diversity, periodicity and the like are fused, has wide sources, various types, complex structure and potential value, and is difficult to process and analyze by a common method. The agricultural big data reserves the basic characteristics of huge scale, various types, low value density, high processing speed, high accuracy, high complexity and the like of the big data, and the information flow in agriculture is extended and deepened. The agricultural big data is composed of structured data and unstructured data, the unstructured data show a rapidly growing trend along with development and construction of agriculture and application of the Internet of things, and the quantity of the unstructured data greatly exceeds that of the structured data. The dynamic monitoring of vegetation biomass has great significance for the decision of regional sustainable development and the research of global change. The dynamic monitoring of vegetation biomass by using the remote sensing technology is not only the requirement for developing the biomass monitoring technology, but also the requirement for the application and development of the remote sensing technology.
The existing forest vegetation information data collecting and analyzing system for agricultural big data cannot rapidly calculate and statistically analyze the vegetation growth survival amount of different age groups, and the traditional sampling calculation method is not accurate enough.
Disclosure of Invention
The invention aims to provide a forest vegetation information data collecting and analyzing system for agricultural big data, and aims to solve the problem that the traditional sampling calculation method is not accurate enough because the existing forest vegetation information data collecting and analyzing system for agricultural big data in the background technology cannot quickly calculate and statistically analyze the growth survival amount of vegetation in different age groups.
In order to achieve the purpose, the invention provides the following technical scheme: a forest vegetation information data collecting and analyzing system for agricultural big data comprises the following steps:
s1: calculating the biomass of forest vegetation in various fields by utilizing forestry fixed sample plot data continuously checked by forest resources and through the biomass estimation models of organs of various tree groups;
s2: carrying out geometric correction on the remote sensing image by utilizing the topographic map, and carrying out principal component transformation, tassel cap transformation and vegetation index calculation on the remote sensing image to generate derived data of the remote sensing image;
s3: unifying the raster sample plot data, the remote sensing data and the derivative data thereof, the raster terrain data and the raster meteorological data to the same coordinate system and projection, and interpolating all the data into grid data with the resolution of 30 m;
s4: performing grid space superposition analysis by using the sample plot data, the remote sensing data and derivative data thereof, the terrain data and the meteorological data to obtain sample plot data, remote sensing data and derivative data thereof, the terrain data and the meteorological data of each sample plot;
s5: stratifying all data into sample data of several different age groups such as young forest, middle forest, near forest, mature forest and the like according to the age group to which the variety of dominant tree species belong;
s6: and respectively carrying out correlation analysis on the sample plot biomass of the young forest, the middle-aged forest, the near-aged forest and the mature forest and corresponding remote sensing data and derivative data, meteorological data and topographic data, and further obtaining the growth survival amount of the vegetation of different age layers.
Preferably, in step S1, a plot GIS database may be established according to the coordinates of the plots.
Preferably, a topographic map is used as a basic map, the remote sensing image is geometrically corrected by selecting obvious same-name ground objects on the map and the remote sensing image, the pixel brightness value is determined by adopting a nearest neighbor method, the TM6 wave band is resampled to the pixel size of 30m multiplied by 30m, and the registration of the remote sensing image and a sample plot GIS database is realized.
Preferably, the meteorological data comprises temperature data and rainfall data, the temperature data comprises annual average temperature data (TA) and accumulated temperature data (T0) of more than 0 ℃, and the rainfall data is annual average rainfall data (PA).
Preferably, the temperature and rainfall data are spatially interpolated using GIS in combination with DEM data and knowledge about geography and meteorology to obtain corresponding areal digital data, and data (IM) of the wettability index are generated on the basis of temperature and rainfall.
Preferably, the terrain data is mainly 1: and (3) extracting contour lines from the digital data of the topographic map by using 25 thousands of the digital data of the topographic map, and acquiring digital elevation data and slope data by using GIS software.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, correlation among biomass of young forests, middle-aged forests, near-aged forests and mature forests and corresponding derivative data, meteorological data and topographic data is analyzed, so that forest coverage rates of vegetation at different ages can be obtained, and forest vegetation information is statistically analyzed by utilizing big data, so that a good monitoring effect is achieved on the whole ecological environment.
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FIG. 1 is a flow chart of the steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, an embodiment of the present invention: a forest vegetation information data collecting and analyzing system for agricultural big data comprises the following steps:
s1: calculating the biomass of forest vegetation in various fields by utilizing forestry fixed sample plot data continuously checked by forest resources and through the biomass estimation models of organs of various tree groups;
s2: carrying out geometric correction on the remote sensing image by utilizing the topographic map, and carrying out principal component transformation, tassel cap transformation and vegetation index calculation on the remote sensing image to generate derived data of the remote sensing image;
s3: unifying the raster sample plot data, the remote sensing data and the derivative data thereof, the raster terrain data and the raster meteorological data to the same coordinate system and projection, and interpolating all the data into grid data with the resolution of 30 m;
s4: performing grid space superposition analysis by using the sample plot data, the remote sensing data and derivative data thereof, the terrain data and the meteorological data to obtain sample plot data, remote sensing data and derivative data thereof, the terrain data and the meteorological data of each sample plot;
s5: stratifying all data into sample data of several different age groups such as young forest, middle forest, near forest, mature forest and the like according to the age group to which the variety of dominant tree species belong;
s6: and respectively carrying out correlation analysis on the sample plot biomass of the young forest, the middle-aged forest, the near-aged forest and the mature forest and corresponding remote sensing data and derivative data, meteorological data and topographic data, and further obtaining the growth survival amount of the vegetation of different age layers.
Further, in step S1, a plot GIS database may be established according to the coordinates of the plots.
Further, by using a topographic map as a basic map, selecting obvious same-name ground objects on the map and the remote sensing image to carry out geometric correction on the remote sensing image, determining the pixel brightness value by adopting a nearest neighbor method, resampling the TM6 waveband to the pixel size of 30m multiplied by 30m, and realizing the registration of the remote sensing image and a sample plot GIS database.
Further, the meteorological data comprises temperature data and rainfall data, the temperature data comprises annual average temperature data (TA) and accumulated temperature data (T0) which is greater than 0 ℃, and the rainfall data is annual average rainfall data (PA).
Further, the GIS is utilized and DEM data and related geoscience and meteorology knowledge are combined to conduct spatial interpolation on the temperature and rainfall data, so that corresponding planar digital data are obtained, and data (IM) of the wettability index are generated on the basis of the temperature and the rainfall.
Further, the terrain data is mainly 1: and (3) extracting contour lines from the digital data of the topographic map by using 25 thousands of the digital data of the topographic map, and acquiring digital elevation data and slope data by using GIS software.
Deriving data for a series of vegetation indices, such as a difference vegetation index, a normalized vegetation index, a vertical vegetation index, a ratio vegetation index, a soil adjustment vegetation index, a deformed soil adjustment vegetation index, and a mid-infrared vegetation index, is generated from the LANDSAT TM data. The algorithm is as follows: difference vegetation index: DVI ═ TM4-a × TM 3; normalizing the difference vegetation index: NDVI ═ (TM4-TM3)/(TM4+ TM 3); vertical vegetation index: PVI ═ (TM4-a × TM3-B)/SQR (1+ a 2); ratio vegetation index: RVI ═ TM4/TM 3; soil adjustment ratio vegetation index: SARVI ═ TM4/(TM3+ B/a); adjusting vegetation index by using deformed soil: TSAVI × (TM4-a × TM3-B)/(TM3+ a × TM4-a × B); mid-infrared vegetation index: VI3 × (TM4-TM5)/(TM4+ TM 5). In the formula, TM3 and TM4 are red light band and near infrared band of LANDSAT TM, values of a and B are determined to be 0.96916 and 0.084726, respectively, according to relevant references, and finally, principal component transformation is performed on LANDSAT TM images, components of each principal component generated are PC1, PC2, PC3, PC4 and PC5, respectively, first, second, third, fourth and fifth principal components, all samples are classified into young forest, middle forest, near forest and mature forest according to the age group to which each sample belongs, and then correlation analysis between biomass and telemetric data is performed on sample data in each age group, respectively.
Through the correlation analysis of the biomass of the sample plots of young forest, middle forest, near forest and mature forest and the corresponding remote sensing data and derivative data thereof, meteorological data and topographic data, the biomass of the young forest and the brightness values of TM1 and TM6 wave bands of LANDSAT are obviously correlated at the level of 0.05, and the correlation coefficients are all-0.33. While at this level, none of the correlations are significant enough. The biomass of young forests is inversely related to the TM1 band, indicating that the higher the biomass, the stronger the uptake in this band, and the lower the TM1 value. The main reason is that the TM1 wave band is a blue light wave band, and when the vegetation is subjected to photosynthesis, 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 biomass of young forest leaves is a relatively large proportion of its total biomass. Thus, the higher the biomass of the young forest, the lower the value of its TM1 band. Through analyzing the correlation between the biomass of young forest, middle-aged forest, near-aged forest and mature forest and the corresponding derivative data, meteorological data and topographic data thereof, the forest coverage rate of vegetation of different age layers can be obtained, and the forest vegetation information is statistically analyzed by utilizing big data, so that the ecological environment monitoring system has a good monitoring effect on the whole ecological environment.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A forest vegetation information data collecting and analyzing system for agricultural big data is characterized by comprising the following steps:
s1: calculating the biomass of forest vegetation in various fields by utilizing forestry fixed sample plot data continuously checked by forest resources and through the biomass estimation models of organs of various tree groups;
s2: carrying out geometric correction on the remote sensing image by utilizing the topographic map, and carrying out principal component transformation, tassel cap transformation and vegetation index calculation on the remote sensing image to generate derived data of the remote sensing image;
s3: unifying the raster sample plot data, the remote sensing data and the derivative data thereof, the raster terrain data and the raster meteorological data to the same coordinate system and projection, and interpolating all the data into grid data with the resolution of 30 m;
s4: performing grid space superposition analysis by using the sample plot data, the remote sensing data and derivative data thereof, the terrain data and the meteorological data to obtain sample plot data, remote sensing data and derivative data thereof, the terrain data and the meteorological data of each sample plot;
s5: stratifying all data into sample data of several different age groups such as young forest, middle forest, near forest, mature forest and the like according to the age group to which the variety of dominant tree species belong;
s6: and respectively carrying out correlation analysis on the sample plot biomass of the young forest, the middle-aged forest, the near-aged forest and the mature forest and corresponding remote sensing data and derivative data, meteorological data and topographic data, and further obtaining the growth survival amount of the vegetation of different age layers.
2. The forest vegetation information data collecting and analyzing system for agricultural big data according to claim 1, wherein: in step S1, a sample plot GIS database may be established according to the coordinates of the sample plot.
3. The forest vegetation information data collecting and analyzing system for agricultural big data according to claim 1, wherein: the method comprises the steps of utilizing a topographic map as a basic map, selecting obvious same-name ground objects on the map and the remote sensing image to carry out geometric correction on the remote sensing image, determining the pixel brightness value by adopting a nearest neighbor method, resampling a TM6 waveband to the pixel size of 30m multiplied by 30m, and realizing registration of the remote sensing image and a sample plot GIS database.
4. The forest vegetation information data collecting and analyzing system for agricultural big data according to claim 1, wherein: the meteorological data comprises temperature data and rainfall data, the temperature data comprises annual average temperature data (TA) and accumulated temperature data (T0) which is higher than 0 ℃, and the rainfall data is annual average rainfall data (PA).
5. The forest vegetation information data collecting and analyzing system for agricultural big data according to claim 1, wherein: spatial interpolation is performed on the temperature and rainfall data by using the GIS in combination with DEM data and knowledge about geography and meteorology, so that corresponding planar digital data is obtained, and data (IM) of a wettability index is generated on the basis of the temperature and rainfall.
6. The forest vegetation information data collecting and analyzing system for agricultural big data according to claim 1, wherein: the terrain data is mainly 1: and (3) extracting contour lines from the digital data of the topographic map by using 25 thousands of the digital data of the topographic map, and acquiring digital elevation data and slope data by using GIS software.
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| KR20140041972A (en) * | 2012-09-25 | 2014-04-07 | 건국대학교 산학협력단 | System for generating plant functional type database map using land cover and forest characteristics information and method therefor |
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| KR20140041972A (en) * | 2012-09-25 | 2014-04-07 | 건국대학교 산학협력단 | System for generating plant functional type database map using land cover and forest characteristics information and method therefor |
Non-Patent Citations (2)
| Title |
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| 杨存建等: "不同龄组的热带森林植被生物量与遥感地学数据之间的相关性分析", 《植物生态学报》, vol. 28, no. 06, pages 862 - 867 * |
| 马泽清等: "基于TM遥感影像的湿地松林生物量研究", 《自然资源学报》, vol. 23, no. 03, pages 467 - 478 * |
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