CN110991333A - Aboveground biomass early warning method - Google Patents
Aboveground biomass early warning method Download PDFInfo
- Publication number
- CN110991333A CN110991333A CN201911208830.2A CN201911208830A CN110991333A CN 110991333 A CN110991333 A CN 110991333A CN 201911208830 A CN201911208830 A CN 201911208830A CN 110991333 A CN110991333 A CN 110991333A
- Authority
- CN
- China
- Prior art keywords
- biomass
- aboveground biomass
- aboveground
- value
- year
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000002028 Biomass Substances 0.000 title claims abstract description 159
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000012417 linear regression Methods 0.000 claims description 5
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 230000012447 hatching Effects 0.000 claims description 2
- 238000010219 correlation analysis Methods 0.000 abstract description 4
- 238000001556 precipitation Methods 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 5
- 238000000611 regression analysis Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003746 surface roughness Effects 0.000 description 2
- 241000218631 Coniferophyta Species 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 241001464837 Viridiplantae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 230000029553 photosynthesis Effects 0.000 description 1
- 238000010672 photosynthesis Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Agronomy & Crop Science (AREA)
- Multimedia (AREA)
- Life Sciences & Earth Sciences (AREA)
- Quality & Reliability (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Image Processing (AREA)
Abstract
The application discloses an aboveground biomass early warning method. The method specifically comprises the following steps: acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain aboveground biomass; projecting the obtained overground biomass values to obtain overground biomass number grid maps of different years; calculating the aboveground biomass average value from the initial year to the target year according to the aboveground biomass grid map, generating an annual aboveground biomass average value grid map, and calculating an aboveground biomass standard deviation according to the aboveground biomass average value; calculating the difference value of the biomass mean values of the target period year and the annual land to obtain a difference value grid map; and outputting the above-ground biomass difference value grid map. The method disclosed by the application can directly reveal the early warning and normal condition of the aboveground biomass in a specific area, and further accurately identify the area needing ecological restoration; meanwhile, based on a geographic information system, the weather data is seamlessly linked with other weather index data such as precipitation, accumulated temperature and the like during correlation analysis.
Description
Technical Field
The invention relates to the field of monitoring and evaluating land resources and ecological environments, in particular to an aboveground biomass early warning method.
Background
Aboveground biomass (AGB) is the weight of a plant (overground part) cut at a specific time and in a unit square area, and is substantially the amount of organic substances accumulated by photosynthesis of a green plant per unit area. The vegetation is the basis of organic matter storage, material circulation and energy flow, and the biomass is the direct representation of the productivity and growth condition of the vegetation and has a very important position in the ecosystem, so the vegetation biomass is also taken as an important index for measuring the regional ecosystem.
Commonly used methods for biomass estimation are mainly classified into field measurement and remote sensing estimation. The field measurement method is suitable for biomass measurement in small areas with single vegetation type and relatively consistent growth vigor, and has the disadvantages of time and labor consumption and difficulty in obtaining biomass in artificial inaccessible areas such as wetlands, swamps and the like. The method is particularly suitable for quick estimation of biomass in a large area based on more applications of the remote sensing estimation algorithm, and in recent years, along with the rise of machine learning algorithms such as random forests and the like, the inversion accuracy of the remote sensing estimation algorithm is also improved.
In the prior art, people only can know the current data of the aboveground biomass, namely the current data of the aboveground biomass of a region is generated by using remote sensing image data through GIS or ENVI wave band operation and ground investigation processing. The students use the combination of the aboveground biomass and other ecological factors to evaluate the comprehensive condition of the ecological environment, and the students also classify the aboveground biomass and then perform the operation of classification numerical values.
However, the current data of aboveground biomass only reveal the spatial distribution value of aboveground biomass at a certain period, and cannot reveal the relative high or low aboveground biomass, that is, a model for evaluating the state of aboveground biomass is not formed. Many other basic ecological data are needed for evaluating the comprehensive condition of the ecological environment, and the data are difficult to obtain; the above-ground biomass is classified and then the classification numerical value is calculated, only the change between the above-ground biomass at two stages is revealed, but the change of the above-ground biomass per se on a long-time sequence is not considered to be a normal phenomenon, and a standard value is used for comparing the above-ground biomass with the target above-ground biomass.
The above problems exist in the prior art.
Disclosure of Invention
The application provides an aboveground biomass early warning method, which aims to solve the problem that aboveground biomass cannot change within a long time in the prior art.
In order to solve the technical problems, one technical scheme adopted by the application is an aboveground biomass early warning method, remote sensing image data of a target area are obtained, the remote sensing image data are preprocessed, and aboveground biomass is obtained through calculation; projecting the obtained overground biomass values to obtain overground biomass number grid maps of different years; calculating the aboveground biomass average value from the initial year to the target year according to the aboveground biomass grid map, generating an annual aboveground biomass average value grid map, and calculating an aboveground biomass standard deviation according to the aboveground biomass average value; calculating the difference value of the biomass mean values of the target period year and the annual land to obtain a difference value grid map; and outputting the above-ground biomass difference value grid map.
Further, the technical scheme also comprises: the method comprises the following steps of obtaining remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain aboveground biomass, wherein the method specifically comprises the following steps: preprocessing a remote sensing image; calculating an image NDVI value; determining a square point, and acquiring an NDVI value of a sample image; combining the ground biomass data actually measured in the target area to obtain a vegetation index; carrying out saturation analysis and segmentation point determination on the NDVI value; and (5) performing operation treatment to obtain aboveground biomass.
Further, the technical scheme also comprises: the remote sensing image preprocessing comprises the following steps: one or more of reprojection, image mosaicing, cropping, radiometric calibration, atmospheric correction, NDVI calculation, maximum synthesis, and data format conversion.
Further, the technical scheme also comprises: calculating aboveground biomass standard deviation according to the aboveground biomass average value; calculating the difference value of the biomass mean value on the target period year and the annual land, and obtaining a difference value grid map comprises the following steps: firstly, calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
Further, the technical scheme also comprises: the method for calculating the annual overground biomass mean value comprises the following steps: the ratio of the sum of the data in the biomass raster map over each period year to the total year is calculated.
Further, the technical scheme also comprises: the mean grid map varies with the year.
Further, the technical scheme also comprises: the difference value is smaller than a preset threshold value and is recorded as early warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.25.
Further, the technical scheme also comprises: the pre-warning and/or the normal are marked with different colors or transparencies or hatching.
Further, the technical scheme also comprises: the above-ground biomass information is acquired through MOD13Q products, and the specific setting data are as follows: the spatial resolution is 1000m/500m/250m, and the time resolution is 8 days; and selecting an NDVI data set, performing projection conversion and vectorization treatment, and projecting the data set to a coordinate system to obtain an aboveground biomass grid map.
Further, the technical scheme also comprises: the above-ground biomass obtained by the arithmetic processing comprises the following steps: analyzing the spatial distribution characteristics of the NDVI values of the China in N years by adopting a unitary linear regression analysis equation, wherein the unitary linear regression analysis equation is as follows:
where K is the slope and NDVIi represents the year i NDVI value.
Has the advantages that: different from the prior art, the beneficial effects of this application are: the method disclosed by the application can directly reveal the early warning and normal condition of the aboveground biomass in a specific area, and further accurately identify the area needing ecological restoration; meanwhile, based on a geographic information system, the weather data is seamlessly linked with other weather index data such as precipitation, accumulated temperature and the like during correlation analysis.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of aboveground biomass forewarning;
fig. 2 is a proportion of the early warning amount to the total region to be measured.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the above-ground biomass condition evaluation, in the prior art, people can only know the current data of the above-ground biomass, namely, the current data of the above-ground biomass of an area is generated by combining GIS or ENVI waveband calculation and ground investigation processing by utilizing remote sensing image data. The students use the combination of the aboveground biomass and other ecological factors to evaluate the comprehensive condition of the ecological environment, and the students also classify the aboveground biomass and then perform the operation of classification numerical values.
However, the current data of aboveground biomass only reveal the spatial distribution value of aboveground biomass at a certain period, and cannot reveal the relative high or low aboveground biomass, that is, a model for evaluating the state of aboveground biomass is not formed. Many other basic ecological data are needed for evaluating the comprehensive condition of the ecological environment, and the data are difficult to obtain; the above-ground biomass is classified and then the classification numerical value is calculated, only the change between the above-ground biomass at two stages is revealed, but the change of the above-ground biomass per se on a long-time sequence is not considered to be a normal phenomenon, and a standard value is used for comparing the above-ground biomass with the target above-ground biomass.
In order to solve the limitation that only the current aboveground biomass condition can be obtained in the prior art, the aboveground biomass early warning method is provided, and specifically, refer to fig. 1, wherein fig. 1 is a flow chart of the aboveground biomass early warning method provided by the application.
As shown in fig. 1, the aboveground biomass early warning method of the embodiment includes the following steps:
step S100: acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain aboveground biomass;
in this embodiment, the method specifically includes: preprocessing a remote sensing image; calculating an image NDVI value; determining a square point, and acquiring an NDVI value of a sample image; combining the ground biomass data actually measured in the target area to obtain a vegetation index; carrying out saturation analysis and segmentation point determination on the NDVI value; and (5) performing operation treatment to obtain aboveground biomass. The arithmetic processing includes: and (4) performing regression analysis, and establishing a regression analysis model.
In the embodiment, the ground can include different geographic environments such as forests, grasslands and the like; obtaining the above-ground biomass initial data by an above-ground biomass quantitative inversion method;
in this embodiment, the aboveground biomass remote sensing image data is acquired through a MOD13Q product, and the specific setting data is as follows: the spatial resolution is 1000m/500m/250m, and the time resolution is 8 days; and selecting an NDVI data set, performing projection conversion and vectorization treatment, and projecting the data set to a coordinate system to obtain an aboveground biomass grid map.
The remote sensing image data preprocessing includes preprocessing such as reprojection, image mosaic, clipping, radiometric calibration, atmospheric correction, NDVI calculation, maximum synthesis, and data format conversion, which one or combination of these is not limited in this embodiment.
In this example, the above-ground biomass was obtained by arithmetic processing. The arithmetic processing includes: and (4) performing regression analysis, and establishing a regression analysis model. For the regression analysis method: there is a certain correlation between the corresponding time series between each grid change within the year of the vegetation index. The NDVI value in the grid tends to decrease or increase along with the time, and in order to reflect the change of the trend, a unary linear regression analysis equation is adopted to analyze the spatial distribution characteristics of the NDVI value in the China in N years.
In the formula, K is a slope, NDVIi represents an i-th year NDVI value, the method can be used for calculating the spatial variation trend of vegetation coverage in a time series, and K >0 represents that vegetation shows an improvement trend, otherwise, vegetation shows a degradation trend. The method is used for calculating the NDVI spatial distribution trend chart of 3 land types in China, wherein K is less than or equal to-0.001 and is a degradation trend, K is more than or equal to 0.001 and is an improvement trend, and the other areas are basically unchanged.
The remote sensing comprises laser radar or large-spot laser radar or optical remote sensing in the prior art; interpolating the obtained aboveground biomass data into raster data and outputting an aboveground biomass raster image; acquiring aboveground biomass parameters obtained by measuring remote sensing images;
in recent years, the research of forest biomass inversion is as follows: aboveground biomass information of northern conifers in wisconsin, usa was inverted by means of vegetation indices such as NDVI extracted from ETM + (Landsat 7) images. 2) And estimating the vegetation biomass of tropical forest in the West-West double Banna by using a TM (Landsat5) image original wave band method. 3) By means of a small spot (diameter: 0.1-2m) and the height of the point cloud and the density of the canopy extracted from the LiDAR data, the aboveground biomass of the temperate forest is inverted. 4) The measurement of the surface roughness was measured by measuring the surface roughness of a large spot (diameter:
52-90m) LiDAR data, extracting characteristic variables related to tree height information, and inverting the biomass of tropical rainforests. Or respectively extracting vegetation characteristic parameters by utilizing a hyperspectral remote sensing technology, an unmanned aerial vehicle photogrammetry technology and a ground photogrammetry technology, respectively establishing inversion models between the vegetation characteristic parameters and the biomass by combining ground sample plot actual measurement data, and carrying out precision analysis on the established models to determine an optimal biomass inversion model. The aboveground biomass parameters are obtained by methods not limited to those listed above.
Step S200: projecting the obtained overground biomass values to obtain overground biomass number grid maps of different years; calculating the aboveground biomass average value from the initial year to the target year according to the aboveground biomass grid map, generating an annual aboveground biomass average value grid map, and calculating an aboveground biomass standard deviation according to the aboveground biomass average value;
in this example, the objective of the present application is to ultimately obtain changes in a continuous time series, i.e., over successive years, rather than merely obtaining aboveground biomass data for a particular year, which is where the present application differs from the prior art.
S201: calculating the aboveground biomass average value from the initial year to the target year according to the aboveground biomass grid map, generating an annual aboveground biomass average value grid map,
the grid Calculator (rater Calculator tool in ArcGIS) is used to calculate the year data in the grid, that is, the ratio of the sum of the data in the biomass grid map to the total year on each period year is calculated, such as: the mean of the annual vegetation index raster data of the year 2000 to the present is calculated to generate an annual vegetation index mean raster map, and the process is that a raster calculator page is opened, and the average value of the raster is obtained by selecting (2000 aboveground biomass ' + ' 2001 aboveground biomass ' + ' 2002 aboveground biomass ' + ' 2003 aboveground biomass ' …)/total number of years.
And obtaining a mean grid image according to the mean value projection. The mean is automatically updated as the year is updated.
For example, taking the aboveground biomass data after 2000 years as an example, a rater Calculator (grid Calculator) in ArcGIS is used, the data pixel data in the grid is the aboveground biomass data corrected in each year, the current year is 2019, and the average value of the aboveground biomass data in the grid data from 2000 to 2018 is calculated. And, the data is automatically updated as the year is updated. If the current year is updated to 2020, the grid data is calculated for 2000 to 2019, and the mean and standard deviation of aboveground biomass are obtained. The mean and standard deviation data will be updated as the year progresses.
S202: calculating the standard deviation of the aboveground biomass according to the aboveground biomass average value, and generating a grid graph of the standard deviation:
the standard deviation of the aboveground biomass can reflect the discrete degree of the aboveground biomass, and provides a standard for the subsequent calculation of the difference of the aboveground biomass.
Step S300: calculating the difference value of the biomass mean values of the target period year and the annual land to obtain a difference value grid map; and outputting the above-ground biomass difference value grid map.
In this embodiment, the following are specifically mentioned: calculating the vegetation index standard deviation according to the vegetation index average value, and generating a standard deviation grid diagram comprises the following steps:
using a Raster Calculator tool in ArcGIS, firstly calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
And (3) comparing the difference value of the overground biomass of the target period year in the raster data with a preset threshold value by using a reclassification tool (reclassification) in ArcGIS, and marking differently according to the comparison result.
The difference value in the difference value grid graph is smaller than a preset threshold value and is recorded as early warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.25.
Comparing the difference value of the aboveground biomass of the target period year in the raster data with preset values, marking the difference values according to the comparison result, directly revealing the early warning or normal condition of the aboveground biomass of the region through a difference value raster graph, and further accurately identifying the region needing ecological restoration; based on a geographic information system, the weather data is seamlessly linked with other weather index data such as vegetation indexes, accumulated temperature and the like when correlation analysis is carried out.
And marking the marks on the difference grid graph according to different expressions, such as different colors, shades and the like.
As shown in fig. 2, the ratio of the total amount of the warning to the total area to be measured is displayed.
The aboveground biomass early warning map can directly reveal the early warning and normal conditions of aboveground biomass in a specific area, and further accurately identify the area needing ecological restoration; meanwhile, based on a geographic information system, the weather data is seamlessly linked with other weather index data such as precipitation, accumulated temperature and the like during correlation analysis.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. An aboveground biomass early warning method is characterized by comprising the following steps:
acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain aboveground biomass;
projecting the obtained overground biomass values to obtain overground biomass number grid maps of different years; calculating the aboveground biomass average value from the initial year to the target year according to the aboveground biomass grid map, generating an annual aboveground biomass average value grid map, and calculating an aboveground biomass standard deviation according to the aboveground biomass average value;
calculating the difference value of the biomass mean values of the target period year and the annual land to obtain a difference value grid map; and outputting the above-ground biomass difference value grid map.
2. The method for early warning of aboveground biomass according to claim 1, wherein remote sensing image data of a target area are obtained, the remote sensing image data are preprocessed, and aboveground biomass is obtained through calculation, and the method specifically comprises the following steps: preprocessing a remote sensing image; calculating an image NDVI value; determining a square point, and acquiring an NDVI value of a sample image; combining the ground biomass data actually measured in the target area to obtain a vegetation index; carrying out saturation analysis and segmentation point determination on the NDVI value; and (5) performing operation treatment to obtain aboveground biomass.
3. The method for early warning of aboveground biomass according to claim 2, wherein the preprocessing of the remote sensing image comprises: one or more of reprojection, image mosaicing, cropping, radiometric calibration, atmospheric correction, NDVI calculation, maximum synthesis, and data format conversion.
4. The aboveground biomass early warning method according to claim 1, wherein aboveground biomass standard deviation is calculated according to the aboveground biomass average value; calculating the difference value of the biomass mean value on the target period year and the annual land, and obtaining a difference value grid map comprises the following steps: firstly, calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
5. The aboveground biomass early warning method according to claim 4, wherein the annual aboveground biomass mean value calculation method comprises: the ratio of the sum of the data in the biomass raster map over each period year to the total year is calculated.
6. The method of claim 5, wherein the mean grid pattern changes with the change of year.
7. The aboveground biomass early warning method according to claim 4, wherein the difference is smaller than a preset threshold and is recorded as early warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.25.
8. The method of claim 7, wherein the pre-alarm and/or the normal is marked with different colors or transparencies or hatching.
9. The aboveground biomass early warning method according to claim 1, wherein aboveground biomass information is obtained through MOD13Q products, and specific setting data are as follows: the spatial resolution is 1000m/500m/250m, and the time resolution is 8 days; and selecting an NDVI data set, performing projection conversion and vectorization treatment, and projecting the data set to a coordinate system to obtain an aboveground biomass grid map.
10. The aboveground biomass early warning method according to claim 2, wherein the above-ground biomass obtaining by the arithmetic processing comprises: analyzing the spatial distribution characteristics of the NDVI values of the China in N years by adopting a unitary linear regression analysis equation, wherein the unitary linear regression analysis equation is as follows:
where K is the slope and NDVIi represents the year i NDVI value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911208830.2A CN110991333A (en) | 2019-11-30 | 2019-11-30 | Aboveground biomass early warning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911208830.2A CN110991333A (en) | 2019-11-30 | 2019-11-30 | Aboveground biomass early warning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110991333A true CN110991333A (en) | 2020-04-10 |
Family
ID=70088840
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911208830.2A Pending CN110991333A (en) | 2019-11-30 | 2019-11-30 | Aboveground biomass early warning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110991333A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786027A (en) * | 2023-11-27 | 2024-03-29 | 黑龙江省网络空间研究中心(黑龙江省信息安全测评中心、黑龙江省国防科学技术研究院) | Method for analyzing time-space change of overground biomass in long time sequence |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102915616A (en) * | 2012-05-11 | 2013-02-06 | 新疆大学 | Grid-accumulation-type regional land desertification early-warning method |
| CN103605901A (en) * | 2013-11-29 | 2014-02-26 | 民政部国家减灾中心 | Regional drought risk assessment method |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
| CN107437262A (en) * | 2017-07-19 | 2017-12-05 | 中国科学院遥感与数字地球研究所 | Crops planting area method for early warning and system |
| CN108875146A (en) * | 2018-05-28 | 2018-11-23 | 福州大学 | A kind of ground mulching consecutive variations detection method considering change procedure |
| CN109615215A (en) * | 2018-12-06 | 2019-04-12 | 西安理工大学 | A kind of characteristic analysis method that regional vegetation restores |
| CN109636171A (en) * | 2018-12-06 | 2019-04-16 | 西安理工大学 | A kind of comprehensive diagnos and risk evaluating method that regional vegetation restores |
| CN109766815A (en) * | 2019-01-03 | 2019-05-17 | 银河航天（北京）科技有限公司 | A kind of pair of object event carries out pre-warning system and method |
| CN110210990A (en) * | 2019-05-30 | 2019-09-06 | 河南省农业科学院农业经济与信息研究所 | Winter wheat Monitoring of freezing injury method based on high score satellite remote sensing date |
| CN110296942A (en) * | 2019-05-21 | 2019-10-01 | 华中农业大学 | A kind of dynamic assessment method of vegetative coverage object element |
-
2019
- 2019-11-30 CN CN201911208830.2A patent/CN110991333A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102915616A (en) * | 2012-05-11 | 2013-02-06 | 新疆大学 | Grid-accumulation-type regional land desertification early-warning method |
| CN103605901A (en) * | 2013-11-29 | 2014-02-26 | 民政部国家减灾中心 | Regional drought risk assessment method |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
| CN107437262A (en) * | 2017-07-19 | 2017-12-05 | 中国科学院遥感与数字地球研究所 | Crops planting area method for early warning and system |
| CN108875146A (en) * | 2018-05-28 | 2018-11-23 | 福州大学 | A kind of ground mulching consecutive variations detection method considering change procedure |
| CN109615215A (en) * | 2018-12-06 | 2019-04-12 | 西安理工大学 | A kind of characteristic analysis method that regional vegetation restores |
| CN109636171A (en) * | 2018-12-06 | 2019-04-16 | 西安理工大学 | A kind of comprehensive diagnos and risk evaluating method that regional vegetation restores |
| CN109766815A (en) * | 2019-01-03 | 2019-05-17 | 银河航天（北京）科技有限公司 | A kind of pair of object event carries out pre-warning system and method |
| CN110296942A (en) * | 2019-05-21 | 2019-10-01 | 华中农业大学 | A kind of dynamic assessment method of vegetative coverage object element |
| CN110210990A (en) * | 2019-05-30 | 2019-09-06 | 河南省农业科学院农业经济与信息研究所 | Winter wheat Monitoring of freezing injury method based on high score satellite remote sensing date |
Non-Patent Citations (6)
| Title |
|---|
| 刘少军等: "基于时态GIS的植被NDVI动态监测的模拟方法研究", 《水土保持研究》 * |
| 刘志军等: "2001-2014年云贵高原植被NDVI时空变化特征", 《山地农业生物学报》 * |
| 古冬丽等: "基于MODIS/NDVI的河北省植被指数时空变化特征及影响因素分析", 《湖北农业科学》 * |
| 方金等: "青藏高原草地生物量遥感动态监测", 《草业科学》 * |
| 甄计国等: "植被指数与退耕还林(草)初期的遥感监测应用", 《遥感技术与应用》 * |
| 黄思宇: "华南地区马铃薯典型种植面积和生长进度遥感监测方法", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786027A (en) * | 2023-11-27 | 2024-03-29 | 黑龙江省网络空间研究中心(黑龙江省信息安全测评中心、黑龙江省国防科学技术研究院) | Method for analyzing time-space change of overground biomass in long time sequence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Morton et al. | Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data | |
| Lyons et al. | Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia | |
| Alqurashi et al. | An assessment of the impact of urbanization and land use changes in the fast-growing cities of Saudi Arabia | |
| CN114740180A (en) | Soil organic carbon estimation method and device based on multi-source remote sensing data | |
| Koko et al. | Spatiotemporal influence of land use/land cover change dynamics on surface urban heat island: A case study of Abuja metropolis, Nigeria | |
| Solvin et al. | Use of UAV photogrammetric data in forest genetic trials: measuring tree height, growth, and phenology in Norway spruce (Picea abies L. Karst.) | |
| Đomlija et al. | Identification and mapping of soil erosion processes using the visual interpretation of LiDAR imagery | |
| Hao et al. | Methodology for optimizing quadrat size in sparse vegetation surveys: A desert case study from the Tarim Basin | |
| Goodbody et al. | Benchmarking acquisition parameters for digital aerial photogrammetric data for forest inventory applications: Impacts of image overlap and resolution | |
| CN114819737A (en) | Carbon reserve estimation method, system and storage medium for road and road area vegetation | |
| Ghofrani et al. | Evaluating coverage changes in national parks using a hybrid change detection algorithm and remote sensing | |
| Kleinod et al. | Detecting vegetation changes in a wetland area in Northern Germany using earth observation and geodata | |
| Wang et al. | Potential of texture metrics derived from high-resolution PLEIADES satellite data for quantifying aboveground carbon of Kandelia candel mangrove forests in Southeast China | |
| Wiegand et al. | Automated extraction of shallow erosion areas based on multi-temporal ortho-imagery | |
| Wicks et al. | Polygon-based aggregation of remotely sensed data for regional ecological analyses | |
| Yang et al. | Deriving ephemeral gullies from VHR image in loess hilly areas through directional edge detection | |
| Mukhopadhyay et al. | Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok, India | |
| CN110991333A (en) | Aboveground biomass early warning method | |
| Jones et al. | Assessing the utility of LiDAR to differentiate among vegetation structural classes | |
| Liu et al. | Tree Crown width estimation, using discrete airborne LiDAR data | |
| Xu et al. | A comparative study on the applicability and effectiveness of NSVI and NDVI for estimating fractional vegetation cover based on multi-source remote sensing image | |
| CN116451840A (en) | Forest carbon reserve measuring and calculating method, system, terminal and storage medium | |
| Thwal et al. | Land cover classification and change detection analysis of multispectral satellite images using machine learning | |
| Gallant | What you should know about land‐cover data | |
| Campbell et al. | LiDAR as an advanced remote sensing technology to augment ecosystem classification and mapping |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 011599 West Hall, F2, Intelligent Manufacturing Industrial Park, Shengle Modern Service Industry Cluster, Horinger County, Hohhot, Inner Mongolia Applicant after: Inner Mongolia Xiaocao digital Ecological Industry Co.,Ltd. Applicant after: Mengcao ecological environment (Group) Co.,Ltd. Address before: 011517 room 2230, enterprise headquarters building, Shengle modern service industry cluster, Shengle economic Park, Helingeer County, Hohhot City, Inner Mongolia Autonomous Region Applicant before: Inner Mongolia mengcao life community big data Co.,Ltd. Applicant before: INNER MONGOLIA MONGOLIAN GRASS ECOLOGICAL ENVIRONMENT (GROUP) Ltd. Address after: 011599 West Hall, F2, Intelligent Manufacturing Industrial Park, Shengle Modern Service Industry Cluster, Horinger County, Hohhot, Inner Mongolia Applicant after: Inner Mongolia Xiaocao Digital Ecological Industry Co.,Ltd. Applicant after: Mengcao ecological environment (Group) Co.,Ltd. Address before: 011599 West Hall, F2, Intelligent Manufacturing Industrial Park, Shengle Modern Service Industry Cluster, Horinger County, Hohhot, Inner Mongolia Applicant before: Inner Mongolia Xiaocao digital Ecological Industry Co.,Ltd. Applicant before: Mengcao ecological environment (Group) Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230705 Address after: 011599 West Hall, F2, Intelligent Manufacturing Industrial Park, Shengle Modern Service Industry Cluster, Horinger County, Hohhot, Inner Mongolia Applicant after: Inner Mongolia Xiaocao Digital Ecological Industry Co.,Ltd. Address before: 011599 West Hall, F2, Intelligent Manufacturing Industrial Park, Shengle Modern Service Industry Cluster, Horinger County, Hohhot, Inner Mongolia Applicant before: Inner Mongolia Xiaocao Digital Ecological Industry Co.,Ltd. Applicant before: Mengcao ecological environment (Group) Co.,Ltd. |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200410 |