CN113670825A - Forest environment remote sensing monitoring system based on comprehensive remote sensing technology - Google Patents
Forest environment remote sensing monitoring system based on comprehensive remote sensing technology Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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Abstract
The invention provides a forest environment remote sensing monitoring system based on an integrated remote sensing technology, wherein an environment data reading module and an image digitization module are in control connection, the image digitization module, a data change analysis module and a remote sensing image matching module are in communication connection in sequence, the environment data reading module acquires a forest state diagram, non-digital patterns are converted into digitalization through the image digitization module and transmitted to the data change analysis module, the data change analysis module extracts extracted information through human-computer interaction interpretation, the forest environment remote sensing monitoring system analyzes and utilizes the remote sensing technology to a forest area to be detected and monitored, can effectively analyze and monitor tree growth vigor, plant diseases and insect pests, illumination intensity and precipitation intensity data, and further makes adjustment beneficial to overall change, the detection of the whole forest environment is realized.
Description
Technical Field
The invention relates to the fields of forest ecological environment, remote sensing monitoring technology and the like, in particular to a forest environment remote sensing monitoring system based on a comprehensive remote sensing technology.
Background
Under the new trend of forestry entering full-scale development, more than 85% of forest management decisions are related to their spatial location. Such as forest resource management, forest fire prevention, forest survey, natural forest protection, wind prevention and sand control, forest pest control and the like. The method is characterized by wide forest land, long production period, complex forest system structure and the like. The traditional method is adopted to carry out decision-making, which is limited to boring data processing, lacks of intuition and visualization of decision-making process, and brings many difficulties and errors to leadership decision-making and operation management; forest resources are always in a dynamic and changing process, and in order to better master the actual situation of the forest resources at each moment, the forest resources are often required to be continuously monitored. At present, a method for extracting and examining on the spot is still used, or the implementation condition of forest resources by applying a remote sensing technology can play a certain monitoring role. However, no matter which forest resource monitoring method is used, the method has certain hysteresis to the change situation of the forest resources.
The forest ecosystem monitoring technology is an important support and information guarantee for realizing forest sustainable utilization and global change research. The remote sensing technology mainly comprises forest remote sensing classification and change monitoring, forest vegetation parameter remote sensing inversion, forest accumulation and biomass remote sensing estimation, forest interference remote sensing monitoring and the like in forest ecosystem monitoring.
For a forest environment remote sensing monitoring system of a remote sensing technology, related researches in the prior art are not many, manual waste is caused by a traditional wetland extraction investigation method, meanwhile, serious burden is brought to forest resources, accurate measurement and analysis on tree growth, plant diseases and insect pests, illumination intensity and precipitation of forests cannot be carried out, and manpower and financial resources are consumed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for analyzing and applying a remote sensing technology to a forest area to be detected and monitored by the remote sensing technology, which can effectively analyze and monitor the growth vigor, plant diseases and insect pests, illumination intensity and precipitation intensity data of trees, and solves the problems of untimely detection of the whole environment of the forest, waste of manpower, material resources and financial resources in the prior art.
The invention provides a forest environment remote sensing monitoring system based on a comprehensive remote sensing technology, which comprises: the system comprises an environmental data reading module, an image digitization module, a data change analysis module and a remote sensing image matching module, wherein the environmental data reading module and the image digitization module are in control connection, the image digitization module, the data change analysis module and the remote sensing image matching module are in communication connection in sequence, the environmental data reading module acquires a forest state diagram, non-digital graphs are converted into digitalization through the image digitization module and transmitted to the data change analysis module, the data change analysis module extracts extracted information through human-computer interaction interpretation, the data change analysis module comprises a data preprocessing module, the data preprocessing module is in control connection with the data change analysis module, the data change analysis module further comprises an image correction module, a map correction unit, a precision approval unit and a remote sensing image correction unit, the image correction module respectively controls the map correction unit, the precision approval unit and the remote sensing image correction unit, the remote sensing image correction unit adopts quadratic or cubic polynomial to correct the remote sensing image, and the remote sensing image matching module registers the corrected remote sensing image with the forest state diagram.
Preferably, the environment data reading module includes: the tree growth vigor measuring unit, the plant disease and insect pest measuring unit, illumination intensity measuring unit and precipitation measuring unit, tree growth vigor measuring unit, plant disease and insect pest measuring unit, illumination intensity measuring unit and precipitation measuring unit respectively with environmental data read between the module control connection, tree growth vigor measuring unit is used for acquireing tree growth parameter index, plant disease and insect pest measuring unit is used for extracting the parameter index relevant with the plant disease and insect pest, illumination intensity measuring unit is used for acquireing the illumination intensity parameter within the hour, precipitation measuring unit is used for acquireing the precipitation of every quarter.
Preferably, the forest environment remote sensing monitoring system based on the comprehensive remote sensing technology further comprises a land correcting unit, and the land correcting unit is in communication connection with the remote sensing image correcting unit.
Preferably, the forest environment remote sensing monitoring system based on the comprehensive remote sensing technology further comprises an image and image pairing unit, an overall registration and local registration unit and a post-correction registration module, wherein the image and image pairing unit, the overall registration and local registration unit and the post-correction registration module are sequentially connected.
Preferably, the forest environment remote sensing monitoring system based on the comprehensive remote sensing technology corrects the forest state diagram through the image and image matching unit on the basis of the forest state diagram to form a base diagram of the forest state diagram, control points are uniformly distributed, and when the image and image matching unit cannot meet the precision requirement, the integral registration and local registration unit is called.
Preferably, control points in the remote sensing image correction unit are uniformly distributed, and when the requirements are difficult to achieve by utilizing quadratic polynomial correction, the correction is carried out through the integral registration and local registration unit by taking a base map of a forest state diagram as a reference.
Preferably, the base map of the forest state diagram and the control points in the remote sensing image correction unit are uniformly distributed with an error smaller than 0.5 mm.
Preferably, the data preprocessing module comprises enhancement processing of the remote sensing image, including denoising and deblurring processing of the image, and enhancement processing of the forest state.
Compared with the prior art, the technical scheme in the application at least has the following technical effects or advantages: carry out remote sensing technology's analysis and application to the forest region that will detect and control through remote sensing technology, can effectually realize carrying out the analysis to trees growth vigour, plant diseases and insect pests, illumination intensity and precipitation intensity data and monitoring to further make the adjustment that is favorable to the whole change of forest, realized detecting the overall environment of forest, use manpower sparingly, material resources, financial resources, its experimental result is true effective.
Drawings
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a schematic diagram of an environment data reading module according to the present invention;
FIG. 3 is a schematic diagram of an image and image matching unit according to the present invention;
FIG. 4 is a schematic diagram illustrating a structural control flow of the basic image library according to the present invention.
In the figure: 1. an environment data reading module; 11. a tree growth measuring unit; 12. a pest measuring unit; 13. an illumination intensity measuring unit; 14. a precipitation amount measuring unit; 2. an image digitizing module; 3. a data change analysis module; 31. a data preprocessing module; 32. an image correction module; 33. a map correction unit; 34. a precision approval unit; 35. a remote sensing image correction unit; 4. a remote sensing image matching module; 5. an image and image pairing unit; 51. a global registration and local registration unit; 52. and a post-correction registration module.
Detailed Description
In order to solve the technical problems, the invention provides a forest environment remote sensing monitoring system based on a comprehensive remote sensing technology, which aims to solve the problems that a tool bearing frame in the prior art is single in structure, difficult to find tools, time-consuming and labor-consuming due to the fact that tools need to be additionally cleaned after maintenance is finished.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example (b): as shown in fig. 1-3, a forest environment remote sensing monitoring system based on comprehensive remote sensing technology comprises: the system comprises an environmental data reading module 1, an image digitization module 2, a data change analysis module 3 and a remote sensing image matching module 4, wherein the environmental data reading module 1 and the image digitization module 2 are in control connection, the image digitization module 2, the data change analysis module 3 and the remote sensing image matching module 4 are in communication connection in sequence, the environmental data reading module 1 obtains a forest state diagram, non-digital patterns are converted into digitalization through the image digitization module 2 and are transmitted to the data change analysis module 3, the data change analysis module 3 extracts extracted information through human-computer interaction interpretation, the data change analysis module 3 comprises a data preprocessing module 31, and the data preprocessing module 31 is in control connection with the data change analysis module 3. The data preprocessing module 31 includes enhancement processing of the remote sensing image, including denoising and deblurring processing of the image, and enhancement processing of the forest state. The data change analysis module 3 further comprises an image correction module 32, a map correction unit 33, a precision approval unit 34 and a remote sensing image correction unit 35, wherein the image correction module 32 controls the map correction unit 33, the precision approval unit 34 and the remote sensing image correction unit 35 respectively, the remote sensing image correction unit 35 adopts quadratic or cubic polynomial to carry out remote sensing image correction, and the remote sensing image matching module 4 carries out registration on the corrected remote sensing image and the forest state diagram.
Specifically, the environment data reading module 1 includes: the tree growth vigor measuring unit 11, the plant disease and insect pest measuring unit 12, illumination intensity measuring unit 13 and precipitation measuring unit 14, the tree growth vigor measuring unit 11, the plant disease and insect pest measuring unit 12, control connection between illumination intensity measuring unit 13 and precipitation measuring unit 14 and environmental data reading module 1 respectively, the tree growth vigor measuring unit 11 is used for obtaining tree growth parameter index, plant disease and insect pest measuring unit 12 is used for extracting parameter index relevant with the plant disease and insect pest, illumination intensity measuring unit 13 is used for obtaining illumination intensity parameter within 24 hours, precipitation measuring unit 14 is used for obtaining the precipitation of per quarter.
In an implementation process, the environmental data reading module 1 obtains data, the environmental data reading module 1 includes tree growth trend data, pest and disease damage range and degree data, illumination intensity data, environmental precipitation data, soil content analysis data and the like, and selects a normalized vegetation index (NDVI), a Differential Vegetation Index (DVI), a Ratio Vegetation Index (RVI), a soil regulation vegetation index (SAVI), an optimized soil regulation vegetation index (OSAVI), an anti-atmospheric vegetation index (ARVI), a converted vegetation index (TVI) and a vertical vegetation index (PVI) for comparison processing.
The remote sensing image and the geographic data are obtained through a Google cloud computing platform, the remote sensing image comprises a first top grade 2m and a second top grade 0.8m, the GEE can obtain remote sensing data processing and analyzing data through online or offline programming, and the obtained data comprises a geographic basic database, a remote sensing image library and a vegetation spectrum library.
The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology further comprises a land correcting unit 36, the land correcting unit 36 is in communication connection with the remote sensing image correcting unit 35, and image correction is based on some interest points of local appearance on an object and is not related to the size and rotation of the image. The tolerance to light, noise, and micro-viewing angle changes is also quite high. Based on these characteristics, they are highly significant and relatively easy to retrieve, easily identify objects and are rarely misidentified in feature databases with large denominations.
The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology further comprises an image and image matching unit 5, an overall registration and local registration unit 51 and a post-correction registration module 52, wherein the image and image matching unit 5, the overall registration and local registration unit 51 and the post-correction registration module 52 are sequentially connected.
The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology corrects a forest state diagram through an image and image matching unit 5 on the basis of the forest state diagram to form a base diagram of the forest state diagram, control points are uniformly distributed (the control points are selected from characteristic points such as road intersections and river inflection points, the control points are not selected too little or too concentrated and are generally distributed in the whole remote sensing diagram), and when the image and image matching unit 5 cannot meet the precision requirement, an integral registration and local registration unit 51 is called.
The control points in the remote sensing image correction unit 35 are uniformly distributed, and when the requirements are difficult to be met by using quadratic polynomial correction, the base map of the forest state diagram is taken as a reference, and correction is performed through the integral registration and local registration unit 51.
In one implementation process, the base map of the forest state diagram and the control points in the remote sensing image correction unit 35 are uniformly distributed with an error smaller than 0.5 mm.
The working principle is as follows: and establishing a basic image library for the forest environment to be measured and monitored, updating the basic database and the image library in real time, and digitizing the non-digital graph by using the image digitizing module 2 to prepare for later-stage computer data processing and analysis. The tree growth vigor, plant diseases and insect pests, the illumination intensity and the precipitation intensity data collected by the tree growth vigor measuring unit 11, the plant disease and insect pest measuring unit 12, the illumination intensity measuring unit 13 and the precipitation amount measuring unit 14 are analyzed, the variation range is extracted, and the found variation information is extracted through man-machine interaction interpretation. The forest environment data to be analyzed and monitored are matched and corrected through the data change analysis module 3 and the remote sensing image matching module 4. The image correction module 32 and the map correction unit 33 adopt an image and image registration technology to correct the images of the forest environment and the like to form a working base map of the forest environment in the current situation, control points are required to be uniformly distributed, and when the image and image registration method cannot meet the accuracy requirement, the integral registration and local registration unit 51 is called to combine integral registration correction and local registration correction. And (3) remote sensing image correction, namely performing remote sensing image correction by using quadratic or cubic polynomial, wherein control points are required to be sufficiently and uniformly distributed, the quadratic polynomial correction is difficult to meet the requirement, and the working base map of the current forest environment is corrected by using a graphic image local registration correction technology. Can effectual realization carry out the analysis to trees growth vigor, plant diseases and insect pests, illumination intensity and precipitation intensity data and monitor to realize long-range whole environment to the forest and detect, use manpower sparingly, material resources, financial resources.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A forest environment remote sensing monitoring system based on comprehensive remote sensing technology is characterized by comprising: the system comprises an environmental data reading module (1), an image digitization module (2), a data change analysis module (3) and a remote sensing image matching module (4). The environment data reading module (1) and the image digitization module (2) are in control connection, the image digitization module (2), the data change analysis module (3) and the remote sensing image matching module (4) are in communication connection in sequence, the environment data reading module (1) obtains a forest state diagram, non-digital graphs are converted into digitalization through the image digitization module (2) and are transmitted to the data change analysis module (3), the data change analysis module (3) extracts extracted information through human-computer interaction interpretation, the data change analysis module (3) comprises a data preprocessing module (31), the data preprocessing module (31) is in control connection with the data change analysis module (3), and the data change analysis module (3) further comprises an image correction module (32), The remote sensing image correction system comprises a map correction unit (33), a precision approval unit (34) and a remote sensing image correction unit (35), wherein the map correction unit (33), the precision approval unit (34) and the remote sensing image correction unit (35) are respectively controlled by an image correction module (32), the remote sensing image correction unit (35) adopts quadratic or cubic polynomial to carry out remote sensing image correction, and the remote sensing image after correction is registered with a forest state diagram by a remote sensing image matching module (4).
2. A forest environment remote sensing monitoring system based on integrated remote sensing technology according to claim 1, characterized in that the environment data reading module (1) comprises: trees growth vigor measuring unit (11), plant diseases and insect pests measuring unit (12), illumination intensity measuring unit (13) and precipitation measuring unit (14), trees growth vigor measuring unit (11), plant diseases and insect pests measuring unit (12), illumination intensity measuring unit (13) and precipitation measuring unit (14) respectively with environmental data read module (1) between control connection, trees growth vigor measuring unit (11) are used for acquireing trees growth parameter index, plant diseases and insect pests measuring unit (12) are used for extracting the parameter index relevant with the plant diseases and insect pests, illumination intensity measuring unit (13) are used for acquireing 24 hours within illumination intensity parameter, precipitation measuring unit (14) are used for acquireing the precipitation of every quarter.
3. The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology of claim 1, characterized in that the forest environment remote sensing monitoring system based on the comprehensive remote sensing technology further comprises a land correction unit (36), and the land correction unit (36) is in communication connection with the remote sensing image correction unit (35).
4. The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology of claim 1, further comprising an image and image pairing unit (5), an overall registration and local registration unit (51) and a post-correction registration module (52), wherein the image and image pairing unit (5), the overall registration and local registration unit (51) and the post-correction registration module (52) are sequentially connected.
5. The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology of claim 1, wherein the forest environment remote sensing monitoring system based on the comprehensive remote sensing technology corrects the forest state diagram through the image and image matching unit (5) on the basis of the forest state diagram to form a base map of the forest state diagram, control points are uniformly distributed, and when the image and image matching unit (5) cannot meet the precision requirement, the integral registration and local registration unit (51) is called.
6. A forest environment remote sensing monitoring system based on comprehensive remote sensing technology as claimed in claim 5, characterized in that control points in the remote sensing image correction unit (35) are uniformly distributed, and when the requirements are difficult to be met by using quadratic polynomial correction, the correction is performed through the global registration and local registration unit (51) with reference to the base map of the forest state diagram.
7. A forest environment remote sensing monitoring system based on comprehensive remote sensing technology as claimed in claim 5 or 6, characterized in that the base map of the forest state diagram and the control points in the remote sensing image correction unit (35) are uniformly distributed with an error of less than 0.5 mm.
8. The forest environment remote sensing monitoring system based on the comprehensive remote sensing technology as claimed in claim 1, wherein the data preprocessing module (31) comprises enhancement processing of the remote sensing image, including denoising and deblurring processing of the image, and enhancement processing of the forest state.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400364A (en) * | 2013-06-06 | 2013-11-20 | 武汉大学 | Monitoring method for forest resource change |
| CN104851087A (en) * | 2015-04-17 | 2015-08-19 | 华中农业大学 | Multi-scale forest dynamic change monitoring method |
| CN104881865A (en) * | 2015-04-29 | 2015-09-02 | 北京林业大学 | Forest disease and pest monitoring and early warning method and system based on unmanned plane image analysis |
| US20160292626A1 (en) * | 2013-11-25 | 2016-10-06 | First Resource Management Group Inc. | Apparatus for and method of forest-inventory management |
| CN106915462A (en) * | 2017-02-14 | 2017-07-04 | 福建兴宇信息科技有限公司 | Forestry pests ＆ diseases intelligent identifying system based on multi-source image information |
| CN108535193A (en) * | 2018-03-19 | 2018-09-14 | 电子科技大学 | A kind of forestry typical case pest and disease damage remote-sensing monitoring method |
| CN110645961A (en) * | 2019-09-27 | 2020-01-03 | 佛山科学技术学院 | Forest resource dynamic change detection method based on remote sensing and NDVI |
| CN111811578A (en) * | 2020-07-20 | 2020-10-23 | 云南财经大学 | Forest growth simulation prediction system based on forest model |
| CN112131953A (en) * | 2020-08-26 | 2020-12-25 | 航天信德智图（北京）科技有限公司 | Rapid forest change detection method research based on high-resolution second image |
| CN112395905A (en) * | 2019-08-12 | 2021-02-23 | 北京林业大学 | Forest pest and disease real-time detection method, system and model establishment method |
| CN112666170A (en) * | 2021-01-06 | 2021-04-16 | 兰州乾元生态科技有限公司 | Sky and ground integrated remote sensing monitoring method for plant diseases and insect pests |
-
2021
- 2021-08-24 CN CN202110975545.4A patent/CN113670825A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400364A (en) * | 2013-06-06 | 2013-11-20 | 武汉大学 | Monitoring method for forest resource change |
| US20160292626A1 (en) * | 2013-11-25 | 2016-10-06 | First Resource Management Group Inc. | Apparatus for and method of forest-inventory management |
| CN104851087A (en) * | 2015-04-17 | 2015-08-19 | 华中农业大学 | Multi-scale forest dynamic change monitoring method |
| CN104881865A (en) * | 2015-04-29 | 2015-09-02 | 北京林业大学 | Forest disease and pest monitoring and early warning method and system based on unmanned plane image analysis |
| CN106915462A (en) * | 2017-02-14 | 2017-07-04 | 福建兴宇信息科技有限公司 | Forestry pests ＆ diseases intelligent identifying system based on multi-source image information |
| CN108535193A (en) * | 2018-03-19 | 2018-09-14 | 电子科技大学 | A kind of forestry typical case pest and disease damage remote-sensing monitoring method |
| CN112395905A (en) * | 2019-08-12 | 2021-02-23 | 北京林业大学 | Forest pest and disease real-time detection method, system and model establishment method |
| CN110645961A (en) * | 2019-09-27 | 2020-01-03 | 佛山科学技术学院 | Forest resource dynamic change detection method based on remote sensing and NDVI |
| CN111811578A (en) * | 2020-07-20 | 2020-10-23 | 云南财经大学 | Forest growth simulation prediction system based on forest model |
| CN112131953A (en) * | 2020-08-26 | 2020-12-25 | 航天信德智图（北京）科技有限公司 | Rapid forest change detection method research based on high-resolution second image |
| CN112666170A (en) * | 2021-01-06 | 2021-04-16 | 兰州乾元生态科技有限公司 | Sky and ground integrated remote sensing monitoring method for plant diseases and insect pests |
Non-Patent Citations (1)
| Title |
|---|
| 王宗梅等: "基于高分辨率遥感影像的森林信息提取方法综述" * |
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