CN116481600A - Plateau forestry ecological monitoring and early warning system and method - Google Patents
Plateau forestry ecological monitoring and early warning system and method Download PDFInfo
- Publication number
- CN116481600A CN116481600A CN202310752306.1A CN202310752306A CN116481600A CN 116481600 A CN116481600 A CN 116481600A CN 202310752306 A CN202310752306 A CN 202310752306A CN 116481600 A CN116481600 A CN 116481600A
- Authority
- CN
- China
- Prior art keywords
- ecological
- transition zone
- early warning
- plateau
- forestry
- 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.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000007704 transition Effects 0.000 claims abstract description 143
- 230000008859 change Effects 0.000 claims abstract description 71
- 238000012806 monitoring device Methods 0.000 claims abstract description 60
- 238000012545 processing Methods 0.000 claims abstract description 27
- 230000005855 radiation Effects 0.000 claims description 23
- 230000011218 segmentation Effects 0.000 claims description 19
- 230000035772 mutation Effects 0.000 claims description 17
- 230000000694 effects Effects 0.000 claims description 15
- 239000002689 soil Substances 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000013527 convolutional neural network Methods 0.000 claims description 11
- 238000005452 bending Methods 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 7
- 238000001556 precipitation Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000009472 formulation Methods 0.000 abstract description 3
- 239000000203 mixture Substances 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 description 7
- 239000012530 fluid Substances 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000002349 favourable effect Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000009194 climbing Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 241000245165 Rhododendron ponticum Species 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 244000144972 livestock Species 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The application provides a plateau forestry ecology monitoring and early warning system and a method, which relate to the technical field of forestry ecology monitoring, wherein the plateau forestry ecology monitoring and early warning method comprises the steps of marking an initial vegetation transition zone, drawing a final value vegetation transition zone mark, marking change recognition, ecology signal recognition and position early warning; the ecological monitoring and early warning system for the plateau forestry comprises an ecological monitoring device, an acquisition module and a processing module. According to the method, the position change conditions of the vegetation transition zone in the initial remote sensing image and the vegetation transition zone in the final value remote sensing image are compared, and the ecological environment at the vegetation transition zone is combined to realize the monitoring and early warning of the plateau forestry ecological system, so that relevant personnel can be facilitated to recognize in time in the early stage of the plateau forestry ecological system, the exploration basis of the reason in the aspect of climate change can be provided for the imbalance of the plateau forestry ecological system, and the targeted formulation of plateau forestry protection measures in the target area is facilitated.
Description
Technical Field
The application relates to the technical field of ecological monitoring of forestry, in particular to an ecological monitoring and early warning system and method for plateau forestry.
Background
The method has the advantages that the Chinese amplitude staff are wide, the climates are various, and the mountain area has rich biological resources, wherein various plants are distributed for the first time in the mountain area of the plateau along with the change of the elevation of the mountain, transition is carried out from dense forests to short bushes to sparse meadows, and the ecological weakness of the plateau forestry is caused, so that an early warning machine for effectively monitoring, evaluating and changing is formed.
In forestry ecosystems in mountain areas of high altitude, vegetation transition zones have very important research value, and the vegetation transition zones represent that the vegetation ecosystems in the areas reach a steady state and balance under natural environment conditions. By researching the change condition of the vegetation transition zone, the vegetation survival and development condition of the area can be better known, the method is favorable for identifying the unbalanced signal of the early ecological system of the plateau forestry, exploring the unbalanced reason and preparing the protection measure of the plateau forestry in a targeted manner.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a plateau forestry ecology monitoring and early warning system and method aiming at how to effectively monitor, evaluate and change an early warning mechanism for the plateau forestry ecology.
In order to achieve the above purpose, the technical scheme adopted in the application includes:
according to a first aspect of the application, there is provided a plateau forestry ecological monitoring and early warning method, comprising:
marking an initial vegetation transition zone, arranging a plurality of ecological monitoring devices at the boundary of the vegetation transition zone in a target area, and drawing linear initial vegetation transition zone marks aiming at the target area in an initial remote sensing image by a control unit after receiving position signals of the ecological monitoring devices;
drawing a final value vegetation transition zone mark, and after acquiring an initial remote sensing image of the target area, acquiring a final value remote sensing image of the target area at intervals of preset time by a control unit, and processing the final value remote sensing image to obtain a linear final value vegetation transition zone mark aiming at the target area;
identifying the mark change, and comparing the mark of the initial vegetation transition zone with the mark of the final vegetation transition zone to obtain the position change condition of the vegetation transition zone in the target area;
and (3) identifying an ecological signal, namely comparing an initial ecological signal corresponding to the initial remote sensing image with a final ecological signal corresponding to the final remote sensing image according to the ecological signal acquired by the ecological monitoring device to obtain an ecological signal change condition of a vegetation transition zone in the target area, wherein the ecological signal comprises the following components: atmospheric temperature, atmospheric humidity, soil temperature, soil humidity, precipitation and solar radiation;
And (3) position early warning, judging the forestry ecological condition in the target area according to the position change condition of the vegetation transition zone and/or the ecological signal change condition, and generating corresponding early warning information.
Optionally, the plateau forestry ecological monitoring and early warning method further comprises the following steps:
acquiring a plurality of median remote sensing images within the preset time, taking the median remote sensing images and the median vegetation transition zone marks aiming at each median remote sensing image as sample data sets, and training a semantic segmentation depth convolution neural network;
inputting the final value remote sensing image into a trained semantic segmentation depth convolution neural network, performing binarization processing on the semantic segmentation depth convolution neural network result, and performing closed operation on the binarization processing result to obtain a final value vegetation transition zone mark corresponding to the final value remote sensing image.
Optionally, the plateau forestry ecological monitoring and early warning method further comprises the following steps:
inputting the initial remote sensing image into a trained semantic segmentation depth convolutional neural network to obtain a vegetation transition zone mark corresponding to the initial remote sensing image, comparing the obtained vegetation transition zone mark with the initial vegetation transition zone mark, and correcting the semantic segmentation depth convolutional neural network according to a comparison result.
Optionally, the boundary of the vegetation transition zone comprises: boundaries of arbor and shrub regions, boundaries of shrub and meadow regions.
Optionally, the marking change identification specifically includes:
and establishing a coordinate system by taking the initial remote sensing image or the final value remote sensing image as a reference, correspondingly generating the initial vegetation transition zone mark and the final value vegetation transition zone mark in the coordinate system, respectively calculating the position data difference values of the corresponding ecological monitoring devices in the initial vegetation transition zone mark and the final value vegetation transition zone mark, sequentially calculating the absolute value of the slope difference value of every two adjacent position data difference values, and if the absolute value of a certain slope difference value is larger than a first threshold value, regarding the ecological monitoring device corresponding to the slope difference value as a position mutation point of the vegetation transition zone.
Optionally, the ecological signal identification step specifically includes:
and respectively calculating the difference value of the numerical value of a certain ecological signal of all the ecological monitoring devices in the initial vegetation transition zone mark and the data of the corresponding ecological signal in the final vegetation transition zone mark, calculating the average value of the difference values, and if the algebraic difference between the certain difference value and the average value is larger than a second threshold value, regarding the ecological monitoring device corresponding to the difference value as an ecological mutation point of the vegetation transition zone.
Optionally, the position early warning specifically includes:
generating first early warning information when the position change condition of the vegetation transition zone is abnormal;
generating second early warning information when the ecological signal of the vegetation transition zone is abnormal;
generating third early warning information when the position change condition of the vegetation transition zone and the ecological signal change are abnormal at the same time;
the third early warning information is prioritized over the first early warning information, and the first early warning information is prioritized over the second early warning information.
According to a second aspect of the present application, there is provided an ecological monitoring and early warning system for plateau forestry, which is applied to the ecological monitoring and early warning method for plateau forestry in the first aspect, the ecological monitoring and early warning system for plateau forestry includes:
the ecological monitoring device is used for collecting ecological signals;
the acquisition module is used for acquiring the remote sensing image of the target area, the position information of the ecological monitoring device and the ecological signal acquired by the ecological monitoring device;
the processing module is used for processing the acquired remote sensing image, processing the position signal of the ecological monitoring device and the ecological signal acquired by the ecological monitoring device, and generating corresponding early warning information.
Optionally, the plateau forestry ecological monitoring and early warning system further comprises an early warning module, wherein the early warning module is used for outputting corresponding early warning information according to the early warning information generated by the processing module.
Optionally, the ecological monitoring device comprises a cover plate, a first plate body, a second plate body, a solar radiation measurer and an atmospheric temperature measurer, wherein the cover plate, the first plate body and the second plate body are sequentially arranged along the vertical direction, the first plate body and the second plate body enclose an air guide cavity together, the atmospheric temperature measurer is arranged between the first plate body and the second plate body and is positioned in the middle of the air guide cavity, and the solar radiation measurer is arranged at the top of the cover plate;
the projection of the first plate body falls into the projection of the cover plate in the vertical direction, the first plate body and the second plate body are coaxially arranged, the end face, close to the second plate body, of the first plate body is formed into a first wind guide curved surface with an upward bending direction and capable of generating a coanda effect, and the end face, close to the first plate body, of the second plate body is formed into a second wind guide curved surface with an upward bending direction and capable of generating a coanda effect, so that the first wind guide curved surface and the second wind guide curved surface can jointly enclose the wind guide cavity.
The beneficial effects are that:
1. according to the technical scheme, the method of the invention realizes the monitoring and early warning of the plateau forestry ecological system by comparing the position change condition of the vegetation transition zone in the initial remote sensing image and the vegetation transition zone in the final value remote sensing image and combining the change of the ecological environment at the vegetation transition zone, can help relevant personnel to identify the plateau forestry ecological system early and timely, and can provide a exploring basis for the reason of climate change for the imbalance of the plateau forestry ecological system, thereby being beneficial to formulation of plateau forestry protection measures in the target area.
2. Other benefits or advantages of the present application will be described in detail with reference to specific structures in the detailed description.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art. Furthermore, it should be understood that the scale of each component in the drawings in this specification is not represented by the scale of actual material selection, but is merely a schematic diagram of structures or positions, in which:
FIG. 1 is a schematic step diagram of a method for ecological monitoring and early warning of plateau forestry provided in an exemplary embodiment of the present application;
FIG. 2 is a schematic structural diagram of an ecological monitoring and early warning system for plateau forestry provided in an exemplary embodiment of the present application;
FIG. 3 is a schematic perspective view of an ecological monitoring device according to an exemplary embodiment of the present application;
FIG. 4 is a structural exploded view of an ecological monitoring device provided in an exemplary embodiment of the present application;
FIG. 5 is a schematic perspective view of a first plate according to an exemplary embodiment of the present application;
fig. 6 is a schematic cross-sectional structure of an ecological monitoring device according to an exemplary embodiment of the present application.
The reference numerals in the drawings indicate:
1-an ecological monitoring device; 11-cover plate; 12-a first plate body; 121-a first wind guiding curved surface; 13-a second plate; 131-a second wind guiding curved surface; 14-solar radiometer; 15-an atmospheric temperature measurer; 16-an air guide cavity; 171-a support plate; 172-supporting rods; 173-a base; 2-an acquisition module; 3-a processing module; 4-an early warning module; 5-a control unit.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Firstly, it should be noted that in the forestry ecosystem in the mountain area of the plateau, the vegetation transition zone is affected in many ways, for example, the high-temperature period of the mountain tree is prolonged under the influence of climate warming, so that the low-temperature limit degree of the growing season is smaller, the mountain tree has better growing environment and longer growing period, and the mountain tree (i.e. the boundary between the arbor area and the shrub area) is further moved to the place with higher altitude; for another example, the competition between different species can also affect the change of vegetation transition zones, in areas of relatively dense shrubs (e.g., rhododendron shrubs), the dense shrubs can cause the seeds of the arbor to fail to obtain sunlight after landing, and can inhibit the climbing of the mountain line; in situations where, for example, human activity may also affect the transition zone of vegetation, when more frequent human activity or excessive grazing occurs, human activity and livestock movement may cause soil hardening or seedling death in the understory or vegetation transition zone, thereby inhibiting "climbing" of mountain lines and "downshifting" of shrubs.
The monitoring and early warning method and system are based on the change condition of the vegetation transition zone of the target area in different periods, and combine the change of the ecological environment (such as atmospheric temperature, atmospheric humidity, soil temperature, soil humidity, precipitation and solar radiation) at the vegetation transition zone to realize the monitoring and early warning of the plateau forestry ecological system, can effectively identify the early imbalance of the plateau forestry ecological system (the vegetation transition zone position is mutated to a certain extent and can be regarded as an early signal of the ecological system imbalance), and can provide a exploring basis for the reason of the climate change for the plateau forestry ecological system imbalance, thereby being beneficial to formulating the plateau forestry protection measures in a targeted manner.
The following describes the technical scheme of the present application in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, according to a first aspect of the present application, there is provided a method for ecological monitoring and early warning of plateau forestry, the method comprising the steps of:
step S1: marking the initial vegetation transition zone, that is, setting a plurality of ecological monitoring devices 1 at the boundary of the vegetation transition zone in the target area (protection area), and the control unit 5 receives the position signals of the plurality of ecological monitoring devices 1 and draws a linear initial vegetation transition zone mark for the target area in the initial remote sensing image.
In this embodiment, the control unit 5 draws the initial vegetation transition zone into the initial remote sensing image according to the received position signal of the ecological monitoring device 1, which is advantageous for defining the basis of contrast, i.e. for defining the initial position of the vegetation transition zone in the target area.
In addition, it can be understood that the ecological monitoring device 1 can be directly arranged on the boundary of the vegetation transition zone, or can be arranged at a certain distance from the boundary, so long as the marking of the boundary of the vegetation transition zone can be realized, and the specific position of the ecological monitoring device 1 is not limited. Furthermore, the control unit 5 of the present application refers to an integrated unit comprising the acquisition module 2 and the processing module 3, which is capable of reading, analyzing and executing relevant instructions from a memory.
Step S2: after the initial remote sensing image of the target area is acquired, the control unit 5 acquires the final remote sensing image of the target area at intervals of preset time, and processes the final remote sensing image to obtain the linear final vegetation transition zone mark aiming at the target area.
In this embodiment, the control unit 5 obtains the final vegetation transition zone mark corresponding to the remote sensing image by processing the final remote sensing image according to the obtained final remote sensing image, so as to compare the final vegetation transition zone mark with the initial vegetation transition zone mark, and further obtain the position change condition of the vegetation transition zone in the target area.
It will be appreciated that, first, the final remote sensing image may be processed in a variety of processing manners, for example, an image edge detection method based on searching for a class (detecting a boundary by searching for a maximum and a minimum in the first derivative of the image, typically locating the boundary in a direction of maximum gradient), or a zero crossing method (searching for a boundary by searching for zero crossings of the second derivative of the image, typically Laplacian zero crossings or zero crossings represented by nonlinear differences) may be used. The present application is not particularly limited thereto. Second, the preset time may be selected according to actual needs, for example, the preset time may be measured in hours (for example, 1h, 8h, 24h, etc.), the preset time may be measured in days (for example, 1 day, 15 days, 30 days, etc.), and the preset time may be measured in months (for example, 1 month, 2 months, 3 months, etc.), which is not limited to specific values of the preset time.
Step S3: and (3) identifying the mark change, and comparing the mark of the initial vegetation transition zone with the mark of the final vegetation transition zone to obtain the position change condition of the vegetation transition zone in the target area.
In this embodiment, the position change condition of the vegetation transition zone in the target area can be intuitively obtained according to the change condition of the initial vegetation transition zone mark and the final vegetation transition zone mark.
Step S4: the ecological signal identification, according to the ecological signal collected by the ecological monitoring device 1, comparing the initial ecological signal corresponding to the initial remote sensing image with the final ecological signal corresponding to the final remote sensing image to obtain the ecological signal change condition of the vegetation transition zone in the target area, wherein the ecological signal comprises: atmospheric temperature, atmospheric humidity, soil temperature, soil humidity, precipitation and solar radiation.
In this embodiment, the ecological monitoring device 1 can collect the ecological signal change condition at the vegetation transition zone in the target area in real time, and in combination with the position change condition of the vegetation transition zone, the exploration foundation in the aspect of the climate change can be provided for researching the change reason of the vegetation transition zone.
It can be understood that the atmospheric temperature, the atmospheric humidity, the soil temperature, the soil humidity, the precipitation amount and the solar radiation are climate factors having great influence on vegetation growth and development, and can provide a reason reference in terms of climate change for vegetation change condition analysis in a target area, thereby being beneficial to relevant personnel to judge the reason of vegetation change in terms of climate change.
In the present embodiment, the step S3 and the step S4 do not have a difference in priority, that is, the step S3 may be performed first, the step S4 may be performed first, or the step S3 and the step S4 may be performed simultaneously.
Step S5: and (3) position early warning, judging the forestry ecological condition in the target area according to the position change condition of the vegetation transition zone and/or the ecological signal change condition, and generating corresponding early warning information.
In this embodiment, the position early warning is to generate corresponding position change early warning information according to the recognized position change condition of the vegetation transition zone and/or the ecological signal change condition, so that relevant personnel can grasp the vegetation transition zone change condition in the target area, thereby being beneficial to timely finding out the relevant personnel in the early stage of the unbalance of the plateau forestry ecological system and protecting the weaker plateau forestry ecological system.
According to the technical scheme, the method of the invention realizes the monitoring and early warning of the plateau forestry ecological system by comparing the position change condition of the vegetation transition zone in the initial remote sensing image and the vegetation transition zone in the final value remote sensing image and combining the change of the ecological environment at the vegetation transition zone, can help relevant personnel to identify the plateau forestry ecological system early and timely, and can provide a exploring basis for the reason of climate change for the imbalance of the plateau forestry ecological system, thereby being beneficial to formulation of plateau forestry protection measures in the target area.
In one embodiment of the present application, the plateau forestry ecology monitoring and early warning method of the present application may further include: acquiring a plurality of median remote sensing images in a preset time, taking the median remote sensing images and a median vegetation transition zone mark aiming at each median remote sensing image as a sample data set, and training a semantic segmentation depth convolution neural network; inputting the final value remote sensing image into a trained semantic segmentation depth convolution neural network, performing binarization processing on the semantic segmentation depth convolution neural network result, and performing closed operation on the binarization processing result to obtain a final value vegetation transition zone mark corresponding to the final value remote sensing image.
In the embodiment, a plurality of median remote sensing images are acquired, so that a plurality of groups of sample marking results can be obtained, and training of the convolutional neural network is performed through the plurality of median remote sensing images and the corresponding median vegetation transition zone marks. Classical deep convolutional neural networks come in a variety of types, e.g., full convolutional networks FCN, segNet, etc., and since early stop schemes have been widely used in the field of deep learning, the application is not specifically limited as to what scheme to use to stop training. In one possible implementation, training may be stopped when the loss function drop rate of the deep convolutional neural network is below a threshold. Thus, the trained deep convolutional neural network model can be obtained. And simultaneously, inputting the final value remote sensing image into a trained semantic deep segmentation convolutional neural network model, propagating the network forwards, performing binarization processing on a final segmentation result, and performing closed operation on the binarization processing result to obtain a corresponding final value vegetation transition zone mark.
In this application, the closed operation of the binarized processing result is to suppress the meshing problem in the thresholding result (because part of the network has a certain vexation convolution operation, the thresholding result may have meshing problem, and the meshing problem may cause repeated detection).
In addition, it is understood that the plurality of median remote sensing images may be remote sensing images acquired at regular intervals within a preset time, or may be remote sensing images acquired randomly, which is not particularly limited in this application.
In one embodiment of the present application, the plateau forestry ecology monitoring and early warning method of the present application may further include: inputting the initial remote sensing image into a trained semantic segmentation depth convolutional neural network to obtain a vegetation transition zone mark corresponding to the initial remote sensing image, comparing the obtained vegetation transition zone mark with the initial vegetation transition zone mark, and correcting the semantic segmentation depth convolutional neural network according to a comparison result.
In this way, the initial remote sensing image is input into the trained semantic segmentation depth convolution neural network to obtain the corresponding vegetation transition zone mark in the depth convolution neural network model at the moment, the initial transition zone mark is compared with the obtained vegetation transition zone mark, and the neural network parameters are adjusted in real time to correct the semantic segmentation depth convolution neural network.
Specifically, a visualization tool such as Tensorboard, MLflow may be used to observe and analyze the loss curve during training, and the analysis results may be combined to adjust the loss curve (e.g., the loss curve is approximately linear, may have a small learning rate, may have insufficient loss drop, may adjust the loss curve to initially drop faster and then gradually slow down, and may have too severe fluctuation of the loss curve, for example, because the gradient direction calculated by each batch is less accurate when the batch size is small, the variance between batches is large, and may instead easily escape from the saddle point.
In one embodiment of the present application, the boundary of the vegetation transition zone of the present application may comprise: boundaries of arbor and shrub regions, boundaries of shrub and meadow regions. Thus, in one aspect, the vegetation transition zone of the embodiment is confirmed by two boundary lines, so that the vegetation transition zone corresponding to the initial remote sensing image can be recorded more accurately (that is, in the initial state, the vegetation transition zone of the target area can be recorded more accurately); on the other hand, the change situation of the arbor region, the shrub region and the meadow region can be obtained more clearly, that is, the change situation of the vegetation transition zone can be reflected more accurately.
In one embodiment of the present application, the signature change identification of the present application may be specifically: and establishing a coordinate system by taking the initial remote sensing image or the final value remote sensing image as a reference, correspondingly generating an initial vegetation transition zone mark and a final value vegetation transition zone mark in the coordinate system, respectively calculating the position data difference values of the corresponding ecological monitoring devices 1 in the initial vegetation transition zone mark and the final value vegetation transition zone mark, sequentially calculating the absolute value of the slope difference of every two adjacent position data difference values, and if the absolute value of a certain slope difference value is larger than a first threshold value, taking the ecological monitoring device 1 corresponding to the slope difference value as a position mutation point of a vegetation transition zone.
Since the initial vegetation transition zone markers are obtained from the position information of the ecological monitoring device 1, the following other vegetation transition zone markers are all generated by the neural network model, and some abrupt values (possibly true data, false data or abnormal data) may exist, and the coordinate data (i.e. the position data in the coordinate system) of the vegetation transition zone markers are linearly distributed, the present embodiment determines whether the position data is an abrupt point by calculating the slope difference between each adjacent related position data. Specifically, the determining method of the present embodiment converts the position data of two periods into a set of linearly changing data, and then calculates the slope difference between each data point and the previous data point, and if the slope difference exceeds the first threshold, the ecological monitoring device 1 corresponding to the data point is regarded as the position abrupt change point of the vegetation transition zone.
For example, in implementing the above-described signature change identification in Python, exemplary code may be:
def find_mutations(data,threshold):
mutations=[ ]
for i in range(1,len(data)):
slope_diff=abs((data[i]-data[i-1])/(i-(i-1)))
if slope_diff>threshold:
mutations.append((i,data[i]))
return mutations
it should be noted that, in this exemplary code, data is a set of linearly changing data (the position data difference value of the corresponding ecological monitoring device 1 in the initial vegetation transition zone mark and the final vegetation transition zone mark), and threshold is a defined first threshold value for determining where the mutation occurs. Meanwhile, in this example, the data determined as the mutation points is also recorded in the mutation list, and finally, indexes and corresponding values of all the mutation points are returned.
Therefore, the position mutation point can be found relatively quickly by calculating the slope difference of the difference value of every two adjacent position data, and meanwhile, the embodiment only needs relatively simple circulation and operation logic, so that the corresponding position mutation point can be found very conveniently and quickly.
In addition, it is understood that, in this embodiment, the first threshold may be adjusted according to the actual situation to achieve an effect that is more suitable for the specific situation, and the value of the first threshold is not specifically limited in this application.
In one embodiment of the present application, the ecological signal identifying step of the present application may specifically be: and respectively calculating the difference value of the numerical value of a certain ecological signal of all the ecological monitoring devices 1 in the initial vegetation transition zone marks and the data of the corresponding ecological signal in the final vegetation transition zone marks, calculating the average value of the difference values, and if the algebraic difference between the certain difference value and the average value is larger than a second threshold value, taking the ecological monitoring device 1 corresponding to the difference value as an ecological mutation point of the vegetation transition zone.
In this way, when the ecological signals are different, whether the relative difference degree is large or not can be judged by analyzing the data change degrees of the ecological signals in different periods (namely, the data of the initial vegetation transition zone mark and the data of the final vegetation transition zone mark) of all the ecological monitoring devices 1, so that the ecological mutation points are determined.
Specifically, in an exemplary embodiment, taking solar radiation as an example, the ecological monitoring device 1 is set to 8, and the corresponding solar radiation intensities at the time of initial vegetation transition zone marking are respectively: 1300. 1299, 1300, 1301, 1300 and 1302 (unit:) The method comprises the steps of carrying out a first treatment on the surface of the The corresponding solar radiation intensities when the final vegetation transition zone is marked are respectively as follows: 1301. 1299, 1301, 1299, 1303, 1302, 1301 and 1305 (unit:. A. Sup./c.)>) The method comprises the steps of carrying out a first treatment on the surface of the At this time, it is possible to calculateAnd obtaining corresponding difference data: 1. 0, 1, -1, 2, 1 and 3 (unit:. A:. A.sub.1)>) The method comprises the steps of carrying out a first treatment on the surface of the The average value of the calculated differences was 1 (unit:) At the same time, a second threshold value of 1 can be set, at which time the ecology mutation point that can be identified is the last ecology monitoring device 1 (i.e. the solar radiation intensity measured at the time of the initial vegetation transition zone marking is 1302 >The ecological monitoring device 1) of the above-mentioned system, that is, the ecological signal change at the last ecological monitoring device 1 is abnormal, so that important monitoring is needed, and investigation can be carried out in the field if necessary.
Furthermore, it will be appreciated that for certain periodically varying ecological data, such as atmospheric temperature, the different periods upon which the comparison is based should be based upon the periodically varying contemporaneous data, e.g., the initial vegetation transition zone indicia is determined at the beginning of 1 month, then the corresponding final vegetation transition zone indicia should also be determined at the beginning of 1 month (including the current year and other years).
In one embodiment of the present application, the location pre-warning of the present application may be specifically: generating first early warning information when the position change condition of the vegetation transition zone is abnormal; generating second early warning information when the ecological signal of the vegetation transition zone is abnormal; generating third early warning information when the position change condition of the vegetation transition zone and the ecological signal change are abnormal at the same time; the third warning information is prioritized over the first warning information, and the first warning information is prioritized over the second warning information.
So, first early warning information can be directed against vegetation transition zone change unusual, is favorable to making relevant personnel can grasp vegetation transition zone change unusual position in real time to can in the ecological unbalanced trend of in-time discovery ecological unbalance in the plateau forestry ecological unbalance early stage, be favorable to taking intervention measure or prevention and cure measure in advance, make the plateau forestry ecology can be comparatively quick and timely maintain. The second early warning information can be specific to the abnormal change of the ecological signal, so that related personnel can predict the change condition of the ecological of the future plateau forestry according to the change condition and reasonably, and targeted measures can be taken conveniently to avoid ecological unbalance of the plateau forestry in advance. The third early warning information can be aimed at the condition that the vegetation transition zone is abnormal and the ecological signal is abnormal and occurs at the same time, so that the ecological unbalance of the plateau forestry can be timely reminded, and the reference of corresponding climate change factors can be provided for relevant personnel to study the ecological unbalance of the plateau forestry, so that the inspire is provided for relevant personnel to formulate the protection measure of the plateau forestry ecology similar to the target area.
As shown in fig. 2 to 6, according to a second aspect of the present application, there is provided an ecological monitoring and early warning system for highland forestry, which is applied to the ecological monitoring and early warning method for highland forestry in the first aspect, the ecological monitoring and early warning system for highland forestry includes: the ecological monitoring device 1, the acquisition module 2 and the processing module 3; the ecological monitoring device 1 is used for collecting ecological signals; the acquisition module 2 is used for acquiring a remote sensing image of the target area, position information of the ecological monitoring device 1 and an ecological signal acquired by the ecological monitoring device 1; the processing module 3 is configured to process the obtained remote sensing image, and process the position signal of the ecological monitoring device 1 and the ecological signal obtained by the ecological monitoring device 1, and generate corresponding early warning information.
Like this, through the ecological monitoring early warning system of plateau forestry of this application, can realize monitoring and early warning to plateau forestry ecosystem, can help relevant personnel in early time discernment at the ecological system of plateau forestry to can provide the exploration basis of the reason in the aspect of the climate change for the ecological system unbalance of plateau forestry, thereby also be favorable to making the plateau forestry safeguard measure in this target area with pertinence.
In an implementation manner of the application, the plateau forestry ecological monitoring and early warning system further comprises an early warning module 4, and the early warning module 4 is used for outputting corresponding early warning information according to the early warning information generated by the processing module 3.
It can be understood that the ecological monitoring and early warning system for the plateau forestry can further comprise a display device for displaying early warning information, so that related personnel can visually check the early warning information, and meanwhile, the ecological unbalance position of the plateau forestry can be visually observed.
In the plateau environment, because the altitude of the plateau area is high, the air is thin, the transparency of the atmosphere is high, the attenuation degree of the atmosphere to solar radiation is small, and the solar radiation intensity is high. At this time, solar radiation will cause the measuring instrument to warm up (the temperature measured by the atmospheric temperature measuring instrument 15 is higher than the temperature of the surrounding air, i.e., solar radiation error), and the existing atmospheric temperature measuring instrument 15 cannot accurately measure the atmospheric temperature. And the accurate measurement of the atmospheric temperature can be helpful for relevant personnel to observe the ecological environment of the plateau forestry more accurately.
In view of this, in one embodiment of the present application, as shown in fig. 3 to 6, the ecological monitoring device 1 of the present application may include a cover plate 11, a first plate body 12, a second plate body 13, a solar radiation measurer 14 and an atmospheric temperature measurer 15, where the cover plate 11, the first plate body 12 and the second plate body 13 are sequentially disposed along a vertical direction, the first plate body 12 and the second plate body 13 together enclose an air guiding cavity 16, the atmospheric temperature measurer 15 is disposed between the first plate body 12 and the second plate body 13 and is located in a middle portion of the air guiding cavity 16, and the solar radiation measurer 14 is disposed at a top portion of the cover plate 11; in the vertical direction, the projection of the first plate body 12 falls within the projection of the cover plate 11, the first plate body 12 and the second plate body 13 are coaxially disposed, the end surface of the first plate body 12 near the second plate body 13 is formed into a first air guiding curved surface 121 with an upward bending direction and capable of generating a coanda effect, and the end surface of the second plate body 13 near the first plate body 12 is formed into a second air guiding curved surface 131 with an upward bending direction and capable of generating a coanda effect, so that the first air guiding curved surface 121 and the second air guiding curved surface 131 can jointly enclose the air guiding cavity 16.
First, it should be noted that the Coanda Effect (Coanda Effect) is also called Coanda Effect or Coanda Effect. Refers to the tendency of a fluid to flow away from the original flow direction, instead following a convex object surface. When there is surface friction (i.e., fluid viscosity) between the fluid and the surface of the object over which it flows, the fluid will flow along the surface of the object as long as the curvature is not large.
In this embodiment, on the one hand, the air guiding cavity 16 enclosed by the first plate body 12 and the second plate body 13 of the present application can more smoothly guide the atmospheric fluid (i.e. flowing air) to the atmospheric temperature measurer 15, so that the atmospheric temperature measurer 15 can measure the atmospheric temperature, and in this process, the bending directions of the first air guiding curved surface 121 and the second air guiding curved surface 131 are upward, and in addition, the shielding effect of the first plate body 12 and the second plate body 13 can effectively reduce the interference of solar radiation on the measurement accuracy of the atmospheric temperature measurer 15 (reduce the thermal pollution). On the other hand, since the projection of the first plate body 12 falls within the projection of the cover plate 11, the cover plate 11 of the present application can effectively shield the first plate body 12, reduce the thermal pollution of solar radiation to the first plate body 12, and further reduce the thermal pollution of solar radiation to the atmospheric temperature measurer 15 located below the first plate body 12, and further reduce the interference of solar radiation to the atmospheric temperature measurer 15.
In addition, the ecological monitoring device 1 of the present application may further include a support plate 171, a support rod 172 and a base 173, wherein the support plate 171 is disposed between the first plate body 12 and the second plate body 13 to maintain a space between the first plate body 12 and the second plate body 13, and further includes smoothness of the atmospheric air flow, which is beneficial to ensuring that the atmospheric air temperature measurer 15 can smoothly measure the atmospheric air temperature; the support bar 172 may be provided between the bottom of the second plate 13 and the base 173 to support the entire ecological monitoring device 1, and the base 173 may be used to be installed under the ground to reduce interference with the ecological environment of the ground in a plateau region (and also to facilitate arrangement of a soil temperature measurer and a soil humidity measurer hereinafter).
Furthermore, it is understood that the ecological monitoring device 1 of the present application may further comprise a corresponding atmospheric humidity measurer, soil temperature measurer, soil humidity measurer and precipitation measurer. To measure the ecological signal in the context of the present application.
According to a third aspect of the application, there is also provided a computer device, including a memory, a processor and a computer program stored in the memory and executable by the processor, the processor implementing the plateau forestry ecological monitoring and early warning method according to any one of the above technical solutions when executing the computer program.
According to a fourth aspect of the present application, there is also provided a computer readable storage medium, a non-volatile readable storage medium having instructions stored therein, which when run on a terminal, cause the terminal to perform an upgrade method of plateau forestry ecological monitoring pre-warning as described in any one of the possible implementations of the first aspect and the first aspect.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a register, a hard disk, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (ApplicationSpecific Integrated Circuit, ASIC). In the context of the present application, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, e.g., the partitioning of elements is merely a logical functional partitioning, and there may be additional partitioning in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not implemented. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, indirect coupling or communication connection of devices or units, electrical, mechanical, or other form.
In this application, units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Furthermore, any combination of the various embodiments of the present application may be made, as long as it does not depart from the spirit of the application, which should also be construed as disclosed herein, although the subject matter has been described in language specific to method logic acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (10)
1. The ecological monitoring and early warning method for the plateau forestry is characterized by comprising the following steps of:
marking an initial vegetation transition zone, arranging a plurality of ecological monitoring devices (1) at the boundary of the vegetation transition zone in a target area, and receiving position signals of the ecological monitoring devices (1) by a control unit (5) and drawing linear initial vegetation transition zone marks aiming at the target area in an initial remote sensing image;
drawing a final value vegetation transition zone mark, wherein the control unit (5) acquires a final value remote sensing image of the target area at intervals of preset time after acquiring an initial remote sensing image of the target area, and processes the final value remote sensing image to obtain a linear final value vegetation transition zone mark aiming at the target area;
Identifying the mark change, and comparing the mark of the initial vegetation transition zone with the mark of the final vegetation transition zone to obtain the position change condition of the vegetation transition zone in the target area;
and (3) identifying an ecological signal, and comparing an initial ecological signal corresponding to an initial remote sensing image with a final ecological signal corresponding to a final remote sensing image according to the ecological signal acquired by the ecological monitoring device (1) to obtain an ecological signal change condition of a vegetation transition zone in the target area, wherein the ecological signal comprises: atmospheric temperature, atmospheric humidity, soil temperature, soil humidity, precipitation and solar radiation;
and (3) position early warning, judging the forestry ecological condition in the target area according to the position change condition of the vegetation transition zone and/or the ecological signal change condition, and generating corresponding early warning information.
2. The plateau forestry ecology monitoring and early warning method according to claim 1, wherein the plateau forestry ecology monitoring and early warning method further comprises:
acquiring a plurality of median remote sensing images within the preset time, taking the median remote sensing images and the median vegetation transition zone marks aiming at each median remote sensing image as sample data sets, and training a semantic segmentation depth convolution neural network;
Inputting the final value remote sensing image into a trained semantic segmentation depth convolution neural network, performing binarization processing on the semantic segmentation depth convolution neural network result, and performing closed operation on the binarization processing result to obtain a final value vegetation transition zone mark corresponding to the final value remote sensing image.
3. The plateau forestry ecology monitoring and early warning method according to claim 2, wherein the plateau forestry ecology monitoring and early warning method further comprises:
inputting the initial remote sensing image into a trained semantic segmentation depth convolutional neural network to obtain a vegetation transition zone mark corresponding to the initial remote sensing image, comparing the obtained vegetation transition zone mark with the initial vegetation transition zone mark, and correcting the semantic segmentation depth convolutional neural network according to a comparison result.
4. The method of claim 1, wherein the boundary of the vegetation transition zone comprises: boundaries of arbor and shrub regions, boundaries of shrub and meadow regions.
5. The plateau forestry ecology monitoring and early warning method according to claim 1, characterized in that,
the mark change identification specifically comprises the following steps:
And establishing a coordinate system by taking an initial remote sensing image or a final value remote sensing image as a reference, correspondingly generating the initial vegetation transition zone mark and the final value vegetation transition zone mark in the coordinate system, respectively calculating the position data difference values of the corresponding ecological monitoring devices (1) in the initial vegetation transition zone mark and the final value vegetation transition zone mark, sequentially calculating the absolute value of the slope difference of each two adjacent position data difference values, and regarding the ecological monitoring device (1) corresponding to the slope difference value as a position mutation point of a vegetation transition zone if the absolute value of a certain slope difference value is larger than a first threshold value.
6. The plateau forestry ecology monitoring and early warning method according to claim 1, characterized in that,
the ecological signal identification step specifically comprises the following steps:
and respectively calculating the difference value of a certain ecological signal in the initial vegetation transition zone mark and the corresponding ecological signal in the final vegetation transition zone mark of all the ecological monitoring devices (1), calculating the average value of the difference values, and if the algebraic difference between the certain difference value and the average value is larger than a second threshold value, regarding the ecological monitoring device (1) corresponding to the difference value as an ecological mutation point of the vegetation transition zone.
7. The plateau forestry ecology monitoring and early warning method according to claim 1, characterized in that,
the position early warning specifically comprises the following steps:
generating first early warning information when the position change condition of the vegetation transition zone is abnormal;
generating second early warning information when the ecological signal of the vegetation transition zone is abnormal;
generating third early warning information when the position change condition of the vegetation transition zone and the ecological signal change are abnormal at the same time;
the third early warning information is prioritized over the first early warning information, and the first early warning information is prioritized over the second early warning information.
8. An ecological monitoring and early warning system for plateau forestry, which is applied to the ecological monitoring and early warning method for plateau forestry according to any one of claims 1 to 7, the ecological monitoring and early warning system for plateau forestry comprising:
the ecological monitoring device (1) is used for acquiring ecological signals;
the acquisition module (2) is used for acquiring remote sensing images of the target area, position information of the ecological monitoring device (1) and ecological signals acquired by the ecological monitoring device (1);
the processing module (3) is used for processing the acquired remote sensing image, processing the position signal of the ecological monitoring device (1) and the ecological signal acquired by the ecological monitoring device (1), and generating corresponding early warning information.
9. The plateau forestry ecological monitoring and early warning system according to claim 8, further comprising an early warning module (4), wherein the early warning module (4) is configured to output corresponding early warning information according to the early warning information generated by the processing module (3).
10. The plateau forestry ecological monitoring and early warning system according to claim 8 or 9, wherein the ecological monitoring device (1) comprises a cover plate (11), a first plate body (12), a second plate body (13), a solar radiation measurer (14) and an atmospheric temperature measurer (15), the cover plate (11), the first plate body (12) and the second plate body (13) are sequentially arranged along the vertical direction, the first plate body (12) and the second plate body (13) jointly enclose an air guide cavity (16), the atmospheric temperature measurer (15) is arranged between the first plate body (12) and the second plate body (13) and is positioned in the middle of the air guide cavity (16), and the solar radiation measurer (14) is arranged at the top of the cover plate (11);
the projection of the first plate body (12) falls into the projection of the cover plate (11) in the vertical direction, the first plate body (12) and the second plate body (13) are coaxially arranged, the end face, close to the second plate body (13), of the first plate body (12) is formed into a first wind guiding curved surface (121) with an upward bending direction and capable of generating a coanda effect, the end face, close to the first plate body (12), of the second plate body (13) is formed into a second wind guiding curved surface (131) with an upward bending direction and capable of generating the coanda effect, and the first wind guiding curved surface (121) and the second wind guiding curved surface (131) can jointly enclose the wind guiding cavity (16).
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310752306.1A CN116481600B (en) | 2023-06-26 | 2023-06-26 | Plateau forestry ecological monitoring and early warning system and method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310752306.1A CN116481600B (en) | 2023-06-26 | 2023-06-26 | Plateau forestry ecological monitoring and early warning system and method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116481600A true CN116481600A (en) | 2023-07-25 |
CN116481600B CN116481600B (en) | 2023-10-20 |
Family
ID=87223572
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310752306.1A Active CN116481600B (en) | 2023-06-26 | 2023-06-26 | Plateau forestry ecological monitoring and early warning system and method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116481600B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116664989A (en) * | 2023-07-28 | 2023-08-29 | 四川发展环境科学技术研究院有限公司 | Data analysis method and system based on intelligent environmental element recognition monitoring system |
Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018081043A1 (en) * | 2016-10-24 | 2018-05-03 | Board Of Trustees Of Michigan State University | Methods for mapping temporal and spatial stability and sustainability of a cropping system |
CN111767801A (en) * | 2020-06-03 | 2020-10-13 | 中国地质大学(武汉) | Remote sensing image water area automatic extraction method and system based on deep learning |
CN111854851A (en) * | 2020-08-25 | 2020-10-30 | 贵州师范大学 | Method for monitoring change of ecological environment in karst depression |
CN112381288A (en) * | 2020-11-13 | 2021-02-19 | 西北民族大学 | Ecological management system for grassland in alpine regions |
CN112906638A (en) * | 2021-03-19 | 2021-06-04 | 中山大学 | Remote sensing change detection method based on multi-level supervision and depth measurement learning |
CN113077133A (en) * | 2021-03-19 | 2021-07-06 | 南京大学 | Identification and tracing method for illegal dumping risk area of hazardous waste based on multi-source data |
CN114005048A (en) * | 2021-11-07 | 2022-02-01 | 福建师范大学 | Multi-temporal data-based land cover change and thermal environment influence research method |
CN114022783A (en) * | 2021-11-08 | 2022-02-08 | 刘冰 | Satellite image-based water and soil conservation ecological function remote sensing monitoring method and device |
CN114359703A (en) * | 2021-11-30 | 2022-04-15 | 山东师范大学 | Method and system for quickly identifying shrub distribution range of shrub grassland |
CN114511784A (en) * | 2022-02-16 | 2022-05-17 | 平安国际智慧城市科技股份有限公司 | Environment monitoring and early warning method, device, equipment and storage medium |
CN114662526A (en) * | 2022-02-08 | 2022-06-24 | 王悦文 | Forest land ecological monitoring method based on remote sensing data and multi-temporal SAR (synthetic aperture radar) images |
CN114897860A (en) * | 2022-05-26 | 2022-08-12 | 中国科学院地理科学与资源研究所 | Deduction method and device under influence of land utilization on carbon monoxide pollution |
US20220309772A1 (en) * | 2021-03-25 | 2022-09-29 | Satellite Application Center for Ecology and Environment, MEE | Human activity recognition fusion method and system for ecological conservation redline |
US20220383633A1 (en) * | 2019-10-23 | 2022-12-01 | Beijing University Of Civil Engineering And Architecture | Method for recognizing seawater polluted area based on high-resolution remote sensing image and device |
CN115508357A (en) * | 2022-10-31 | 2022-12-23 | 盐城师范学院 | Monitoring system based on research of diversity of regional ecosystem |
CN115620132A (en) * | 2022-09-30 | 2023-01-17 | 中国科学院西安光学精密机械研究所 | Unsupervised comparative learning ice lake extraction method |
-
2023
- 2023-06-26 CN CN202310752306.1A patent/CN116481600B/en active Active
Patent Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018081043A1 (en) * | 2016-10-24 | 2018-05-03 | Board Of Trustees Of Michigan State University | Methods for mapping temporal and spatial stability and sustainability of a cropping system |
US20220383633A1 (en) * | 2019-10-23 | 2022-12-01 | Beijing University Of Civil Engineering And Architecture | Method for recognizing seawater polluted area based on high-resolution remote sensing image and device |
CN111767801A (en) * | 2020-06-03 | 2020-10-13 | 中国地质大学(武汉) | Remote sensing image water area automatic extraction method and system based on deep learning |
CN111854851A (en) * | 2020-08-25 | 2020-10-30 | 贵州师范大学 | Method for monitoring change of ecological environment in karst depression |
CN112381288A (en) * | 2020-11-13 | 2021-02-19 | 西北民族大学 | Ecological management system for grassland in alpine regions |
CN112906638A (en) * | 2021-03-19 | 2021-06-04 | 中山大学 | Remote sensing change detection method based on multi-level supervision and depth measurement learning |
CN113077133A (en) * | 2021-03-19 | 2021-07-06 | 南京大学 | Identification and tracing method for illegal dumping risk area of hazardous waste based on multi-source data |
US20220309772A1 (en) * | 2021-03-25 | 2022-09-29 | Satellite Application Center for Ecology and Environment, MEE | Human activity recognition fusion method and system for ecological conservation redline |
CN114005048A (en) * | 2021-11-07 | 2022-02-01 | 福建师范大学 | Multi-temporal data-based land cover change and thermal environment influence research method |
CN114022783A (en) * | 2021-11-08 | 2022-02-08 | 刘冰 | Satellite image-based water and soil conservation ecological function remote sensing monitoring method and device |
CN114359703A (en) * | 2021-11-30 | 2022-04-15 | 山东师范大学 | Method and system for quickly identifying shrub distribution range of shrub grassland |
CN114662526A (en) * | 2022-02-08 | 2022-06-24 | 王悦文 | Forest land ecological monitoring method based on remote sensing data and multi-temporal SAR (synthetic aperture radar) images |
CN114511784A (en) * | 2022-02-16 | 2022-05-17 | 平安国际智慧城市科技股份有限公司 | Environment monitoring and early warning method, device, equipment and storage medium |
CN114897860A (en) * | 2022-05-26 | 2022-08-12 | 中国科学院地理科学与资源研究所 | Deduction method and device under influence of land utilization on carbon monoxide pollution |
CN115620132A (en) * | 2022-09-30 | 2023-01-17 | 中国科学院西安光学精密机械研究所 | Unsupervised comparative learning ice lake extraction method |
CN115508357A (en) * | 2022-10-31 | 2022-12-23 | 盐城师范学院 | Monitoring system based on research of diversity of regional ecosystem |
Non-Patent Citations (2)
Title |
---|
江鑫: "利用高分辨率森林覆盖影像实现高山林线的自动提取", 遥感学报, vol. 26, no. 3 * |
盛辉: "基于多时相遥感数据的海岸线自动提取方法", 海洋科学, vol. 45, no. 5 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116664989A (en) * | 2023-07-28 | 2023-08-29 | 四川发展环境科学技术研究院有限公司 | Data analysis method and system based on intelligent environmental element recognition monitoring system |
CN116664989B (en) * | 2023-07-28 | 2023-09-29 | 四川发展环境科学技术研究院有限公司 | Data analysis method and system based on intelligent environmental element recognition monitoring system |
Also Published As
Publication number | Publication date |
---|---|
CN116481600B (en) | 2023-10-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Friedli et al. | Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions | |
Peper et al. | Comparison of five methods for estimating leaf area index of open-grown deciduous trees | |
Zeshan et al. | Monitoring land use changes and their future prospects using GIS and ANN-CA for Perak River Basin, Malaysia | |
CN116481600B (en) | Plateau forestry ecological monitoring and early warning system and method | |
Jiang et al. | Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera | |
Confalonieri et al. | PocketPlant3D: Analysing canopy structure using a smartphone | |
US20180308229A1 (en) | Method and information system for detecting at least one plant planted on a field | |
Pearse et al. | Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation | |
CN103208028A (en) | Waterfowl habitat suitability evaluation method based on combination of remote sensing and geographical information system (GIS) | |
CN104236486B (en) | A kind of cotton leaf area index quick nondestructive assay method | |
Li et al. | Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images | |
CN109946714A (en) | A kind of method for building up of the forest biomass model based on LiDAR and ALOS PALSAR multivariate data | |
CN107480721A (en) | A kind of ox only ill data analysing method and device | |
CN115294147A (en) | Method for estimating aboveground biomass of single trees and forests based on unmanned aerial vehicle laser radar | |
Thi Phan et al. | Method for estimating rice plant height without ground surface detection using laser scanner measurement | |
Mokroš et al. | Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry | |
CN116108990A (en) | Grape downy mildew prediction method based on subleaf boundary layer humidity | |
Liu et al. | Applicability of the crop water stress index based on canopy–air temperature differences for monitoring water status in a cork oak plantation, northern China | |
CN106097372A (en) | Crop plant water stress Phenotypic examination method based on image procossing | |
Beyaz et al. | Canopy analysis and thermographic abnormalities determination possibilities of olive trees by using data mining algorithms | |
CN112488230A (en) | Crop water stress degree judging method and device based on machine learning | |
CN116911012A (en) | Overhead power line protection area hidden danger tree species height prediction model and construction method thereof | |
Yang et al. | Variability in minimal-damage sap flow observations and whole-tree transpiration estimates in a coniferous forest | |
Georganos | Exploring the spatial relationship between NDVI and rainfall in the semi-arid Sahel using geographically weighted regression | |
Clark et al. | Three decades of annual growth, mortality, physical condition, and microsite for ten tropical rainforest tree species. |
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 | ||
GR01 | Patent grant | ||
GR01 | Patent grant |