Forest resource dynamic monitoring system and method

Abstract

The invention relates to the technical field of forest resource monitoring, in particular to a forest resource dynamic monitoring system and method, and the system comprises a data collection layer, a data transmission layer, a data processing and analysis layer and a user interaction layer. The data acquisition layer is used for collecting multi-source original data, extending to each corner of a forest region, from an unmanned aerial vehicle in the air, from a sensor on the ground and from a satellite in the outer space, grabbing various data such as a crown, terrain and environment, and laying a foundation for subsequent monitoring analysis; according to the invention, unmanned aerial vehicle aerial survey, a ground sensor network and a satellite remote sensing receiving module are integrated, multi-source data collaborative acquisition is carried out, macroscopic to microscopic levels of a forest are covered in an omnibearing manner, an unmanned aerial vehicle can flexibly capture local high-precision images, a ground sensor senses a microenvironment in real time, and a satellite guarantees large-area long-term observation; the data acquisition period is greatly shortened, the temporal-spatial resolution of forest resource monitoring is remarkably improved, and tree growth and distribution details are accurately drawn.

Description

Technical Field

[0001] The present invention relates to the technical field of forest resource monitoring, and in particular to a forest resource dynamic monitoring system and method. Background Art

[0002] As the world pays more and more attention to ecological and environmental protection, the precise monitoring and scientific management of forest resources, as a core component of terrestrial ecosystems, are becoming increasingly critical.

[0003] Traditional forest resource monitoring methods have many limitations: on the one hand, they rely on manual field surveys, which are not only labor-intensive and inefficient, but also difficult to conduct high-frequency inspections of large areas of remote forest areas, resulting in a long data update cycle and difficulty in capturing the dynamic changes of forest resources in a timely manner; on the other hand, a single data acquisition channel, such as relying solely on ground observation stations, can only obtain local and limited-dimensional environmental and resource information, and cannot construct a comprehensive picture of forest resources, and lacks a coordinated analysis from a macro perspective.

[0004] Although satellite remote sensing technology can provide macro-scale data, its resolution is limited and it is difficult to accurately identify the growth status of individual trees; drone aerial surveys are limited by endurance and weather conditions, and data collection is not consistent; ground sensors have a small monitoring range, and isolated data cannot be integrated into the overall dynamic analysis of forest resources. These scattered monitoring methods lack coordination and integration, resulting in data fragmentation, which cannot provide forestry management departments with a systematic, comprehensive and timely decision-making basis, seriously hindering the scientific conservation and rational development and utilization of forest resources. To this end, a dynamic monitoring system and method for forest resources is proposed. Summary of the invention

[0005] In view of this, the present invention provides a forest resource dynamic monitoring system and method to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0006] The technical solution of the present invention is implemented as follows: a dynamic monitoring system for forest resources, including a data collection layer, a data transmission layer, a data processing and analysis layer and a user interaction layer;

[0007] The data collection layer collects raw data from multiple sources, extending to every corner of the forest area, from drones in the air, to sensors on the ground, to satellites in space, capturing various data such as forest canopy, terrain, and environment, laying the foundation for subsequent monitoring and analysis;

[0008] The data transmission layer is responsible for transmitting data, building a bridge between the data collection end and the processing end, and transmitting the data collected by sensors, drones, and satellites scattered everywhere to the data processing center, so that subsequent analysis can be carried out on time;

[0009] The data processing mentioned above mines the value of data by first sorting and purifying the messy raw data, and then using intelligent algorithms for in-depth analysis to gain insight into the dynamic changes in the quantity, quality and distribution of forest resources, and transform the raw data into useful intelligence for decision-making;

[0010] The analysis layer and user interaction layer connect people and systems, equip managers with convenient terminals to obtain information and feedback on on-site conditions at the front line. On the other hand, a web platform is built for superior departments to remotely coordinate and manage, allowing managers to seamlessly connect with the monitoring system and issue instructions and allocate resources based on the monitoring results.

[0011] Further preferably, the data collection layer includes a UAV aerial survey module, a ground sensor network and a satellite remote sensing receiving module. The UAV aerial survey module uses the onboard optical camera and lidar to capture forest details over a large area, the ground sensor network continuously collects forest microenvironment data, and the satellite remote sensing receiving module overlooks the macro forest with optical satellite images and radar satellite data.

[0012] Further preferably, the data transmission layer includes a wireless communication network and a data cache module. The wireless communication network uses communication technologies such as 4G / 5G and Beidou short messages to quickly send data scattered throughout the forest area to the processing center. When the data cache module encounters accidents such as communication failures and network congestion, the newly collected data is temporarily stored here and uploaded in full and orderly after the network is restored.

[0013] Further preferably, the data processing and analysis layer includes a data preprocessing subsystem, an intelligent analysis module and a visualization subsystem. The data preprocessing subsystem organizes data of different formats, containing noise and chaotic coordinates, unifies the format, removes noise, and calibrates the coordinates, so that subsequent intelligent algorithms can accurately process the data. The intelligent analysis module uses deep learning, machine learning and GIS technology. Deep learning interprets images to accurately classify tree species and estimate growth parameters. Machine learning predicts growth trends. GIS integrates spatial information to present the spatial distribution of forest resources and explores the quantitative and qualitative changes of forest resources behind the data. The visualization subsystem converts the analysis results into intuitive two-dimensional maps and three-dimensional models.

[0014] Further preferably, the user interaction layer includes a monitoring terminal and a Web management platform. When the monitoring terminal is patrolling in the field, it can call up the latest monitoring data and receive early warnings anytime and anywhere. If the actual situation is found to be inconsistent with the system, feedback and corrections will be given. The Web management platform can log in to the webpage to have an overall view, view historical and real-time reports, remotely issue monitoring instructions, and flexibly adjust parameters.

[0015] Further preferably, the vegetation coverage calculation formula is as follows:

[0016]

[0017] Among them, FVC is the vegetation coverage, and NDVI is the normalized vegetation index, which is calculated through specific bands of satellite remote sensing images. ρ NIR , RED They are the reflectance of the near-infrared band and the red light band; NDVI soil is the Normalized Difference Vegetation Index of bare soil, NDVI veg It is the normalized vegetation index of pure vegetation coverage pixels. Vegetation coverage can intuitively reflect the coverage degree of forest on the surface and is used to assess the overall scale of forest resources.

[0018] A method for dynamic monitoring of forest resources comprises the following steps:

[0019] Step 1: Data collection process;

[0020] Step 2: Data analysis process;

[0021] Step 3: Early warning and response process.

[0022] Further preferably, in the data collection process, the drone conducts full-area cruise aerial surveys on a weekly or monthly basis, the ground sensors continuously collect environmental data, and the satellite pushes remote sensing images based on its orbital period to construct a multi-time scale data set. When extreme weather events such as fire and rainstorm occur in the forest area, or when illegal logging is suspected, the drone takes off immediately to focus on monitoring the affected area, and the ground sensors encrypt the data collection frequency to obtain detailed data before and after the event to assist in subsequent disaster assessment and damage tracing. The data analysis process extracts texture, shape, and spectral features from the image data, and extracts temperature and humidity change trends, light duration and other features from the sensor data as the basic elements for identifying and evaluating forest resources, and compares the current data features with the historical data for the same period to calculate Calculate the change rate of the quantity and quality indicators of forest resources, evaluate the growth and extinction of forests; compare with the preset ecological threshold to judge the ecological health status of the forest. Once an indicator exceeds the threshold, the early warning mechanism is immediately triggered. The early warning and response process is based on the data analysis results. When abnormal situations such as the outbreak trend of forest pests and diseases, excessive decline in forest coverage, and increased fire hazards are monitored, the system automatically generates early warning information, which is graded according to the severity and pushed to the relevant management personnel terminal through SMS and system messages. After receiving the early warning, the management personnel will allocate human and material resources according to the warning level and details, and organize targeted actions such as pest control, fire fighting, and illegal logging investigation. After the action is completed, the monitoring system continues to follow up, evaluate the disposal effect, and feedback to the subsequent monitoring and decision-making process.

[0023] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0024] 1. The present invention integrates drone aerial survey, ground sensor network and satellite remote sensing receiving module, and collects multi-source data in a coordinated manner, covering the macro and micro levels of the forest in all directions. The drone can flexibly capture local high-precision images, the ground sensor can perceive the microenvironment in real time, and the satellite can ensure long-term observation of large areas. The three complement each other, greatly shortening the data acquisition cycle, significantly improving the spatiotemporal resolution of forest resource monitoring, and accurately outlining the growth and distribution details of trees.

[0025] 2. The present invention uses a stable and efficient data transmission layer, whether it is temporary aerial survey data from drones in remote forest areas or continuously updated ground sensor data, to be seamlessly transmitted to the data processing center. It is equipped with a data cache module to prevent packet loss and ensure data continuity. Various types of data are fused and analyzed after pre-processing, breaking the previous data island situation, fully presenting the complex structure and dynamic evolution process of the forest ecosystem, and laying a solid data foundation for subsequent decision-making.

[0026] 3. The data processing and analysis layer of the present invention uses intelligent algorithms to deeply analyze massive monitoring data. It can not only accurately identify tree species and estimate timber volume, but also predict forest ecological change trends, converting raw data into highly forward-looking decision-making information. The visualization subsystem makes obscure data intuitive and visual, assisting managers to quickly understand forest conditions, formulate targeted strategies, ensure the sustainable development of forest resources, and achieve both ecological and economic benefits.

[0027] 4. The monitoring terminal and Web management platform of the user interaction layer of the present invention enable front-line personnel to interact with management in real time. Management personnel can obtain first-hand monitoring data anytime and anywhere, respond to emergencies in a timely manner, and the system will continue to follow up and evaluate the results after handling, so that forest resource management forms a closed loop, improves management efficiency, reduces disaster losses and illegal destruction risks, and protects forest ecological security.

[0028] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0031] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0032] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] like Figure 1 As shown, an embodiment of the present invention provides a dynamic monitoring system for forest resources, including a data collection layer, a data transmission layer, a data processing and analysis layer, and a user interaction layer;

[0034] The data collection layer collects raw data from multiple sources, extending to every corner of the forest area, from drones in the air, to sensors on the ground, to satellites in space, capturing various data such as forest canopy, terrain, and environment, laying the foundation for subsequent monitoring and analysis;

[0035] The data transmission layer is responsible for transmitting data and building a bridge between the data collection end and the data processing end. It transmits the data collected by sensors, drones, and satellites scattered everywhere to the data processing center so that subsequent analysis can be carried out on time.

[0036] Data processing, mining data value, first sorting and purifying the messy raw data, and then using intelligent algorithms for in-depth analysis, gaining insight into the dynamic changes in the quantity, quality, and distribution of forest resources, and transforming the raw data into useful intelligence for decision-making;

[0037] The analysis layer and the user interaction layer connect people and systems, and equip managers with convenient terminals to obtain information and feedback on on-site conditions at the front line. On the other hand, a web platform is built for superior departments to coordinate and manage remotely, allowing managers to seamlessly connect with the monitoring system and issue instructions and allocate resources based on the monitoring results.

[0038] In one embodiment, the data collection layer includes a UAV aerial survey module, a ground sensor network and a satellite remote sensing receiving module. The UAV aerial survey module uses the onboard optical camera and lidar to capture forest details over a large area. The ground sensor network continuously collects forest microenvironment data. The satellite remote sensing receiving module uses optical satellite images and radar satellite data to overlook the macro forest.

[0039] In one embodiment, the data transmission layer includes a wireless communication network and a data cache module. The wireless communication network uses communication technologies such as 4G / 5G and Beidou short messages to quickly send data scattered throughout the forest area to the processing center. When the data cache module encounters accidents such as communication failures and network congestion, the newly collected data will be temporarily stored here and uploaded in full and orderly after the network is restored.

[0040] In one embodiment, the data processing and analysis layer includes a data preprocessing subsystem, an intelligent analysis module and a visualization subsystem. The data preprocessing subsystem organizes data of different formats, noise and chaotic coordinates, unifies the format, removes noise and calibrates the coordinates, so that the subsequent intelligent algorithm can accurately process the data. The intelligent analysis module uses deep learning, machine learning and GIS technology. Deep learning interprets images to accurately classify tree species and estimate growth parameters. Machine learning predicts growth trends. GIS integrates spatial information to present the spatial distribution of forest resources and explores the quantitative and qualitative changes of forest resources behind the data. The visualization subsystem turns the analysis results into intuitive two-dimensional maps and three-dimensional models.

[0041] In one embodiment, the user interaction layer includes a monitoring terminal and a Web management platform. When the monitoring terminal is patrolling in the field, it can call up the latest monitoring data and receive early warnings anytime and anywhere. If the actual situation is found to be inconsistent with the system, feedback and corrections are given. The Web management platform logs in to the web page to have an overview of the overall situation, view historical and real-time reports, remotely issue monitoring instructions, and flexibly adjust parameters.

[0042] In one embodiment, the vegetation coverage calculation formula is as follows:

[0043]

[0044] Among them, FVC is the vegetation coverage, and NDVI is the normalized vegetation index, which is calculated through specific bands of satellite remote sensing images. ρ NIR , RED They are the reflectance of the near-infrared band and the red light band; NDVI soil is the Normalized Difference Vegetation Index of bare soil, NDVI veg It is the normalized vegetation index of pure vegetation coverage pixels. Vegetation coverage can intuitively reflect the coverage degree of forest on the surface and is used to assess the overall scale of forest resources.

[0045] A method for dynamic monitoring of forest resources comprises the following steps:

[0046] Step 1: Data collection process;

[0047] Step 2: Data analysis process;

[0048] Step 3: Early warning and response process.

[0049] In one embodiment, in the data collection process, the drone conducts full-area cruise aerial surveys on a weekly or monthly basis, the ground sensors continuously collect environmental data, and the satellite pushes remote sensing images based on its orbital period to build a multi-time scale data set. When extreme weather events such as fires and rainstorms occur in the forest area, or when illegal logging is suspected, the drone takes off immediately to focus on monitoring the affected area. The ground sensors encrypt the data collection frequency to obtain detailed data before and after the event to assist in subsequent disaster assessment and damage tracing. The data analysis process extracts texture, shape, and spectral features from the image data, and extracts temperature and humidity change trends, light duration and other features from the sensor data as the basic elements for identifying and evaluating forest resources. The current data features are compared with the historical data of the same period to calculate The change rate of the quantity and quality indicators of forest resources is used to evaluate the growth and extinction of forests; compared with the preset ecological thresholds, the ecological health status of the forest is judged. Once an indicator exceeds the threshold, the early warning mechanism is immediately triggered. The early warning and response process is based on the data analysis results. When abnormal situations such as the outbreak trend of forest pests and diseases, excessive decline in forest coverage, and increased fire hazards are monitored, the system automatically generates early warning information, which is graded according to the severity and pushed to the relevant management personnel terminals via SMS and system messages. After receiving the early warning, the management personnel will allocate human and material resources according to the warning level and details, and organize targeted actions such as pest control, fire fighting, and illegal logging investigation. After the action is completed, the monitoring system continues to follow up, evaluate the disposal effect, and feedback to the subsequent monitoring and decision-making process.

[0050] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A dynamic monitoring system for forest resources, characterized in that: It includes data collection layer, data transmission layer, data processing and analysis layer and user interaction layer; The data collection layer collects raw data from multiple sources, extending to every corner of the forest area, from drones in the air, to sensors on the ground, to satellites in space, capturing various data such as forest canopy, terrain, and environment, laying the foundation for subsequent monitoring and analysis; The data transmission layer is responsible for transmitting data, building a bridge between the data collection end and the processing end, and transmitting the data collected by sensors, drones, and satellites scattered everywhere to the data processing center, so that subsequent analysis can be carried out on time; The data processing mentioned above mines the value of data by first sorting and purifying the messy raw data, and then using intelligent algorithms for in-depth analysis to gain insight into the dynamic changes in the quantity, quality and distribution of forest resources, and transform the raw data into useful intelligence for decision-making; The analysis layer and user interaction layer connect people and systems, equip managers with convenient terminals to obtain information and feedback on on-site conditions at the front line. On the other hand, a web platform is built for superior departments to remotely coordinate and manage, allowing managers to seamlessly connect with the monitoring system and issue instructions and allocate resources based on the monitoring results.

2. A dynamic monitoring system for forest resources according to claim 1, characterized in that: The data collection layer includes a UAV aerial survey module, a ground sensor network and a satellite remote sensing receiving module. The UAV aerial survey module uses the optical camera and lidar on board to capture forest details over a large area. The ground sensor network continuously collects forest microenvironment data. The satellite remote sensing receiving module overlooks the macro forest with optical satellite images and radar satellite data.

3. A dynamic monitoring system for forest resources according to claim 1, characterized in that: The data transmission layer includes a wireless communication network and a data cache module. The wireless communication network uses communication technologies such as 4G / 5G and Beidou short messages to quickly send data scattered throughout the forest area to the processing center. When the data cache module encounters accidents such as communication failures and network congestion, the newly collected data will be temporarily stored here and uploaded in full and orderly after the network is restored.

4. A dynamic monitoring system for forest resources according to claim 1, characterized in that: The data processing and analysis layer includes a data preprocessing subsystem, an intelligent analysis module and a visualization subsystem. The data preprocessing subsystem organizes data of different formats, noise and chaotic coordinates, unifies the format, removes noise and calibrates the coordinates, so that the subsequent intelligent algorithm can accurately process the data. The intelligent analysis module uses deep learning, machine learning and GIS technology. Deep learning interprets images to accurately classify tree species and estimate growth parameters. Machine learning predicts growth trends. GIS integrates spatial information, presents the spatial distribution of forest resources, and explores the quantitative and qualitative changes of forest resources behind the data. The visualization subsystem converts the analysis results into intuitive two-dimensional maps and three-dimensional models.

5. A dynamic monitoring system for forest resources according to claim 1, characterized in that: The user interaction layer includes a monitoring terminal and a Web management platform. When the monitoring terminal is patrolling in the field, it can call up the latest monitoring data and receive early warnings anytime and anywhere. If the actual situation is found to be inconsistent with the system, feedback and corrections will be given. The Web management platform can log in to the webpage to have an overall view, view historical and real-time reports, remotely issue monitoring instructions, and flexibly adjust parameters.

6. A dynamic monitoring system for forest resources according to claim 1, characterized in that: The calculation formula of vegetation coverage is as follows: Among them, FVC is the vegetation coverage, and NDVI is the normalized vegetation index, which is calculated through specific bands of satellite remote sensing images. ρ NIR , RED They are the reflectance of the near-infrared band and the red light band; NDVI soil is the Normalized Difference Vegetation Index of bare soil, NDVI veg It is the normalized vegetation index of pure vegetation coverage pixels. Vegetation coverage can intuitively reflect the degree of forest coverage on the surface and is used to assess the overall scale of forest resources.

7. A method for dynamic monitoring of forest resources, supporting a dynamic monitoring system for forest resources as claimed in any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: Data collection process; Step 2: Data analysis process; Step 3: Early warning and response process.

8. A forest resource dynamic monitoring system and method according to claim 1, characterized in that: In the data collection process, drones conduct full-area cruise surveys on a weekly or monthly basis, ground sensors continuously collect environmental data, and satellites push remote sensing images based on their orbital cycles to build a multi-time scale data set. When extreme weather events such as fires and rainstorms occur in forest areas, or when illegal logging is suspected, drones take off immediately to focus on monitoring the affected areas. Ground sensors encrypt data collection frequency to obtain detailed data before and after the event to assist in subsequent disaster assessment and damage tracing. In the data analysis process, texture, shape, and spectral features are extracted from image data, and temperature and humidity change trends, light duration and other features are extracted from sensor data as basic elements for identifying and evaluating forest resources. Current data features are compared with historical data from the same period to calculate forest resources. The change rate of the quantity and quality indicators of the source is used to evaluate the growth and extinction of the forest; compared with the preset ecological threshold, the ecological health status of the forest is judged. Once an indicator exceeds the threshold, the early warning mechanism is immediately triggered. The early warning and response process is based on the data analysis results. When abnormal situations such as the outbreak trend of forest pests and diseases, excessive decline in forest coverage, and increased fire hazards are monitored, the system automatically generates early warning information, which is classified by severity and pushed to the relevant management personnel terminal through SMS and system messages. After receiving the early warning, the management personnel will deploy human and material resources according to the warning level and details, and organize targeted actions such as pest control, fire fighting, and illegal logging investigation. After the action is completed, the monitoring system continues to follow up, evaluate the disposal effect, and feedback to the subsequent monitoring and decision-making process.

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