Forest resource management platform based on satellite data

Abstract

The invention relates to the technical field of forest data management, in particular to a forest resource management platform based on satellite data, which comprises a data acquisition module, a data processing module, an emergency early warning module and a personal APP module, and is characterized in that the data processing module divides forest resources into elderly ancient wood resources and general forest resources. A first-level core protection area and a second-level buffer area are respectively set up for different old ancient trees, indexes such as forest stand volume V, forest stand canopy density C and tree density D are accurately measured and calculated for general tree resources, a rotation area and a logging area are scientifically planned according to the indexes, a dynamic monitoring and evaluation mechanism is established, sustainable utilization of forest resources is ensured, and the forest resource utilization rate is increased. Refining and scientization of forest resource management are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of forest data management, and in particular to a forest resource management platform based on satellite data. Background Art

[0002] Forest resources are one of the most important natural resources on earth. They not only provide humans with abundant material resources such as wood, medicinal materials, and food, but also play an irreplaceable role in maintaining ecological balance, regulating climate, conserving water and soil, purifying air, and protecting biodiversity. With the growth of the global population and economic development, human demand for forest resources continues to increase. Forest resources are facing many threats such as over-exploitation, logging, fire, pests and diseases, and the difficulty of forest resource management is increasing.

[0003] Traditional forest resource management methods mainly rely on manual field surveys and monitoring, which have many limitations. On the one hand, manual surveys are inefficient and require a lot of manpower, material resources and time costs, making it difficult to achieve high-frequency and refined monitoring of large-scale forest resources. For example, in the survey of forest vegetation coverage, manual measurement can only obtain data from limited sample points, which cannot fully reflect the true situation of the entire forest area, resulting in poor data accuracy and timeliness. On the other hand, the coverage of manual monitoring is limited. In remote forest areas with complex terrain and inconvenient transportation, it is difficult to conduct comprehensive and in-depth surveys, and monitoring blind spots are prone to occur, so that some forest disasters (such as fires, pests and diseases) cannot be discovered and handled in time, thus causing serious losses to forest resources. In addition, traditional management methods are also insufficient in data integration and analysis. Forest resource data from different sources and different periods are often stored in a scattered manner, lacking an effective integration platform, making it difficult to conduct comprehensive analysis and in-depth mining, and unable to provide strong data support for the scientific planning, rational utilization and precise protection of forest resources. For example, when formulating a forest harvesting plan, the lack of comprehensive and accurate forest resource data may lead to unreasonable harvesting volume and affect the sustainable development of forests.

[0004] In summary, traditional forest resource management methods can no longer meet the needs of modern forest resource management, and there is an urgent need for an efficient, accurate, and intelligent forest resource management method to cope with the increasingly severe challenges of forest resource protection and management. The forest resource management platform based on satellite data of the present invention aims to use satellite technology to achieve all-round, real-time monitoring and scientific management of forest resources, improve the efficiency and level of forest resource management, and protect the stability and sustainable development of forest ecosystems. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a forest resource management platform based on satellite data, thereby solving the technical problems mentioned in the background technology.

[0006] To achieve the above object, the present invention is realized by the following technical solutions:

[0007] A forest resource management platform based on satellite data, comprising a data acquisition module, a data processing module, an emergency warning module and a personal APP module; the data processing module divides forest resources into ancient and venerable trees and general forest resources, and the general tree resources calculate a comprehensive index I according to the stand volume V, the stand density C and the tree density D R = 0.3×V + 0.4×C + 0.3×D, when I R < I R1 wherein I R1 is set to 0.6, and this forest area is planned as a rotation area; when the comprehensive index I F = 0.4×V + 0.4×C + 0.2×D satisfies I F > I F1 wherein I F1 is set to 0.7, and the tree density D > D 1 where D 1 is accurately set according to the growth characteristics of different tree species and the optimal forest ecological carrying capacity model. For coniferous forests, D 1 is 700 trees per hectare, and for broad-leaved forests, D 1 is 900 trees per hectare, then this area is planned as a logging area; when the comprehensive index I of the rotation area R increases by more than 0.2 for two consecutive years, and at the same time the tree density D is stable in the appropriate interval [D min2 , D max2 , for common coniferous-broad-leaved mixed rotation forests, D min2 is set to 550 trees per hectare, D max2 is 750 trees per hectare, and the average tree height H of the stand increases by more than 0.5 meters per year. At this time, this area is moderately converted to a logging area; conversely, if the comprehensive index I of the logging area F < 0.6 after logging, and the stand density C < 0.4, and the tree density is sparse to affect ecological stability, it is immediately converted to a rotation area to implement ecological restoration and reconstruction planning.

[0008] In a possible implementation manner, the protection of ancient and venerable trees uses tree age detection radar technology to identify ancient and venerable trees, uses the GIS system for positioning and marking, sets up different protection areas for ancient trees of different ages, installs monitoring equipment in the protection areas, and establishes exclusive protection files to record the information of ancient trees.

[0009] In a possible implementation, during the process of collecting forest resource data, the data collection module constructs a database by using a method based on image segmentation and change detection. Threshold segmentation method is used for image segmentation. Based on the gray value difference of image pixels, for the blue band B, green band G, red band R, and near-infrared band NIR of the satellite images used for forest resource monitoring, the threshold of the near-infrared band region is set as The corresponding threshold of the red band is where S is considered as the season factor, and the corresponding values for the four seasons are 0 - 3. The pixel values at different coordinates (x, y) satisfy and Then it indicates that (x, y) belongs to the forest area, otherwise, it does not belong to the forest area.

[0010] In a possible implementation, the emergency warning module includes a logging supervision sub-module, a pest warning sub-module, and a fire warning sub-module.

[0011] In a possible implementation, the logging supervision sub-module uses big data analysis combined with an ecological rhythm model to set the logging time period [T start , T end . With satellite supervision synchronization, when the current time T current > T end or T current < T start , the warning mechanism is immediately activated, and the ranger is notified that the logging time is abnormal. The number and distribution changes of trees are monitored through high-resolution satellite remote sensing images. Let N t be the number of trees in the monitored area at time t, N t-1 be the number of trees at the previous monitoring time t - 1. ΔN = N t - N t-1 represents the change in the number of trees between two monitoring times. Let P t be the number of tree pixels in the forest area at time t, P t-1 be the number of tree pixels in the forest area at the previous time. ΔP = P t - P t-1 . During the set logging time period [T start , T end , if ΔN min ≤ ΔN ≤ ΔN max and ΔP min ≤ ΔP ≤ ΔP max , the system determines that it is normal logging operation. If ΔN ≤ ΔN min and ΔP ≤ ΔP min , the illegal logging warning is immediately triggered, and the ranger is notified that the logging scale is abnormal.

[0012] In a possible implementation, the pest and disease warning sub-module relies on a satellite remote sensing system to conduct multi-spectral scanning and imaging of the forest area three times a week. Let the average NDVI of healthy vegetation in the forest area be μ NDVI , with a standard deviation of σ NDVI . If the results of four consecutive forest area monitoring sessions satisfy NDVI < μ NDVI - kσ NDVI , where k is an empirical coefficient, and the EVI decline exceeds 15%, mark the area as a suspected pest and disease zone. The forest rangers conduct a grid-based detailed inspection of this zone, and in combination with meteorological data, use big data deep learning algorithms to judge the outbreak situation of pests and diseases.

[0013] In a possible implementation, the forest fire warning sub-module constructs a real-time forest fire risk assessment system based on the surface temperature T s and the relative humidity RH. Based on the vegetation temperature anomaly index where is the historical average surface temperature of the same period, σT s is the standard deviation of the surface temperature, the vegetation water supply index and the meteorological drought index MDI for classification warning, and corresponding measures are taken at each level. When T ani > 0.8, VSWI < 0.4 and MDI ≥ 3, initiate a first-level warning. The forest rangers use drones and satellites to focus on the temperature of this area at 20 minutes per time. When T ani > 1.2, VSWI < 0.2 and MDI ≥ 4, initiate a second-level warning. 60% of the fire-fighting forces quickly move forward to this area, and fire trucks and fire bomb extinguishing devices are arranged in advance. Supplies are quickly allocated according to the preset plan. When T ani > 1.5, VSWI < 0.1 and MDI ≥ 5, initiate an emergency warning. All fire-fighting forces participate in extinguishing the fire, and a firebreak is set up. The firebreak is dynamically set according to the terrain slope S ≤ 45° and S > 45° according to the formulas and to ensure blocking the spread of the fire.

[0014] In a possible implementation, the personal APP module is used to receive warning information, record patrol conditions, and locate trees. When a warning is triggered, the personal APP module pushes the warning level, the coordinates of the incident location, and key indicator data in the form of pop-up windows, large characters, and voice broadcasts.

[0015] In a possible implementation, the data processing module and the emergency warning module are both set on the cloud platform, the data acquisition module is set on the satellite, and the personal APP module is installed on the mobile terminal of the forest ranger.

[0016] Beneficial effects compared with the prior art:

[0017] 1. In this solution, the data processing module is set up on the cloud platform. After receiving the data collected by satellites, it uses the tree age detection radar technology to accurately identify ancient and old trees, locates and marks them with the GIS system, sets up a first-level core protection area and a second-level buffer zone for different ancient and old trees respectively. A variety of sensors and monitoring devices are installed in the protection areas, and exclusive protection files are established for the ancient trees to record information. For general tree resources, through the collaborative operation of lidar satellites, high-resolution satellite images, and equipment carried by drones, indicators such as stand volume, canopy density, and tree density are accurately measured. Based on this, the rotation area and logging area are scientifically planned, and a dynamic monitoring and evaluation mechanism is established to ensure the sustainable use of forest resources, realizing the refinement and scientific management of forest resources;

[0018] 2. In this solution, the emergency warning module is the key guarantee for forest ecological security. The logging supervision sub-module uses big data analysis combined with the ecological rhythm model to set the logging time period, and satellite supervision is synchronized. The number and distribution changes of trees are monitored at fixed intervals through high-resolution satellite remote sensing images, and the nature of the operation is determined by comparing with the legal logging threshold, effectively preventing illegal logging. The pest warning sub-module relies on the satellite remote sensing system to conduct multi-spectral scanning and imaging of the forest area multiple times a week, pays attention to the vegetation index, and forest rangers conduct grid-based inspections of suspicious areas. Combining meteorological data and using big data deep learning algorithms to accurately warn of pests and diseases and organize prevention and control in a timely manner. The fire warning sub-module constructs a real-time evaluation system based on multi-source data, conducts hierarchical warnings based on vegetation temperature anomaly index, vegetation water supply index, and meteorological drought index, etc. At each level, measures such as lookout tower observation, drone inspection, fire fighting force pre-positioning, and dynamic planning of fire fighting routes are taken. Combining meteorological and terrain information, it provides decision-making support for fire fighting operations and comprehensively protects forest safety;

[0019] 3. In this solution, the personal APP module is specifically designed for forest rangers and is installed on mobile terminals. Once an alarm is triggered, the APP immediately pushes the alarm level, accurate coordinates of the incident location, and key index data in the form of pop-up windows, eye-catching large characters, and voice broadcasts to ensure timely information transmission. It also has a patrol record function. When forest rangers conduct patrols, they can take pictures, record sounds, and note abnormalities and upload logs. The management terminal accurately dispatches tasks based on this to achieve efficient collaboration. In addition, the positioning function of the APP enables forest rangers to accurately locate specific trees, understand the detailed situation of the trees, enables forest rangers to comprehensively grasp the forest situation, improves the work efficiency of forest rangers, and enhances the coordination and accuracy of forest management. Brief Description of the Drawings

[0020] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following will be described in detail with reference to the preferred embodiments of the present invention and the accompanying drawings.

[0021] Figure 1Framework diagram of the forest resource management platform based on satellite data of the present invention;

[0022] Figure 2 Operation diagram of the forest resource management platform based on satellite data of the present invention. Detailed implementation manners

[0023] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various different forms. Therefore, the present invention is not limited to the embodiments described below. In addition, in order to describe the present invention more clearly, components not connected to the invention will be omitted from the drawings;

[0024] The technical solutions in the embodiments of the present application are to solve the problems in the above-mentioned background technology. The general idea is as follows:

[0025] Embodiment:

[0026] Please refer to Figure 1 and Figure 2 As shown, this embodiment introduces a forest resource management platform based on satellite data. The management platform realizes the management of forest resources through a forest resource management system based on satellite data. The management system includes a data acquisition module, a data processing module, an emergency warning module, and a personal APP module.

[0027] 1. Data acquisition module

[0028] The data acquisition module is installed on the satellite and is used for the determination of the forest resource area and the acquisition of data. During the acquisition of forest resource data, a method based on image segmentation and change detection is used to construct a comprehensive and accurate database. Among them, threshold segmentation method is used for image segmentation. Specifically as follows:

[0029] The threshold segmentation method performs segmentation operations based on the gray value differences of image pixels. For the satellite images used in forest resource monitoring, it contains multiple spectral band information, such as blue light band (denoted as B), green light band (denoted as G), red light band (denoted as R), and near-infrared band (denoted as NIR), etc. Different ground objects have different spectral reflection characteristics in these bands. Forest vegetation usually has a higher reflectivity in the near-infrared band and a relatively lower reflectivity in the red light band. This "vegetation red edge" feature is one of the key bases for distinguishing forests from other ground objects.

[0030] Let f(x, y) represent the pixel value of the satellite image at the coordinate (x, y). For multi-spectral images, f(x, y) can be regarded as a vector [B(x, y), G(x, y), R(x, y), NIR(x, y)]. Through a large number of field investigations and sample analyses, the reflectivity threshold range for distinguishing forests from non-forests in the near-infrared band is determined, denoted as and and the corresponding threshold range in the red light band and To determine whether a pixel is a forest area as follows:

[0031] If and then it indicates that (x, y) belongs to the forest area, otherwise, it does not belong to the forest area.

[0032] Considering the seasonal factor, let the seasonal factor be S (taking values from 0 to 3, corresponding to the four seasons), and through data analysis, the adjustment coefficients α NIR (S), α R (S) and β NIR (S), β R (S) of the threshold values in different bands with the change of seasons are obtained. The threshold value of the near-infrared band after dynamic adjustment is The threshold value of the red light band is The threshold values after dynamic adjustment are more in line with the real threshold situation of forests in the four seasons.

[0033] 2. Data processing module

[0034] The data processing module is set on the cloud platform. After receiving the forest resource data transmitted by the data acquisition module, it divides the forest resources into regions according to the data, which is convenient for better planning and utilization of forest resources.

[0035] 2.1 Protection of ancient and venerable trees

[0036] With the help of the tree age detection radar technology, based on the propagation characteristics of electromagnetic waves in the internal structure of trees, by transmitting and receiving radar signals, the internal structure of the trees is imaged and analyzed. Combining with the annual ring analysis, the ancient and venerable trees with a tree age exceeding 200 years (let the tree age be T, T≥200) are accurately identified, and the GIS system is used for positioning and marking to establish a multi-level and three-dimensional protection framework.

[0037] For the rare ancient trees with a tree age exceeding 200 years, a first-level core protection area is set up, and the radius R of the protection area EG1 :[[]]END]] (k EG1 takes values from 7 to 10 and is dynamically adjusted according to the endangered degree of the ancient trees and the key degree of the ecological niche; C FW1 is the diameter of the crown of the ancient tree, measured in centimeters on the spot; E IR is the ecological impact radius, obtained by simulating through a geographical model by comprehensively considering factors such as the surrounding topography, hydrogeology, and the association of animal and plant communities; H ETis the historical ecological trace index, which considers the intangible values such as ancient trees witnessing ecological changes and carrying cultural memories, and takes values from 1 to 5). A number of highly sensitive ecological sensors are installed in the protected area to form an Internet of Things real-time monitoring network. In addition to monitoring conventional environmental indicators, a stress and strain monitor for ancient trees is added to capture subtle changes in the internal structure of the tree trunks to prevent potential toppling hazards; once any indicator deviates from the preset appropriate range, such as the temperature exceeding [T min1 ,T max1 , the humidity being lower than H min1 or higher than H max1 , etc., the intelligent early warning system will be immediately activated to mobilize a professional maintenance team for an emergency response.

[0038] A secondary buffer zone is set up for ancient trees with a tree age of 200 - 500 years, and the radius R of the protected area EG2 : (k EG2 takes values from 4 to 6, C FW2 is the crown diameter of ancient trees at this level, S AD is the aggregation degree of similar ancient trees in the vicinity, which is obtained through GIS spatial analysis and statistics, and takes values from 0 to 100). Advanced equipment such as hyperspectral imagers, thermal imagers, and lidar scanners carried by drones are used for regular inspections, twice a month in the peak season (spring and summer) and once a month in the off-season (autumn and winter). Deep learning image analysis algorithms are used to compare subtle growth state changes such as the color, texture, and canopy density of ancient tree branches and leaves. Combining with the vegetation health index model (incorporating cutting-edge indicators such as chlorophyll fluorescence parameters and water stress indices) to evaluate the health status of ancient trees, and the data is transmitted back to the cloud management center in real time to achieve remote intelligent diagnosis and trend prediction.

[0039] For the protection of the above ancient trees, a dedicated protection file is also established. The file details the basic identity information of ancient trees, including the precise coordinates (longitude accurate to three decimal places after the second, and latitude similarly), the tree species name (using the international standard botanical name, with local common names and aliases attached), a detailed report on the determination of the tree age (using dendrochronology to accurately count and cross-verify with the carbon-14 dating method, and the error range of the tree age data is controlled within ±5 years). It also records the health status of ancient trees, recording the types of pests and diseases occurring over the years, the first discovery time, the damage degree (quantitatively evaluated using the pest and disease damage index), the control measures and effects in the form of a time axis. Regularly (monthly) collect data from ecological sensors, draw curves of environmental factors such as temperature, humidity, light intensity, and soil moisture content, mark the abnormal periods and durations exceeding the appropriate range, and conduct correlation analysis between environmental fluctuations and abnormal growth manifestations of ancient trees.

[0040] 2.2 General Tree Resource Planning

[0041] The stand volume V is obtained through a combination of lidar satellites and on-site plot surveys with a spacing of 200 square meters. Using the volume equation V = aRb H c Precisely measured, where a, b, and c are parameters, R is the diameter at breast height of the tree, and H is the height of the tree, ensuring that the data error of the stand volume is controlled within an extremely small range; the stand canopy density C is accurately obtained through multi-spectral analysis and artificial intelligence image interpretation of high-resolution satellite images, precisely outlining the canopy contour with an error rate of less than 5%, and the tree density D is obtained through the cooperation of a laser mapping instrument carried by an unmanned aerial vehicle and an image recognition algorithm, with a statistical accuracy of up to 98% for determination. The comprehensive index is calculated as: I R = 0.3×V + 0.4×C + 0.3×D (the weight coefficients are optimized and calibrated through big data simulation analysis of a large number of forest samples and long-term field experiments). When I R < I R1 (I R1 obtained through statistical analysis of regional forest resource dynamic monitoring data over ten years or more and calculation of the ecosystem carrying capacity model, with a value range of 0.4 - 0.6, and the value is dynamically adjusted according to the forest succession stage and the soil fertility recovery period), this forest area is planned as a rotation area. Subsequently, multiple varieties of native tree species suitable for growth will be introduced for mixed afforestation in this area, and a personalized fertilization plan will be formulated based on the soil nutrient detection results. Advanced means such as biochar and nitrogen-fixing bacterial agents will be used to improve the soil texture and fertility, comprehensively promoting the optimization of the stand structure and accelerating the positive succession of the forest ecosystem.

[0042] Similarly, the logging area is determined according to the high-precision data collection and analysis process. When the comprehensive index I F = 0.4×V + 0.4×C + 0.2×D satisfies I F > I F1 (I F1 is set to 0.7 - 0.9, and is dynamically fine-tuned according to the mature commercial forest standard, the market supply and demand trend of timber, and the forest sustainable management plan) and the tree density D > D 1 (D 1 is accurately set according to the growth characteristics of different tree species and the forest ecosystem optimal carrying capacity model. For coniferous forests, D 1 is 700 trees per hectare, and for broad-leaved forests, D 1 is 900 trees per hectare), then this area will be planned as a logging area. However, the logging process strictly follows scientific guidelines, and a certain proportion of young trees are retained. The retention ratio P Y is calculated as follows: (D Y is the density of young trees, and a and b are fitting parameters based on the tree species growth curve, calibrated according to the forest type; S Y is the spatial distribution uniformity of young trees, obtained through GIS spatial analysis and statistics, with a value range of 0 - 100). Retaining young trees ensures the subsequent natural expansion and reproduction ability of the forest, reducing the cost of artificial tree planting and ecological disturbance.

[0043] Establish a dynamic monitoring and evaluation mechanism, using monthly, quarterly and annual time series analysis of satellite remote sensing data combined with field sample site review. R Growth for two consecutive years and the increase exceeded ΔI R (ΔI R The value is set to 0.1-0.2, adjusted according to forest recovery capacity and management objectives), and the tree density D is stabilized in the appropriate range [D min2 ,D max2 ](Based on the target stand structure setting, the D of common coniferous-broadleaved mixed rotation forests min2 Assuming 550 plants / ha, D max2 750 trees / ha), and the average annual growth rate of tree height in the stand exceeds H min (The value is set to 0.3-0.5 meters, adjusted according to the tree species). At this time, according to the expert assessment, the ecosystem has recovered well and can be considered to be moderately converted to a logging area; on the contrary, if the logging area is restored after the comprehensive index I F Down to I R1 Below, and the stand canopy density C is lower than C min (C min The threshold is set at 0.3-0.4 to ensure basic ecological function. If the tree density is so sparse that it affects ecological stability, the area will be immediately converted to a rotation area to implement ecological restoration and reconstruction planning. Accurate decisions will be made based on scientific data throughout the process to ensure the sustainable use of forest resources.

[0044] 3. Emergency warning module

[0045] This module is the core defense line of forest ecological security. Logging supervision strictly controls the legality of resource exploitation; pest and disease warning can achieve early detection and early control; fire situation classification warning is based on precise data and responds quickly at different critical moments, protecting the forest from damage in all directions and maintaining the stability of the ecosystem.

[0046] 3.1 Logging Supervision Submodule

[0047] Using big data analysis combined with ecological rhythm models to accurately set the logging time period in the logging area [T start ,T end ]. This period comprehensively considers the proportion of the duration of the physiological dormancy period of trees P dp (such as cold temperate coniferous forest P dp In winter, it can reach 60%-70%, which is adjusted according to the cold-resistance characteristics of the tree species), the proportion of wildlife activity low period P wa (Through long-term infrared monitoring and statistical analysis, the low period of deer activity corresponds to winter, accounting for about 40%-50%) and the inventory preparation cycle before the peak season of timber market demand C wp(Based on the fluctuations of market data over the years, plan logging 2-3 months in advance) and other factors. After setting, satellite monitoring is precisely synchronized with it. Once the current time T current >T end or T current <T start The early warning mechanism is activated immediately. The early warning notifies the forest ranger, and the content of the notification includes: "The logging time is illegal! The current time T current , overtime duration T start -T current or T current -T end , the coordinates of the violation area (X a ,Y a )”) Warning information is pushed to the forest ranger’s personal APP module on the mobile terminal through the management platform pop-up window flashing, high-decibel warning sound, and text message to ensure that logging operations strictly abide by time limits.

[0048] Based on high-resolution satellite remote sensing images, the number and distribution of trees are recorded in real time according to a fixed monitoring cycle (every day / time). t is the number of trees in the monitoring area at time t, N t-1 is the number of trees at the last monitoring time t-1, ΔN = N t -N t-1 represents the change in the number of trees between two monitoring times; let P t is the number of tree pixels in the forest area at time t, P t-1 is the number of tree pixels in the forest area at the last moment, ΔP = P t -P t-1 The nature of the operation is determined by comparing it with the pre-set legal logging threshold. For example, based on forest resource assessment and logging plan, the legal logging threshold range is determined to be ΔN min ≤ΔN≤ΔN max (Mature pine plantation, ΔN min Assuming -50 plants / ha, ΔN max Assumed to be 100 plants / ha) and ΔP min ≤ΔP≤ΔP max (Assume ΔP min is -200 pixels, ΔP max is 300 pixels).

[0049] During the legal logging period, if ΔN min ≤ΔN≤ΔN max And ΔP min ≤ΔP≤ΔP max ,,The system determines that it is a normal logging operation, and only automatically records the logging data (tree species, felled tree location, breast diameter, etc.) and updates the forest resource database. current Not in [Tstart , T end within, or ΔN ≤ ΔN min and ΔP ≤ ΔP min , immediately trigger the illegal logging warning, and the cloud platform will instantly push the warning information to the forest ranger's mobile terminal (the displayed content is "Illegal logging alert! Location (X s , Y s ). Suspected illegal logging scale: tree reduction amount ΔN, pixel reduction amount ΔP"). With the help of the navigation function of the handheld terminal, the forest ranger quickly rushes to the suspicious area. The management center synchronously calls the satellite high-definition image to magnify and analyze the illegal logging area, combines with the geographic information system to track the surrounding road network and predict the possible transportation routes, and notifies the surrounding law enforcement stations to set up checkpoints for interception; at the same time, dispatches drones to conduct low-altitude and high-speed inspections, uses high-definition cameras and thermal imagers to obtain clear images of the scene, and assists in identifying the identity characteristics (clothing, body posture, etc.) of the illegal loggers and the illegal logging tools (saw model, vehicle appearance), so as to collect evidence for severely punishing illegal logging acts according to law in all aspects and safeguard the safety of forest resources.

[0050] 3.2 Pest and disease warning sub-module

[0051] The satellite remote sensing system conducts full-scale and high-precision multi-spectral scanning imaging of the forest area 3 times a week. Focus on vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), etc. These two are just like the accurate measurement scales of the forest health status, and can present the photosynthetic efficiency, growth momentum and internal physiological functions of the vegetation in real time and sensitively. Let the average value of NDVI of healthy vegetation in the forest area be μ NDVI , and the standard deviation be σ NDVI , when the monitoring results of a certain forest area for 4 consecutive times satisfy NDVI < μ NDVI - kσ NDVI (k is an empirical coefficient, and its value is dynamically adjusted between 1.5 - 2.5 according to tree species and seasons), and the EVI shows a significant synchronous downward trend, with a decline amplitude exceeding 15%, this area will immediately be marked as a suspicious area with abnormal vegetation health and become the top priority for subsequent strict investigation.

[0052] After the forest area was marked, the rangers conducted a detailed grid inspection of the suspicious areas in the area based on satellite positioning and navigation, evenly set up a sampling point every 500 square meters to collect leaf, branch and soil samples, and used high-precision GPS to mark the location of abnormal trees, with the error strictly controlled within 3 meters. The site is also equipped with a portable intelligent meteorological monitoring device to continuously monitor the local temperature, humidity, wind speed and direction and other key meteorological factors, and the data is synchronized to the cloud intelligent analysis platform in real time. If in the suspicious area, the proportion of abnormal samples in the cumulative sampling points within a week exceeds the warning red line of 30%, and combined with comprehensive analysis of meteorological data, once it is found that the temperature and humidity conditions in the next 48 hours are suitable for the breeding of pests and diseases (such as the temperature is maintained at 20-30℃, and the humidity is higher than 70%, this range is a breeding ground for most fungal and bacterial pests and diseases), and the wind speed is 1-3 meters / second (breeze is conducive to the spread of pest and disease spores and eggs), the system will immediately use big data deep learning algorithms to comprehensively integrate massive multi-source data such as dynamic changes in satellite images, detailed test results of on-site samples, real-time fluctuations in meteorological elements, and so on, to accurately "diagnose" the urgency and severity of the outbreak of pests and diseases.

[0053] When the system determines that pests and diseases are coming, the early warning information is immediately and accurately pushed to the smart mobile terminals of forest rangers and forest protection technicians, and then an efficient response mechanism is triggered. The forest rangers and forest protection personnel closest to the area are selected first. The system plans the best route for them according to the road conditions and topography of the forest area, avoiding steep slopes, muddy sections and other difficult areas to ensure that they can reach the forest area in the shortest time. At the same time, the follow-up support team is quickly formed to deploy manpower according to the estimated severity of the disaster. For example, in the case of moderate disasters, a reinforcement team including 10 professional prevention and control personnel will be organized within half an hour to rush to the surrounding area of ​​the area to be treated and stand by, and the area will be precisely controlled by chemical control, scientifically placed by biological control, and efficiently blocked by physical control. During the entire prevention and control process, drones patrol the entire process from high altitude, and send back images in real time to assist commanders in monitoring the prevention and control effects and dynamically adjust the plan until the pests and diseases are completely controlled. The forest area is continuously monitored to prevent recurrence and ensure that the forest ecosystem returns to health and stability.

[0054] 3.3 Fire warning submodule

[0055] This module focuses on the "early detection, early warning, and early disposal" of forest fires. It relies on multi-source data and intelligent algorithms to set up defenses at different levels and accurately respond to fire threats. It continuously collects forest area meteorological elements, vegetation physiological status (surface temperature T s , vegetation moisture content (WC) and topographic parameters (digital elevation model DEM, accuracy of 5-10 meters) to build a real-time fire risk assessment system.

[0056] Level 1 warning: When the vegetation temperature abnormal index ( is the historical average surface temperature of the same period, σT s (σT is the standard deviation of the surface temperature), the improved vegetation water supply index (RH is the relative humidity), and when the meteorological drought index MDI ≥ 3 (according to national standards, comprehensively considering precipitation and evaporation, etc.) reaches moderate drought, the early warning is activated. The lookout frequency of the watchtower is increased to once every 30 minutes. The forest rangers use intelligent telescopes to focus on investigating high-risk areas according to the wind direction and the vegetation flammability level (classified by tree species); the unmanned aerial vehicle is equipped with an infrared thermal imaging and multi-spectral camera, and the low-altitude full-area patrol is encrypted to once every 20 minutes to accurately capture potential hot spots; the satellite switches to the high-frequency hot spot scanning mode, covering the key forest areas every 20 minutes, using the thermal infrared channel to identify temperature anomaly points, and the minimum detectable temperature difference reaches 0.8°C.

[0057] Secondary warning: When MDI ≥ 4, 60% of the fire-fighting forces are quickly pre-positioned to key nodes, such as the "throat" areas of the fire, such as the edge of the forest area and the valley wind channel. According to the terrain slope S (extracted from the DEM) and the wind speed and direction, fire trucks, fire extinguishing bombs and other equipment are arranged in advance to ensure a coverage radius of 300 meters; the helicopter is equipped with a high-performance infrared thermal imager and takes off, with a flight altitude of 800 - 1200 meters, uses image recognition algorithms to locate the high-temperature core area, and transmits the fire field situation back in real time; the ground fire-fighting teams are on standby, and the material reserve depot quickly allocates according to the preset plan, and the material allocation time is controlled within 30 minutes to ensure that they can be put into battle at any time.

[0058] Emergency warning: Once MDI ≥ 5, all personnel immediately start fighting the fire. According to the real-time wind field (accuracy 0.1 m / s, jointly monitored by the laser wind measurement radar and the meteorological station, and the meteorological station data update frequency is 1 minute / time), the terrain and landform DEM (analyzing slope, aspect, valley trend, with an analysis accuracy of 5 meters) and the intelligent fire spread model (based on cellular automata, combined with fuel load and distribution modeling, and the fuel load monitoring error is within 10%), dynamically plan the fire-fighting route to ensure the fire-fighting efficiency and personnel safety; the width of the firebreak is dynamically set according to the terrain slope S according to the formula meters to ensure blocking the spread of the fire.

[0059] The fire department can quickly formulate prevention and fire-fighting plans based on the early warning of fire situations, allocate fire-fighting resources such as fire trucks and fire helicopters to the fire scene. The management platform can also analyze meteorological information such as wind direction and wind speed in real time, and combine with terrain data to predict the spread direction and speed of the fire, providing dynamic decision-making support for fire-fighting operations. During the fire-fighting process, satellite remote sensing continuously monitors the fire scene and updates the fire situation information in real time, so that the command center can flexibly adjust the fire-fighting strategy according to the actual situation. At the same time, using drones equipped with thermal imagers and other devices to conduct close-range monitoring in the core area of the fire, obtaining more detailed information on the combustion situation, such as the location of the fire source, the distribution of fire intensity, etc., and transmitting it back to the command center to assist firefighters in carrying out fire-fighting operations more accurately, improving the efficiency of fire-fighting and minimizing the loss of forest resources and the damage to the ecological environment caused by the fire.

[0060] 4. Personal APP Module

[0061] This module is specially designed for forest rangers and is installed on the mobile terminals of forest rangers. When a forest ranger logs in to the mobile APP, once a fire or pest warning is triggered, the APP will instantly pop up a push notification, with large and eye-catching characters flashing on the screen, clearly showing the warning level (graded warning in red, orange, and yellow), the precise coordinates of the incident location (longitude and latitude accurate to four decimal places, one-key navigation directly to the scene), key indicator data (such as the soaring temperature value during a fire, the sudden drop ratio of humidity, wind speed value, and the decline amplitude of the pest area ratio, etc.). At the same time, a voice broadcast will interpret the content in detail like a considerate guide, ensuring that forest rangers can capture key information immediately no matter where they are, respond quickly, and accurately rush to the scene.

[0062] In addition, the APP also has convenient functions for patrol records and task dispatching. During the patrol, forest rangers can take pictures, record voices, and note abnormal situations at any time, and upload them with one key to generate an electronic patrol log, which is seamlessly connected to the management platform. The management terminal can accurately dispatch tasks according to the real-time situation, such as fire hazard investigation and pest sample collection, and the instructions will be directly sent to the forest ranger APP, realizing efficient collaborative operations and making the forest protection action run smoothly like a precise gear set without any delay.

[0063] Forest rangers can also locate specific trees through the APP. When maintaining ancient trees and arriving at the ancient tree protection area, they can directly and accurately locate specific ancient trees through the positioning function of the APP, so as to understand the specific situation of the ancient trees, including but not limited to the age, location, crown diameter, etc. of the ancient trees, which is convenient for forest rangers to query and understand each tree and have a clear understanding of the situation in the forest.

[0064] Finally, it should be noted that: Obviously, the above embodiments are merely examples given to clearly illustrate the present invention, rather than limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A forest resource management platform based on satellite data, characterized in that: Including data collection module, data processing module, emergency warning module and personal APP module; The data processing module divides forest resources into old trees and general forest resources. The general tree resources calculate the comprehensive index I according to the forest volume V, forest canopy density C and tree density D. R =0.3×V+0.4×C+0.3×D, when I R <I R1 When I R1 It is set to 0.6, and the forest area is planned as a rotation area; When the comprehensive index I F =0.4×V+0.4×C+0.2×D satisfies I F >I F1 , where I F1 Set to 0.7, and the tree density D>D1, where D1 is precisely set according to the growth characteristics of different tree species and the optimal carrying capacity model of forest ecology, D1 of coniferous forest is 700 trees / hectare, and D1 of broad-leaved forest is 900 trees / hectare, then the area is planned as a logging area; Comprehensive index of the current rotation area I R The increase has exceeded 0.2 for two consecutive years, and the tree density D has remained stable in the appropriate range [D min2 ,D max2 ], D of common coniferous-broadleaved mixed rotation forest min2 Assuming 550 plants / ha, D max2 When the number of trees per hectare is 750 and the average annual growth rate of tree height H exceeds 0.5 m, the area is moderately transformed into a logging area; On the contrary, if the comprehensive index of the logging area after logging is F <0.6, and the stand canopy density C<0.

4. The tree density is so sparse that it affects the ecological stability. It should be immediately converted into a rotation area to implement ecological restoration and reconstruction planning.

2. A forest resource management platform based on satellite data as claimed in claim 1, characterized in that: The protection of old ancient trees uses tree age detection radar technology to identify old ancient trees, uses the GIS system to locate and mark them, establishes different protection areas for ancient trees of different ages, installs monitoring equipment in the protection areas, and establishes exclusive protection archives to record the information of ancient trees.

3. The forest resource management platform based on satellite data as claimed in claim 1, characterized in that: The data acquisition module uses a method based on image segmentation and change monitoring to build a database during the forest resource data acquisition process. The image segmentation uses a threshold segmentation method. The threshold segmentation method is based on the gray value difference of image pixels. For the blue light band B, green light band G, red light band R and near infrared band NIR of the satellite image used for forest resource monitoring, the near infrared band threshold is set to The corresponding threshold for the red band is Where S is considered as a seasonal factor, and the values ​​of the four seasons are 0-3. The pixel values ​​at different coordinates (x, y) satisfy and This indicates that (x, y) belongs to the forest area, otherwise, it does not belong to the forest area.

4. The forest resource management platform based on satellite data as claimed in claim 1, characterized in that: The emergency warning module includes a logging supervision submodule, a pest and disease warning submodule and a fire situation warning submodule.

5. A forest resource management platform based on satellite data as claimed in claim 4, characterized in that: The logging supervision submodule uses big data analysis combined with the ecological rhythm model to set the logging time period [T start ,T end ], satellite supervision synchronization, current time T current >T end or T current <T start When the logging time is abnormal, the number and distribution of trees are monitored through high-resolution satellite remote sensing images. t is the number of trees in the monitoring area at time t, N t-1 is the number of trees at the last monitoring time t-1, ΔN = N t -N t-1 represents the change in the number of trees between two monitoring times, let P t is the number of tree pixels in the forest area at time t, P t-1 is the number of tree pixels in the forest area at the last moment, ΔP = P t -P t-1 , when setting the logging time period [T start ,T end ], if ΔN min ≤ΔN≤ΔN max And ΔP min ≤ΔP≤ΔP max , the system determines that it is a normal logging operation, if ΔN≤ΔN min And ΔP≤ΔP min , immediately triggering an early warning for illegal logging and notifying forest rangers of abnormal logging scale.

6. A forest resource management platform based on satellite data as claimed in claim 4, characterized in that: The pest warning submodule relies on the satellite remote sensing system to perform multi-spectral scanning imaging of the forest area three times a week. The NDVI average of healthy vegetation in the forest area is μ NDVI , with standard deviation σ NDVI , the forest area has been monitored for four consecutive times and the results meet the NDVI<μ NDVI -k σNDVI , where k is the empirical coefficient, and the EVI decreases by more than 15%, the area is marked as a suspected pest and disease zone, and the forest rangers conduct a detailed grid-based inspection of the area, combine meteorological data, and use big data deep learning algorithms to determine the outbreak of pests and diseases.

7. The forest resource management platform based on satellite data as claimed in claim 4, characterized in that: The fire warning submodule is based on the ground surface temperature T s and relative humidity RH to build a real-time fire risk assessment system, based on the vegetation temperature anomaly index in is the historical average surface temperature during the same period, σT s is the standard deviation of surface temperature and vegetation water supply index and meteorological drought index MDI graded warning, each level takes corresponding measures. ani >0.8, VSWI <0.4 and MDI ≥3, the first-level warning is activated, and the forest rangers use drones and satellites to focus on the temperature in the area every 20 minutes. ani >1.2, VSWI <0.2 and MDI ≥4, activate the second-level warning, 60% of the firefighting force will be quickly deployed to the area, fire trucks and fire extinguishing bombs will be deployed in advance, and materials will be quickly deployed according to the preset plan. ani >1.5, VSWI <0.1 and MDI ≥5, the emergency warning is activated, all firefighting forces participate in fire fighting, and isolation belts are set up. The isolation belts are calculated according to the formula according to the terrain slope S≤45° and S>45°. and Dynamically set the scope of the isolation zone to ensure that the spread of fire is blocked.

8. The forest resource management platform based on satellite data as claimed in claim 1, characterized in that: The personal APP module is used to receive warning information, record patrol conditions and locate trees. When the warning is triggered, the personal APP module pushes the warning level, the coordinates of the incident site and key indicator data in the form of pop-up windows, large characters and voice broadcasts.

9. The forest resource management platform based on satellite data as claimed in claim 1, characterized in that: The data processing module and the emergency warning module are both arranged on the cloud platform, the data acquisition module is arranged on the satellite, and the personal APP module is installed on the forest ranger's mobile terminal.

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