Continuous monitoring method and system for forest stock and execution method therefor

11869,023

18/213912

June 26, 2023

The invention concerns a continuous monitoring method and system for forest stock and its execution method, including: 1, sample plots sampling design; 2, intelligent sample plots layout; 3, automatic sample plot data collection; 4, dynamic update of stock: detecting plot type change subclasses through remote sensing, and updating graphic and attribute forest resource change maps information; building a dynamic forest stand update model through intelligent sample plot data for plot type unchanged subclasses, and then updating attribute information of forest subclasses; 5, precision test and correction; 6, monitoring output: outputting current period stock monitoring data; 7, determining whether a monitoring period arrives. The invention shortens the survey and monitoring period, provides accurate and comparable monitoring results, significantly reduces costs, the workload and risks of work organization, quality inspection, and production safety, particularly suitable for forest resource stock survey and monitoring in counties and forest farms, with significant comprehensive benefits.

Sichuan Provincial Institute of Forestry and Grassland Inventory and Planning (Sichuan, CN); Sichuan Yangdi Shikong Technology Co., Ltd (Sichuan, CN)

Sichuan Provincial Institute of Forestry and Grassland Inventory and Planning (Chengdu, CN), Sichuan Yangdi Shikong Technology Co., Ltd (Chengdu, CN)

1. A continuous monitoring method for forest stock, comprising the following steps: (1) sampling design of sample plots: completing layout of monitoring sample plots, and determining a sample plot population, a sampling method, and spatial locations of sample plots; (2) layout of intelligent sample plots: completing layout of first measurement and monitoring devices for the sample plots; the layout of the monitoring devices comprising: installing tree diameter measurement sensors to measure diameters and perimeters of sample trees; connecting the tree diameter measurement sensors to data collection terminals through wireless an ad hoc network technology to complete data collection and input; networking the tree diameter measurement sensors and the data collection terminals with a mobile communication gateway or a Beidou short message gateway through the wireless ad hoc network technology to complete data summarization and transmission; sending, by the mobile communication gateway, data back to a communication server through a mobile communication base station of a communication operator, or sending, by the Beidou short message gateway, the data to a Beidou director through a Beidou satellite; and transmitting, by the communication server or the Beidou director, the data to a continuous monitoring application system for forest stock through an optical network; (3) automatic collection of sample plot data: (4) dynamic update of stock: detecting plot type change subclasses through remote sensing, and updating graphic and attribute information of forest resource change maps simultaneously; building a dynamic forest stand update model through intelligent sample plot data for plot type unchanged subclasses, and then updating attribute information of forest subclasses, wherein specific steps are as follows: (4.1) computing the stock of each intelligent sample plot according to the data collected in step (3); (4.2) computing forest stock and sampling precision of current monitored regions based on the intelligent sample plot, wherein the forest stock is computed by the following formula: [mathematical expression included] where V all_Plot is the forest stock of the current monitored regions based on the intelligent sample plot, v ij is the stock of the i th sample plot of the j th population, s ij is the area of the i th sample plot of the j th population, S j is the total area of the j th population, n is the number of sample plots of the j th population, and m is the total number of the current monitored regions; wherein the sampling precision is computed by the following formula: [mathematical expression included] where P v j is sampling precision of the current sample plot population, t a is a reliability index, S v j is an arithmetic square root of a sample variance of the j th population, and V j is a sample mean of the j th population; (4.3) determining whether the sampling precision meets the sampling design, and if so, performing step (4.4); otherwise, performing step (1) to adjust the sampling design and complement intelligent sample plots; (4.4) determining whether to combine with first class survey, and if so, computing forest stock of the current monitored regions based on the intelligent sample plot as current period forest stock, and performing step (4.17); otherwise, performing step (4.5); (4.5) determining whether remote sensing images are obtained in a monitoring period, and if so, performing step (4.6); otherwise, only updating the dynamic forest stand model and performing step (4.11); (4.6) carrying out remote sensing change detection and update, with remote sensing change detection as the main approach, supplemented by on-site survey and file update, and zoning a spatial scope of plot type change subclasses to form a remote sensing interpretation map database; (4.7) filling in on-site survey factors for remote sensing interpretation maps based on on-site survey and file update to form an on-site survey database; (4.8) performing spatial update analysis on the on-site survey database and a base period forest resource subclass database, and performing spatial and attribute updates of the on-site survey factors on the base period forest resource subclass database to generate a current period forest resource subclass database; (4.9) performing spatial joint analysis on the current period forest resource subclass database and the base period forest resource subclass database, and only retaining previous and subsequent plot type change subclasses for the joint results as a forest resource change database; (4.10) summarizing differences between the current period subclass stock and the base period subclass stock of the forest resource change database to obtain a subclass stock variation of the plot type change subclasses, wherein a computation formula is as follows: [mathematical expression included] Where ΔV Area_change is the subclass stock variation of the plot type change subclasses, V cur_area i is the subclass stock of the i th subclass in the forest resource change database, V base_area i is base period subclass stock of the i th subclass in the forest resource change database, and 0 is the total number of subclasses in the forest resource change database; (4.11) determining whether to combine with second class survey, and if so, performing step (4.12); otherwise, performing step (4.14); (4.12) computing the total stock of sample plots in the plot type unchanged subclasses, and solving the difference between the ratio of the total stock to the total stock of base period sample plots and 1 as a dynamic forest stand model update parameter, wherein a computation formula is as follows: [mathematical expression included] where k is the dynamic forest stand model update parameter, V cur_Plot is the total stock of sample plots in the plot type unchanged subclasses, and V base_Plot is the total stock of the base period sample plots; (4.13) multiplying the stock of the plot type unchanged subclasses by the dynamic forest stand model update parameter, and obtaining a forest stock variation of the forest stand structure change subclasses after summarization, wherein a computation formula is as follows: [mathematical expression included] where ΔV Struct_vary is the forest stock variation of the forest stand structure change subclasses, ΔV base_Struct is the stock of the plot type unchanged subclasses, V cur_Plot is the total stock of sample plots in the plot type unchanged subclasses, and V base_Plot is the total stock of the base period sample plots; (4.14) computing the total stock of sample plots in the plot type unchanged subclasses in each stratum or quota, and solving the difference between the ratio of the total stock to the total stock of base period sample plots and 1 as a dynamic forest stand model update parameter in each stratum or quota, wherein a computation formula is as follows: [mathematical expression included] where k j is the dynamic forest stand model update parameter in the j th stratum or quota, V cur_Plot j is the total stock of sample plots in the plot type unchanged subclasses in the j th stratum or quota, and V base_Plot j is the total stock of base period sample plots in the j th stratum or quota; (4.15) multiplying base subclass stock of the plot type unchanged subclasses in each stratum or quota by the dynamic forest stand model update parameter, and obtaining a forest stock variation of the forest stand structure change subclasses in each stratum or quota after summarization, wherein a computation formula is as follows: [mathematical expression included] [mathematical expression included] [mathematical expression included] where ΔV Struct_vary is the forest stock variation of the forest stand structure change subclasses, V cur_Plot j is the total stock of sample plots in the plot type unchanged subclasses in the j th stratum or quota, V base_Plot j is the total stock of base period sample plots in the j th stratum or quota, V base_Struct ij is base period subclass stock of the i th plot type unchanged subclass in the j th stratum or quota, V cur_Plot j is the stock of sample plots in the plot type unchanged subclasses in the j th stratum or quota, VbasePlot is the stock of base period sample plots in the j th stratum or quota, s is a quantity of plot type unchanged subclasses in the j th stratum or quota, and t is a quantity of strata or quotas; (4.16) computing a forest stock variation of forest resource subclasses by the following formula: Δ V=ΔV Area_change +ΔV Struct_vary where ΔV is the forest stock variation of the forest resource subclasses, ΔV Area_change is the subclass stock variation of the plot type change subclasses, and ΔV Struct_vary is the forest stock variation of the forest stand structure change subclasses; (4.17) ending the process; (6) monitoring output: outputting current period stock monitoring data; (7) determining whether a monitoring period arrives, and if so, ending the process; otherwise, returning to step (3) for continuous monitoring.

2. The continuous monitoring method for forest stock according to claim 1 , characterized in that the method further comprises a step between step (4.17) and step (6): (5) precision test and correction; in step (5), the forest stock variation is superposed on a monitoring base, precision of the monitoring results is tested, and the data that do not meet precision requirements are corrected after reasons are found, so that both the intelligent sample plot data and the forest resource subclass data meet the precision requirements; specific steps of step (5) are as follows: (5.1) determining whether to combine with first class survey, and if so, computing total stock of monitored regions based on intelligent sample plots as current period forest stock, otherwise, performing step (5.2); (5.2) determining whether quota sampling is necessary, and if so, performing step (5.5); otherwise, comparing a total stock value of monitored subclasses with a surveyed stock value of the sampling population, where the total stock value of the monitored subclasses is a total value of monitored base period forest stock and the forest stock variation, and the surveyed stock value of the sampling population is the total stock of the monitored regions based on intelligent sample plot computation in the current period; (5.3) determining whether the difference between the total stock value of the monitored subclasses and the surveyed stock value of the sampling population is within ±1 times a standard error, and if so, skipping correction and determining that the current period forest stock is the total stock value of the monitored subclasses; otherwise, performing step (5.4); (5.4) correcting the subclasses with large deviations from forest resource subclass stock, so that the difference between the total stock value of the monitored subclasses and the surveyed stock value of the sampling population is within the standard error, and the current period forest stock is the total stock value of the corrected monitored subclasses; (5.5) computing and comparing stock per hectare of the monitored subclasses in each quota and sampling survey stock per hectare, determining whether the difference between the two exceeds a specified threshold, and if so, skipping correction and determining that the current period forest stock is the total stock value of the monitored subclasses in each quota; otherwise, performing step (5.6); (5.6) correcting the subclasses with large deviations from forest resource subclass stock, so that the difference between the stock per hectare of the monitored subclasses in each quota and the sampling survey stock per hectare is within a specified threshold, and the current period forest stock is the total stock value of the corrected monitored subclasses in each quota; (5.7) ending the process.

3. The continuous monitoring method for forest stock according to claim 2 , characterized in that specific steps of step (6) are as follows: (6.1) determining whether current period results are qualified upon precision test, and if so, outputting current period result databases and adding timestamps for archiving and storage, otherwise, performing step (6.3); (6.2) outputting main indexes of the results; (6.3) ending the process.

4. A continuous monitoring system for forest stock, characterized in that it is used to complete steps (4) to (7) in the continuous monitoring method for forest stock according to claim 3 , comprising: a user login and management module, configured to log in to the continuous monitoring system for forest stock; an intelligent sample plot data receiving and storage module, configured to receive and parse intelligent sample plot data, save the data to a forest sample plot spatio-temporal database, and update the database; a remote sensing change detection module, configured to obtain remote sensing change determination maps in two consecutive periods by using multi-period remote sensing images; a remote sensing determination map survey and editing module, configured to complete input of remote sensing interpretation map factors after on-site verification and file update of remote sensing interpretation maps; a sample plot stock computation module, configured to compute stock of each sample plot and stock of each sampling population in the current period by using updated sample tree survey information in the forest sample plot spatio-temporal database; a forest resource change database generation module, configured to update base period forest resource subclass data by using remote sensing interpretation map verification results, obtain a forest resource change map through graphic and attribute comparison analysis, and compute current period forest resource subclass stock and forest stock variation; a forest stand model update computation module, configured to compute a dynamic model update parameter, and obtain a forest stock variation caused by current period forest stand structure changes; a sampling precision and eigenvalue computation module, configured to generate sampling precision and eigenvalues of the monitoring population by statistics; a monitoring spatio-temporal database update module, configured to add timestamps to current period results for archiving and storage after the current period results are qualified upon precision test; a monitoring result computation and statistics module, configured to collect statistics on main indexes and statistical data tables of monitoring results.

5. An execution method for the continuous monitoring system for forest stock according to claim 4 , characterized in that it comprises the following steps: (1) logging in to an application system by a user using the user login and management module; (2) collecting and updating a current period sample plot tree database by using the intelligent sample plot data receiving and storage module; (3) obtaining forest resource interpretation maps according to remote sensing images in two consecutive periods by using the remote sensing change detection module; (4) obtaining an on-site survey database by using the remote sensing determination map survey and editing module after field survey, file update, and graphic and attribute editing of the forest resource interpretation maps; (5) superposing base period forest resource subclass data on the on-site survey database to generate a forest resource change database by using the forest resource change database generation module, and collecting statistics on a forest stock variation caused by plot type changes; (6) obtaining a dynamic forest stand model update parameter for plot type unchanged subclasses, updating stock of all the plot type unchanged subclasses by using the forest stand model update computation module, and collecting statistics on a forest stock variation caused by forest stand structure changes; (7) computing eigenvalues of a sampling population, forest stock of sample plots, and stock of forest subclasses by using the sampling precision and eigenvalue computation module, and correcting data according to precision control requirements; (8) outputting a current period result database and adding timestamps for archiving and storage by using the monitoring spatio-temporal database update module after results are qualified upon precision test; (9) collecting statistics on main indexes and statistical data tables of monitoring results by the user using the monitoring result computation and statistics module on demand.

6. The continuous monitoring method for forest stock according to claim 1 , characterized in that specific steps of the layout of the monitoring devices are as follows: (2.1) arriving at the sample plot, measuring the sample plot, testing a signal type of the sample plot with the data collection terminal, and selecting a gateway type; (2.2) selecting a sample tree in a center of the sample plot to lay a bracket, fixing a gateway, testing signals, and keeping the gateway turned on after success; (2.3) connecting the data collection terminal to the gateway, setting a data collection frequency, and determining next automatic startup time and duration of the gateway and a tree diameter measurement sensor; (2.4) selecting a location for measuring a diameter of the sample tree and fix the tree diameter measurement sensor to the tested sample tree; (2.5) starting the tree diameter measurement sensor, and connecting the data collection terminal to the tree diameter measurement sensor while ensuring that a displayed code of the connected tree diameter measurement sensor is consistent with a label code on a shell of the tree diameter measurement sensor; (2.6) pulling out a pull rope from a rope outlet of the tree diameter measurement sensor, winding the pull rope on the sample tree by one circle, and then buckling the pull rope into an anti-unwinding rope fixing port of the tree diameter measurement sensor; (2.7) checking in the data collection terminal whether the diameter of the sample tree has a measured value or significantly deviates from an actual value, and if so, starting the tree diameter measurement sensor again; and after the data collection terminal is reset, pulling the pull rope again for installation; (2.8) connecting the tree diameter measurement sensor to the data collection terminal again, and inputting, by the data collection terminal, a tree species and a gauge type; (2.9) transmitting, by the tree diameter measurement sensor, measured values to the gateway, performing clock synchronization, and obtaining next startup time and duration of the tree diameter measurement sensor; (2.10) in response to the tree diameter measurement sensor being in an unconnected case, automatically entering the tree diameter measurement sensor to a dormant state after a first fixed time interval; (2.11) repeating steps (2.4)-(2.10) to complete measurement of all sample trees and installation of the tree diameter measurement sensors in the sample plot; (2.12) transmitting, by the gateway which is the mobile communication gateway, through the mobile communication base station, the data back to the communication server and then the data is summarized into a network server of the continuous monitoring application system by the communication server; or transmitting, by the gateway which is the Beidou short message gateway, through the satellite, the data back to the Beidou director and then the data is summarized into the network server by the Beidou director; (2.13) disconnecting the data collection terminal from the gateway, automatically entering the gateway to the dormant state after a second fixed time interval.

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