Forestry surveying and mapping method based on remote sensing big data

Abstract

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a forestry surveying and mapping method based on remote sensing big data, which comprises the following steps: integrating remote sensing images, unmanned aerial vehicle images, temperature sensor data and humidity sensor data to perform space-time alignment processing, filling missing data through time interpolation, and performing abnormal value detection at the same time; and combining the processed data to calculate ecological indexes of the forest area, and outputting a forest health ecological assessment result. According to the method, by integrating remote sensing images, unmanned aerial vehicle images and temperature and humidity sensor data, the accuracy and integrity of data are improved, species identification and ecological function contribution analysis are achieved, key species and contribution of the key species to an ecological system can be accurately identified, and by analyzing the dynamic change trend of forest resources and climate influence, the forest resources can be accurately identified. According to the method, resource configuration can be effectively optimized, the management efficiency and the risk management and control capability are improved, the precision and timeliness of forest resource monitoring, assessment and management are improved, and the sustainable utilization and ecological protection capabilities are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing mapping, and in particular to a forestry mapping method based on remote sensing big data. Background Art

[0002] Remote sensing mapping technology uses satellites, drones, aviation platforms and other equipment, using electromagnetic waves, light waves, radar waves and other sensors to obtain information about the earth's surface and its environment, and uses corresponding data processing and analysis methods to draw accurate geographic information images or models. This technical field covers multiple aspects such as remote sensing data acquisition, processing, analysis and application, and is widely used in land use, environmental monitoring, disaster warning, urban planning, agricultural and forestry resource surveys and other fields. Remote sensing mapping uses spatial information obtained from a height that cannot be directly contacted from the ground. It has the characteristics of a wide acquisition range, strong temporal continuity, high real-time performance, and gradually improved accuracy. It has significant advantages, especially in large-scale, dynamically changing regional surveys and monitoring.

[0003] The forestry surveying and mapping method based on remote sensing big data mainly uses remote sensing technology and big data analysis to accurately monitor and evaluate forest resources. This method integrates multiple data sources such as satellite remote sensing images, drone data, and ground field surveys, and combines cloud computing, big data processing and other technologies to conduct detailed analysis of forest coverage, tree species distribution, forest land changes, and tree growth. It has a wide range of uses and can effectively support forest resource surveys, forestry ecological monitoring, forest fire warnings, carbon storage assessments, and other aspects, and is of great significance for promoting the sustainable management and protection of forest resources.

[0004] Although the remote sensing mapping of existing technologies can obtain a large amount of spatial information, it often faces several significant problems in the actual operation process. First, although the remote sensing data obtained by equipment such as satellites and drones have a high coverage and real-time performance, the huge amount of data and the single source lead to the prominent problem of time and space alignment between different data sources, which may cause the monitoring results of some areas to be biased or missed. Secondly, the existing technologies mostly rely on static data analysis and traditional ground surveys. When facing dynamically changing forest resources, they lack effective dynamic monitoring mechanisms and early warning capabilities, and cannot timely reflect the changing trends of resource utilization efficiency and the actual risks of the ecological environment. This makes some monitoring results lack sufficient responsiveness in areas with rapid changes or uncertainties, and cannot provide accurate basis for decision-making. Finally, the existing technologies are relatively simple in analyzing the relationship between species distribution and ecological functions, lack cross-domain comprehensive data support, and often cannot deeply explore the complex relationship between species and ecological functions, resulting in the results of ecological health assessments being not comprehensive enough, limiting the accuracy and operability of resource management. Therefore, the limitations of existing technologies are mainly reflected in the singleness of data sources, the lag in processing logic and the limitations of analysis dimensions. They are unable to fully reflect the dynamic changes of forest ecology and the contribution of species in the ecosystem, affecting the effectiveness of ecological monitoring and forest resource management. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a forestry surveying and mapping method based on remote sensing big data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a forestry surveying and mapping method based on remote sensing big data, comprising the following steps:

[0007] S1: Integrate remote sensing images, drone images, temperature sensors and humidity sensor data for spatiotemporal alignment, fill missing data through temporal interpolation, perform outlier detection, calculate ecological indicators of forest areas based on processed data, and output forest health ecological assessment results;

[0008] S2: Based on the above forest health ecological assessment results, identify the species in the forest through remote sensing images, drone images, temperature sensor and humidity sensor data, and analyze the contribution of species to forest ecological functions in combination with environmental factors. Compare species distribution with ecological health, identify the distribution of key species that affect ecological functions, and output a species distribution ecological function contribution report;

[0009] S3: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, analyze the dynamic changes of forest ecology and resources, determine the changing trend of forest resources, identify the increase and decrease patterns of forest resources and combine with climate factors to predict the changes in forest ecology in the future and produce a future forecast report on forest resources;

[0010] S4: Combine the forest health ecological assessment results and the species distribution ecological function contribution report to conduct spatial distribution analysis of forest resources, evaluate the resource utilization efficiency of the difference areas, identify and distinguish between high-efficiency and low-efficiency resource utilization areas through preset thresholds, analyze the reasons for regional differences, and output a forest resource difference report;

[0011] S5: Based on the forest resource difference report and the future forest resource forecast report, reallocate resources for areas with inefficient resource utilization, combine climate change and environmental monitoring data to assess potential ecological risk areas, stimulate early warning and risk management, and form a forest resource management optimization and risk control plan.

[0012] As a further solution of the present invention, the forest health ecological assessment results include forest ecological indicators, missing data supplementation results and outlier detection results; the species distribution ecological function contribution report includes species identification results, species ecological function contribution analysis and key species distribution identification; the future forest resource forecast report includes resource change trend analysis, resource increase and decrease patterns and climate factor impact analysis; the forest resource difference report includes resource utilization efficiency assessment, high-efficiency and low-efficiency area calibration and regional difference cause analysis; the forest resource management optimization and risk control plan includes resource reconfiguration plan, potential risk area assessment and risk management strategy.

[0013] As a further solution of the present invention, the remote sensing images, drone images, temperature sensor and humidity sensor data are integrated for spatiotemporal alignment processing, and missing data are filled by time interpolation. At the same time, outlier detection is performed, and the ecological indicators of the forest area are calculated by combining the processed data. The specific steps of outputting the forest health ecological assessment results are as follows:

[0014] S101: Based on remote sensing images, drone images, temperature sensors and humidity sensor data, accurately align time and space coordinates, sort the timestamps of each data source, convert and unify the spatial coordinate scale, adjust the time difference and spatial deviation between data sources, and generate time-space alignment data;

[0015] S102: Based on the time-space aligned data, perform time period interpolation, resample all data according to time intervals, and insert values ​​at missing data points to fill in the gaps, thereby generating filled data;

[0016] S103: Based on the filled data, outlier detection is performed and the normal fluctuation range of the data is defined, the relationship between each data point and the range is checked, outliers that exceed the range are marked and outliers are processed by interpolation, and the forest health ecological assessment results are output.

[0017] As a further solution of the present invention, based on the forest health ecological assessment results, the species in the forest are identified through remote sensing images, drone images, temperature sensor and humidity sensor data, and the contribution of species to the forest ecological function is analyzed in combination with environmental factors. The species distribution is compared with ecological health to identify the distribution of key species that affect the ecological function. The specific steps of outputting the species distribution ecological function contribution report are as follows:

[0018] S201: Based on the forest health ecological assessment results, extract species outlines of forest areas through remote sensing images and drone images, analyze species distribution ranges in combination with temperature and humidity data, identify species, mark the core growth areas of species, and generate species identification data;

[0019] S202: Based on the species identification data and in combination with environmental factors, the relationship between species growth and temperature and humidity factors is extracted, and the impact of species on forest ecological functions is analyzed by numerical matching of species and environmental factors, and the ecological contribution index within the growth area is calculated to generate species ecological function contribution data;

[0020] S203: Based on the ecological function contribution data of the species, compare the species distribution with the results of the forest health ecological assessment, identify key species and their role in ecological functions, conduct a comprehensive analysis based on the spatial distribution and ecological function contribution of the species, and generate a species distribution ecological function contribution report.

[0021] As a further solution of the present invention, the calculation formula of the ecological contribution index of the species is specifically:

[0022]

[0023] First, the Ecological Contribution Index represents ECI i , species represents i, environmental factor represents j, R ij represents the response rate of species i under environmental factor j, E ij represents the actual measured value of environmental factor j at location i, H i represents the optimum humidity for species i, P i Represents the actual humidity.

[0024] As a further solution of the present invention, based on the forest health ecological assessment results and the species distribution ecological function contribution report, the dynamic changes of forest ecology and resources are analyzed, the changing trend of forest resources is determined, the increase and decrease rules of forest resources are identified and combined with climate factors, the changes in forest ecology in the future are predicted, and the specific steps for obtaining the future forecast report of forest resources are as follows:

[0025] S301: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, extract resource data for each forest area, and sort out the resource changes in each period in chronological order, perform spatial distribution analysis in combination with geographic information, and identify the increase and decrease of resources in the area to generate forest resource change data;

[0026] S302: Based on the forest resource change data, compare the resource change with the temperature, humidity, precipitation, etc. of the climate, analyze the impact of climate factors on the increase or decrease of forest resources, identify the increase or decrease trend of resources, and perform comparative processing on the resource change to generate resource increase or decrease trend data;

[0027] S303: Based on the resource increase and decrease trend data, analyze the change trend of forest resources, combine with climate change data, identify seasonal fluctuations and long-term change trends, and predict the potential changes in forest resources in the future, and generate a future forecast report on forest resources.

[0028] As a further solution of the present invention, the calculation formula for the influence of the climate factors on the change of forest resources in a specific location is specifically:

[0029]

[0030] First, ΔR i represents the impact of climate factors on the change of forest resources in a specific location, k represents the expected change in resources without any climate impact, m represents the impact coefficient of temperature, and Q i represents the actual observed temperature, D i represents the actual observed humidity, l represents the influence coefficient of precipitation, P i Represents the actual observed precipitation.

[0031] As a further solution of the present invention, the spatial distribution analysis of forest resources is carried out in combination with the forest health ecological assessment results and the species distribution ecological function contribution report, the resource utilization efficiency of the difference area is evaluated, and the resource utilization efficient and inefficient areas are identified and distinguished through preset thresholds, and the reasons for regional differences are analyzed. The specific steps of outputting the forest resource difference report are:

[0032] S401: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, the density distribution of regional resources is analyzed by comparing spatial coordinates with species type data, and regional division is performed to generate forest resource spatial distribution data;

[0033] S402: Based on the forest resource spatial distribution data, a preset threshold value of resource utilization is set, resource density and ecological function contribution of each area are quantified, resource utilization efficiency is calculated, high-efficiency and low-efficiency areas are identified, and the two types of areas are defined to generate resource utilization efficiency regional division data;

[0034] S403: Based on the resource utilization efficiency regional division data, compare the regional climate, soil, and species composition, analyze the reasons for the differences in resource utilization within the region, and generate forest resource difference data.

[0035] As a further solution of the present invention, the resource utilization efficiency calculation formula is specifically:

[0036]

[0037] First, RE i Resource utilization efficiency, c represents the adjustment coefficient, D i represents resource density, E i Represents the ecological function contribution of the region, S i Represents the actual area of ​​the region.

[0038] As a further solution of the present invention, based on the forest resource difference report and the future forest resource forecast report, resources are reconfigured for areas with inefficient resource utilization, and combined with climate change and environmental monitoring data, potential ecological risk areas are evaluated to stimulate early warning and risk management, and the specific steps of forming a forest resource management optimization and risk control plan are as follows:

[0039] S501: Based on the forest resource difference data, extract the resource distribution of the inefficient area, conduct statistical and comparative analysis of regional resource density, land use efficiency and ecological function, identify areas with uneven resource allocation, and generate a resource allocation plan for the inefficient area;

[0040] S502: Based on the inefficient regional resource allocation plan, combined with climate change data and environmental monitoring data, analyze the correlation between climate change trends and soil and vegetation, calibrate risk levels through cross-regional data comparison, and generate potential ecological risk area data;

[0041] S503: Based on the potential ecological risk area data, environmental carrying capacity assessment and resource mobility analysis are conducted, combined with climate adaptability factors, to generate forest resource management optimization and risk control plans.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] In the present invention, by integrating remote sensing images, drone images and temperature and humidity sensor data, the accuracy and completeness of the data are improved. Species identification and ecological function contribution analysis can accurately identify key species and their contribution to the ecosystem. By analyzing the dynamic change trends of forest resources and climate impacts, resource allocation can be effectively optimized, management efficiency and risk control capabilities can be improved, the accuracy and timeliness of forest resource monitoring, assessment and management can be improved, and sustainable utilization and ecological protection capabilities can be enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a schematic diagram of the steps of the present invention.

[0046] Figure 2 It is a flow chart of the steps of S1 of the present invention.

[0047] Figure 3 This is a flow chart of the steps of S2 of the present invention.

[0048] Figure 4 This is a flow chart of the steps of S3 of the present invention.

[0049] Figure 5 This is a flow chart of the steps of S4 of the present invention.

[0050] Figure 6 This is a flow chart of the steps of S5 of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0052] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0053] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0054] See also Figure 1 , a forestry surveying and mapping method based on remote sensing big data, comprising the following steps:

[0055] S1: Integrate remote sensing images, drone images, temperature sensors and humidity sensor data for spatiotemporal alignment, fill missing data through temporal interpolation, perform outlier detection, calculate ecological indicators of forest areas based on processed data, and output forest health ecological assessment results;

[0056] S2: Based on the results of forest health ecological assessment, identify species in the forest through remote sensing images, drone images, temperature sensors and humidity sensor data, and analyze the contribution of species to forest ecological functions in combination with environmental factors. Compare species distribution with ecological health, identify the distribution of key species that affect ecological functions, and output a species distribution ecological function contribution report;

[0057] S3: Based on the results of forest health ecological assessment and the report on the contribution of species distribution to ecological functions, analyze the dynamic changes of forest ecology and resources, determine the changing trend of forest resources, identify the law of increase and decrease of forest resources and, combined with climate factors, predict the changes in forest ecology in the future and produce a future forecast report on forest resources;

[0058] S4: Combine the results of forest health ecological assessment and the species distribution ecological function contribution report to analyze the spatial distribution of forest resources, evaluate the resource utilization efficiency of different regions, identify and distinguish between high-efficiency and low-efficiency resource utilization regions through preset thresholds, analyze the reasons for regional differences, and output a forest resource difference report;

[0059] S5: Based on the forest resource difference report and the future forest resource forecast report, reallocate resources for areas with inefficient resource utilization, combine climate change and environmental monitoring data to assess potential ecological risk areas, stimulate early warning and risk management, and form a forest resource management optimization and risk control plan;

[0060] The results of forest health ecological assessment include forest ecological indicators, missing data supplementation results and outlier detection results. The species distribution ecological function contribution report includes species identification results, species ecological function contribution analysis and key species distribution identification. The future forest resource forecast report includes resource change trend analysis, resource increase and decrease patterns and climate factor impact analysis. The forest resource difference report includes resource utilization efficiency assessment, high-efficiency and low-efficiency area calibration and regional difference cause analysis. The forest resource management optimization and risk control plan includes resource reconfiguration plan, potential risk area assessment and risk management strategy.

[0061] See also Figure 2 , the specific steps of S1 are:

[0062] S101: Based on remote sensing images, drone images, temperature sensors and humidity sensor data, accurately align time and space coordinates, sort the timestamps of each data source, convert and unify the spatial coordinate scale, adjust the time difference and spatial deviation between data sources, and generate time-space alignment data;

[0063] First, data is obtained from different sensors and imaging devices. These data include geographic coordinates, timestamps, and environmental parameters such as temperature and humidity. These data differ in time and space and require precise calibration and alignment. Spatial alignment involves mapping data from different sources to a unified coordinate system, while temporal alignment requires synchronizing the timestamps of the data to ensure consistency during data analysis. This usually involves complex algorithms, including time synchronization algorithms and coordinate conversion algorithms. Through these algorithms, data from different sources can be effectively integrated to improve data availability and accuracy. For example, remote sensing images can be geocoded, and the high-resolution features of drone images can be combined with the wide coverage features of remote sensing data. Temperature and humidity data can be used to adjust or verify information from other data sources, ultimately achieving spatiotemporal consistency of all data sources and generating spatiotemporal aligned data.

[0064] S102: Based on the spatiotemporal alignment data, time period interpolation is performed, all data are resampled according to time intervals, and values ​​are inserted into missing data points to fill in the gaps, thereby generating filled data;

[0065] Interpolate all data for the time period according to the formula

[0066]

[0067] Compute the time-aligned interpolated data.

[0068] In the formula, T′ i Represents the interpolated data of the current time step, T′ i-1 represents the data of the previous time step, ΔT′ represents the time interval, and Y′ iRepresents the target data value of the current step, Y′ i-1 represents the target data value of the previous time step, X′ i Represents the actual timestamp of the current data, X′ i-1 Represents the previous timestamp.

[0069] Assume that there are time series data points (1000, 20), (1010, 23), (1020, 25), and we need to interpolate the data at time 1015. First, calculate ΔT′=1015-1010=5, and then calculate the slope

[0070]

[0071] Apply the formula T′ i =23+5×0.2=24, so the interpolated data at time 1015 is 24.

[0072] S103: Based on the filled data, outlier detection is performed and the normal fluctuation range of the data is defined, the relationship between each data point and the range is checked, outliers beyond the range are marked and outliers are processed by interpolation, and the forest health ecological assessment results are output;

[0073] First, an anomaly detection model needs to be established, which usually involves statistical methods or machine learning algorithms to determine the normal fluctuation range of the data, such as using the standard deviation or interquartile range to define the anomaly threshold. The outlier detection algorithm identifies anomalies by comparing the deviation of the actual data points from this range. Once the abnormal data is identified, these points are processed using interpolation methods, such as filling with the average value of adjacent data points. This method is simple and effective and can correct outliers without affecting the overall trend of the data, which is very important for subsequent data analysis and decision making. In this way, the accuracy and reliability of the data can be ensured, and finally the outliers that are out of range are marked and the outliers are processed by interpolation to output the forest health ecological assessment results.

[0074] See also Figure 3 , the specific steps of S2 are:

[0075] S201: Based on the results of forest health ecological assessment, extract species outlines in forest areas through remote sensing images and drone images, analyze species distribution ranges in combination with temperature and humidity data, identify species, mark the core growth areas of species, and generate species identification data;

[0076] First, it is necessary to screen out representative images from a large amount of image data, and use image processing algorithms such as edge detection and texture analysis to determine the physical outline of the species. Then, combined with temperature and humidity data, data fusion technology is used to more accurately locate the species distribution in the image. This process may involve the synchronization and fusion of multiple sensor data. By analyzing the hot spots and environmental preferences of different species, the core growth areas of the species are marked. In practical applications, this step is crucial for the management of protected areas and the formulation of species conservation plans. It can help managers better understand and monitor the growth environment and distribution dynamics of species and generate species identification data.

[0077] S202: Based on the species identification data and combined with environmental factors, the relationship between species growth and temperature and humidity factors is extracted, and the impact of species on forest ecological functions is analyzed by numerical matching of species and environmental factors. The ecological contribution index within the growth area is calculated to generate species ecological function contribution data;

[0078] The calculation formula of the species' ecological contribution index is as follows:

[0079]

[0080] First, ECI i represents the ecological contribution index, i represents species representative, j represents environmental factors, R ij represents the response rate of species i under environmental factor j, E ij represents the actual measured value of environmental factor j at location i, H i represents the optimum humidity for species i, P i Represents the actual humidity.

[0081] Response rate R ij Usually obtained from ecological research data, it reflects the sensitivity of species to specific environmental factors. ij Directly measured by environmental monitoring equipment, such as a temperature or humidity meter. i and P i The value of is usually determined by biological research and indicates the difference between the ideal living conditions required by a species and the actual living environment.

[0082] Assume that the number of environmental factors n = 2, representing temperature and humidity. Assume that the optimum humidity H for a specific species at location i is i =75% and observed humidity P i =70%. Response rate R i1 and R i2 Represent the species' response rates to temperature and humidity, respectively. Assuming the temperature response rate R i1 =0.8 (obtained from ecological data), humidity response rate R i2=0.6. Actual measured value of environmental factor E i1 =22℃ and E i2 =70%, these values ​​were measured at the site using environmental monitoring equipment.

[0083] Substitute into the formula to calculate:

[0084]

[0085]

[0086]

[0087] ECI i =26.65

[0088] The results show that the ecological contribution index of the species at the site is 26.65, which reflects the comprehensive ecological function efficiency of the species under given environmental factors. This value can indicate the contribution of the species to the ecosystem under specific environmental conditions. A higher ECI i A high value indicates that the species has a strong ecological function under current environmental conditions and contributes to the stability and sustainable development of the ecosystem.

[0089] S203: Based on the species ecological function contribution data, compare the species distribution with the forest health ecological assessment results, identify key species and their role in ecological functions, conduct a comprehensive analysis based on the spatial distribution and ecological function contribution of species, and generate a species distribution ecological function contribution report;

[0090] By integrating the data obtained in the early stage, using geographic information systems and ecological modeling tools to analyze the spatial distribution patterns of species, key species and their roles in ecological functions are identified. This usually involves spatial analysis and statistical modeling of ecological data. By geocoding and spatial clustering the ecological contribution index of different species, a comprehensive analysis is conducted based on the spatial distribution of species and their ecological function contributions, providing a scientific basis for protection strategies and resource management, and ultimately generating a species distribution ecological function contribution report. This method helps to more accurately identify and manage key species that affect forest health.

[0091] See also Figure 4 , the specific steps of S3 are:

[0092] S301: Based on the results of forest health ecological assessment and the species distribution ecological function contribution report, extract the resource data of each forest area, and sort out the resource changes in each period in chronological order, combine geographic information to conduct spatial distribution analysis, and identify the increase and decrease of resources in the area to generate forest resource change data;

[0093] The first step is to collect resource data, including forest coverage, tree species distribution, biodiversity index, etc., followed by data classification and organization. The collected data needs to be arranged and organized according to geographic information and time sequence, so that the changes in forest resources at different time points can be observed. Next is spatial distribution analysis, which requires the application of GIS technology to associate data with geographic location. Through this method, the spatial distribution and change trends of resources in different regions can be clearly seen, and specific areas where resources have increased or decreased can be identified. These steps are extremely important for the formulation of forest management and protection policies, and generate forest resource change data.

[0094] S302: Based on the forest resource change data, compare the resource changes with the temperature, humidity, precipitation, etc. of the climate, analyze the impact of climate factors on the increase and decrease of forest resources, identify the increase and decrease trend of resources, and perform comparative processing on resource changes to generate resource increase and decrease trend data;

[0095] The specific calculation formula for the impact of climate factors on the change of forest resources in a specific location is:

[0096]

[0097] First, ΔR i represents the impact of climate factors on the change of forest resources in a specific location, k represents the expected change in resources without any climate impact, m represents the impact coefficient of temperature, and Q i represents the actual observed temperature, D i represents the actual observed humidity, l represents the influence coefficient of precipitation, P i Represents the actual observed precipitation.

[0098] k is a constant term that represents the expected resource change in the absence of any climate impact. To calculate this value, the resource data for the same location over the past five years were analyzed. In years without significant climate anomalies, the average resource change was k = 1.5 units.

[0099] m is the influence coefficient of temperature. Through regression analysis of the data of the past five years, the correlation between temperature and resource change was calculated, and m = 0.03 was obtained. This means that for every 1°C increase, the resource change increases by 0.03 units on average.

[0100] Q i is the actual observed temperature at the location, for example, the average temperature observed in the current year is Q i =25℃.

[0101] D i is the actual observed humidity at the location, used to adjust for the effect of temperature, assuming the current humidity is D i =80%, i.e. Di =0.8.

[0102] l is the influence coefficient of precipitation, which is obtained through a regression analysis similar to temperature and is l = 0.02, indicating that for every increase of 1 mm of precipitation, the resource change increases by an average of 0.02 units.

[0103] P i is the actual observed precipitation at the location, for example, if the total precipitation for the current year is P i =500mm.

[0104] Substitute specific numbers to calculate:

[0105]

[0106]

[0107] ΔR i =1.5+0.9375+10

[0108] ΔR i =12.4375

[0109] The results show that given current climate conditions, the resources at the site are expected to increase by about 12.44 units. This calculation reflects the direct effects of temperature and precipitation and the moderating effect of humidity on the temperature effect, providing a quantitative method to assess the specific impact of climate change on forest resources, allowing resource management decisions to be adjusted based on actual climate data.

[0110] S303: Based on the resource increase and decrease trend data, analyze the change trend of forest resources, combine with climate change data, identify seasonal fluctuations and long-term change trends, and predict the potential changes in forest resources in the future period, and generate a future forecast report on forest resources;

[0111] First, through statistical analysis methods, such as time series analysis, seasonal fluctuations and long-term trends in resource changes are identified. Next, climate models are used to predict future climate conditions, such as temperature increases or changes in precipitation. These models are usually based on large amounts of meteorological data and advanced computing algorithms. These predicted data are then matched with resource change trends to predict potential changes in forest resources. This process is crucial for future resource management and protection strategies. Ultimately, future forest resource forecast reports are generated. These reports provide a scientific basis for policymakers to help them make more effective decisions.

[0112] See also Figure 5 , the specific steps of S4 are:

[0113] S401: Based on the results of forest health ecological assessment and the report on the ecological function contribution of species distribution, the density distribution of regional resources is analyzed by comparing spatial coordinates with species type data, and regional division is performed to generate spatial distribution data of forest resources;

[0114] First, the location data of different species and the environmental characteristic data are superimposed and analyzed to reveal the relationship between species distribution and environmental factors. Then, statistical methods and spatial analysis tools are used to quantify the density of species distribution. Then, each region is divided into different areas, which requires comprehensive consideration of ecological functions, species density and geographical characteristics. The division of each area must be accurate to specific coordinates and boundaries. Finally, the spatial distribution data of forest resources is generated, which will directly affect the formulation of resource management and protection strategies.

[0115] S402: Based on the spatial distribution data of forest resources, set the preset threshold of resource utilization, quantify the resource density and ecological function contribution of each area, calculate the resource utilization efficiency, identify high-efficiency and low-efficiency areas, and define the two types of areas to generate resource utilization efficiency regional division data;

[0116] The specific formula for calculating resource utilization efficiency is:

[0117]

[0118] First, RE i Resource utilization efficiency, c represents the adjustment coefficient, D i represents resource density, E i Represents the ecological function contribution of the region, S i Represents the actual area of ​​the region.

[0119] c is the adjustment coefficient, which is set based on the analysis of historical data. It is used to balance the comparison between different regions. It is calculated from the average resource utilization efficiency data of the past five years and is set to 1.2.

[0120] D i is the resource density of the region, which is obtained by dividing the number of resources by the area of ​​the region. For example, data obtained through GIS system statistics show that there are 2,000 trees covering 10,000 square meters in a certain area, so

[0121]

[0122] trees / m2.

[0123] E i It is the ecological function contribution of the region. The evaluation score is based on the ecological services provided by the region, such as carbon sequestration and water purification. It is assumed that the score is obtained through the ecological service evaluation model, which is 300 points.

[0124] S i is the actual area of ​​the region, obtained directly from GIS data, for example, the area is 10,000 square meters.

[0125] Calculation process:

[0126]

[0127]

[0128]

[0129] RE i =0.72

[0130] The results show that the regional resource use efficiency is 0.72, which means that each square meter of the area contributes an average of 0.72 units of ecological function. This shows that the region has a higher resource use efficiency than other regions, which helps decision makers determine the priorities of resource management strategies and protection measures. In this way, resources can be allocated more efficiently and those areas that contribute more to the ecosystem can be protected.

[0131] S403: Based on the regional division data of resource utilization efficiency, compare the climate, soil, and species composition of the region, analyze the reasons for the differences in resource utilization within the region, and generate forest resource difference data;

[0132] Detailed analysis of the climate conditions of each region, such as temperature, precipitation, and soil type, can be used to identify the main environmental factors that lead to differences in resource utilization through comparisons. Further analysis may include soil fertility testing, water source assessments, and species adaptability studies. Each factor requires data collection and geostatistical analysis. This process is crucial for understanding the ecological and environmental drivers behind resource utilization efficiency in different regions, and ultimately generates forest resource difference data that helps decision makers develop more targeted management strategies to ensure sustainable resource utilization.

[0133] See also Figure 6 , the specific steps of S5 are:

[0134] S501: Based on forest resource difference data, extract resource distribution in inefficient areas, conduct statistical and comparative analysis of regional resource density, land use efficiency and ecological function, identify areas with uneven resource allocation, and generate resource allocation plans for inefficient areas;

[0135] First, areas with low resource density are screened out from the overall data set, and spatial analysis tools are used to determine the specific locations and boundaries of these areas. Then, in-depth statistical analysis of land use efficiency and ecological functions of these inefficient areas is conducted, including land cover type, soil quality assessment, and calculation of biodiversity index. Through these indicators, the causes of unbalanced resource allocation can be identified, such as insufficient soil fertility or inappropriate ecological protection measures. Finally, this information is integrated to generate resource allocation improvement plans for each inefficient area. These plans will guide future resource management and optimization measures.

[0136] S502: Based on the inefficient regional resource allocation plan, combined with climate change data and environmental monitoring data, analyze the correlation between climate change trends and soil and vegetation, calibrate the risk level through cross-regional data comparison, and generate potential ecological risk area data;

[0137] Combine climate change data with environmental monitoring data to analyze the correlation between climate change trends and soil and vegetation.

[0138]

[0139] Identify risk levels.

[0140] In the formula, R ′ Represents the risk level of the area, C i ′ Represents the climate change index, E i ′ Represents the environmental sensitivity index, N ′ Represents the number of regions.

[0141] Assume there are three regions, where the climate change index C is 1.2, 1.5, 1.3, the environmental sensitivity index E is 0.8, 1.0, 0.9, and the number of regions N is 3. According to the formula

[0142]

[0143] This calculation result shows that the overall risk level is 1.21, indicating a moderate environmental risk.

[0144] S503: Based on the data of potential ecological risk areas, environmental carrying capacity assessment and resource mobility analysis are carried out, combined with climate adaptability factors to generate forest resource management optimization and risk control plans;

[0145] First, the environmental carrying capacity of each region is assessed, which is usually done by simulating the consumption and regeneration capacity of natural resources in different ecosystems. Next, resource mobility is analyzed, especially the recycling of water resources and soil nutrients, combined with climate adaptability factors such as regional climate patterns and the impact of temperature changes on biological communities. Through these analyses, forest resource management optimization and risk control plans are generated. These plans aim to improve resource utilization efficiency, reduce ecological risks, and provide a scientific basis for the sustainable management of forest resources.

[0146] 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 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 forestry surveying and mapping method based on remote sensing big data, characterized in that: The following steps are involved: Integrate remote sensing images, drone images, temperature sensors, and humidity sensor data for spatiotemporal alignment, fill in missing data through temporal interpolation, perform outlier detection, calculate ecological indicators of forest areas based on processed data, and output forest health ecological assessment results; Based on the forest health ecological assessment results, identify the species in the forest through remote sensing images, drone images, temperature sensors and humidity sensor data, and analyze the contribution of species to the forest ecological function in combination with environmental factors. Compare species distribution with ecological health, identify the distribution of key species that affect ecological functions, and output a species distribution ecological function contribution report; Based on the forest health ecological assessment results and the species distribution ecological function contribution report, analyze the dynamic changes of forest ecology and resources, determine the changing trend of forest resources, identify the increase and decrease patterns of forest resources and, combined with climate factors, predict the changes in forest ecology in the future and produce a future forecast report on forest resources; Combined with the forest health ecological assessment results and the species distribution ecological function contribution report, conduct spatial distribution analysis of forest resources, evaluate resource utilization efficiency in different regions, identify and distinguish high-efficiency and low-efficiency resource utilization regions through preset thresholds, analyze the reasons for regional differences, and output a forest resource difference report; Based on the forest resource difference report and the future forest resource forecast report, resources will be reallocated in areas with inefficient resource utilization. Combined with climate change and environmental monitoring data, potential ecological risk areas will be assessed, early warning and risk management will be stimulated, and a forest resource management optimization and risk control plan will be formed.

2. The forestry surveying and mapping method based on remote sensing big data according to claim 1 is characterized in that: The forest health ecological assessment results include forest ecological indicators, missing data supplementation results and outlier detection results. The species distribution ecological function contribution report includes species identification results, species ecological function contribution analysis and key species distribution identification. The future forest resource forecast report includes resource change trend analysis, resource increase and decrease patterns and climate factor impact analysis. The forest resource difference report includes resource utilization efficiency assessment, high-efficiency and low-efficiency area calibration and regional difference cause analysis. The forest resource management optimization and risk control plan includes resource reconfiguration plan, potential risk area assessment and risk management strategy.

3. The forestry surveying and mapping method based on remote sensing big data according to claim 1 is characterized in that: The specific steps of integrating remote sensing images, drone images, temperature sensors and humidity sensor data for spatiotemporal alignment processing, filling missing data through time interpolation, and performing outlier detection are as follows: Based on remote sensing images, drone images, temperature sensors, and humidity sensor data, accurate alignment of time and space coordinates is performed, timestamps of each data source are sorted, and spatial coordinate scales are converted and unified, and time differences and spatial deviations between data sources are adjusted to generate time-space alignment data; Based on the time-space aligned data, time period interpolation is performed, all data are resampled according to time intervals, and values ​​are inserted at missing data points to fill in the gaps, thereby generating filled data; Based on the filled data, outlier detection is performed and the normal fluctuation range of the data is defined, the relationship between each data point and the range is checked, outliers beyond the range are marked and the outliers are processed by interpolation, and the forest health ecological assessment results are output.

4. The forestry surveying and mapping method based on remote sensing big data according to claim 1, characterized in that: Based on the results of the forest health ecological assessment, remote sensing images, drone images, temperature sensors and humidity sensor data are used to identify species in the forest, and combined with environmental factors, the contribution of species to forest ecological functions is analyzed. Species distribution and ecological health are compared to identify the distribution of key species that affect ecological functions. The specific steps for outputting the species distribution ecological function contribution report are as follows: Based on the forest health ecological assessment results, extract species outlines in forest areas through remote sensing images and drone images, analyze species distribution ranges in combination with temperature and humidity data, identify species, mark the core growth areas of species, and generate species identification data; Based on the species identification data, combined with environmental factors, the relationship between species growth and temperature and humidity factors is extracted, and the impact of species on forest ecological functions is analyzed by numerical matching of species and environmental factors, and the ecological contribution index within the growth area is calculated to generate species ecological function contribution data; Based on the ecological function contribution data of the species, the species distribution is compared with the results of forest health ecological assessment, key species and their role in ecological functions are identified, and a comprehensive analysis is conducted based on the spatial distribution and ecological function contribution of species to generate a species distribution ecological function contribution report.

5. The forestry surveying and mapping method based on remote sensing big data according to claim 4 is characterized in that: The specific steps are: the calculation formula of the ecological contribution index of the species is: First, the Ecological Contribution Index represents ECI i , species represents i, environmental factor represents j, R ij represents the response rate of species i under environmental factor j, E ij represents the actual measured value of environmental factor j at location i, H i represents the optimum humidity for species i, P i Represents the actual humidity.

6. The forestry surveying and mapping method based on remote sensing big data according to claim 1, characterized in that: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, the dynamic changes of forest ecology and resources are analyzed, the changing trend of forest resources is determined, the increase and decrease of forest resources is identified, and combined with climate factors, the changes in forest ecology in the future are predicted. The specific steps for obtaining the future forecast report of forest resources are as follows: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, extract the resource data of each forest area, and sort out the resource changes in each period in chronological order, combine geographic information to conduct spatial distribution analysis, and identify the increase and decrease of resources in the area to generate forest resource change data; Based on the forest resource change data, compare the resource changes with the temperature, humidity, precipitation, etc. of the climate, analyze the impact of climate factors on the increase or decrease of forest resources, identify the increase or decrease trend of resources, and perform comparative processing on resource changes to generate resource increase or decrease trend data; Based on the resource increase and decrease trend data, the changing trend of forest resources is analyzed, and combined with climate change data, seasonal fluctuations and long-term change trends are identified, and the potential changes in forest resources in the future are predicted to generate a future forecast report on forest resources.

7. The forestry surveying and mapping method based on remote sensing big data according to claim 6 is characterized in that: The specific steps are: the calculation formula for the impact of the climate factors on the change of forest resources in a specific location is: First, ΔR i represents the impact of climate factors on the change of forest resources in a specific location, k represents the expected change in resources without any climate impact, m represents the impact coefficient of temperature, and Q i represents the actual observed temperature, D i represents the actual observed humidity, l represents the influence coefficient of precipitation, P i Represents the actual observed precipitation.

8. The forestry surveying and mapping method based on remote sensing big data according to claim 1, characterized in that: Combined with the forest health ecological assessment results and the species distribution ecological function contribution report, the spatial distribution analysis of forest resources is carried out to evaluate the resource utilization efficiency of the difference areas. By using the preset threshold, the efficient and inefficient resource utilization areas are identified and distinguished, and the reasons for the regional differences are analyzed. The specific steps for outputting the forest resource difference report are as follows: Based on the forest health ecological assessment results and the species distribution ecological function contribution report, the density distribution of regional resources is analyzed by comparing spatial coordinates with species type data, and each region is divided to generate forest resource spatial distribution data; Based on the spatial distribution data of forest resources, preset thresholds for resource utilization are set, resource density and ecological function contribution of each area are quantified, resource utilization efficiency is calculated, high-efficiency and low-efficiency areas are identified, and the two types of areas are defined to generate resource utilization efficiency regional division data; Based on the resource utilization efficiency regional division data, the regional climate, soil, and species composition are compared, the reasons for the differences in resource utilization within the region are analyzed, and forest resource difference data are generated.

9. The forestry surveying and mapping method based on remote sensing big data according to claim 8, characterized in that: The specific steps are: The resource utilization efficiency calculation formula is specifically: First, RE i Resource utilization efficiency, c represents the adjustment coefficient, D i represents resource density, E i Represents the ecological function contribution of the region, S i Represents the actual area of ​​the region.

10. The forestry surveying and mapping method based on remote sensing big data according to claim 1, characterized in that: Based on the forest resource difference report and the future forest resource forecast report, the specific steps for reallocating resources in areas with inefficient resource utilization, assessing potential ecological risk areas, stimulating early warning and risk management, and forming a forest resource management optimization and risk control plan are as follows: Based on the forest resource difference data, the resource distribution of inefficient areas is extracted, and statistical and comparative analysis of regional resource density, land use efficiency and ecological function is performed to identify areas with uneven resource allocation and generate resource allocation plans for inefficient areas; Based on the inefficient regional resource allocation plan, combined with climate change data and environmental monitoring data, the correlation analysis between climate change trends and soil and vegetation is carried out. Through cross-regional data comparison, the risk level is calibrated and potential ecological risk area data is generated; Based on the data of potential ecological risk areas, environmental carrying capacity assessment and resource mobility analysis are conducted, combined with climate adaptability factors to generate forest resource management optimization and risk control plans.

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