Ecological effectiveness evaluation method of forest protection and restoration based on multispectral UAV

Abstract

The present invention provides a forest protection and restoration ecological effectiveness evaluation method based on a multispectral UAV, including: obtaining relevant data to obtain the spatial scope of the evaluation area, and selecting a reference ecosystem based on the evaluation area; delimiting the image acquisition range of the multispectral UAV according to the evaluation area and the reference ecosystem to obtain multispectral remote sensing data; combining forest protection and restoration measures, selecting ecological indicators, and constructing a forest protection and restoration ecological effectiveness evaluation index system based on ecological indicators; using mandatory indicators and at least two optional indicators as evaluation indicators to establish a forest protection and restoration ecological effectiveness index; calculating the evaluation indicators of the evaluation area and the reference ecosystem according to the multispectral remote sensing data, and grading and weighting the evaluation indicators; calculating the comprehensive score of ecological effectiveness according to all evaluation indicators and corresponding weights, and analyzing to obtain the evaluation results. The present invention can realize the rapid quantitative evaluation of restoration effectiveness and improve the convenience and scientificity of evaluation work.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a method for evaluating the ecological effectiveness of forest protection and restoration based on a multi-spectral unmanned aerial vehicle. Background Art

[0002] The ecological effectiveness assessment of forest protection and restoration is a key task to ensure the quality and effectiveness of forest ecological restoration. It is of great significance for adjusting and optimizing engineering measures and layout, and achieving maximum benefit and sustainability.

[0003] At present, my country has achieved a lot of research results in the construction of ecological protection and restoration effectiveness evaluation system, but the field of ecological restoration involves a wide range of areas, and there is still a lack of scientific evaluation standards, unified indicator systems and evaluation methods for the evaluation of the ecological effectiveness of forest protection and restoration. Due to the long cycle and slow effectiveness of forest ecological restoration, the traditional manual ground survey method is costly and labor-intensive, and some evaluation indicators are difficult to obtain. Affected by subjective factors such as the cognitive level and professional ability of investigators, different personnel often have large differences in their evaluation of the same indicator, resulting in poor comparability of evaluation results in different restoration intervals, which makes it difficult to meet the actual work needs of low-cost, rapid, scientific and objective evaluation of ecological effectiveness in forest protection and restoration areas.

[0004] Therefore, there is an urgent need for an indicator system and technical method that can be used to evaluate the ecological effectiveness of forest protection and restoration, so as to achieve rapid quantitative evaluation of the effects of forest protection and restoration. Summary of the invention

[0005] Based on this, it is necessary to provide an ecological effectiveness evaluation method for forest protection and restoration based on multispectral drones to address the above-mentioned technical issues.

[0006] A method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral unmanned aerial vehicle comprises the following steps: obtaining relevant data of a forest protection and restoration area to be evaluated, obtaining the spatial scope of the evaluation area, and selecting a corresponding reference ecosystem based on the ecological conditions of the evaluation area; defining an image acquisition scope according to the scope and terrain conditions of the evaluation area and the reference ecosystem, and acquiring data through a multispectral unmanned aerial vehicle to obtain multispectral remote sensing data; selecting ecological indicators for reflecting changes in forest ecological conditions in combination with forest protection and restoration measures, and constructing an ecological effectiveness evaluation index system for forest protection and restoration based on the ecological indicators, wherein the ecological indicators include mandatory indicators and selective indicators, wherein the mandatory indicators include vegetation coverage, aboveground biomass and canopy density; using the mandatory indicators and at least two selective indicators as evaluation indicators to establish a forest protection and restoration ecological effectiveness index; calculating the evaluation indicators of the evaluation area and the reference ecosystem based on the collected multispectral remote sensing data, and grading and weighting the evaluation indicators; calculating a comprehensive score of ecological effectiveness according to all evaluation indicators and corresponding weights of the evaluation area, and analyzing the evaluation results of the ecological effectiveness of forest protection and restoration.

[0007] In one embodiment, the selective indicators include newly afforested area, incidence of pests and diseases, normalized vegetation index, leaf chlorophyll index, difference vegetation index, ratio vegetation index, enhanced vegetation index, green normalized vegetation index, normalized difference red edge vegetation index, and optimized soil adjustment vegetation index.

[0008] In one of the embodiments, the relevant data of the forest protection and restoration area to be evaluated is obtained to obtain the spatial scope of the evaluation area, and based on the ecological conditions of the evaluation area, a corresponding reference ecosystem is selected, including: collecting engineering design drawings and implementation plans of the forest protection and restoration area to be evaluated, obtaining the real-time full-cycle start time and specific restoration measures of the project, and determining the spatial scope of the evaluation area; according to the ecological conditions of the topography and vegetation type of the evaluation area, an area in the same geographical climate zone with the same ecosystem as the evaluation area, similar ecological conditions, no ecological restoration measures implemented, and little interference from human activities is selected as a reference ecosystem for synchronous monitoring.

[0009] In one of the embodiments, the image acquisition range is delineated according to the scope and terrain conditions of the assessment area and the reference ecosystem, and data is collected by a multispectral UAV to obtain multispectral remote sensing data, including: according to the scope and terrain conditions of the assessment area and the reference ecosystem, the image acquisition range is delineated, a UAV route mission is created, the route flight altitude is set to be no higher than 120m, the orthogonal GSD is better than 10cm / pixel, the forward overlap and the lateral overlap are not less than 85%, and a multispectral UAV is used for data collection; aerial triangulation is performed on the collected images, the regional images are mosaicked and spliced ​​according to the flight sequence, and the visible light images are subjected to uniform color correction processing; the processed images are saved in TIFF format, and the data space reference is converted to the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum to obtain multispectral remote sensing data.

[0010] In one embodiment, the calculating of the assessment indicators of the assessment area and the reference ecosystem based on the collected multispectral remote sensing data includes:

[0011] The pixel dichotomy method is used to calculate vegetation coverage through the normalized vegetation index. The formula is:

[0012]

[0013] Where, FVC is the vegetation coverage; NDVI is the normalized difference vegetation index; N soil is the NDVI value of pure bare soil pixel; n veg It is the NDVI value of pure vegetation pixels or areas with high vegetation coverage;

[0014] Extract vegetation spectral index, texture characteristics or original band spectral characteristics as characteristic variables, establish statistical regression models for other measured biomass data, and invert forest aboveground biomass;

[0015] Based on the collected multispectral remote sensing data, the green leaf algorithm is used to transform the image color space. The formula is:

[0016]

[0017] In the formula, R, G, and B represent the spectral reflectance of the red, green, and blue bands, respectively;

[0018] The pixels with GLA < 0 after color space transformation were defined as black background areas, and the K-Means algorithm was used for classification, and the canopy density was obtained by pixel statistics.

[0019] Through the comparison and interpretation of multi-temporal high-resolution remote sensing images, the newly added afforestation spots are outlined, and the newly added afforestation area is calculated through area statistics;

[0020] Through the method of vegetation spectral feature analysis and visual interpretation, the diseased trees with disease-susceptible color changes are marked and the incidence of diseases and insect pests is calculated. The formula is:

[0021]

[0022] Where N d Indicates the number of diseased trees, N a Indicates the total number of trees;

[0023] The calculation formula of the normalized vegetation index is:

[0024]

[0025] In the formula, NIR is the reflectivity of the near-infrared band, and R is the reflectivity of the red band;

[0026] The leaf chlorophyll index is used to determine the chlorophyll and nitrogen content of vegetation leaves, and the calculation formula is:

[0027]

[0028] In the formula, R EDGE is the red edge band reflectivity;

[0029] The difference vegetation index is used to detect vegetation biomass in sparse vegetation areas, and the calculation formula is:

[0030] DVI=NIR-R

[0031] The ratio vegetation index is used to monitor the health status of vegetation in densely vegetated areas, and the calculation formula is:

[0032]

[0033] The enhanced vegetation index is used to monitor vegetation changes in densely vegetated areas, and the calculation formula is:

[0034]

[0035] Where, B is the blue band reflectivity;

[0036] The green normalized vegetation index is used to reflect the nitrogen content during vegetation growth, and the calculation formula is:

[0037]

[0038] Where, G is the reflectivity of the green band;

[0039] The normalized difference red edge vegetation index is used to reflect the chlorophyll content of vegetation in high vegetation coverage areas, and the calculation formula is:

[0040]

[0041] The optimized soil adjusted vegetation index is used to reflect the health status of vegetation in the early stage of growth, and the calculation formula is:

[0042]

[0043] In one embodiment, the grading and weighting of the evaluation indicators includes: taking the monitoring value of the reference ecosystem as the reference value and the monitoring value of the evaluation area as the true value, respectively calculating the change between the true value and the reference value, grading the evaluation indicators according to the change, and using the average normalization method to standardize the change data of each evaluation indicator, mapping the change to between -1 and 1, and the calculation formula is:

[0044] x=x r -x c

[0045]

[0046] In the formula, x is the change between the actual value and the reference value, x r is the actual value, x c is the reference value, x′ is the standardized change, X mean is the average value of the variation, x max is the maximum value of the change, x min is the minimum value of the change; the evaluation indicators are graded according to the size of the standardized change, and the evaluation indicator results are divided into five levels using the equal interval method; the evaluation indicators are weighted according to the correlation and importance of each evaluation indicator to the remediation measures.

[0047] In one embodiment, the comprehensive score of ecological effectiveness is calculated based on all evaluation indicators and corresponding weights of the evaluation area to obtain the evaluation result of the ecological effectiveness of forest protection and restoration, including: using the FEI model to calculate the comprehensive score of ecological effectiveness, the formula is:

[0048]

[0049] Where FEREI is the forest ecological restoration effectiveness index. The larger the value, the better the forest ecological restoration effect, and vice versa. S i is the score of the i-th indicator, W iis the weight of the i-th indicator, i is the indicator number, and n is the number of indicators; when the FEREI value is in the interval [85,100], it indicates that the ecological effectiveness evaluation result is excellent; when the FEREI value is in the interval [70,85), it indicates that the ecological effectiveness evaluation result is good; when the FEREI value is in the interval [60,70), it indicates that the ecological effectiveness evaluation result is qualified; when the FEREI value is in the interval [0,60), it indicates that the ecological effectiveness evaluation result is unqualified.

[0050] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: by acquiring relevant data of the forest protection and restoration area to be evaluated, the spatial scope of the evaluation area is obtained, and based on the ecological conditions of the evaluation area, the corresponding reference ecosystem is selected, and according to the terrain conditions of the evaluation area and the reference ecosystem, the image acquisition range is delineated, and data is collected by multispectral UAVs to obtain multispectral remote sensing data. By comparing the evaluation area with the ecosystem, the preliminary data collection and analysis work is avoided and the workload is reduced; in combination with forest protection and restoration measures, ecological indicators are selected to reflect changes in forest ecological conditions, and a forest protection and restoration ecological effectiveness evaluation index system is constructed based on the selected ecological indicators. The ecological indicators include coverage The mandatory and optional indicators of density, aboveground biomass and canopy density are selected to facilitate the selection of corresponding indicators for ecological restoration evaluation based on the regional ecological conditions; the mandatory indicators and at least two optional indicators are used as evaluation indicators to establish a forest protection and restoration ecological effectiveness index, and the evaluation indicators of the evaluation area and the reference ecosystem are calculated based on the collected multispectral remote sensing data, and the evaluation indicators are graded and weighted to facilitate the subsequent calculation of ecological effectiveness scores; the comprehensive ecological effectiveness score is calculated based on all evaluation indicators and corresponding weights of the evaluation area, and the evaluation results of the ecological effectiveness of forest protection and restoration are analyzed, so as to achieve rapid quantitative evaluation of the effect of forest ecological restoration and improve the efficiency and scientificity of the evaluation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a flowchart of a method for evaluating the ecological effectiveness of forest protection and restoration based on a multi-spectral UAV in one embodiment. DETAILED DESCRIPTION

[0052] Before describing the specific embodiments of the present invention, the overall concept of the present invention is described as follows:

[0053] The present invention is mainly developed based on the ecological effectiveness evaluation process of forest protection and restoration. The current ground survey method has high cost and large workload, and it is difficult to obtain some evaluation indicators, resulting in low efficiency and scientificity of restoration evaluation.

[0054] Therefore, the present invention proposes a forest protection and restoration ecological effectiveness assessment method based on multispectral UAVs. The method obtains relevant data of the forest protection and restoration area to be assessed, obtains the spatial scope of the assessment area, selects the corresponding reference ecosystem based on the ecological conditions of the assessment area, and defines the image acquisition range according to the terrain conditions of the assessment area and the reference ecosystem. The multispectral UAV is used to collect data to obtain multispectral remote sensing data. By comparing the assessment area with the ecosystem, the preliminary data collection and analysis work is avoided and the workload is reduced. In combination with forest protection and restoration measures, ecological indicators are selected to reflect changes in forest ecological conditions, and an ecological effectiveness assessment index system for forest protection and restoration is constructed based on the selected ecological indicators. The indicators include mandatory and optional indicators of coverage, aboveground biomass and canopy density, so that the corresponding indicators can be selected for ecological restoration assessment according to the regional ecological conditions; the mandatory indicators and at least two optional indicators are used as evaluation indicators to establish a forest protection and restoration ecological effectiveness index, and the evaluation indicators of the evaluation area and the reference ecosystem are calculated according to the collected multispectral remote sensing data, and the evaluation indicators are graded and weighted to facilitate the subsequent calculation of ecological effectiveness scores; the comprehensive ecological effectiveness score is calculated based on all evaluation indicators and corresponding weights of the evaluation area, and the evaluation results of the ecological effectiveness of forest protection and restoration are analyzed, so as to achieve rapid quantitative evaluation of the effects of forest ecological restoration and improve the efficiency and scientific nature of the evaluation work.

[0055] After introducing the overall concept of the present invention, in order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail by specific implementation methods in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] In one embodiment, Figure 1 As shown, a method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral UAV is provided, which includes the following steps:

[0057] Step S101, obtaining relevant data of the forest protection and restoration area to be evaluated, obtaining the spatial scope of the evaluation area, and selecting a corresponding reference ecosystem based on the ecological conditions of the evaluation area.

[0058] Specifically, when evaluating the restoration effectiveness of the forest protection and restoration area to be evaluated, first, obtain the relevant data of the corresponding restoration area to obtain the spatial scope of the evaluation area, and based on the ecological conditions of the evaluation area, select the corresponding reference ecosystem for simultaneous monitoring. Based on the simultaneous monitoring of the evaluation area and the reference ecosystem, it is convenient to conduct a comparative evaluation of the ecological restoration effect of the evaluation area, improve the evaluation efficiency and the scientificity and reliability of the evaluation results.

[0059] Among them, step S101 includes: collecting engineering design drawings and implementation plans of the forest protection and restoration area to be evaluated, obtaining the real-time start time and specific restoration measures of the project throughout the entire cycle, and determining the spatial scope of the evaluation area; according to the ecological conditions of the topography and vegetation types of the evaluation area, select areas in the same geographical climate zone with the same ecosystem as the evaluation area, similar ecological conditions, no ecological restoration measures implemented, and little interference from human activities as reference ecosystems for synchronous monitoring.

[0060] Specifically, relevant data and information on engineering design and implementation plans of the forest protection and restoration areas to be evaluated are collected to understand the start time and specific restoration measures of the entire project implementation cycle, and to clarify the spatial scope of the evaluation area. Based on the ecological background conditions such as the topography and vegetation types of the evaluation area, areas in the same geographical climate zone with the same ecosystem as the evaluation area, similar ecological background conditions, no ecological restoration measures, and little interference from human activities are selected as reference ecosystems for synchronous monitoring.

[0061] Step S102, according to the scope and terrain conditions of the assessment area and the reference ecosystem, the image acquisition range is delineated, and data is collected through a multispectral UAV to obtain multispectral remote sensing data.

[0062] Specifically, after the assessment area is restored through forest protection measures, the scope of image collection is delineated based on the scope and terrain conditions of the assessment area and reference ecosystem, and the image collection range of the multispectral UAV is set. Data is collected by the multispectral UAV to obtain the corresponding multispectral remote sensing data for subsequent restoration effectiveness evaluation. At the same time, data collection through drones effectively reduces the labor and time costs of forest ecological monitoring and improves the work efficiency of the evaluation. In addition, drones can realize full-area detection and evaluation of the assessment area, so as to more accurately grasp the spatial change trend of restoration effectiveness.

[0063] Among them, step S102 includes: according to the scope and terrain conditions of the assessment area and the reference ecosystem, delineating the image collection scope, creating a drone route mission, setting the route flight altitude to be no higher than 120m, the orthogonal GSD to be better than 10cm / pixel, the forward overlap and the side overlap to be no less than 85%, and using a multispectral drone for data collection; performing aerial triangulation on the collected images, mosaicking and splicing the regional images according to the flight sequence, and performing uniform color correction on the visible light images; saving the processed images in TIFF format, converting the data space reference to the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum, and obtaining multispectral remote sensing data.

[0064] Specifically, according to the scope and terrain conditions of the assessment area and reference ecosystem, the image collection scope is delineated, the drone route mission is created, the flight altitude of the route is set to be no higher than 120m, the orthogonal GSD is better than 10cm / pixel, the forward overlap and lateral overlap are not less than 85%, and multispectral drones are used for data collection. DJI Terra software is used to perform aerial triangulation of the collected images, mosaic and splice the regional images according to the flight sequence, and perform uniform color correction on the visible light images. The processed data is saved in .TIFF format, and the data spatial reference is converted to the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum. Finally, the multispectral remote sensing data collected by the drone is obtained for subsequent ecological effectiveness evaluation.

[0065] Step S103, in combination with forest protection and restoration measures, select ecological indicators used to reflect changes in forest ecological conditions, and construct a forest protection and restoration ecological effectiveness evaluation index system based on the ecological indicators. The ecological indicators include mandatory indicators and optional indicators, among which the mandatory indicators include vegetation coverage, aboveground biomass and canopy density.

[0066] Specifically, combined with forest protection and restoration measures, indicators that can reflect changes in forest ecological conditions are selected to construct an ecological effectiveness evaluation index system for forest protection and restoration, which improves the scientific nature and operability of the effectiveness evaluation work. It is worth noting that the selected indicators should be able to reflect the ecological changes brought about by engineering measures, and can be obtained through multi-spectral drones and related technologies.

[0067] Among them, the selective indicators include newly afforestation area, incidence of pests and diseases, normalized vegetation index, leaf chlorophyll index, difference vegetation index, ratio vegetation index, enhanced vegetation index, green normalized vegetation index, normalized difference red edge vegetation index, and optimized soil adjustment vegetation index.

[0068] Specifically, we can construct a set of forest protection and restoration ecological effectiveness evaluation index system from the aspects of forest structure, forest quality and forest growth, including vegetation coverage* (FVC), aboveground biomass* (AGB), canopy density* CD, newly afforested area (NAA), pest and disease incidence rate (DPIR), normalized difference vegetation index (NDVI), leaf chlorophyll index (LCI), difference vegetation index (DVI), ratio vegetation index (RVI), enhanced vegetation index (EVI), green normalized vegetation index (GNDVI), normalized difference red edge vegetation index (NDRE), optimized soil adjustment vegetation index (OSAVI), etc. Among them, * is a mandatory indicator, and the rest are optional indicators.

[0069] Step S104, using the mandatory indicators and at least two optional indicators as evaluation indicators to establish a forest protection and restoration ecological effectiveness index.

[0070] Specifically, on the basis of the mandatory indicators, according to the vegetation type and restoration measures characteristics of the assessment area, 2-6 indicators that can reflect the effects of forest protection and restoration are selected from the selective indicators to establish the forest protection and restoration ecological effectiveness index (FEI). For example, if the selected indicators are newly afforestation area, incidence of pests and diseases, and page chlorophyll index, then the FEI is expressed as a function equation of three mandatory indicators and three selective indicators, that is, FEI = f(FVC, AGB, CD, NAA, DPIR, LCI). By constructing a scientific and easy-to-measure forest ecological restoration effectiveness evaluation indicator system, it is convenient to conduct rapid quantitative evaluation of the effects of forest ecological restoration.

[0071] Step S105, calculating the assessment indicators of the assessment area and the reference ecosystem based on the collected multispectral remote sensing data, and grading and weighting the assessment indicators.

[0072] Specifically, based on the multispectral remote sensing data collected by drones, the evaluation indicators of the assessment area and the reference ecological coefficients are calculated, the calculated evaluation indicators are standardized and graded, and the indicators are weighted according to the correlation and importance of the evaluation indicators and restoration measures, so as to calculate the restoration effectiveness of the assessment area according to the evaluation indicators; based on the synchronous monitoring and calculation of the assessment area and the reference ecosystem, the evaluation efficiency and scientific nature of the ecological restoration effect can be improved.

[0073] The calculation steps of the evaluation index include: using pixel dichotomy method to calculate vegetation coverage through normalized vegetation index, the formula is:

[0074]

[0075] Where, FVC is the vegetation coverage; NDVI is the normalized difference vegetation index; N soil is the NDVI value of pure bare soil pixel; N veg It is the NDVI value of pure vegetation pixels or areas with high vegetation coverage;

[0076] Extract vegetation spectral index, texture characteristics or original band spectral characteristics as characteristic variables, establish statistical regression models for other measured biomass data, and invert forest aboveground biomass;

[0077] Based on the collected multispectral remote sensing data, the green leaf algorithm is used to transform the image color space. The formula is:

[0078]

[0079] In the formula, R, G, and B represent the spectral reflectance of the red, green, and blue bands, respectively; the pixel points with GLA < 0 after color space transformation are defined as the black background area, and the K-Means algorithm is used for classification, and the pixel statistics method is used to obtain the canopy closure;

[0080] Through the comparison and interpretation of multi-temporal high-resolution remote sensing images, the newly added afforestation spots are outlined, and the newly added afforestation area is calculated through area statistics;

[0081] When pests and diseases occur in forests, trees will show some pathological changes, such as leaf fall, withering, and yellowing. Through the analysis of vegetation spectral characteristics and visual interpretation, the diseased trees with diseased colors are marked and the incidence of pests and diseases is calculated. The formula is:

[0082]

[0083] Where N d Indicates the number of diseased trees, N a Indicates the total number of trees;

[0084] The Normalized Difference Vegetation Index is an important parameter that reflects vegetation growth and nutritional information, and is also an intermediary for evaluating vegetation coverage and aboveground biomass. The value is between -1 and 1. Positive values ​​indicate vegetation coverage, and increase with increasing coverage; 0 and negative values ​​indicate non-vegetation coverage such as bare soil, rocks, and water bodies. The calculation formula is:

[0085]

[0086] In the formula, NIR is the reflectivity of the near-infrared band, and R is the reflectivity of the red band;

[0087] The leaf chlorophyll index is used to determine the chlorophyll and nitrogen content of vegetation leaves. The calculation formula is:

[0088]

[0089] In the formula, R EDGE is the red edge band reflectivity;

[0090] The difference vegetation index can well reflect the changes in vegetation coverage and biomass and is sensitive to soil background changes. It is used to detect vegetation biomass in sparse vegetation areas. The calculation formula is:

[0091] DVI=NIR-R

[0092] The ratio vegetation index is suitable for monitoring the health status of vegetation in densely vegetated areas. It has a high correlation with leaf area index, biomass, chlorophyll content, etc. The calculation formula is:

[0093]

[0094] The enhanced vegetation index adds a blue band to enhance vegetation information and corrects the influence of soil background and aerosol scattering. It is used to monitor vegetation changes in densely vegetated areas. The calculation formula is:

[0095]

[0096] Where, B is the blue band reflectivity;

[0097] The green normalized vegetation index is used to reflect the nitrogen content in the process of vegetation growth to a certain extent. The calculation formula is:

[0098]

[0099] Where, G is the reflectivity of the green band;

[0100] The normalized difference red edge vegetation index is used to reflect the chlorophyll content of vegetation in high vegetation coverage areas. The calculation formula is:

[0101]

[0102] The optimized soil adjustment vegetation index is used to reduce the impact of soil background in the early stage of vegetation growth and reflect the health status of vegetation in the early stage of growth. The calculation formula is:

[0103]

[0104] The steps of grading and weighting the evaluation indicators include: taking the monitoring value of the reference ecosystem as the reference value and the monitoring value of the evaluation area as the true value, respectively calculating the change of the true value and the reference value, grading the evaluation indicators according to the change, and using the average normalization method to standardize the change data of each evaluation indicator, mapping the change to between -1 and 1. The calculation formula is:

[0105] x=x r -x c

[0106]

[0107] In the formula, x is the change between the actual value and the reference value, x r is the actual value, x c is the reference value, x′ is the standardized change, X mean is the average value of the variation, x max is the maximum value of the change, x min is the minimum value of the change; the evaluation indicators are graded according to the size of the standardized change, and the evaluation indicator results are divided into five levels using the equal interval method; the evaluation indicators are weighted according to the correlation and importance of each evaluation indicator to the remediation measures.

[0108] Specifically, the monitoring value of the reference ecosystem is identified as the reference value under the ideal state (less human interference activities), and the monitoring value of the assessment area is used as the actual value. The change between the reference value and the actual value is used as the basis for the classification of the effectiveness evaluation indicators. The average normalization method is used to standardize the change data of each indicator, and the change is mapped between -1 and 1 to eliminate the difference in the numerical magnitude between indicators. The evaluation indicators are graded according to the size of the standardized change, and the evaluation results of each indicator are divided into 5 levels using the equal interval method: significantly improved (0.6, 1), slightly improved (0.2 ~ 0.6], no significant change (-0.2 ~ 0.2], slightly reduced (-0.6 ~ -0.2], significantly reduced (-1 ~ -0.6], and assigned 100, 80, 60, 40, and 20 points respectively. Finally, each indicator is weighted according to the relevance and importance of each indicator to the restoration measures.

[0109] Step S106, calculating the comprehensive score of ecological effectiveness based on all evaluation indicators and corresponding weights of the evaluation area and analyzing to obtain the evaluation results of the ecological effectiveness of forest protection and restoration.

[0110] Specifically, based on all assessment indicators and corresponding weights in the assessment area, the comprehensive ecological effectiveness score is calculated, and the assessment results of the ecological effectiveness of forest protection and restoration are obtained based on the analysis of the comprehensive ecological effectiveness score, thereby achieving a rapid quantitative assessment of the ecological effectiveness of forest protection and restoration, and improving the convenience and scientificity of the assessment work.

[0111] Wherein, step S106 includes: using the FEI model to calculate the comprehensive score of ecological effectiveness, the formula is:

[0112]

[0113] Where FEREI is the forest ecological restoration effectiveness index. The larger the value, the better the forest ecological restoration effect, and vice versa. S i is the score of the i-th indicator, W i is the weight of the i-th indicator, i is the indicator number, and n is the number of indicators; when the FEREI value is in the interval [85,100], it indicates that the ecological effectiveness evaluation result is excellent; when the FEREI value is in the interval [70,85), it indicates that the ecological effectiveness evaluation result is good; when the FEREI value is in the interval [60,70), it indicates that the ecological effectiveness evaluation result is qualified; when the FEREI value is in the interval [0,60), it indicates that the ecological effectiveness evaluation result is unqualified.

[0114] Specifically, an integrated ecological effectiveness score is calculated based on the scores and weights of each evaluation indicator, and the ecological effectiveness results of forest protection and restoration are analyzed based on the integrated ecological effectiveness score. The higher the integrated ecological effectiveness score, the better the forest ecological restoration results, and vice versa, the worse the restoration results. The corresponding evaluation results are thus obtained, realizing a rapid quantitative evaluation of the ecological effectiveness of deep forest protection and restoration, and meeting the actual work needs of conducting low-cost, rapid, scientific and objective ecological effectiveness evaluations in forest protection and restoration areas.

[0115] In this embodiment, the spatial scope of the assessment area is obtained by acquiring relevant data of the forest protection and restoration area to be assessed, and based on the ecological conditions of the assessment area, the corresponding reference ecosystem is selected. According to the terrain conditions of the assessment area and the reference ecosystem, the image acquisition range is delineated, and data is collected by multispectral drones to obtain multispectral remote sensing data. By comparing the assessment area with the ecosystem, the preliminary data collection and analysis work is avoided and the workload is reduced. In combination with forest protection and restoration measures, ecological indicators are selected to reflect changes in forest ecological conditions, and a forest protection and restoration ecological effectiveness evaluation index system is constructed based on the selected ecological indicators. The ecological indicators include coverage, aboveground biomass, and and canopy density, as well as optional indicators, so as to select corresponding indicators for ecological restoration evaluation according to regional ecological conditions; using the required indicators and at least two optional indicators as evaluation indicators, establish a forest protection and restoration ecological effectiveness index, calculate the evaluation indicators of the evaluation area and the reference ecosystem according to the collected multispectral remote sensing data, and grade and weight the evaluation indicators to facilitate the subsequent calculation of ecological effectiveness scores; calculate the comprehensive ecological effectiveness score based on all evaluation indicators and corresponding weights of the evaluation area, analyze the evaluation results of the ecological effectiveness of forest protection and restoration, thereby realizing a rapid quantitative evaluation of the forest ecological restoration effect, and improving the efficiency and scientific nature of the evaluation work.

[0116] Compared with the existing technology, the present invention, based on multi-spectral UAV remote sensing technology, has constructed a set of scientific, objective, simple, feasible, economical and fast forest protection and restoration ecological effectiveness evaluation index system and technical methods, and achieved rapid quantitative evaluation of forest protection and restoration effects by comparing the effectiveness with the reference ecosystem. The present invention adopts an evaluation method based on a reference ecosystem, avoiding the data collection and analysis work in the early stage of ecological restoration, optimizing the evaluation workflow, and effectively reducing the workload; using UAVs to monitor evaluation indicators effectively reduces the labor and time costs of forest ecological monitoring and improves the efficiency of evaluation work; constructing a representative, easy-to-monitor, and quantifiable effectiveness evaluation index system, improving the scientificity and operability of effectiveness evaluation work; compared with the traditional point survey method of ground samples, the use of multi-spectral UAVs can monitor the entire evaluation area, so that the spatial change trend of restoration effectiveness can be more accurately grasped.

[0117] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0118] Obviously, those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0119] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for evaluating the ecological effectiveness of forest protection and restoration based on multispectral drones, characterized in that: include: Obtain relevant data of the forest protection and restoration area to be evaluated, obtain the spatial scope of the evaluation area, and select the corresponding reference ecosystem based on the ecological conditions of the evaluation area, including: collecting engineering design drawings and implementation plans of the forest protection and restoration area to be evaluated, obtaining the real-time start time and specific restoration measures of the project throughout the entire cycle, and determining the spatial scope of the evaluation area; according to the ecological conditions of the topography and vegetation type of the evaluation area, select areas in the same geographical climate zone with the same ecosystem as the evaluation area, similar ecological conditions, no ecological restoration measures implemented, and little interference from human activities as reference ecosystems for synchronous monitoring; According to the scope and terrain conditions of the assessment area and reference ecosystem, the image collection range is delineated, and data is collected by multispectral UAV to obtain multispectral remote sensing data; In combination with forest protection and restoration measures, ecological indicators used to reflect changes in forest ecological conditions are selected, and an ecological effectiveness evaluation index system for forest protection and restoration is constructed based on the ecological indicators. The ecological indicators include mandatory indicators and optional indicators, wherein the mandatory indicators include vegetation coverage, aboveground biomass and canopy density, and the optional indicators include newly afforested area, incidence of pests and diseases, normalized vegetation index, leaf chlorophyll index, difference vegetation index, ratio vegetation index, enhanced vegetation index, green normalized vegetation index, normalized difference red edge vegetation index and optimized soil adjustment vegetation index; Taking the mandatory indicators and at least two optional indicators as evaluation indicators, establish the forest protection and restoration ecological effectiveness index; Calculate the assessment indicators of the assessment area and reference ecosystem based on the collected multispectral remote sensing data, standardize and grade the assessment indicators, and weight the assessment indicators based on their relevance and importance to forest protection and restoration measures; The comprehensive score of ecological effectiveness is calculated based on all evaluation indicators and corresponding weights of the evaluation area, and the evaluation results of the ecological effectiveness of forest protection and restoration are analyzed.

2. The method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral UAV according to claim 1 is characterized in that: According to the scope and terrain conditions of the assessment area and reference ecosystem, the image acquisition range is delineated, and data is collected through a multispectral UAV to obtain multispectral remote sensing data, including: According to the scope and terrain conditions of the assessment area and reference ecosystem, the image collection scope is delineated, and the drone route mission is created. The flight altitude of the route is set to be no higher than 120m, the orthogonal GSD is better than 10cm / pixel, and the forward overlap and lateral overlap are not less than 85%. Multispectral drones are used for data collection; Perform aerial triangulation on the collected images, mosaic the regional images according to the flight sequence, and perform uniform color correction on the visible light images; The processed images were saved in TIFF format, and the data spatial reference was converted to the 2000 National Geodetic Coordinate System and the 1985 National Height Datum to obtain multispectral remote sensing data.

3. The method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral UAV according to claim 1 is characterized in that: The step of calculating the assessment indicators of the assessment area and the reference ecosystem based on the collected multispectral remote sensing data includes: The pixel dichotomy method is used to calculate vegetation coverage through the normalized vegetation index. The formula is: Where, FVC is the vegetation coverage; NDVI is the normalized difference vegetation index; N soil is the NDVI value of pure bare soil pixel; N veg It is the NDVI value of pure vegetation pixels or areas with high vegetation coverage; Extract vegetation spectral index, texture characteristics or original band spectral characteristics as characteristic variables, establish statistical regression models for other measured biomass data, and invert forest aboveground biomass; Based on the collected multispectral remote sensing data, the green leaf algorithm is used to transform the image color space. The formula is: In the formula, R, G, and B represent the spectral reflectance of the red, green, and blue bands, respectively; The pixels with GLA < 0 after color space transformation were defined as black background areas, and the K-Means algorithm was used for classification, and the canopy density was obtained by pixel statistics. Through the comparison and interpretation of multi-temporal high-resolution remote sensing images, the newly added afforestation spots are outlined, and the newly added afforestation area is calculated through area statistics; Through the method of vegetation spectral feature analysis and visual interpretation, the diseased trees with disease-susceptible color changes are marked and the incidence of diseases and insect pests is calculated. The formula is: Where N d Indicates the number of diseased trees, N a Indicates the total number of trees; The calculation formula of the normalized vegetation index is: In the formula, NIR is the reflectivity of the near-infrared band, and R is the reflectivity of the red band; The leaf chlorophyll index is used to determine the chlorophyll and nitrogen content of vegetation leaves, and the calculation formula is: In the formula, R EDGE is the red edge band reflectivity; The difference vegetation index is used to detect vegetation biomass in sparse vegetation areas, and the calculation formula is: DVI=NIR-R, The ratio vegetation index is used to monitor the health status of vegetation in densely vegetated areas, and the calculation formula is: The enhanced vegetation index is used to monitor vegetation changes in densely vegetated areas, and the calculation formula is: Where, B is the blue band reflectivity; The green normalized vegetation index is used to reflect the nitrogen content during vegetation growth, and the calculation formula is: Where, G is the reflectivity of the green band; The normalized difference red edge vegetation index is used to reflect the chlorophyll content of vegetation in high vegetation coverage areas, and the calculation formula is: The optimized soil adjusted vegetation index is used to reflect the health status of vegetation in the early stage of growth, and the calculation formula is:

4. The method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral UAV according to claim 1 is characterized in that: The evaluation indicators are standardized and graded, and weighted according to their relevance and importance to forest protection and restoration measures, including: The monitoring value of the reference ecosystem is used as the reference value, and the monitoring value of the assessment area is used as the true value. The changes of the true value and the reference value are calculated respectively. The assessment indicators are graded according to the changes. The change data of each assessment indicator is standardized by the average normalization method, and the change is mapped to between -1 and 1. The calculation formula is: x=x r -x c , In the formula, x is the change between the actual value and the reference value, x r is the actual value, x c is the reference value, x′ is the standardized change, X mean is the average value of the variation, x max is the maximum value of the change, x min is the minimum value of the change; The evaluation indicators are graded according to the size of the standardized changes, and the evaluation indicator results are divided into five levels using the equal interval method; The evaluation indicators are weighted according to their relevance and importance to the restoration measures.

5. The method for evaluating the ecological effectiveness of forest protection and restoration based on a multispectral UAV according to claim 1 is characterized in that: The above-mentioned comprehensive ecological effectiveness score is calculated based on all evaluation indicators and corresponding weights in the evaluation area, and the evaluation results of the ecological effectiveness of forest protection and restoration are analyzed, including: The FEI model is used to calculate the comprehensive score of ecological effectiveness, and the formula is: Where FEREI is the forest ecological restoration effectiveness index. The larger the value, the better the forest ecological restoration effect, and vice versa. S i is the score of the i-th indicator, W i is the weight of the i-th indicator, i is the indicator number, and n is the number of indicators; Among them, when the FEREI value is in the interval [85,100], it indicates that the ecological effectiveness evaluation result is excellent; When the FEREI value is in the range [70,85), it indicates that the ecological effectiveness evaluation result is good; When the FEREI value is in the interval [60,70), it indicates that the ecological effectiveness evaluation result is qualified; When the FEREI value is in the interval [0,60), it indicates that the ecological effectiveness evaluation result is unqualified.

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