Forest resource exploration method based on multi-source satellite and ecological resource evaluation system

Abstract

The invention discloses a multi-source satellite-based forest resource exploration method and an ecological resource evaluation system, and relates to the technical field of forest resource exploration. The method comprises the following steps: dividing a monitoring range into a plurality of homomorphic regions according to remote sensing reflectivity of different positions in the monitoring range under each remote sensing wave band; marking a plurality of marked forest homomorphic areas covered by forest vegetation in all the homomorphic areas; retrieving and analyzing other forest homomorphic regions in all homomorphic regions according to the remote sensing reflectivity of the marked forest homomorphic region under each remote sensing wave band; and selecting a representative position point in each forest homomorphic region, taking a forest resource exploration result of the representative position point as resource exploration results of all positions in the corresponding forest homomorphic region, and summarizing to obtain resource exploration results of all positions covered by forest vegetation in the monitoring range. According to the invention, the efficiency of forest resource exploration is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of forest resource exploration, and in particular relates to a forest resource exploration method based on multi-source satellites and an ecological resource evaluation system. Background Art

[0002] Forest resources are an important part of the earth's ecosystem and play a key role in global ecological balance, climate regulation and biodiversity conservation. With the rapid development of social economy and the continuous intensification of human activities, global forest resources are facing increasingly severe threats, including deforestation, land degradation, and biodiversity loss. Therefore, how to efficiently and accurately monitor the status of forest resources and assess the health of forest ecosystems has become an important issue in the field of global ecological protection.

[0003] Traditional forest resource survey methods mainly rely on large-scale ground surveys. Although ground surveys have high accuracy, they are limited in coverage, expensive, and time-consuming, making them unsuitable for large-scale forest resource monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a forest resource survey method and an ecological resource assessment system based on multi-source satellites, which reduces the workload of field surveys of forest resources and improves the efficiency of forest resource surveys by analyzing remote sensing graphics under multiple remote sensing bands.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides a forest resource survey method based on multi-source satellites, comprising:

[0007] Obtain remote sensing graphics of the monitoring range under multiple remote sensing bands and analyze them to obtain the remote sensing reflectivity of different locations within the monitoring range under each remote sensing band;

[0008] The monitoring range is divided into several homogeneous regions according to the remote sensing reflectance of different locations within the monitoring range in each remote sensing band;

[0009] Marking a plurality of marked forest isomorphic areas covered by forest vegetation in all isomorphic areas;

[0010] According to the remote sensing reflectance of the marked forest homomorphic area in each remote sensing band, other forest homomorphic areas are retrieved and analyzed in all homomorphic areas;

[0011] Representative locations are selected in each forest homogeneous area, and the forest resource exploration results of the representative locations are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized.

[0012] The present invention also discloses a forest resource survey method based on multi-source satellites, comprising:

[0013] Receive representative location points of each forest homomorphic region;

[0014] Conduct field surveys at representative locations in each forest homogeneous area to obtain resource exploration results for each representative location;

[0015] The forest resource exploration results of the representative location points are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized.

[0016] The present invention also discloses a forest resource survey method based on multi-source satellites, comprising:

[0017] Receive resource exploration results for all locations covered by forest vegetation within the monitoring area;

[0018] Obtain the correspondence between resource exploration results and assessment levels, and obtain the assessment level of forest vegetation coverage within the monitoring range based on the resource exploration results of all locations covered by forest vegetation within the monitoring range;

[0019] Displays the forest vegetation coverage assessment level within the monitoring area.

[0020] The present invention also discloses a multi-source satellite-based forest ecological resource assessment system, comprising:

[0021] A remote sensing analysis unit is used to obtain remote sensing graphics of the monitoring range under multiple remote sensing bands and analyze them to obtain remote sensing reflectivity of different positions in the monitoring range under each remote sensing band;

[0022] The monitoring range is divided into several homogeneous regions according to the remote sensing reflectance of different locations within the monitoring range in each remote sensing band;

[0023] Marking a plurality of marked forest isomorphic areas covered by forest vegetation in all isomorphic areas;

[0024] According to the remote sensing reflectance of the marked forest homomorphic area in each remote sensing band, other forest homomorphic areas are retrieved and analyzed in all homomorphic areas;

[0025] Select representative locations in each forest homogeneous area, use the forest resource exploration results of the representative locations as the resource exploration results of all locations in the corresponding forest homogeneous area, and summarize the resource exploration results of all locations covered by forest vegetation within the monitoring range;

[0026] A field survey unit for receiving representative location points for each forest homogeneous area;

[0027] Conduct field surveys at representative locations in each forest homogeneous area to obtain resource exploration results for each representative location;

[0028] The forest resource exploration results of the representative location point are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized;

[0029] An evaluation and display unit for receiving resource exploration results of all locations covered by forest vegetation within the monitoring range;

[0030] Obtain the correspondence between resource exploration results and assessment levels, and obtain the assessment level of forest vegetation coverage within the monitoring range based on the resource exploration results of all locations covered by forest vegetation within the monitoring range;

[0031] Displays the forest vegetation coverage assessment level within the monitoring area.

[0032] The present invention analyzes remote sensing graphics under multiple remote sensing bands obtained by multi-source satellite monitoring through a remote sensing analysis unit to obtain a forest homogeneous area covered by forest vegetation. It only needs to conduct field surveys on representative representative location points in the forest homogeneous area to achieve comprehensive, rapid and accurate resource surveys of all forest resources within the monitoring range. It can also facilitate the field survey unit to quickly survey forest resources and the evaluation and display unit to conduct ecological resource assessment of forest resources within the monitoring range.

[0033] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 A schematic diagram of functional units and information flows of a multi-source satellite-based forest ecological resource assessment system in one embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a process flow of a remote sensing analysis unit according to an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of a process flow of a field survey unit according to an embodiment of the present invention;

[0038] Figure 4A schematic diagram of a step flow of an evaluation display unit according to an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of the step flow of step S2 in one embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of the step flow of step S23 in one embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram of the step flow of step S4 in one embodiment of the present invention;

[0042] Figure 8 This is a schematic diagram of a step flow chart of step S43 in an embodiment of the present invention;

[0043] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0044] 1- Remote sensing analysis unit, 2- Field survey unit, 3- Evaluation and display unit. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0046] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0047] See also Figures 1 to 4 As shown, the present invention provides a multi-source satellite-based forest ecological resource assessment system, which includes a remote sensing analysis unit 1, a field survey unit 2, and an assessment display unit 3 in terms of functional units. The remote sensing analysis unit 1 guides the field survey unit 2 to conduct a field survey of forest resources by analyzing remote sensing graphics under multiple remote sensing bands obtained by multi-source satellite monitoring, thereby reducing the workload of the subsequent assessment display unit 3 in ecological resource assessment and improving work efficiency.

[0048] During operation, the remote sensing analysis unit 1 in the system can firstly execute step S1 to obtain remote sensing graphics analysis of the monitoring range under multiple remote sensing bands to obtain remote sensing reflectances of different positions in the monitoring range under each remote sensing band. Remote sensing reflectance (Rrs for short) is an important concept in the field of remote sensing, which is usually used to describe the degree of reflection of electromagnetic waves by water bodies or land target objects, especially in plant environment monitoring, it is the core parameter for analyzing the optical characteristics and composition of plants.

[0049] In forest resource surveys, the selection of satellite remote sensing bands is critical because different bands have different sensitivities to the biophysical characteristics of forests (such as vegetation cover, chlorophyll content, humidity, etc.).

[0050] The following are commonly used satellite bands and their roles in forest resource surveys:

[0051] 1. Visible light band (VIS: 400–700nm)

[0052] The blue band (450–495nm) is used to monitor water bodies and forest health. It is more sensitive to areas with low vegetation cover or high moisture content.

[0053] The green band (495–570nm) is most sensitive to the reflection of green leaves of vegetation. It helps to estimate the health of forests and vegetation cover.

[0054] Red light band (620–750nm), the band with the strongest absorption by vegetation (chlorophyll absorption). Used for vegetation index calculation (such as NDVI).

[0055] 2. Near infrared band (NIR: 750–1400nm)

[0056] The near-infrared band is very sensitive to the reflectance of vegetation canopy and is the core band for monitoring forest health, vegetation cover, and calculating vegetation indices (such as NDVI and EVI). It can well distinguish between vegetation and non-vegetation areas.

[0057] 3. Short-wave infrared band (SWIR: 1400–2500nm)

[0058] SWIR-1 (1400–1800nm), reflects the moisture content of vegetation and monitors drought conditions. It can distinguish between wetland vegetation and non-wetland vegetation.

[0059] SWIR-2 (2000–2500nm) monitors soil and mineral composition and studies the soil conditions at the bottom of the forest. It is very effective for monitoring forest residues and carbon content after fire.

[0060] 4. Thermal infrared band (TIR: 8–14 μm)

[0061] Thermal infrared bands are used to monitor surface temperature, forest fires, evapotranspiration, and regional climate conditions. They can identify hotspots of forest fires and analyze forest evapotranspiration.

[0062] 5. Microwave band (Radar: 1mm–1m)

[0063] Microwave remote sensing (such as SAR: synthetic aperture radar) penetrates clouds and vegetation canopies and can be used for forest biomass monitoring, structural analysis (such as tree height and density), and soil moisture and forest cover monitoring.

[0064] 6. Hyperspectral bands

[0065] Providing finer spectral resolution, it can be used to detect specific tree species, chlorophyll concentration, moisture content and disease monitoring in forests. It is often used for accurate classification of forest resources.

[0066] See also Figures 1 to 5 As shown, since the area of ​​the monitoring range is extremely large, it is difficult to fully survey and evaluate each place. Therefore, it is necessary to divide the areas with the same or similar status together and process them uniformly. This requires the remote sensing analysis unit 1 to execute step S2 to divide the monitoring range into several homomorphic areas according to the remote sensing reflectivity of different positions in each remote sensing band within the monitoring range. Specifically, step S21 can be executed first to randomly select several position points as reference position points within the monitoring range. Next, step S22 can be executed to calculate and obtain the difference between the remote sensing reflectivity of each reference position point and other position points in the monitoring range under each remote sensing band. Next, step S23 can be executed to divide the position points with the same remote sensing reflection state into the same homomorphic point set according to the difference between the remote sensing reflectivity of the reference position point and other position points under each remote sensing band. Finally, step S24 can be executed to take the area composed of position points that are adjacent in position and belong to the same homomorphic point set as a homomorphic area, and obtain several homomorphic areas within the monitoring range. The homomorphic area includes homomorphic areas with characteristics such as bare soil, rock, forest, grassland, lake, and shrub.

[0067] See also Figure 6As shown, since the remote sensing reflectance of the position points in the same homomorphic area in each remote sensing band is similar, in view of this, step S231 can be first executed to take the sum of the differences between the remote sensing reflectance of the reference position point and other position points in each remote sensing band as the remote sensing reflection difference between the reference position point and other position points, and calculate the remote sensing reflection difference between each reference position point and each other position point. Next, step S232 can be executed for each other position point, and it is classified into the same homomorphic point set with the reference position point with the smallest remote sensing reflection difference. Next, step S233 can be executed for each homomorphic point set to calculate the mean position coordinates of all the position points in the homomorphic point set. Next, step S234 can be executed to determine whether the corresponding mean position coordinates of each homomorphic point set are the reference position points. If so, it means that the position points in the homomorphic point set are consistent at this time, so step S235 can be executed next to obtain the homomorphic point set. If otherwise, it means that the position points in the homomorphic point set are not completely consistent at this time, so step S236 can be executed next to use the position point corresponding to the mean position coordinate of each homomorphic point set as the reference position point after iteration. And continue to execute steps S231 to S236 to obtain the iterated homomorphic point set according to the iterated reference position point division until the mean position coordinate corresponding to each homomorphic point set is the reference position point. The above steps complete the classification of the position points within the detection range by iterative updating, and divide the position points with the same satellite remote sensing state into the same homomorphic point set.

[0068] In order to supplement the implementation process of the above steps S231 to S236, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0069]

[0070]

[0071]

[0072]

[0073] This code calculates the remote sensing reflectance difference between the benchmark point and all points, divides the position points into homomorphic point sets using an iterative optimization method, and updates the benchmark points at the same time until the mean position coordinates of each homomorphic point set stabilize and become the benchmark point, which can accurately classify point sets with the same remote sensing reflectance state.

[0074] During the running process of the code, the data is first input. Points represents the remote sensing data points within the monitoring range, including the position and band reflectance; basePoints represents the initial reference point. Then the difference calculation and classification are performed. According to the reflectance difference between each point and the reference point, it is classified into the set corresponding to the reference point with the smallest difference. Next, the mean position is updated, and the mean position coordinates of each homomorphic point set are calculated and updated to the new reference point. After that, iterative optimization is continued, and the above process is repeated until the reference point is stable. This code is suitable for classification analysis and classification tasks of remote sensing data, especially for segmenting areas of objects with similar spectral characteristics.

[0075] Please continue reading Figures 1 to 4 As shown in Figures 7 to 8, in the process of analyzing and dividing forest homomorphic areas, the system can first execute step S3 to mark multiple marked forest homomorphic areas covered by forest vegetation in all homomorphic areas. Next, step S4 can be executed to retrieve and analyze other forest homomorphic areas in all homomorphic areas based on the remote sensing reflectivity of the marked forest homomorphic areas in each remote sensing band. Specifically, step S41 can be first executed to use the remote sensing reflectivity of the reference position point in each homomorphic area in each remote sensing band as the remote sensing reflectivity of the homomorphic area in each remote sensing band. Next, step S42 can be executed to calculate and obtain the sum of the differences in the remote sensing reflectance difference between any two marked forest homomorphic areas in each remote sensing band as the remote sensing reflectance difference between the marked forest homomorphic areas. Next, step S43 can be executed to obtain the maximum tolerance of the remote sensing reflectance difference between the forest homomorphic areas based on the remote sensing reflectance difference between any two marked forest homomorphic areas.

[0076] The purpose of the maximum tolerance in this scheme is to screen out other forest homomorphic areas outside the marked forest homomorphic area. Therefore, in the process of calculating the maximum tolerance of the remote sensing reflectance difference between forest homomorphic areas, step S431 can be first executed to calculate the maximum value of the remote sensing reflectance difference between each marked forest homomorphic area and each other marked forest homomorphic area as the adjacent tolerance of the marked forest homomorphic area. Next, step S432 can be executed to calculate the maximum and minimum values ​​of the adjacent tolerances of all marked forest homomorphic areas, and calculate the ratio of the difference between the maximum and minimum values ​​to the number of all marked forest homomorphic areas as the average adjacent tolerance of all marked forest homomorphic areas. Finally, step S433 can be executed to add the maximum value of the adjacent tolerances of all marked forest homomorphic areas to the adjacent tolerance as the maximum tolerance of the remote sensing reflectance difference between forest homomorphic areas.

[0077] In the process of screening out other forest homomorphic areas outside the marked forest homomorphic area, step S44 can be first executed to calculate the remote sensing reflection difference between each marked forest homomorphic area and each marked forest homomorphic area for other homomorphic areas outside the marked forest homomorphic area, and determine whether they are all less than the maximum tolerance. If so, step S45 can be executed next to determine that it belongs to the forest homomorphic area, if not, step S46 can be executed next to determine that it does not belong to the forest homomorphic area. Finally, step S47 can be executed to summarize and obtain other forest homomorphic areas outside the marked forest homomorphic area.

[0078] In order to provide supplementary explanation for the implementation process of the above-mentioned step S41 to step S7, source code of some functional modules is provided, and comparative explanation is provided in the comment section.

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] This code implements a remote sensing reflectance characteristic analysis method based on marked forest areas, which is used to filter out other homomorphic areas belonging to forests from all homomorphic areas. The algorithm further classifies unmarked areas into forest or non-forest areas by calculating the adjacent tolerance and maximum tolerance between marked areas. This method can effectively utilize known marked data and enhance the accuracy of remote sensing data analysis.

[0085] The above code first inputs data during operation. AllRegions contains all homomorphic regions, and markedRegions is the marked forest area. Then the adjacent tolerance is calculated, and the tolerance is calculated according to the reflectivity difference of the marked area. Next, the maximum tolerance is determined, and the extended maximum tolerance is used to filter unmarked areas. The forest area is also filtered, and the unmarked area is compared with the marked area to determine whether it belongs to the forest area. This code is suitable for regional classification problems based on spectral characteristics in remote sensing analysis, especially for the scalability analysis of existing marked areas, such as forest resource assessment and other fields.

[0086] Please continue reading Figures 1 to 4As shown, the system can then execute step S5 to select a representative location point in each forest homogeneous area, and the reference location point in each forest homogeneous area can be used as the representative location point. The forest resource exploration results of the representative location point are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized;

[0087] During operation, the field survey unit 2 in this solution can first execute step S021 to receive the representative location point of each forest homogeneous area. Next, step S022 can be executed to perform field survey according to the representative location point of each forest homogeneous area to obtain the resource exploration result of each representative location point. Finally, step S023 can be executed to use the forest resource exploration result of the representative location point as the resource exploration result of all locations in the corresponding forest homogeneous area, and summarize the resource exploration results of all locations covered by forest vegetation within the monitoring range.

[0088] During operation, the evaluation and display unit 3 in this solution can firstly execute step S031 to receive the resource exploration results of all locations covered by forest vegetation within the monitoring range. Next, step S032 can be executed to obtain the corresponding relationship between the resource exploration results and the evaluation level, and the forest vegetation coverage evaluation level within the monitoring range can be obtained according to the resource exploration results of all locations covered by forest vegetation within the monitoring range. Finally, step S033 can be executed to display the forest vegetation coverage evaluation level within the monitoring range.

[0089] In the field of resource exploration, the correspondence between exploration results and evaluation levels usually relies on internationally accepted resource classification standards, which classify resources into different levels according to their geological reliability, economic feasibility and maturity of development technology.

[0090] The United Nations International Framework for Classification of Solid Mineral Resources (UNFC-2009) classifies resources into three dimensions: E (economic), F (feasibility), and G (geological reliability), with each dimension scored from high to low. The corresponding relationship between the assessment levels is as follows:

[0091]

[0092]

[0093] E (Economy):

[0094] E1: It is currently economically feasible.

[0095] E2: Potential economic feasibility, more evaluation is needed.

[0096] E3: Not feasible at present and needs to wait for technological advancement or market development.

[0097] F (Feasibility):

[0098] F1: The development technology is mature and can be adopted immediately.

[0099] F2: The developed technology is potentially feasible and requires further research.

[0100] F3: Development technology is immature or unknown.

[0101] G (geological reliability):

[0102] G1: The geological conditions are highly determined.

[0103] G2: The geological conditions are relatively certain.

[0104] G3: The geological conditions are relatively uncertain.

[0105] G4: Insufficient geological information, based on assumptions.

[0106] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0107] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0108] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0109] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A forest resource survey method based on multi-source satellites, characterized in that: include, Obtain remote sensing graphics of the monitoring range under multiple remote sensing bands and analyze them to obtain the remote sensing reflectivity of different locations within the monitoring range under each remote sensing band; The monitoring range is divided into several homogeneous regions according to the remote sensing reflectance of different locations within the monitoring range in each remote sensing band; Marking a plurality of marked forest homomorphic areas covered by forest vegetation in all homomorphic areas; According to the remote sensing reflectance of the marked forest homomorphic area in each remote sensing band, other forest homomorphic areas are retrieved and analyzed in all homomorphic areas; Representative locations are selected in each forest homogeneous area, and the forest resource exploration results of the representative locations are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized.

2. The method according to claim 1, characterized in that The step of dividing the monitoring range into a plurality of homomorphic regions according to the remote sensing reflectance of different positions in the monitoring range in each remote sensing band, include, Randomly select a number of position points within the monitoring range as reference position points; Calculate and obtain the difference between the remote sensing reflectance of each reference location point and other locations within the monitoring range in each remote sensing band; According to the difference between the remote sensing reflectance of the reference point and other points in each remote sensing band, the points with the same remote sensing reflectance state are divided into the same homomorphic point set; An area consisting of position points that are adjacent in position and belong to the same isomorphic point set is taken as the isomorphic area, and a plurality of the isomorphic areas within the monitoring range are obtained.

3. The method according to claim 2, characterized in that The step of dividing the position points with the same remote sensing reflectance state into the same homomorphic point set according to the difference between the remote sensing reflectance of the reference position point and other position points in each remote sensing band, include, The sum of the differences between the remote sensing reflectance of the reference position point and other position points in each remote sensing band is taken as the remote sensing reflectance difference between the reference position point and other position points, and the remote sensing reflectance difference between each reference position point and each other position point is calculated; For each other location point, classify it into the same homomorphic point set with the reference location point with the smallest difference in remote sensing reflectance; For each of the homomorphic point sets, calculating the mean position coordinates of all position points in the homomorphic point set; Determine whether the mean position coordinate corresponding to each of the homomorphic point sets is a reference position point; If so, the homomorphic point set is obtained.

4. The method according to claim 3, characterized in that The step of dividing the position points with the same remote sensing reflectance state into the same homomorphic point set according to the difference between the remote sensing reflectance of the reference position point and other position points in each remote sensing band, further includes: If not, taking the position point of each homomorphic point set corresponding to the mean position coordinate as the reference position point after iteration; The iterated homomorphic point set is obtained according to the iterated reference position point division, until each homomorphic point set corresponding to the mean position coordinate is a reference position point.

5. The method according to any one of claims 2 to 4, characterized in that: The step of searching and analyzing other forest homomorphic areas in all homomorphic areas according to the remote sensing reflectance of the marked forest homomorphic area in each remote sensing band, include, Taking the remote sensing reflectance of each reference position point in each of the homomorphic regions in each remote sensing band as the remote sensing reflectance of the homomorphic region in each remote sensing band; The sum of the differences in remote sensing reflectance differences between any two marked forest homomorphic areas in each remote sensing band is calculated as the remote sensing reflectance difference between the marked forest homomorphic areas; The maximum tolerance of remote sensing reflectance difference between forest homogeneous areas is obtained according to the remote sensing reflectance difference between any two marked forest homogeneous areas; For the other homomorphic areas outside each marked forest homomorphic area, the remote sensing reflectance difference between them and each marked forest homomorphic area is calculated to determine whether they are all less than the maximum tolerance; If so, it is considered to be a forest homogeneous area; If not, it is deemed not to belong to the forest homogeneous area; The other forest homomorphic regions outside the marked forest homomorphic region are summarized.

6. The method according to claim 5, characterized in that The step of obtaining the maximum tolerance of the remote sensing reflectance difference between forest homogeneous areas according to the remote sensing reflectance difference between any two marked forest homogeneous areas, include, For each marked forest homomorphic area, the maximum value of the remote sensing reflectance difference between it and each other marked forest homomorphic area is calculated as the adjacent tolerance of the marked forest homomorphic area; Calculate the maximum and minimum values ​​of the neighboring tolerances of all labeled forest homomorphic regions, and calculate the ratio of the difference between the maximum and minimum values ​​and the number of all labeled forest homomorphic regions as the average neighboring tolerance of all labeled forest homomorphic regions; The maximum value of the adjacent tolerances of all marked forest homogeneous areas plus the adjacent tolerance is taken as the maximum tolerance of the remote sensing reflectance difference between forest homogeneous areas.

7. The method according to any one of claims 2 to 4, characterized in that: The step of selecting representative location points in each forest homomorphic region, include, The benchmark location points in each forest homomorphic area are taken as representative location points.

8. A method for forest resource survey based on multi-source satellites, characterized in that: include, Receiving a representative position point of each forest homogeneous area in a multi-source satellite forest resource survey method according to any one of claims 1 to 7; Conduct field surveys at representative locations in each forest homogeneous area to obtain resource exploration results for each representative location; The forest resource exploration results of the representative location points are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized.

9. A method for surveying forest resources based on multi-source satellites, characterized in that: include, Receiving resource exploration results of all locations covered by forest vegetation within a monitoring range in a multi-source satellite forest resource survey method as described in claim 8; Obtain the correspondence between resource exploration results and assessment levels, and obtain the assessment level of forest vegetation coverage within the monitoring range based on the resource exploration results of all locations covered by forest vegetation within the monitoring range; Displays the forest vegetation coverage assessment level within the monitoring area.

10. A multi-source satellite-based forest ecological resource assessment system, characterized in that: include, A remote sensing analysis unit is used to obtain remote sensing graphics of the monitoring range under multiple remote sensing bands and analyze them to obtain remote sensing reflectivity of different positions in the monitoring range under each remote sensing band; The monitoring range is divided into several homogeneous regions according to the remote sensing reflectance of different locations within the monitoring range in each remote sensing band; Marking a plurality of marked forest homomorphic areas covered by forest vegetation in all homomorphic areas; According to the remote sensing reflectance of the marked forest homomorphic area in each remote sensing band, other forest homomorphic areas are retrieved and analyzed in all homomorphic areas; Select representative locations in each forest homogeneous area, use the forest resource exploration results of the representative locations as the resource exploration results of all locations in the corresponding forest homogeneous area, and summarize the resource exploration results of all locations covered by forest vegetation within the monitoring range; A field survey unit for receiving representative location points for each forest homogeneous area; Conduct field surveys at representative locations in each forest homogeneous area to obtain resource exploration results for each representative location; The forest resource exploration results of the representative location point are used as the resource exploration results of all locations in the corresponding forest homogeneous area, and the resource exploration results of all locations covered by forest vegetation within the monitoring range are summarized; An evaluation and display unit for receiving resource exploration results of all locations covered by forest vegetation within the monitoring range; Obtain the correspondence between resource exploration results and assessment levels, and obtain the assessment level of forest vegetation coverage within the monitoring range based on the resource exploration results of all locations covered by forest vegetation within the monitoring range; Displays the forest vegetation coverage assessment level within the monitoring area.

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