CN116297223A - Forest deforestation recovery remote sensing monitoring method and system - Google Patents
Forest deforestation recovery remote sensing monitoring method and system Download PDFInfo
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Abstract
The invention discloses a forest deforestation recovery remote sensing monitoring method and a system, wherein the method comprises the following steps: acquiring a forest recovery time map based on a remote sensing technology; based on the forest structure index and the forest recovery time map, obtaining each index recovery value after the forest is cut; obtaining a recovery degree graph of the forests after being cut based on the recovery values of the indexes; and obtaining a recovery rate graph after the forest is cut based on the forest recovery time map and the recovery degree graph, and realizing remote sensing monitoring of forest cutting recovery. The invention focuses on the restoration of the forest structure, so that the dynamic state of the forest deforestation restoration can be mastered more comprehensively. And the algorithm mechanism is simple, the operability is strong, and the recovery degree or recovery rate of each forest index only needs one core formula. In addition, the algorithm result can not only present the restoration degree of the pixel level forest after being cut down, but also flexibly set the restoration rate of the sub-area presentation part according to the size of the target area.
Description
Technical Field
The invention belongs to the technical field of remote sensing data application, and particularly relates to a forest deforestation recovery remote sensing monitoring method and system.
Background
At present, the monitoring of the deforestation and restoration of a large-scale forest mainly utilizes a ground image obtained by a satellite remote sensing technology to interpret the space change information of the forest. The forest recovery information obtained by the prior art can only count the boundary and the area, and lacks the recovery information of forest structure parameters like tree height and AGB, so that the recovery condition of forest carbon storage capacity cannot be accurately estimated. Thus, there is a need for better description and assessment of the restoration after deforestation using quantitative restoration of forest structure parameters.
In recent years, large-space-scale remote sensing products generated by various satellite sensors such as optical sensors, radars and microwaves and various platforms such as aviation and aerospace acquire data are emerging in a dispute, information sources for forest multidimensional observation are enriched, and application potential of the remote sensing products in forest deforestation recovery monitoring is greatly improved. Among these, remote sensing products describing parameters of forest structures are of particular interest. Researchers use novel global ecological system dynamic survey (GEDI) spaceborne laser radar developed by NASA to obtain three-dimensional structure observation data of the surface vegetation on a large scale, and a global tree height product with 30m resolution is produced by combining a remote sensing optical image; a 250m resolution global land surface area index (LAI) product and an effective light and radiation absorption ratio (Fraction of photosynthetic active radiation, FPAR) product, etc. were produced in combination with the MODIS surface reflectance product. The development of the forest structure parameter product provides feasibility for monitoring the forest structure recovery after the forest structure is cut down, and if the design algorithm calculates the recovery degree or recovery rate of each index, the recovery condition of the forest after the forest is cut down can be more comprehensively known, and the basis can be provided for the evaluation of the forest carbon balance in a large range.
Disclosure of Invention
The invention aims to solve the defects of the prior art, provides a forest deforestation recovery remote sensing monitoring method and a system, constructs a set of forest deforestation recovery monitoring algorithm based on the existing remote sensing data products, calculates the recovery degree, recovery rate and the like of forest structural indexes such as forest deforestation height, AGB and the like, and realizes the monitoring of forest deforestation recovery conditions.
In order to achieve the above object, the present invention provides the following solutions:
a forest deforestation recovery remote sensing monitoring method comprises the following steps:
s1, acquiring a forest recovery time map based on a remote sensing technology;
s2, obtaining various index recovery values after the forest is cut based on the forest structure index and the forest recovery time map;
s3, obtaining a recovery degree graph of the forests after being cut based on the recovery values of the indexes;
s4, based on the forest recovery time map and the recovery degree map, a recovery rate map after forest cutting is obtained, and remote sensing monitoring of forest cutting recovery is achieved.
Preferably, the method for obtaining the forest recovery time map comprises the following steps:
based on the remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained;
acquiring intersection of the target area vector range diagram, the surface coverage classification map, the forest deforestation range and the time map to obtain a target area forest deforestation recovery type diagram;
extracting a forest land restoration range based on the forest deforestation restoration type map;
based on the remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in the forest land restoration range by crossing with the forest land restoration range;
and subtracting the felling time from a target year value on a pixel scale to obtain the forest recovery time map.
Preferably, the forest structure index includes: tree height, forest land biomass AGB, leaf area index LAI, and photosynthetically active radiation fraction FPAR.
Preferably, the method for obtaining the recovery values of each index after the forest is cut comprises the following steps:
obtaining the tree height of a target year, the forest overground biomass AGB, the leaf area index LAI and the photosynthetic effective radiation fraction FPAR, and performing spatial superposition with the forest recovery time map to obtain various index values of a forest recovery area;
and obtaining all index recovery values after the forest is cut based on all index values.
Preferably, the method for obtaining the recovery degree map after forest deforestation comprises the following steps:
acquiring recovery time based on the forest recovery time map;
on the same pixel position, corresponding each index recovery value to the recovery time, generating random points, extracting numerical values, and counting the index recovery values of each recovery time;
setting the recovery time when the index recovery value does not change as a recovery saturation time RST, and setting the index recovery value at that time as a recovery saturation value RSV;
and on the pixel level, calculating the recovery values of the indexes of the target year and the percentage of the recovery saturation time RST of the corresponding indexes, and obtaining the recovery degree graph after the forest is cut.
Preferably, the recovery rate map after deforestation is obtained, comprising the steps of:
dividing a target area into a preset number of subareas, and calculating the recovery saturation value RSV and the recovery saturation time RST of each index of each subarea;
calculating the ratio of the recovery saturation value RSV and the recovery saturation time RST of each index of each sub-region to obtain the index recovery rate corresponding to the sub-region;
and obtaining the recovery rate diagram of the target area based on the index recovery rate of the sub-area.
The invention also provides a forest deforestation recovery remote sensing monitoring system, which comprises: a recovery time module, a recovery value module, a recovery degree module and a recovery rate module;
the recovery time module is used for obtaining a forest recovery time map based on a remote sensing technology;
the recovery value module is used for obtaining various index recovery values after the forest is cut based on the forest structure index and the forest recovery time map;
the recovery degree module is used for obtaining a recovery degree graph after the forest is cut based on the recovery values of the indexes;
and the recovery rate module is used for obtaining a recovery rate diagram after the forest is cut based on the forest recovery time map and the recovery degree diagram, and realizing remote sensing monitoring of the forest cutting recovery.
Preferably, the process of obtaining the forest recovery time map includes:
based on the remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained;
acquiring intersection of the target area vector range diagram, the surface coverage classification map, the forest deforestation range and the time map to obtain a target area forest deforestation recovery type diagram;
extracting a forest land restoration range based on the forest deforestation restoration type map;
based on the remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in the forest land restoration range by crossing with the forest land restoration range;
and subtracting the felling time from a target year value on a pixel scale to obtain the forest recovery time map.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a forest deforestation recovery monitoring algorithm constructed based on the existing multi-source remote sensing product, which generates a rich forest recovery information graph comprising the recovery type and recovery time after deforestation and the recovery degree and recovery rate of various forest structure parameters. Different from the statistics of the conventional forest restoration range and area, the invention focuses on the restoration of the forest structure, so that the dynamic state of the forest deforestation restoration can be mastered more comprehensively. And the algorithm mechanism is simple, the operability is strong, and the recovery degree or recovery rate of each forest index only needs one core formula. In addition, the algorithm result can not only present the restoration degree of the pixel level forest after being cut down, but also flexibly set the restoration rate of the sub-area presentation part according to the size of the target area.
The forest deforestation recovery information provided by the invention has important value for quantitatively estimating the forest carbon balance, and is beneficial to the services in many societies and science, such as the management, protection, sustainable utilization and the like of forest resources. This technique is particularly important for areas where forest cutting is more severe, such as southeast asia. In southeast asia, large-area forests are cut down in recent years to plant oil palm plantation with higher oil yield, and carbon reserves in the areas where the forests are cut down and continuously restored are dynamically changed for a long time, so that comprehensive real-time restoration information is needed to be provided for the cut down areas to support forest management departments to make scientific decisions. The application of the technology can well meet the requirement, reflect the recovery capability of the forest in the target area while providing information, and provide scientific basis for long-term forest management and protection and evaluation of the influence of forest variation on the tropical forest ecosystem.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a forest cutting recovery remote sensing monitoring method according to an embodiment of the present invention;
FIG. 2 is a graph showing the recovery levels of tree height, AGB, LAI and FPAR after forest deforestation in Malaysia in 1990-2019 according to an embodiment of the present invention;
FIG. 3 is a graph of recovery rates of tree height, AGB, LAI and FPAR after deforestation in the peninsula malaysia in 1990-2019 according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, a remote sensing monitoring method for forest cutting recovery includes the following steps:
s1, acquiring a forest recovery time map based on a remote sensing technology;
s2, obtaining various index recovery values after forest deforestation based on the forest structure index and a forest recovery time map;
s3, obtaining a recovery degree graph of the forests after deforestation based on the recovery values of the indexes;
s4, based on the forest recovery time map and the recovery degree map, a recovery rate map after the forest is cut is obtained, and remote sensing monitoring of forest cutting recovery is achieved.
The method for obtaining the forest recovery time map comprises the following steps:
based on a remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained; specifically, the application of the remote sensing technology: remote sensing products related to target area forest deforestation restoration, including land cover classification products, forest variation products, tree height products, LAI products and FPAR products, are downloaded first. The forest change remote sensing product comprises a forest felling range and a forest felling time.
The vector range diagram of the target area, the surface coverage classification map and the forest felling range and time map are intersected to obtain a recovery type diagram of the target area after forest felling;
extracting a forest land restoration range based on the forest deforestation restoration type map; since this embodiment focuses mainly on restoration of forests, the types of restoration after felling are classified into two categories: woodland and others, and extracts the scope of this type of restoration of the woodland for subsequent calculation.
Based on a remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in a forest land restoration range by intersection with the forest land restoration range;
on the pel scale, the deforestation time (deforestation time in the range of woodland restoration) is subtracted from the target year value (e.g., 2019) to obtain a map of forest restoration times (restoration time map for which the restoration type is woodland).
Forest structure index, comprising: tree height, forest land biomass AGB, leaf area index LAI, and photosynthetically active radiation fraction FPAR. Therefore, the forest recovery time map specifically includes: tree height map, AGB map, LAI map, and FPAR map.
Specifically, AGB is calculated from the tree height and the differential rate growth equation for the regional tree. And (3) performing spatial superposition on the forest recovery time map obtained in the step (S1) and tree heights, AGB, LAI and FPAR products of the target year, and extracting various index values of a forest recovery area to serve as various index recovery values (assuming that various indexes of the current year of deforestation are 0 value) after deforestation. And on the same pixel position, each index recovery value corresponds to the recovery time, the index recovery values of each recovery time (such as 1 year, 2 years and 3 years … …) are counted in a mode of generating random point extraction values, the recovery time (such as 20 years) when the index recovery value is not changed is set as the recovery saturation time RST, and the index recovery value at the moment is set as the recovery saturation value RSV. And on the pixel level, calculating the percentage of the recovery value of each index of the target year and the recovery saturation value RSV of the corresponding index to obtain a recovery degree graph after forest deforestation.
The method for obtaining the recovery values of each index after the forest is cut comprises the following steps:
obtaining the tree height of a target year, the biomass AGB on the forest land, the leaf area index LAI and the photosynthetic effective radiation fraction FPAR, and performing spatial superposition with a forest recovery time map to obtain various index values of a forest recovery area;
and obtaining various index recovery values after the forest is cut based on various index values.
The method for obtaining the recovery degree graph after the forest is cut down comprises the following steps:
acquiring recovery time based on the forest recovery time map;
on the same pixel position, corresponding each index recovery value with recovery time, generating random points, extracting numerical values, and counting the index recovery values of each recovery time; an index recovery value comprising: a woodland tree Gao Hui restoration value, a woodland AGB restoration value, a woodland LAI restoration value, and a woodland FPAR restoration value.
Setting the recovery time when the index recovery value is unchanged as the recovery saturation time RST, and setting the index recovery value at the moment as the recovery saturation value RSV;
on the pixel level, calculating the recovery values of each index of the target year and the percentage of RST of the corresponding index to obtain a recovery degree graph after forest deforestation, wherein the recovery degree graph specifically comprises: a high degree of restoration of the tree after felling, an AGB degree of restoration after felling, an LAI degree of restoration after felling and an FPAR degree of restoration after felling. The calculation formula of the recovery degree related to the step is as follows:
in the formula, RL TH 、RL AGB 、RL LAI And RL(s) FPAR Representing the restoration degree of the tree height, AGB, LAI and FPAR pixel levels respectively; r is R TH 、R AGB 、R LAI And R is FPAR Restoration values representing tree height, AGB, LAI and FPAR pixel levels respectively; RSV TH 、RSB AGB 、RSV LAI And RSV FPAR RSV representing tree height, AGB, LAI and FPAR, respectively.
In particular, a zonal recovery rate solution method for weakening a spatial scale is adopted to obtain a recovery rate diagram after forest deforestation, and the method comprises the following steps:
dividing a target area into a preset number of subareas, and calculating a recovery saturation value RSV and a recovery saturation time RST of each index of each subarea;
calculating the ratio of the recovery saturation value RSV and the recovery saturation time RST of each index of each sub-region to obtain the index recovery rate of the corresponding sub-region;
based on the index recovery rate of the subarea, obtaining a recovery rate diagram of the target area specifically comprises the following steps: a high recovery rate plot of the tree after felling, an AGB recovery rate plot after felling, a LAI recovery rate plot after felling, and an FPAR recovery rate plot after felling.
In particular, the recovery rate after deforestation should be represented by the ratio of the index recovery value to the recovery time, and the index recovery value on some pixels may reach saturation in advance, and the recovery rate calculated by using the index recovery values may be smaller, so it is necessary to know when the index recovery value on these pixels reaches saturation. However, individual indices (such as tree height) currently do not have map products of consecutive years, so there is difficulty in obtaining time information that the index restoration value reaches saturation in advance. In this regard, the present embodiment proposes a solution for the resolution of the regional recovery rate that weakens the spatial scale. Dividing the target area into a plurality of subareas, calculating the RSV and RST of each index of each subarea according to the method of the step S2, and then obtaining the ratio of the RSV and RST of each index of each subarea as the index recovery rate of the corresponding subarea. The calculation formula of the recovery rate involved in the step is as follows:
wherein: RR (RR) THi 、RR AGBi 、RR LAIi And RR FPARi Representing the recovery rates of the ith sub-region tree height, AGB, LAI and FPAR, respectively; RSV THi 、RSV AGBi 、RSV LAIi And RSV FPARi RSV representing the ith sub-region tree height, AGB, LAI and FPAR, respectively; RST THi 、RST AGBi 、RST LAIi And RST FPARi RST, representing the ith sub-region tree height, AGB, LAI and FPAR, respectively.
In this embodiment, according to the technical scheme step S1, the present invention takes the malaysia peninsula with serious forest cutting in recent years as an example, and downloads the existing remote sensing products related to forest cutting restoration in the area, including the fine classification products of 30m surface coverage worldwide in 2020: https:// data.casearth.cn/sdo/detail/5fbc7904819aec ea2dd7061, 1990-2021 world 30m tropical forest variant product: https:// forobs. Jrc. Ec. Europa. Eu/TMF/data. Php#gee, 2019 global 30m forest canopy height product: https:// glad. Umd. Edu/Dataset/gedi/, global 250m LAI product 2019: http:// glass-product. Bnu. Edu. Cn/induction/lai. Html and 2019 global 250m FPAR product: http:// glass-product. Bnu. Edu. Cn/induction/FPAR. Html). Firstly, respectively cutting the products by using a vector boundary diagram of a peninsula malaysia to obtain various product diagrams of peninsula malaysia ranges, and then intersecting a 1990-2019 forest deforestation range in tropical forest change products with a 2020 earth surface coverage classification product to obtain all earth surface coverage recovery types in the peninsula malaysia forest deforestation range and classifying the types into two types: woodlands and others. And then, the forest land recovery range is intersected with the forest cutting time in the tropical forest variation product, and a cutting time map of the Malaysia peninsula forest land recovery range is obtained. Finally, subtracting the felling year value in the forest land restoration range from the value of 2019 on the pixel scale to obtain a Malaysia peninsula forest felling restoration time map.
According to step S2, first, the method is obtained by step S1The resulting tree height map of peninsula malaysia and the differential growth equation for regional trees generate AGB maps of peninsula malaysia 2019. And (3) obtaining four index recovery values of a forest recovery area by utilizing intersection of the Malaysia peninsula forest felling recovery time map obtained in the step (S1) and four product maps of 2019 tree height, AGB, LAI and FPAR. Generating random points in a forest recovery area, extracting all index recovery values and recovery time at the same pixel position, counting all index recovery average values of different recovery times, setting the recovery time when the index recovery average value is not changed any more as a to-be-determined RST, and setting the index recovery value at the moment as the to-be-determined RSV. And (3) repeating the steps by increasing the random dot number group, and setting the to-be-determined RST and RSV at the moment as a result of finally obtaining the RST and RSV when the to-be-determined RST and RSV of each index extracted by increasing the random dot number are not changed any more. In this case, RST is obtained TH 、RST AGB 、RST LAI RST FPAR Values of 18 years, 20 years and 20 years, respectively, and RST TH 、RST AGB 、RST LAI RST FPAR The values of (2) are 31m, 359Mg/ha, 7 and 10, respectively. The degree of restoration of the post-deforestation tree height, AGB, LAI and FPAR of the peninsula malaysia forest in 1990-2019 was then calculated and a map was generated according to equations (1), (2), (3) and (4), respectively, as shown in FIG. 2.
According to the technical scheme, step S4, the Malaysia peninsula is divided into 5 subregions according to administrative boundaries, wherein the subregions comprise a northwest region, a northeast region, a southwest region, a middle eastern region and a southeast region. The RSV and RST of each index of each sub-area are calculated according to the method of step S2, and then the recovery rates of the tree height, AGB, LAI and FPAR after the forest is cut in each sub-area are calculated according to formulas (5), (6), (7) and (8) and a map is generated, as shown in fig. 3.
Example two
The invention also provides a forest deforestation recovery remote sensing monitoring system, which comprises: a recovery time module, a recovery value module, a recovery degree module and a recovery rate module;
the recovery time module is used for obtaining a forest recovery time map based on a remote sensing technology;
the recovery value module is used for obtaining each index recovery value after the forest is cut based on the forest structure index and the forest recovery time map;
the recovery degree module is used for obtaining a recovery degree graph after the forest is cut based on the recovery values of the indexes;
and the recovery rate module is used for obtaining a recovery rate diagram after the forest is felled based on the forest recovery time map and the recovery degree diagram, and realizing remote sensing monitoring of the forest felling recovery.
Specifically, the process of obtaining the forest recovery time map is as follows:
based on a remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained;
the vector range diagram of the target area, the surface coverage classification map and the forest felling range and time map are intersected to obtain a recovery type diagram of the target area after forest felling;
extracting a forest land restoration range based on the forest deforestation restoration type map; based on a remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in a forest land restoration range by intersection with the forest land restoration range;
on the pixel scale, the deforestation time map is obtained by subtracting the deforestation time from the target year value.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (8)
1. The forest deforestation recovery remote sensing monitoring method is characterized by comprising the following steps of:
s1, acquiring a forest recovery time map based on a remote sensing technology;
s2, obtaining various index recovery values after the forest is cut based on the forest structure index and the forest recovery time map;
s3, obtaining a recovery degree graph of the forests after being cut based on the recovery values of the indexes;
s4, based on the forest recovery time map and the recovery degree map, a recovery rate map after forest cutting is obtained, and remote sensing monitoring of forest cutting recovery is achieved.
2. The method for remotely monitoring forest cutting restoration according to claim 1, wherein the method for obtaining the forest restoration time map is as follows:
based on the remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained;
acquiring intersection of the target area vector range diagram, the surface coverage classification map, the forest deforestation range and the time map to obtain a target area forest deforestation recovery type diagram;
extracting a forest land restoration range based on the forest deforestation restoration type map;
based on the remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in the forest land restoration range by crossing with the forest land restoration range;
and subtracting the felling time from a target year value on a pixel scale to obtain the forest recovery time map.
3. The method for remotely monitoring forest cutting recovery according to claim 1, wherein the forest structure index comprises: tree height, forest land biomass AGB, leaf area index LAI, and photosynthetically active radiation fraction FPAR.
4. A method for remotely sensing and monitoring forest cutting restoration according to claim 3, wherein the method for obtaining the index restoration values after forest cutting is as follows:
obtaining the tree height of a target year, the forest overground biomass AGB, the leaf area index LAI and the photosynthetic effective radiation fraction FPAR, and performing spatial superposition with the forest recovery time map to obtain various index values of a forest recovery area;
and obtaining all index recovery values after the forest is cut based on all index values.
5. The method for remotely monitoring forest cutting restoration according to claim 1, wherein the method for obtaining the restoration degree map after forest cutting is as follows:
acquiring recovery time based on the forest recovery time map;
on the same pixel position, corresponding each index recovery value to the recovery time, generating random points, extracting numerical values, and counting the index recovery values of each recovery time;
setting the recovery time when the index recovery value does not change as a recovery saturation time RST, and setting the index recovery value at that time as a recovery saturation value RSV;
and on the pixel level, calculating the recovery values of the indexes of the target year and the percentage of the recovery saturation time RST of the corresponding indexes, and obtaining the recovery degree graph after the forest is cut.
6. The method for remotely monitoring forest logging restoration according to claim 5, wherein obtaining the restoration rate map after forest logging comprises the steps of:
dividing a target area into a preset number of subareas, and calculating the recovery saturation value RSV and the recovery saturation time RST of each index of each subarea;
calculating the ratio of the recovery saturation value RSV and the recovery saturation time RST of each index of each sub-region to obtain the index recovery rate corresponding to the sub-region;
and obtaining the recovery rate diagram of the target area based on the index recovery rate of the sub-area.
7. A forest logging restoration remote sensing monitoring system, comprising: a recovery time module, a recovery value module, a recovery degree module and a recovery rate module;
the recovery time module is used for obtaining a forest recovery time map based on a remote sensing technology;
the recovery value module is used for obtaining various index recovery values after the forest is cut based on the forest structure index and the forest recovery time map;
the recovery degree module is used for obtaining a recovery degree graph after the forest is cut based on the recovery values of the indexes;
and the recovery rate module is used for obtaining a recovery rate diagram after the forest is cut based on the forest recovery time map and the recovery degree diagram, and realizing remote sensing monitoring of the forest cutting recovery.
8. The forest logging restoration remote sensing monitoring system of claim 7, wherein the process of obtaining the forest restoration time map is:
based on the remote sensing technology, a target area vector range diagram, a ground surface coverage classification map and a forest deforestation range and time map are obtained;
acquiring intersection of the target area vector range diagram, the surface coverage classification map, the forest deforestation range and the time map to obtain a target area forest deforestation recovery type diagram;
extracting a forest land restoration range based on the forest deforestation restoration type map;
based on the remote sensing technology, obtaining the deforestation time, and obtaining the deforestation time in the forest land restoration range by crossing with the forest land restoration range;
and subtracting the felling time from a target year value on a pixel scale to obtain the forest recovery time map.
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