Forest spatial distribution data reconstruction method and device

Abstract

The invention relates to a data reconstruction method and device for forest space distribution, belongs to the technical field of forest data processing, and solves the problems that in the prior art, large deviation exists between forest space distribution data and the real situation, and large uncertainty exists. The invention discloses a data reconstruction method and device for forest spatial distribution. The method comprises the following steps: calculating a reconstructed forest area proportion according to a product of a correction coefficient and a forest spatial distribution probability; and when the area ratio of the reconstructed forest reaches a theoretical upper limit value, updating the statistical area of the historical reconstructed forest according to a difference value between the statistical area of the historical reconstructed forest and the area sum of the area ratio of the reconstructed forest reaching the theoretical upper limit value until the area ratio of each reconstructed forest does not reach the theoretical upper limit value, and obtaining the space distribution of the reconstructed forest in the research area. According to the method, the reconstructed forest spatial distribution can provide finer and more accurate spatial distribution characteristics and long-time sequence coverage, and the reliability consistent with actual survey data can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of forest data processing, and in particular to a method and device for reconstructing forest spatial distribution data. Background Art

[0002] Historical forest cover changes provide important support for capturing landscape distribution patterns, the role of human activities, and the impact of climate change. However, in the current global and regional historical land use spatial distribution data, there is still great uncertainty in the spatiotemporal distribution mapping of forests, which is a difficulty in related research. For example, the "Global Environmental History Dataset" (HYDE Dataset) only quantifies the land use types of human activities and uniformly classifies the remaining land as natural vegetation, making it difficult to distinguish between different natural vegetation types such as forests and grasslands; the "Land Use Assimilation Dataset" (LUH2 Dataset) simulates the range of forests through a climate productivity model, which also has obvious deviations from the actual spatial distribution of forests. At the regional scale, the reconstruction of forest spatial distribution data through the prediction of potential forest range and human impact on forests also faces errors such as obvious deviations from the actual spatial distribution of forests.

[0003] Large-scale remote sensing image data and inversion technology can provide more realistic and reliable information on the distribution of objects, and are expected to become an important basis for the historical reconstruction of forest spatial distribution. However, in the remote sensing inversion of forests, high-resolution remote sensing data is limited, and the coverage period is basically after 2000, which is difficult to meet the time requirements of historical reconstruction. The time range of medium and low remote sensing images can be extended to the 1980s, but its spatial resolution limits the ability to identify the range of trees, especially for narrow forest belts and mixed agricultural and forestry objects. The recognition accuracy is low, which further leads to the difficulty of capturing the real dynamic changes of forest edges. For example, the forest coverage rate in a certain place has increased significantly since the 1980s, but the existing remote sensing products are basically difficult to reflect the above-mentioned trend of change. Some products even believe that the forest area in a certain place is showing a decreasing trend. Therefore, there is also great uncertainty in the existing research on the use of remote sensing land use data to reconstruct the spatial distribution of forests.

[0004] In summary, the existing data processing methods for forest spatial distribution data generate forest spatial distribution data that have large deviations from the actual situation and are subject to great uncertainty. Summary of the invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide a method and device for reconstructing forest spatial distribution data, so as to solve one or more of the above problems existing in the prior art.

[0006] The present disclosure provides a method for reconstructing forest spatial distribution data, including the following steps:

[0007] Determine the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data;

[0008] Determine the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and take the sum of the average forest area percentage and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

[0009] Delineate the potential maximum range of forest distribution based on the remote sensing distribution probability of the forest, and calculate the spatial allocation area available for forest reconstruction within the potential maximum range;

[0010] Calculating the forest natural distribution probability based on the natural factor data, and calculating the forest spatial distribution probability based on the forest remote sensing distribution probability and the forest natural distribution probability;

[0011] Collating statistical data of forest area in the target area to generate historically reconstructed forest statistical area;

[0012] Calculating the initial reconstruction forest space area according to the forest spatial distribution probability;

[0013] Determine the correction coefficient of forest spatial distribution according to the ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area;

[0014] Calculating the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability;

[0015] When the proportion of the reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the reconstructed forest area that reaches the theoretical upper limit, until the proportions of the reconstructed forest areas do not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed forest in the study area.

[0016] The data reconstruction method of the forest spatial distribution of the disclosed embodiment calculates the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; when the proportion of the reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the reconstructed forest area proportions that reach the theoretical upper limit, until the proportions of each reconstructed forest area do not reach the theoretical upper limit, and the forest spatial distribution reconstructed in the study area is obtained. The reconstructed forest spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0017] As one of the optional embodiments, the process of determining the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area includes the steps of:

[0018] Reclassify the land use types and extract the forest types of remote sensing data, establish mapping units, unify the spatial resolution according to the optimal area principle, calculate the forest area ratio of each mapping unit, and determine the average forest area ratio.

[0019] As one of the optional embodiments, the process of determining the forest remote sensing interpretation classification probability of the reconstructed mapping unit includes the steps of:

[0020] Resampling the remote sensing data according to the remote sensing image unit size and the optimal area principle, overlaying the resampled data, calculating the average value of each grid unit, extracting the grid units with an average value of 1 as completely consistent areas, and extracting the grid units with an average value of 0 as areas to be identified;

[0021] Perform stratified random sampling on completely consistent areas to obtain a training sample set for remote sensing image interpretation;

[0022] According to the multi-band composite values ​​of remote sensing images and a series of remote sensing indices, a land use interpretation feature set is generated. A classifier is constructed for the remote sensing image interpretation training sample set through a machine learning algorithm. The random forest algorithm is applied to interpret the image of the identified area, and the grid cells with forest interpretation results and their classification probabilities are extracted. The classification probability is assigned to the numerical value of the forest remote sensing interpretation probability of the corresponding grid cell, and the remote sensing interpretation probability of the grid cells in the remaining range is assigned to 0.

[0023] The average value of forest remote sensing interpretation probability of each mapping unit is calculated and recorded as forest remote sensing interpretation classification probability.

[0024] As one of the optional embodiments, the process of delineating the potential maximum range of forest distribution according to the forest remote sensing distribution probability comprises the steps of:

[0025] The reconstructed mapping units where the sum of forest remote sensing distribution probabilities in each year is greater than 0 are defined as the maximum range of forest remote sensing distribution, and the range of forest distribution in the global potential vegetation type map is superimposed to define the potential maximum range of reconstructed forest distribution;

[0026] The process of calculating the spatial allocation area available for forest reconstruction within the potential maximum range is as follows:

[0027]

[0028] in, The spatial allocation area available for forest reconstruction in year y, A l is the land area of ​​the reconstructed mapping unit, and Reconstruct the urban area and cultivated land area of ​​the mapping unit in year y respectively.

[0029] As one of the optional embodiments, the process of calculating the forest natural distribution probability according to the natural factor data, and calculating the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability is as follows:

[0030] F=1-w i ×F i ;

[0031] Among them, F is the probability of natural distribution of forests, w i is the weight of the natural factor, w i =1 / n, n is the number of natural factors, F i is the normalized value of the natural factor;

[0032]

[0033] Among them, S y is the forest spatial distribution probability in year y, S′ y+1 is the proportion of forest reconstructed area in each reconstructed mapping unit in year y+1, R y is the remote sensing distribution probability of forests, and N is the year with remote sensing data.

[0034] As one of the optional embodiments, the process of calculating the initial reconstruction forest space area according to the forest space distribution probability is as follows:

[0035]

[0036] Where A is the area of ​​the reconstructed mapping unit, S y is the forest spatial distribution probability in year y.

[0037] As one of the optional embodiments, the process of determining the theoretical upper limit value includes the steps of:

[0038] The maximum forest area ratio within the forest distribution range is determined according to the remote sensing data of the target area, and the maximum forest area ratio is used as the theoretical upper limit value.

[0039] The disclosed embodiment also provides a data reconstruction device for forest spatial distribution, including:

[0040] A mean value calculation module, used to determine the forest distribution range of the target area and the average value of the forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data;

[0041] A probability calculation module, used to determine the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and take the sum of the average forest area percentage and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

[0042] An area calculation module is used to delineate the potential maximum range of forest distribution according to the remote sensing distribution probability of the forest, and calculate the spatial allocation area available for forest reconstruction within the potential maximum range;

[0043] A probability reconstruction module, used to calculate the forest natural distribution probability according to the natural factor data, and calculate the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability;

[0044] A first area reconstruction module is used to sort out the statistical data of the forest area of ​​the target area and generate a historically reconstructed forest statistical area;

[0045] A second area reconstruction module, used for calculating the initial reconstruction forest space area according to the forest space distribution probability;

[0046] A coefficient calculation module, used to determine the correction coefficient of forest spatial distribution according to the ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area;

[0047] A proportion calculation module, used to calculate the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability;

[0048] The spatial calculation module is used to update the historically reconstructed forest statistical area according to the difference between the historically reconstructed forest statistical area and the sum of the areas of the reconstructed forest area percentages that reach the theoretical upper limit value when the reconstructed forest area percentage reaches the theoretical upper limit value, until the reconstructed forest area percentages do not reach the theoretical upper limit value, thereby obtaining the spatial distribution of the reconstructed forest in the study area.

[0049] The data reconstruction device for forest spatial distribution of the disclosed embodiment calculates the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; when the proportion of reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportion of reconstructed forest area that reaches the theoretical upper limit, until the proportion of each reconstructed forest area does not reach the theoretical upper limit, and the forest spatial distribution reconstructed in the study area is obtained. The reconstructed forest spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0050] At least one embodiment of the present disclosure further provides a data control device, including:

[0051] one or more memories non-transitorily storing computer-executable instructions;

[0052] One or more processors are configured to execute computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the forest spatial distribution data reconstruction method according to any embodiment of the present disclosure.

[0053] The above-mentioned data control device calculates the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; when the proportion of reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportion of reconstructed forest area that reaches the theoretical upper limit, until the proportion of each reconstructed forest area does not reach the theoretical upper limit, and the reconstructed forest spatial distribution of the study area is obtained. The reconstructed forest spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with the actual survey data.

[0054] At least one embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, a method for reconstructing forest spatial distribution data according to any embodiment of the present disclosure is implemented.

[0055] The above-mentioned non-transient computer-readable storage medium calculates the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; when the proportion of reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportion of reconstructed forest area that reaches the theoretical upper limit, until the proportion of each reconstructed forest area does not reach the theoretical upper limit, and the reconstructed forest spatial distribution of the study area is obtained. The reconstructed forest spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with the actual survey data. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of a method for reconstructing forest spatial distribution data according to a disclosed embodiment;

[0057] Figure 2 A flow chart of a method for reconstructing forest spatial distribution data according to a preferred embodiment;

[0058] Figure 3 A module structure diagram of a data reconstruction device for forest spatial distribution according to a disclosed embodiment;

[0059] Figure 4 A schematic block diagram of a data control device provided for at least one embodiment of the present disclosure;

[0060] Figure 5 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0062] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0063] In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed descriptions of some known functions and known components.

[0064] The disclosed embodiment provides a method for reconstructing forest spatial distribution data.

[0065] Figure 1 FIG. 1 is a flow chart of a method for reconstructing forest spatial distribution data according to a disclosed embodiment. Figure 1 As shown, a method for reconstructing forest spatial distribution data in a disclosed embodiment includes steps S100 to S106:

[0066] S100, determining the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data;

[0067] S101, determining the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and taking the sum of the average forest area percentage and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

[0068] S102, delineating a potential maximum range of forest distribution according to the remote sensing distribution probability of the forest, and calculating a spatial allocation area available for forest reconstruction within the potential maximum range;

[0069] S103, calculating the forest natural distribution probability according to the natural factor data, and calculating the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability;

[0070] S104, collating the statistical data of the forest area in the target area to generate a historically reconstructed forest statistical area;

[0071] S105, calculating the initial reconstruction forest space area according to the forest space distribution probability;

[0072] S106, determining a correction coefficient of forest spatial distribution according to a ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area;

[0073] S107, calculating the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability;

[0074] S108, when the proportion of the reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportions of the reconstructed forest areas that reach the theoretical upper limit, until the proportions of the reconstructed forest areas do not reach the theoretical upper limit, thereby obtaining the spatial distribution of the reconstructed forests in the study area.

[0075] The target area is a pre-defined geographical scope, which can be reflected in the remote sensing data in the form of data. The pre-collected remote sensing data has a corresponding geographical scope including the target area and the area outside the target area. The target area can be divided into a forest distribution area and a non-forest distribution area.

[0076] Preferably, Figure 2 FIG. 1 is a flow chart of a method for reconstructing forest spatial distribution data according to a preferred embodiment of the present invention. Figure 2 As shown, the process of determining the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area in step S100 includes step S200:

[0077] S200, reclassify the land use types of remote sensing data and extract forest types, establish mapping units, unify the spatial resolution according to the optimal area principle, calculate the forest area ratio of each mapping unit, and determine the average forest area ratio.

[0078] Among them, remote sensing data is used to extract forest data, and a reconstruction mapping unit is established based on the forest data. Forest data includes land use data such as CULUCC, CCI-LC, CLCD, GlobeLand3, HansenGFC, PALSAR / PALSAR-2 and MCD12Q1. According to the classification system of remote sensing data, land use types are reclassified and forest types are extracted, grid cells with forest distribution are assigned 1, grid cells without forest distribution are assigned 0, and preprocessing such as spatial registration and data format unification is performed. A reconstruction mapping unit for forest data is established, and its resolution is based on the lowest resolution of all remote sensing land use data, or the spatial resolution of all remote sensing data is unified according to the optimal area principle with reference to the research requirements for the resolution of reconstructed forest data. In this embodiment, the resolution of the MCD12Q1 product is selected as a reference to unify the spatial resolution of each remote sensing land use data; for each remote sensing land use data, the forest area ratio of each mapping unit is calculated; in each year, the average forest area ratio of all remote sensing data in each mapping unit is calculated, which is recorded as the statistical probability R1 of the remote sensing product.

[0079] Preferably, if Figure 2 As shown, the process of determining the forest remote sensing interpretation classification probability of the reconstructed mapping unit in step S101 includes steps S201 to S204:

[0080] S201, resampling the remote sensing data according to the remote sensing image unit size and the optimal area principle, overlaying the resampled data, calculating the average value of each grid unit, extracting the grid units with an average value of 1 as completely consistent areas, and extracting the grid units with an average value of 0 as areas to be identified;

[0081] S202, performing stratified random sampling on completely consistent areas to obtain a training sample set for remote sensing image interpretation;

[0082] S203, generating a land use interpretation feature set based on the multi-band composite value of the remote sensing image and a series of remote sensing indices, constructing a classifier for the remote sensing image interpretation training sample set through a machine learning algorithm, applying a random forest algorithm, performing image interpretation on the to-be-identified area, extracting grid cells with forest interpretation results and their classification probabilities, assigning the classification probability to the numerical value of the forest remote sensing interpretation probability of the corresponding grid cell, and assigning the remote sensing interpretation probability of the grid cells in the remaining range to 0;

[0083] S204, calculating the average value of the remote sensing interpretation probability of each unit forest, and recording it as the remote sensing interpretation classification probability of the forest.

[0084] The remote sensing data of the target area is obtained, and low-quality pixels in the remote sensing data are masked, wherein the low-quality pixels include cloud, snow and cloud shadow pixels; high-quality multi-band data in the remote sensing data is extracted and the vegetation index, building index, bare soil index, burning index, snow index and water index are calculated; taking the remote sensing data as Landsat SR image as an example, the high-quality multi-band data includes 6 bands, namely, bands 1 to 5 and band 7 of Landsat 5 / 7, and 6 bands, namely, bands 2 to 7 of Landsat 8; wherein the formulas for calculating the vegetation index, building index, bare soil index, burning index, snow index and water index are as follows:

[0085] Vegetation index: NDVI = (Nir-Red) / (Nir+Red);

[0086] EVI=2.5×(Nir-Red) / (Nir+6×Red-7.5×Blue+1);

[0087] Building index: NDBI = (Mir-Nir) / (Mir+Nir);

[0088] Bare soil index: BSI = ((Swir2+Red)-(NIR+Blue)) / ((Swir2+Red)+(NIR+Blue));

[0089] Combustion index: NBR = (Swir1-Swir2) / (Swir1+Swir2);

[0090] Snow index: NDSI=(Green-Swir1) / (Green+Swir1);

[0091] Water index: MNDWI = (Green-Swir1) / (Green+Swir1);

[0092] Where Blue, Green, Red, Nir, Swir1, and Swir2 are Band 1-blue, Band 2-green, Band 3-red, Band 4-near infrared, Band 5-shortwave infrared 1, and Band 7-shortwave infrared 2 of Landsat 5TM / 7ETM+ images, or Band 2-blue, Band 3-green, Band 4-red, Band 5-near infrared, Band 6-shortwave infrared 1, and Band 7-shortwave infrared 2 of Landsat 8OLI images.

[0093] Taking the spatial resolution of Landsat SR images as an example, the collected original multi-source land use data are resampled according to the optimal area principle. In each year, all land use resampled data are superimposed, the average value of each grid cell is calculated, and the grid cells with an average value of 1 are extracted as the completely consistent area of ​​forest products in that year, and the grid cells with an average value of 0 are extracted as the area to be identified.

[0094] In each year, stratified random sampling is performed on completely consistent areas to obtain a training sample set for remote sensing image interpretation; a land use interpretation feature set is generated based on the multi-band composite values ​​of remote sensing images and a series of remote sensing indices, a classifier is constructed for the remote sensing image interpretation training sample set through a machine learning algorithm, and the random forest algorithm is applied to interpret the image of the identified area, and the grid cells with forest interpretation results and their classification probabilities are extracted, and the classification probability is assigned to the numerical value of the forest remote sensing interpretation probability of the corresponding grid cell, and the remote sensing interpretation probability of the grid cells in the remaining range is assigned to 0; the established forest reconstruction mapping unit is used to calculate the average value of the forest remote sensing interpretation probability of each unit, which is recorded as the remote sensing interpretation classification probability R2.

[0095] Among them, the series of remote sensing indexes include the vegetation index, building index, bare soil index, burning index, snow index and water body index; the multi-band synthesis value of the remote sensing image includes the median synthesis of each band of the remote sensing image data in four periods within a year and the standard deviation of the band values ​​of all images throughout the year, among which the four periods are January to March, April to June, July to September and October to December respectively.

[0096] Preferably, the process of delineating the potential maximum range of forest distribution according to the forest remote sensing distribution probability in step S102 comprises the steps of:

[0097] The reconstructed mapping units where the sum of forest remote sensing distribution probabilities in each year is greater than 0 are defined as the maximum range of forest remote sensing distribution, and the range of forest distribution in the global potential vegetation type map is superimposed to define the potential maximum range of reconstructed forest distribution;

[0098] The process of calculating the spatial allocation area available for forest reconstruction within the potential maximum range is as follows:

[0099]

[0100] in, The spatial allocation area available for forest reconstruction in year y, A l is the land area of ​​the reconstructed mapping unit, and Reconstruct the urban area and cultivated land area of ​​the mapping unit in year y respectively.

[0101] In year y, the statistical probability of remote sensing products R1 and the probability of remote sensing interpretation classification R2 are added together to form the forest remote sensing distribution probability, that is, R y =R1+R2.

[0102] The forest remote sensing distribution probability R of all years y The sum is calculated, and the mapping units with results greater than 0 are defined as the maximum range of forest remote sensing distribution. The range of forest distribution in the global potential vegetation type map is superimposed to define the potential maximum range of reconstructed forest distribution.

[0103] The spatial allocation area available for forest reconstruction was calculated within the potential maximum range of reconstructed forest distribution, where historical reconstruction data on built-up land and cultivated land were applied to quantify the direct occupation of potential forest land by human activities.

[0104] The urban area in this embodiment comes from the HYDE 3.3 database, and the cultivated land area comes from the newly reconstructed result.

[0105] In the embodiment of the present disclosure, data on natural factors that affect the possibility of deforestation are collected. The embodiment of the present disclosure selects elevation, slope, and land production potential for normalization. If there are other key natural factors that affect deforestation in the target area, they can be added to calculate the probability of deforestation. Among them, the indicators such as elevation and slope use the maximum and minimum value normalization function, and the land production potential uses the maximum value normalization function. The formula is as follows:

[0106] Altitude normalization function: E′=(Emax-E) / (Emax-Emin);

[0107] Slope normalization function: S′=(Smax-S) / (Smax-Smin);

[0108] The maximum normalized function of land production potential: P′=P / Pmax;

[0109] Among them, E′, S′, and P′ are the normalized values ​​of elevation, slope, and land production potential, respectively; E, S, and P are the original values ​​of elevation, slope, and land production potential, respectively; E max , S max , P max are the maximum values ​​of elevation, slope, and land production potential, respectively. min and S min are the minimum values ​​of elevation and slope, respectively.

[0110] Based on this, the process of calculating the forest natural distribution probability according to the natural factor data in step S103, and calculating the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability, is as follows:

[0111] F=1-wi ×F i ;

[0112] Among them, F is the probability of natural distribution of forests, w i is the weight of the natural factor, w i =1 / n, n is the number of natural factors, F i is the normalized value of the natural factor;

[0113]

[0114] Among them, S y is the forest spatial distribution probability in year y, S′ y+1 is the proportion of forest reconstructed area in each reconstructed mapping unit in year y+1, R y is the remote sensing distribution probability of forests, and N is the year with remote sensing data.

[0115] In the potential maximum range of reconstructed forest distribution, the equal weight sum method is used to calculate the probability of deforestation, and the remaining range is the probability of natural distribution of forests under human interference. In the embodiment of the present disclosure, the number of factors is 3 in this example, F i It is the normalized value of natural factors affecting deforestation, including E′, S′, and P′.

[0116] Furthermore, we collected forest areas inferred from local chronicles and their data in the historical period of the study area, as well as forest areas from land surveys and forest resource inventories in modern times, to establish an initial forest statistical area dataset for the study area at each time point; we used the forest areas at key time nodes for linear inference to generate a long-term historical series of reconstructed forest statistical areas. Here, y represents the year of historical reconstruction.

[0117] Preferably, the process of calculating the initial reconstruction forest space area according to the forest space distribution probability in step S105 is as follows:

[0118]

[0119] Where A is the area of ​​the reconstructed mapping unit, S y is the forest spatial distribution probability in year y.

[0120] Furthermore, the forest area is reconstructed using the historical data of year y and initial reconstruction forest space area Calculate the correction coefficient α of forest spatial distribution in year y y , the formula is as follows:

[0121]

[0122] Reconstruct the historical forest statistical area in year y Assigned to each mapping unit, using the forest spatial distribution correction factor α y and forest spatial distribution probability S y , calculate the proportion of reconstructed forest area A′ of each mapping unit y , the formula is as follows:

[0123] A′ y =α y ×S y ;

[0124] After the forest area is corrected, if A′ of any reconstructed mapping unit y Exceeding the historical forest area available for forest reconstruction Assign it to the corresponding year On this basis, the maximum range of forest distribution in the target area is divided into A′ y Reach the upper limit and A′ y The two parts that do not reach the upper limit, the mapping units that reach the upper limit will no longer participate in the calculation, and the historical reconstruction of forest statistical area Subtract the sum of forest areas of all mapping units that have reached the upper limit, and use it as the forest allocation area for the next correction process; mapping units that do not reach the upper limit enter the next process and repeat the above steps of forest spatial distribution correction coefficient and forest area proportion of each mapping unit. y When the spatial distribution of forest reconstruction is not more than the available spatial allocation area for forest reconstruction, the steps are repeated to end, that is, the spatial distribution of forest reconstruction in the target area in year y is obtained, and the proportion of forest reconstruction area S′ of each reconstruction mapping unit is calculated. y . Apply the reconstructed S′ y , execute the above loop operation for year y-1 to complete the reconstruction of forest spatial distribution for all years.

[0125] Preferably, the process of determining the theoretical upper limit value comprises the steps of:

[0126] The maximum forest area ratio within the forest distribution range is determined according to the remote sensing data of the target area, and the maximum forest area ratio is used as the theoretical upper limit value.

[0127] The disclosed embodiment establishes a data reconstruction method for forest spatial distribution that effectively integrates the time series area of ​​ground survey and the remote sensing inversion position, which can effectively extract the forest spatial information provided by the remote sensing inversion method and the forest statistical time series information of the ground survey, and integrate the above information, effectively overcoming the problems of underestimation of forest reconstruction area and spatial distribution error of the existing method. At the same time, based on the key steps of multi-source remote sensing data extraction and local remote sensing interpretation, the effective information of forest spatial distribution of existing land use products is extracted, and the forest distribution and its possibility of the remaining potential range are judged, which effectively solves the problem that the existing remote sensing inversion method and data have weak ability to identify small-scale trees. Compared with the traditional reconstruction method, the disclosed embodiment constrains the upper limit of the area that can be allocated for forest reconstruction based on the scope of human impact on land use, establishes a cyclic iterative process based on the previous forest distribution and statistical area allocation, and realizes the historical reconstruction of forest spatial distribution of long time series, which can provide more refined and accurate spatial distribution characteristics and coverage of long time series, and can also achieve reliability consistent with ground survey data.

[0128] The disclosed embodiment also provides a data reconstruction device for forest spatial distribution.

[0129] Figure 3 FIG. 1 is a block diagram of a data reconstruction device for forest spatial distribution according to a disclosed embodiment. Figure 3 As shown, a data reconstruction device for forest spatial distribution in one embodiment includes:

[0130] A mean value calculation module 100 is used to determine the forest distribution range of the target area and the average value of the forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data;

[0131] The probability calculation module 101 is used to determine the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and take the sum of the forest area percentage average value and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

[0132] An area calculation module 102 is used to define a potential maximum range of forest distribution according to the forest remote sensing distribution probability, and calculate the spatial allocation area available for forest reconstruction within the potential maximum range;

[0133] The probability reconstruction module 103 is used to calculate the forest natural distribution probability according to the natural factor data, and calculate the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability;

[0134] A first area reconstruction module 104 is used to sort out the statistical data of the forest area of ​​the target area and generate a historically reconstructed forest statistical area;

[0135] A second area reconstruction module 105, configured to calculate an initial reconstructed forest space area according to the forest space distribution probability;

[0136] A coefficient calculation module 106, used to determine a correction coefficient of forest spatial distribution according to a ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area;

[0137] A proportion calculation module 107, used to calculate the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability;

[0138] The spatial calculation module 108 is used to update the historically reconstructed forest statistical area according to the difference between the historically reconstructed forest statistical area and the sum of the areas of the reconstructed forest area percentages that reach the theoretical upper limit value when the reconstructed forest area percentage reaches the theoretical upper limit value, until the reconstructed forest area percentages do not reach the theoretical upper limit value, thereby obtaining the spatial distribution of the forest reconstructed in the study area.

[0139] The data reconstruction device for forest spatial distribution of the disclosed embodiment calculates the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; when the proportion of reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportion of reconstructed forest area that reaches the theoretical upper limit, until the proportion of each reconstructed forest area does not reach the theoretical upper limit, and the forest spatial distribution reconstructed in the study area is obtained. The reconstructed forest spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0140] At least one embodiment of the present disclosure further provides a data control device. Figure 4 A schematic block diagram of a data control device provided by at least one embodiment of the present disclosure. Figure 4 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memory 200 is used to store computer executable instructions non-transiently; the processor 201 is used to run the computer executable instructions, and when the computer executable instructions are run by the processor 201, the processor 201 may execute one or more steps in the forest spatial distribution data reconstruction method according to any embodiment of the present disclosure.

[0141] The specific implementation and related explanation of each step of the forest spatial distribution data reconstruction method can be found in the relevant content of the embodiment of the forest spatial distribution data reconstruction method, which will not be repeated here. It should be noted that Figure 4The components of the data control device 20 shown are merely exemplary and non-limiting. The data control device 20 may also have other components according to actual application requirements.

[0142] In one embodiment, the processor 201 and the memory 200 can communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 can communicate via a network connection. The network may include a wireless network, a wired network, and / or any combination of a wireless network and a wired network, and the present disclosure does not limit the type and function of the network. For another example, the processor 201 and the memory 200 can also communicate via a bus connection. The bus may be a peripheral component interconnect standard (PCI) bus or an extended industrial standard architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 may be arranged at a remote data server end (cloud) or a distributed energy system end (local end), or may be arranged at a client end (e.g., a mobile device such as a mobile phone). For example, the processor 201 may be a device having data processing capability and / or instruction execution capability such as a central processing unit (CPU), a tensor processor (TPU), or a graphics processor GPU, and may control other components in the data prediction device 20 to perform desired functions. The central processing unit (CPU) may be an X86 or ARM architecture, etc.

[0143] In one embodiment, the memory 200 may include any combination of one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disk read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer-executable instructions may be stored on the computer-readable storage medium, and the processor 201 may execute the computer-executable instructions to implement various functions of the data prediction device 20. Various applications and various data, as well as various data used and / or generated by the application, etc. may also be stored in the memory 200.

[0144] It should be noted that the data control device 20 can achieve technical effects similar to those of the aforementioned forest spatial distribution data reconstruction method, and the repeated parts will not be repeated.

[0145] At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium. Figure 5 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present disclosure. Figure 5As shown, one or more computer executable instructions 301 may be non-transitory stored on a non-transitory computer readable storage medium 30. For example, when the computer executable instructions 301 are executed by a computer, the computer may execute one or more steps in the forest spatial distribution data reconstruction method according to any embodiment of the present disclosure.

[0146] In one embodiment, the non-transitory computer-readable storage medium 30 may be applied to the above-mentioned data control device 20 , for example, it may be the memory 200 in the data control device 20 .

[0147] In one embodiment, the description of the non-transitory computer-readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data control device 20, and the repeated parts will not be repeated.

[0148] It should be noted that the memory 200 stores different non-transiently stored computer executable instructions, and the data control device 20 corresponds to a firmware upgrade device. When the computer executable instructions are executed by the processor 201, the processor 201 can execute one or more steps in the forest spatial distribution data reconstruction method according to any embodiment of the present disclosure.

[0149] There are a few points to note about this disclosure:

[0150] (1) The drawings of the embodiments of the present disclosure only relate to the structures related to the embodiments of the present disclosure, and other structures may refer to the general design.

[0151] (2) For the sake of clarity, the thickness and size of layers or structures are exaggerated in the drawings used to describe the embodiments of the present invention. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element, or there may be intervening elements.

[0152] (3) In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other to obtain new embodiments. The above are only specific implementations of the present disclosure, but the protection scope of the present disclosure is not limited thereto. The protection scope of the present disclosure shall be subject to the protection scope of the claims.

[0153] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for reconstructing forest spatial distribution data, characterized in that: Includes steps: Determine the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data; Determine the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and take the sum of the average forest area percentage and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability; Delineate the potential maximum range of forest distribution based on the remote sensing distribution probability of the forest, and calculate the spatial allocation area available for forest reconstruction within the potential maximum range; Calculating the forest natural distribution probability based on the natural factor data, and calculating the forest spatial distribution probability based on the forest remote sensing distribution probability and the forest natural distribution probability; Collating statistical data of forest area in the target area to generate historically reconstructed forest statistical area; Calculating the initial reconstruction forest space area according to the forest spatial distribution probability; Determine the correction coefficient of forest spatial distribution according to the ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area; Calculating the proportion of reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; When the proportion of the reconstructed forest area reaches the theoretical upper limit, the historical reconstructed forest statistical area is updated according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the reconstructed forest area that reaches the theoretical upper limit, until the proportions of the reconstructed forest areas do not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed forest in the study area.

2. The method for reconstructing forest spatial distribution data according to claim 1, characterized in that: The process of determining the forest distribution range of the target area and the average forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area comprises the steps of: Reclassify the land use types and extract the forest types of remote sensing data, establish mapping units, unify the spatial resolution according to the optimal area principle, calculate the forest area ratio of each mapping unit, and determine the average forest area ratio.

3. The method for reconstructing forest spatial distribution data according to claim 2, characterized in that: The process of determining the forest remote sensing interpretation classification probability of the reconstructed mapping unit comprises the steps of: Resampling the remote sensing data according to the remote sensing image unit size and the optimal area principle, overlaying the resampled data, calculating the average value of each grid unit, extracting the grid units with an average value of 1 as completely consistent areas, and extracting the grid units with an average value of 0 as areas to be identified; Perform stratified random sampling on completely consistent areas to obtain a training sample set for remote sensing image interpretation; According to the multi-band composite values ​​of remote sensing images and a series of remote sensing indices, a land use interpretation feature set is generated. A classifier is constructed for the remote sensing image interpretation training sample set through a machine learning algorithm. The random forest algorithm is applied to interpret the image of the identified area, and the grid cells with forest interpretation results and their classification probabilities are extracted. The classification probability is assigned to the numerical value of the forest remote sensing interpretation probability of the corresponding grid cell, and the remote sensing interpretation probability of the grid cells in the remaining range is assigned to 0. The average value of forest remote sensing interpretation probability of each mapping unit is calculated and recorded as forest remote sensing interpretation classification probability.

4. The method for reconstructing forest spatial distribution data according to claim 1, characterized in that: The process of delineating the potential maximum range of forest distribution based on the forest remote sensing distribution probability comprises the steps of: The reconstructed mapping units where the sum of forest remote sensing distribution probabilities in each year is greater than 0 are defined as the maximum range of forest remote sensing distribution, and the range of forest distribution in the global potential vegetation type map is superimposed to define the potential maximum range of reconstructed forest distribution; The process of calculating the spatial allocation area available for forest reconstruction within the potential maximum range is as follows: in, The spatial allocation area available for forest reconstruction in year y, A l is the land area of ​​the reconstructed mapping unit, and Reconstruct the urban area and cultivated land area of ​​the mapping unit in year y respectively.

5. The method for reconstructing forest spatial distribution data according to claim 1, characterized in that: The process of calculating the forest natural distribution probability according to the natural factor data, and calculating the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability is as follows: F=1-w i ×F i ; Among them, F is the probability of natural distribution of forests, w i is the weight of the natural factor, w i =1 / n, n is the number of natural factors, F i is the normalized value of the natural factor; Among them, S y is the forest spatial distribution probability in year y, S ′ y+1 is the proportion of forest reconstructed area in each reconstructed mapping unit in year y+1, R y is the remote sensing distribution probability of forests, and N is the year with remote sensing data.

6. The method for reconstructing forest spatial distribution data according to claim 1, characterized in that: The process of calculating the initial reconstruction forest space area according to the forest space distribution probability is as follows: Where A is the area of ​​the reconstructed mapping unit, S y is the forest spatial distribution probability in year y.

7. The method for reconstructing forest spatial distribution data according to claim 1, characterized in that: The process of determining the theoretical upper limit value comprises the steps of: The maximum forest area ratio within the forest distribution range is determined according to the remote sensing data of the target area, and the maximum forest area ratio is used as the theoretical upper limit value.

8. A data reconstruction device for forest spatial distribution, characterized in that: include: A mean value calculation module, used to determine the forest distribution range of the target area and the average value of the forest area ratio within the forest distribution range according to the reconstruction mapping unit of the target area; wherein the reconstruction mapping unit is related to the remote sensing data; A probability calculation module, used to determine the forest remote sensing interpretation classification probability of the reconstructed mapping unit, and take the sum of the average forest area percentage and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability; An area calculation module is used to delineate the potential maximum range of forest distribution according to the remote sensing distribution probability of the forest, and calculate the spatial allocation area available for forest reconstruction within the potential maximum range; A probability reconstruction module, used to calculate the forest natural distribution probability according to the natural factor data, and calculate the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability; A first area reconstruction module is used to sort out the statistical data of the forest area of ​​the target area and generate a historically reconstructed forest statistical area; A second area reconstruction module, used for calculating the initial reconstruction forest space area according to the forest space distribution probability; A coefficient calculation module, used to determine the correction coefficient of forest spatial distribution according to the ratio of the initial reconstructed forest spatial area to the historical reconstructed forest statistical area; A proportion calculation module, used to calculate the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability; The spatial calculation module is used to update the historically reconstructed forest statistical area according to the difference between the historically reconstructed forest statistical area and the sum of the areas of the reconstructed forest area percentages that reach the theoretical upper limit value when the reconstructed forest area percentage reaches the theoretical upper limit value, until the reconstructed forest area percentages do not reach the theoretical upper limit value, thereby obtaining the spatial distribution of the reconstructed forest in the study area.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for reconstructing forest spatial distribution data as described in any one of claims 1 to 7 is implemented.

10. A data control device, characterized in that: include: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to run computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the forest spatial distribution data reconstruction method as described in any one of claims 1 to 7.

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