Data reconstruction method and device for forest spatial distribution

Abstract

The present application relates to a method and device for reconstructing forest spatial distribution data, belonging to the technical field of forest data processing, and solves the problems that there are large deviations between the existing forest spatial distribution data and the actual situation, and there is great uncertainty. The method and device for reconstructing forest spatial distribution data of the present application calculate 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 value, 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 reaching the theoretical upper limit value, until each reconstructed forest area proportion does not reach the theoretical upper limit value, and the reconstructed forest spatial distribution of the study area is obtained. The reconstructed forest spatial distribution of the present application can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data.

Description

Technical Field

[0001] This 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 a great deal of uncertainty in forest spatio-temporal distribution mapping, which is a difficult point in related research. For example, the "Historical Database of the Global Environment" (HYDE dataset) only quantifies the land use types of human activities and classifies the remaining land as natural vegetation uniformly, making it difficult to distinguish different natural vegetation types such as forests and grasslands; the "Land Use Harmonization dataset" (LUH2 dataset) simulates the extent of forests through a climate productivity model, which also has an obvious deviation from the actual spatial distribution of forests. At the regional scale, reconstructing forest spatial distribution data through the prediction of potential forest extent and the impact of humans on forests also faces error problems such as an obvious deviation from the actual forest spatial distribution.

[0003] Large-scale remote sensing image data and inversion techniques can provide more real and reliable information on the distribution of ground 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, making it difficult to meet the time requirements for historical reconstruction. The time range of medium- and low-resolution remote sensing images can be extended to the 1980s, but their spatial resolution limits the ability to identify the extent of forest trees. In particular, the recognition accuracy of ground objects such as narrow forest belts and agroforestry mixtures is relatively low, further leading to the problem of difficulty in capturing the true dynamic changes at the forest edge. For example, since the 1980s, the forest coverage rate in a certain place has increased significantly, but the existing remote sensing products can basically not reflect the above-mentioned change trend. Even some products believe that the forest area in a certain place shows a decreasing trend. Therefore, there is also a great deal of uncertainty in the existing research on using remote sensing land use data for forest spatial distribution reconstruction.

[0004] In summary, the existing data processing methods for forest spatial distribution data generate forest spatial distribution data with a large deviation from the actual situation and a great deal of uncertainty. Summary of the Invention

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

[0006] Embodiments of the present disclosure provide a method for reconstructing forest spatial distribution data, including the steps of:

[0007] Determine the forest distribution range of the target area and the average proportion of the forest area within the forest distribution range based on the reconstructed mapping units of the target area; wherein, the reconstructed mapping units are related to remote sensing data;

[0008] Determine the forest remote sensing interpretation classification probability of the reconstructed mapping units, and use the sum of the average proportion of the forest area and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

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

[0010] Calculate the forest natural distribution probability based on natural factor data, and calculate the forest spatial distribution probability according to the forest remote sensing distribution probability and the forest natural distribution probability;

[0011] Collate the statistical data of the forest area in the target area to generate the historical reconstructed forest statistical area;

[0012] Calculate the initial reconstructed forest spatial area according to the forest spatial distribution probability;

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

[0014] Calculate the proportion of the 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 value, update the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas corresponding to the proportion of the reconstructed forest area reaching the theoretical upper limit value, until each proportion of the reconstructed forest area does not reach the theoretical upper limit value, so as to obtain the reconstructed forest spatial distribution in the study area.

[0016] The data reconstruction method for the forest spatial distribution in the embodiments of the present disclosure 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 value, update the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas corresponding to the proportion of the reconstructed forest area reaching the theoretical upper limit value, until each proportion of the reconstructed forest area does not reach the theoretical upper limit value, so as to obtain the reconstructed forest spatial distribution in the study area. The reconstructed forest spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve reliability consistent with the 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 proportion of forest area within the forest distribution range according to the reconstructed mapping unit of the target area includes the steps:

[0018] Perform reclassification of land use types and extraction of forest types from remote sensing data, establish mapping units, unify the spatial resolution according to the optimal area principle, calculate the proportion of forest area in each mapping unit, and determine the average proportion of forest area.

[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:

[0020] Resample the remote sensing data according to the size of the remote sensing image unit and the optimal area principle, overlay the resampled data, calculate the average value of each grid cell, extract the grid cells with an average value of 1 as the completely consistent area, and extract the grid cells with an average value of 0 as the area to be identified;

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

[0022] Generate 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. Construct a classifier for the remote sensing image interpretation training sample set through a machine learning algorithm. Apply the random forest algorithm to perform image interpretation on the area to be identified, extract the grid cells with the interpretation result of forest and their classification probabilities, assign the classification probability as the value of the forest remote sensing interpretation probability of the corresponding grid cell, and assign the remote sensing interpretation probability of the grid cells in the remaining range as 0;

[0023] Statistically calculate the average value of the forest remote sensing interpretation probabilities of each mapping unit, denoted as the 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 includes the steps:

[0025] Designate the reconstructed mapping units with the sum of forest remote sensing distribution probabilities greater than 0 in each year as the maximum range of forest remote sensing distribution, and overlay the range of forest distribution in the global potential vegetation type map to delineate the potential maximum range of reconstructed forest distribution;

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

[0027]

[0028] Wherein, The allocated area of space available for forest reconstruction in year y, A l is the land area of the reconstruction mapping unit, and are the urban area and cultivated land area of the reconstruction mapping unit in year y, respectively.

[0029] As one of the optional embodiments, the process of 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 is as follows:

[0030] F = 1 - w i ×F i ;

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

[0032]

[0033] where S y is the forest spatial distribution probability in year y, S' y+1 is the proportion of the forest reconstruction area of each reconstruction mapping unit in year y + 1, R y is the forest remote sensing distribution probability, and N is the year with remote sensing data.

[0034] As one of the optional embodiments, the process of calculating the initial reconstructed forest spatial area based on the forest spatial distribution probability is as follows:

[0035]

[0036] where A is the area of the reconstruction mapping unit, and 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] Determining the maximum proportion of the forest area within the forest distribution range based on the remote sensing data of the target area, and taking the maximum proportion of the forest area as the theoretical upper limit value.

[0039] The embodiments of the present disclosure also provide a data reconstruction device for forest spatial distribution, including:

[0040] A mean calculation module, configured to determine the forest distribution range of the target area and the average proportion of the forest area within the forest distribution range according to the reconstructed mapping units of the target area; wherein, the reconstructed mapping units are related to remote sensing data;

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

[0042] An area calculation module, configured to delimit the 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;

[0043] A probability reconstruction module, configured to calculate the forest natural distribution probability according to 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, configured to collate the statistical data of the forest area of the target area to generate the historical reconstructed forest statistical area;

[0045] A second area reconstruction module, configured to calculate the initial reconstructed forest spatial area according to the forest spatial distribution probability;

[0046] A coefficient calculation module, configured to determine the correction coefficient of the 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, configured to calculate the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest spatial distribution probability;

[0048] A spatial calculation module, configured to update the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas corresponding to the proportion of the reconstructed forest area reaching the theoretical upper limit value when the proportion of the reconstructed forest area reaches the theoretical upper limit value, until the proportion of each reconstructed forest area does not reach the theoretical upper limit value, so as to obtain the forest spatial distribution of the study area after reconstruction.

[0049] The data reconstruction device for the forest spatial distribution in the embodiments of the present disclosure calculates the proportion of the reconstructed forest area based on 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 value, 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 value, until none of the proportions of the reconstructed forest areas reach the theoretical upper limit value, and the reconstructed forest spatial distribution of the study area is obtained. The reconstructed forest spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data.

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

[0051] One or more memories that non-transiently store computer-executable instructions;

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

[0053] The above-mentioned data control device calculates the proportion of the reconstructed forest area based on 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 value, 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 value, until none of the proportions of the reconstructed forest areas reach the theoretical upper limit value, and the reconstructed forest spatial distribution of the study area is obtained. The reconstructed forest spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data.

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

[0055] The above non-transitory computer-readable storage medium 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 value, 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 value, until the proportions of each reconstructed forest area do not reach the theoretical upper limit value, and the forest spatial distribution reconstructed in the study area is obtained. The reconstructed forest spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-term coverage, but also achieve reliability consistent with the actual survey data. Description of the Drawings

[0056] Figure 1 It is a flowchart of a data reconstruction method for the forest spatial distribution of an open embodiment;

[0057] Figure 2 It is a flowchart of a data reconstruction method for the forest spatial distribution of a preferred embodiment;

[0058] Figure 3 It is a structural diagram of the modules of a data reconstruction device for the forest spatial distribution of an open embodiment;

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

[0060] Figure 5 It is a schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present disclosure. Detailed Embodiments

[0061] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0062] Unless otherwise defined, technical terms or scientific terms used in this disclosure shall have the ordinary meanings as understood by those of ordinary skill in the art to which this disclosure pertains. The "first", "second" and similar terms used in this disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to indicate relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0063] To keep the following description of the embodiments of this disclosure clear and concise, detailed descriptions of some known functions and known components are omitted in this disclosure.

[0064] An embodiment of this disclosure provides a method for data reconstruction of forest spatial distribution.

[0065] Figure 1 A flowchart of the method for data reconstruction of forest spatial distribution according to an embodiment of the disclosure is as Figure 1 shown. The method for data reconstruction of forest spatial distribution according to an embodiment of the disclosure includes steps S100 to S106:

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

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

[0068] S102, delimiting the potentially maximum range of forest distribution according to the forest remote sensing distribution probability, and calculating the available spatial allocation area for forest reconstruction within the potentially 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 of the target area to generate the historical reconstructed forest statistical area;

[0071] S105, calculate the initial reconstructed forest space area according to the forest space distribution probability;

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

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

[0074] S108, when the proportion of the reconstructed forest area reaches the theoretical upper limit value, update the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the proportion of the reconstructed forest area reaching the theoretical upper limit value, until the proportion of each reconstructed forest area does not reach the theoretical upper limit value, and obtain the forest space distribution reconstructed in the study area.

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

[0076] Preferably, Figure 2 is a flowchart of a data reconstruction method for the forest space distribution of a preferred embodiment. As Figure 2 shown, the process of determining the forest distribution range of the target area and the average value of the proportion of the forest area within the forest distribution range in step S100 includes step S200:

[0077] S200, perform reclassification of land use types and extraction of forest types from remote sensing data, establish mapping units, unify the spatial resolution according to the optimal area principle, calculate the proportion of the forest area of each mapping unit, and determine the average value of the proportion of the forest area.

[0078] Among them, remote sensing data is used to extract forest data, and a reconstructed mapping unit is established based on the forest data. The 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 the remote sensing data, reclassification of land use types and extraction of forest types are carried out. The raster cells with forest distribution are assigned a value of 1, and the raster cells without forest distribution are assigned a value of 0. Preprocessing such as spatial registration and data format unification is performed. A reconstructed mapping unit of the forest data is established, and its resolution is referenced to the lowest resolution of all remote sensing land use data, or the resolution requirements of the research for reconstructing forest data are referred to, and the spatial resolution of all remote sensing data is unified according to the optimal area principle. In this embodiment, the resolution of the MCD12Q1 product is selected as the reference to unify the spatial resolution of each remote sensing land use data; for each remote sensing land use data, the proportion of forest area in each mapping unit is calculated; in each year, the average value of the proportion of forest area of all remote sensing data in each mapping unit is calculated and denoted as the statistical probability R1 of the remote sensing product.

[0079] Preferably, as Figure 2 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, resample the remote sensing data according to the remote sensing image unit size and the optimal area principle, overlay the resampled data, calculate the average value of each raster cell, extract the raster cells with an average value of 1 as the completely consistent area, and extract the raster cells with an average value of 0 as the area to be identified;

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

[0082] S203, generate a land use interpretation feature set according to the multi-band composite value of the remote sensing image and a series of remote sensing indices, construct a classifier for the remote sensing image interpretation training sample set through a machine learning algorithm, apply the random forest algorithm, perform image interpretation on the area to be identified, extract the raster cells with the interpretation result of forest and their classification probabilities, assign the classification probability to the value of the forest remote sensing interpretation probability of the corresponding raster cell, and assign the remote sensing interpretation probability of the raster cells in the remaining ranges to 0;

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

[0084] Obtain remote sensing data of the target area, mask the low-quality pixels in the remote sensing data, and the low-quality pixels include cloud, snow, and cloud shadow pixels; extract the high-quality multi-band data in the remote sensing data and calculate the vegetation index, building index, bare soil index, burn index, snow index, and water body index; taking the remote sensing data as Landsat SR image as an example, the high-quality multi-band data includes 6 bands, namely, the first to fifth bands and the seventh band of Landsat 5 / 7, and 6 bands, namely, the second to seventh bands of Landsat 8; among them, the formulas for calculating the vegetation index, building index, bare soil index, burn index, snow index, and water body 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] Burn index: NBR = (Swir1 - Swir2) / (Swir1 + Swir2);

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

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

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

[0093] Taking the selection of the spatial resolution of Landsat SR images as an example, resample the collected original multi-source land use data according to the optimal area principle; in each year, overlay all the resampled land use data, calculate the average value of each grid cell, extract the grid cells with an average value of 1 as the completely consistent area of forest products in that year, and extract the grid cells with an average value of 0 as the area to be identified;

[0094] In each year, conduct stratified random sampling on the completely consistent area to obtain a training sample set for remote sensing image interpretation; generate a set of land use interpretation features based on the multi-band composite values and a series of remote sensing indices of the remote sensing images, construct a classifier for the remote sensing image interpretation training sample set through a machine learning algorithm, apply the random forest algorithm to interpret the remote sensing image of the area to be identified, extract the grid cells with the interpretation result of forest and their classification probabilities, assign the classification probability as the value of the forest remote sensing interpretation probability of the corresponding grid cell, and assign the remote sensing interpretation probability of the grid cells in the remaining range as 0; use the established forest reconstruction mapping unit to calculate the average value of the forest remote sensing interpretation probabilities of each unit, denoted as the remote sensing interpretation classification probability R2.

[0095] Among them, the series of remote sensing indices include the vegetation index, building index, bare soil index, burning index, snow index and water body index; the multi-band composite values of the remote sensing images include the median synthesis of each band in 4 periods within a year of the remote sensing image data and the standard deviation of the band values of all images throughout the year, where the 4 periods are January - March, April - June, July - September, and October - December respectively.

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

[0097] Delimit the maximum range of forest remote sensing distribution as the reconstruction mapping units with the sum of the forest remote sensing distribution probabilities in each year greater than 0, and overlay the range of forest distribution in the global potential vegetation type map to delimit the potential maximum range of reconstructed forest distribution;

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

[0099]

[0100] Among them, is the available spatial allocation area for forest reconstruction in year y, A l is the land area of the reconstruction mapping unit, and are the urban area and cultivated land area of the reconstruction mapping unit in year y respectively.

[0101] In year y, the statistical probability R1 of remote sensing products is added to the classification probability R2 of remote sensing interpretation, denoted as the forest remote sensing distribution probability, i.e., R y = R1 + R2.

[0102] Sum up the forest remote sensing distribution probabilities R y for all years. Designate the mapping units with results greater than 0 as the maximum range of forest remote sensing distribution, and overlay the range of forest distribution in the global potential vegetation type map to delimit the potential maximum range of reconstructed forest distribution.

[0103] Calculate the available spatial allocation area for forest reconstruction within the potential maximum range of reconstructed forest distribution. Among them, apply the historical reconstruction data of construction land and cultivated land to quantify the direct occupation of potential forest land by human activities.

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

[0105] In the embodiment of the present disclosure, collect the natural factor data affecting the possibility of deforestation. In the embodiment of the present disclosure, elevation, slope, and land production potential are selected for normalization processing. If there are other key natural factors affecting deforestation in the target area, they can be supplemented and added to calculate the deforestation probability; among them, indicators such as altitude and slope use the maximum-minimum normalization function, and land production potential uses the maximum normalization function. The formulas are as follows:

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

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

[0108] Land production potential maximum normalization function: P′ = P / Pmax;

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

[0110] Based on this, in step S103, 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:

[0111] F = 1 - wi ×F i ;

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

[0113]

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

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

[0116] Furthermore, collect the forest areas inferred from local chronicles and their materials in the historical period of the study area, as well as the forest areas from national land surveys and forest resource inventories in the modern period, and establish an initial forest statistical area dataset for the study area at each time point; use the forest areas at key time nodes for linear extrapolation to generate a long - time - series historical reconstructed forest statistical area where y represents the year of historical reconstruction.

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

[0118]

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

[0120] Furthermore, use the historical reconstructed forest statistical area in year y and the initial reconstructed forest spatial area to calculate the correction coefficient α of the forest spatial distribution in year y y , and the formula is as follows:

[0121]

[0122] The historical reconstructed forest statistical area for year y is allocated to each cartographic unit, using the forest spatial distribution correction coefficient α y and the forest spatial distribution probability S y , and the proportion A′ of the reconstructed forest area of each cartographic unit is calculated y , and the formula is as follows:

[0123] A′ y = α y × S y ;

[0124] After the correction of the forest area, if the A′ of any reconstructed cartographic unit y exceeds the historical reconstructed forest statistical area available for forest reconstruction it is assigned to the corresponding year On this basis, the maximum range of the forest distribution in the target area is divided into two parts where A′ y reaches the upper limit and A′ y does not reach the upper limit. The cartographic units that reach the upper limit no longer participate in the calculation, and the historical reconstructed forest statistical area minus the sum of the forest areas of all cartographic units that reach the upper limit is used as the forest allocation area for the next correction process; the cartographic units that do not reach the upper limit enter the next process and perform the above-mentioned repeated steps of the forest spatial distribution correction coefficient and the forest area proportion per cartographic unit. When the forest area proportion A′ y of all cartographic units does not exceed the available spatial allocation area for forest reconstruction, the repetition step ends, that is, the reconstructed forest spatial distribution of the target area for year y is obtained, and the proportion S of the forest reconstruction area of each reconstructed cartographic unit is calculated ′ y . Applying the reconstructed S ′ y , perform the above loop operation for year y - 1 to complete the reconstruction of the forest spatial distribution for all years.

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

[0126] Determine the maximum proportion of the forest area within the forest distribution range according to the remote sensing data of the target area, and use the maximum proportion of the forest area as the theoretical upper limit value.

[0127] The embodiments of the present disclosure establish a data reconstruction method for the forest spatial distribution that effectively integrates the temporal area of ground surveys and the positions of remote sensing inversion, which can effectively extract the forest spatial information provided by remote sensing inversion means and the forest statistical temporal information of ground surveys, and fuse the above information to effectively overcome problems such as the underestimation of forest reconstruction area and spatial distribution errors in existing methods. At the same time, based on the key steps of multi-source remote sensing data extraction and local remote sensing interpretation, the effective information on the forest spatial distribution of existing land use products is extracted, and the forest distribution and its possibility in the remaining potential ranges are discriminated, effectively solving the problem of the weak ability of existing remote sensing inversion methods and data to identify small-scale forest trees. Compared with traditional reconstruction methods, the embodiments of the present disclosure constrain the upper limit of the allocable area for forest reconstruction by the human-impacted land use range, establish a cyclic iterative process of statistical area allocation based on the previous forest distribution, and realize the historical reconstruction of the forest spatial distribution in a long time series, which can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve reliability consistent with ground survey data.

[0128] The embodiments of the present disclosure also provide a data reconstruction device for the forest spatial distribution.

[0129] Figure 3 For the module structure diagram of the data reconstruction device for the forest spatial distribution in an embodiment of the disclosure, as Figure 3 shown, the data reconstruction device for the forest spatial distribution in one embodiment includes:

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

[0131] A probability calculation module 101, configured to determine the forest remote sensing interpretation classification probability of the reconstruction mapping unit, and use the sum of the average value of the proportion of forest area and the forest remote sensing interpretation classification probability as the forest remote sensing distribution probability;

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

[0133] A probability reconstruction module 103, configured to calculate the forest natural distribution probability according to 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, configured to sort out the statistical data of the forest area of the target area to generate the historical reconstructed forest statistical area;

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

[0136] The coefficient calculation module 106 is configured to determine the correction coefficient of the forest space distribution according to the ratio of the initial reconstructed forest space area to the historical reconstructed forest statistical area;

[0137] The proportion calculation module 107 is configured to calculate the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest space distribution probability;

[0138] The space calculation module 108 is configured to, when the proportion of the reconstructed forest area reaches the theoretical upper limit value, update the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the reconstructed forest areas that reach the theoretical upper limit value, until each proportion of the reconstructed forest area does not reach the theoretical upper limit value, so as to obtain the reconstructed forest space distribution of the research area.

[0139] The data reconstruction device for the forest space distribution according to the embodiments of the present disclosure calculates the proportion of the reconstructed forest area according to the product of the correction coefficient and the forest space distribution probability; when the proportion of the reconstructed forest area reaches the theoretical upper limit value, it updates the historical reconstructed forest statistical area according to the difference between the historical reconstructed forest statistical area and the sum of the areas of the reconstructed forest areas that reach the theoretical upper limit value, until each proportion of the reconstructed forest area does not reach the theoretical upper limit value, so as to obtain the reconstructed forest space distribution of the research area. The reconstructed forest space distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve reliability consistent with the actual survey data.

[0140] At least one embodiment of the present disclosure further provides a data control device. Figure 4 It is a schematic block diagram of a data control device provided by at least one embodiment of the present disclosure. For example, as Figure 4 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 non-transiently store computer-executable instructions; 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 can be made to execute one or more steps in the data reconstruction method for the forest space distribution according to any embodiment of the present disclosure.

[0141] For the specific implementation of each step of the data reconstruction method for the forest space distribution and the related explanatory content, reference may be made to the relevant content in the embodiments of the data reconstruction method for the forest space distribution described above, which will not be elaborated here. It should be noted that Figure 4The components of the data control device 20 shown are exemplary rather than restrictive. According to actual application needs, the data control device 20 may also have other components.

[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 through a network connection. The network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network. The present disclosure does not limit the type and function of the network here. For another example, the processor 201 and the memory 200 can also communicate through a bus connection. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 can be set at the remote data server side (cloud) or the distributed energy system side (local side), or can also be set at the client side (for example, a mobile device such as a mobile phone). For example, the processor 201 can be a Central Processing Unit (CPU), a Tensor Processing Unit (TPU), or a Graphics Processing Unit (GPU), etc., which has data processing capabilities and / or instruction execution capabilities, and can control other components in the data control device 20 to perform desired functions. The Central Processing Unit (CPU) can be of the X86 or ARM architecture, etc.

[0143] In one embodiment, the memory 200 can include any combination of one or more computer program products. The computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM) and / or cache memory, etc. Non-volatile memory can include, for example, Read-Only Memory (ROM), hard disk, Erasable Programmable Read-Only Memory (EPROM), Portable Compact Disc Read-Only Memory (CD-ROM), USB memory, flash memory, etc. One or more computer-executable instructions can be stored on the computer-readable storage media. The processor 201 can run the computer-executable instructions to implement various functions of the data control device 20. Various application programs and various data can also be stored in the memory 200, as well as various data used and / or generated by the application programs, etc.

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

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

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

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

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

[0149] For the present disclosure, the following points also need to be noted:

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

[0151] (2) For clarity, in the accompanying drawings used to describe the embodiments of the present invention, the thickness and size of layers or structures are enlarged. It can be 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 can be "directly" on or under the other element, or there can be intermediate elements.

[0152] (3) Without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other to obtain new embodiments. The above are only the specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. The protection scope of the present disclosure should be subject to the protection scope of the claims.

[0153] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as the scope described in this specification.

[0154] The above embodiments only illustrate several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patented application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended 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 forests 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 are the urban area and cultivated land area of ​​the reconstructed 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 forests 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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