CN110955663A - Large-scale regional land resource asset liability statement compiling method - Google Patents
Large-scale regional land resource asset liability statement compiling method Download PDFInfo
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- CN110955663A CN110955663A CN201911215913.4A CN201911215913A CN110955663A CN 110955663 A CN110955663 A CN 110955663A CN 201911215913 A CN201911215913 A CN 201911215913A CN 110955663 A CN110955663 A CN 110955663A
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Abstract
The invention provides a large-scale regional land resource asset liability statement compiling method, which comprises the steps of collecting regional boundary data, calendar year land utilization data and land coverage data and storing the regional boundary data, the calendar year land utilization data and the land coverage data into a basic database; classifying and extracting the information stored in the basic database to obtain a plurality of ground feature type thematic databases of different types; utilizing the area information of the type change of the ground objects of the pattern spots to perform statistics to form a land resource transfer matrix; carrying out spatial statistics on the feature type thematic data of each year in the accounting period in a geographic information system according to regions, and compiling into a land resource physical quantity statistical table; then, converting the physical assets into value assets by adopting a reference land price correction method according to the land resource transfer matrix and the land resource physical quantity statistical table; and summarizing the physical assets and the value assets of the plurality of surface feature types, and compiling a soil resource asset liability statement summary table. The comprehensive accounting of different types of land resources is realized, and simultaneously, the conversion process from physical quantity to value quantity is also realized.
Description
Technical Field
The invention relates to the field of land resource statistics, in particular to a large-scale regional land resource balance sheet compiling method.
Background
Natural resources are the material basis on which the human society relies on survival and development, and are important strategic resources of the country. However, since economic growth has been dependent on resource consumption, it has caused environmental problems such as resource over-consumption, ecosystem deterioration, water shortage, land desertification, and domestic waste pollution. The problem of resource ecological environment is gradually threatened to the survival and development of human society.
Land resources are one of the most important natural resources. The land resources comprise crop production lands such as cultivated land and garden land, and are bearing foundations of ecological systems such as forests, grasslands and wetlands, and national food safety and ecological safety are concerned. Therefore, the country places a high importance on the protection and intensive economical utilization of land resources, and needs to strengthen the protection and management of land resources.
Patent CN109739943A discloses a statistical processing method and storage medium for natural resource vector earth surface coverage change, in which administrative divisions are used as statistical units, data in multiple different historical periods are matched, accurately spliced and cut, statistical analysis of different ground classification codes is performed after space superposition of earth surface coverage layers, and finally different types of statistical data reports are selected as required for output. Because the statistical analysis is carried out by taking administrative divisions as statistical units, the volume of each data processing task is reduced, a large amount of historical data which are covered by natural resource vector earth surfaces can be simultaneously processed by utilizing parallel processing resources of the existing computer, the processing speed and the processing efficiency are improved, and the inquiry can be carried out in a mode of combination of administrative division codes and earth distribution codes so as to realize the grading, earth classification, classification area summarization and multi-dimensional extraction of different administrative divisions.
However, the technical scheme of the patent only can carry out statistical analysis on the land resource physical quantity, and cannot provide quick and systematic intelligent accounting results for the work of accounting, auditing and the like of the land resource physical quantity in a large-scale area range.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a large-scale regional land resource and asset balance sheet compiling method, which is based on a reference land price correction method and solves the problem that only the statistical analysis can be carried out on the land resource and entity quantity in the prior art.
The invention provides a large-scale regional land resource asset liability statement compiling method which comprises the following steps:
s1, collecting area boundary data, past year land utilization data and past year land coverage data according to the area and time period of land resource asset accounting, and storing the collected data into a basic database;
s2, classifying and extracting the land utilization data and land coverage number of the past year to obtain a plurality of feature type thematic databases of different categories;
s3, carrying out spatial comparison on the information in each feature type thematic database in a geographic information system, comparing the feature type change condition of each pattern patch year by year, and carrying out statistics on the area of the feature type change condition of each pattern patch to form a land resource transfer matrix;
s4, carrying out spatial statistics on the land feature type thematic data of each year in the accounting period in a geographic information system according to regions, and compiling the statistical results into a land resource physical quantity statistical table;
s5, converting the physical assets into value assets by adopting a standard land price correction method according to the land resource transfer matrix and the land resource physical quantity statistical table;
and S6, summarizing the physical assets and the value assets of the multiple ground feature types, compiling a land resource asset liability statement summary table, and sorting to obtain a result database. The comprehensive accounting of different types of land resources is realized, and the conversion process from physical quantity to value quantity is realized.
Further, in S1, the historical land use data and the historical land cover data are acquired by interpretation of historical remote sensing images, or acquired by direct use of the results of land change survey and the results of geographical national condition monitoring.
Further, in S3, the land resource transfer matrix includes stock information of a plurality of different types of land resources, and land resource type variation information.
Further, the land resource physical quantity statistical table in S4 includes the accounting beginning data, the accounting end data, and the accounting period change data, and is output in the form of the physical quantity table and the value quantity table, respectively.
Further, in S5, the total economic value is calculated by using the reference price correction method according to the following formula:
in the formula, V is the total economic value of land resources in the checked area, i is the ith land resource thematic data pattern spot, and n is the total number of pattern spots in the thematic data. PsA reference land price f corresponding to land resources in the regioniCorrecting the pattern spot terrain factor correction weight value M of ith land resource thematic dataiAnd (4) the area of the pattern spot of the ith land resource thematic data pattern spot.
Further, the pattern spot terrain factor correction weight is calculated according to the average gradient of the pattern spot in the land resource thematic data, namely the pattern spot terrain factor correction weight is calculated according to the formula:
and t is sin s, wherein f is a terrain factor correction weight, and s is the average slope of the image spot.
Further, in S5, the value amount asset includes two dimensions of resource economic value and ecological service value.
Further, in S6, the land resource balance sheet summary is used for accounting the economic value and ecological value of the land resource.
According to the technical scheme, the invention has the beneficial effects that:
1. the invention provides a large-scale regional land resource asset liability statement compiling method, which comprises the steps of collecting regional boundary data, historical land utilization data and historical land coverage data and storing the regional boundary data, the historical land utilization data and the historical land coverage data into a basic database; classifying and extracting the information stored in the basic database to obtain a plurality of ground feature type thematic databases of different types; utilizing the area information of the type change of the ground objects of the pattern spots to perform statistics to form a land resource transfer matrix; carrying out spatial statistics on the feature type thematic data of each year in the accounting period in a geographic information system according to regions, and compiling into a land resource physical quantity statistical table; then, converting the physical assets into value assets by adopting a reference land price correction method according to the land resource transfer matrix and the land resource physical quantity statistical table; and summarizing the physical assets and the value assets of the multiple ground feature types, compiling a soil resource asset liability statement summary table, and collating to obtain a result database. The comprehensive accounting of different types of land resources is realized, and simultaneously, the conversion process from physical quantity to value quantity is also realized.
2. According to the large-scale regional land resource asset liability statement compiling method provided by the invention, the spot terrain factor correction weight is calculated according to the average gradient of the spots in the land resource thematic data, and then the economic value total is calculated by adopting a reference land price correction method.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart diagram of a large-scale regional land resource balance sheet compiling method.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, a large-scale regional land resource balance sheet compiling method includes the following steps:
firstly, utilizing a land change survey result and a geographical national condition monitoring result, or interpreting and acquiring area boundary data, land utilization data and land coverage data of the years in an accounting area and a time period through remote sensing images of the years, and storing the acquired data into a basic database;
secondly, classifying and extracting the land utilization data and the land coverage number of the past year to obtain a plurality of land feature type thematic databases of different categories;
for a certain accounting area, 5 types of land resources of cultivated land, garden land, forest land, grassland and water area are extracted from land coverage data of all the years by taking 2015-2018 as an accounting period, and a cultivated land special subject database, a garden land special subject database, a forest land special subject database, a grassland special subject database and a water area special subject database are obtained. And classifying the rest of the pattern spots into other lands to obtain a special subject database of the other lands. Subdividing the 6 types of ground object categories into two types of ground objects, wherein the specific division is shown in the following table:
thirdly, the information in the special database of the feature types is compared in space in the geographic information system, the feature type change condition of each map spot is compared year by year, the area of the feature type change condition of each map spot is counted, as shown in the following table 2, a statistical table of the feature type change condition of each map spot in the area a is shown, the feature type of the map spot M2 in the following table is changed, and the map spot M2 is marked as a change map spot.
And (4) counting the change conditions of all the image spots to generate a land resource transfer matrix as shown in the following table 3. The land resource transfer matrix is used as a part of a statistical table in the liability table system, and can integrally reflect the land resource change condition in the accounting period.
Fourthly, carrying out spatial statistics on the feature type thematic data of each year in the accounting period in a geographic information system according to regions, and compiling statistical results into a land resource and entity amount statistical table to obtain a land resource and entity amount statistical table; and calculating the total ecological service value of the land resources according to the physical quantity scales of different land resources in the checked area. The land resource physical quantity and the value quantity together constitute a land resource physical quantity statistical table, as shown in table 4.
And fifthly, converting the physical assets into value assets by adopting a reference land price correction method according to the land resource transfer matrix and the land resource physical quantity statistical table.
The reference land price correction method is to utilize a land resource value asset accounting model to calculate the total land resource economic value according to the reference land price of different land resources in an accounted area, a land quality correction factor and a zone bit correction factor.
And the land resource value asset accounting model comprises the step of calculating the total land resource economic value by utilizing the map spot terrain factor correction weight, the map spot ground area and the reference land price.
The calculation formula is as follows:
in the formula, V is the total economic value of land resources in the checked area, i is the ith land resource thematic data pattern spot, and n is the total number of pattern spots in the thematic data. PsThe land price of the area corresponding to the land resource comprises cultivated land, garden land, forest land, grassland, water area and other land. f. ofiCorrecting the pattern spot terrain factor correction weight value M of ith land resource thematic dataiAnd (4) mapping the physical quantity assets of the ith land resource thematic data, namely the area of the map spot.
The correction weight of the map spot topographic factor in the formula is calculated by using the average gradient of the map spot in the land resource thematic data. The method specifically comprises the following steps:
the terrain factor correction weight can be obtained by calculating the average gradient of the pattern spots in the land resource thematic data. The formula for calculating the terrain factor correction weight is as follows:
t＝sin s
in the above formula, f is the terrain factor correction weight, and s is the average slope of the pattern spot.
And sixthly, summarizing the value assets of various land resources, compiling a summary table of land resource assets and liabilities, and sorting to obtain a result database.
And summarizing the land resource value amount assets, and compiling a land resource asset liability statement summary table as shown in table 5.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A large-scale regional land resource and asset balance sheet compiling method is characterized by comprising the following steps:
s1, collecting area boundary data, past year land utilization data and past year land coverage data according to the area and time period of land resource asset accounting, and storing the collected data into a basic database;
s2, classifying and extracting the land utilization data and land coverage number of the past year to obtain a plurality of feature type thematic databases of different categories;
s3, carrying out spatial comparison on the information in each feature type thematic database in a geographic information system, comparing the feature type change condition of each pattern patch year by year, and carrying out statistics on the area of the feature type change condition of each pattern patch to form a land resource transfer matrix;
s4, carrying out spatial statistics on the land feature type thematic data of each year in the accounting period in a geographic information system according to regions, and compiling the statistical results into a land resource physical quantity statistical table;
s5, converting the physical assets into value assets by adopting a standard land price correction method according to the land resource transfer matrix and the land resource physical quantity statistical table;
and S6, summarizing the physical assets and the value assets of the multiple ground feature types, compiling a land resource asset liability statement summary table, and sorting to obtain a result database.
2. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: in S1, the historical land utilization data and the historical land coverage data are obtained by interpretation of historical remote sensing images, or by direct utilization of the land change survey results and the geographic national condition monitoring results.
3. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: at S3, the land resource transfer matrix includes stock information of a plurality of different categories of land resources, and land resource type variation information.
4. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: the land resource physical quantity statistical table in S4 includes accounting beginning data, accounting end data, and accounting period change data, and is output in the form of a physical quantity table and a value quantity table, respectively.
5. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: in S5, the total economic value is calculated by the following formula using the reference land-value correction method:
in the formula, V is the total economic value of land resources in the checked area, i is the map spot of the ith land resource thematic data, n is the total number of the map spots in the thematic data, PsA reference land price f corresponding to land resources in the regioniCorrecting the pattern spot terrain factor correction weight value M of ith land resource thematic dataiAnd (4) the area of the pattern spot of the ith land resource thematic data pattern spot.
6. The method of claim 5A large-scale regional land resource asset liability statement compiling method is characterized by comprising the following steps: the pattern spot terrain factor correction weight is obtained by calculating according to the average gradient of the pattern spot in the land resource thematic data, namely the pattern spot terrain factor correction weight has the following calculation formula:
and t is sin s, wherein f is a terrain factor correction weight, and s is the average slope of the image spot.
7. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: in S5, the value amount asset includes two dimensions of resource economic value and ecological service value.
8. The large-scale regional land resource liability statement compiling method according to claim 1, characterized in that: in S6, the land resource balance sheet summary is used to account for both economic and ecological dimensions of land resource.
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| CN111538732A (en) * | 2020-05-11 | 2020-08-14 | 甘肃省地质调查院 | Natural resource asset liability management system based on big data |
| CN111915669A (en) * | 2020-08-03 | 2020-11-10 | 北京吉威空间信息股份有限公司 | Land survey linear ground object pattern spot method based on total amount control |
| CN113222672A (en) * | 2021-05-30 | 2021-08-06 | 国家基础地理信息中心 | Service pricing method for ecosystem of alpine grassland in pastoral area |
| CN114459441A (en) * | 2022-01-22 | 2022-05-10 | 云南财经大学 | Land ecological asset liability measuring and calculating method and system |
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Cited By (5)
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