CN112651143A - Estimation method for biomass on haloxylon ammodendron ground - Google Patents
Estimation method for biomass on haloxylon ammodendron ground Download PDFInfo
- Publication number
- CN112651143A CN112651143A CN202110105986.9A CN202110105986A CN112651143A CN 112651143 A CN112651143 A CN 112651143A CN 202110105986 A CN202110105986 A CN 202110105986A CN 112651143 A CN112651143 A CN 112651143A
- Authority
- CN
- China
- Prior art keywords
- biomass
- haloxylon
- ground
- estimation model
- estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for estimating the biomass on haloxylon ammodendron ground, which realizes the estimation of the biomass on the haloxylon persicum ground by constructing a ground biomass estimation model of a sandy soil habitat haloxylon persicum and a ground biomass estimation model of a gravel soil habitat haloxylon persicum; wherein, the above-ground biomass estimation model of the sandy soil habitat haloxylon and the above-ground biomass estimation model of the gravel soil habitat haloxylon are both power function equations, and are respectively W =0.138(DH)1.397And W =0.189(DH)1.433. The method utilizes the physiological indexes of the plants to establish a biomass estimation model, establishes the aboveground biomass estimation model of the haloxylon ammodendron in the sandy and gravelly soil habitat, has the estimation precisions of 82.825 percent and 83.688 percent respectively, has the average relative errors of 14.392 percent and 13.455 percent, has higher fitting precision, and can be applied to the estimation of the haloxylon ammodendron biomass.
Description
Technical Field
The invention relates to the technical field of biomass estimation, in particular to a method for estimating biomass on haloxylon ammodendron ground.
Background
Land desertification is one of ten environmental problems facing mankind today. The desert ecosystem is distributed in arid regions, the species of animals and plants are rare, the ecological environment is extremely fragile, arid and semiarid lands 5.17 multiplied by 107 km2 are shared in the world, wherein lands 3.62 multiplied by 107 km2 are threatened by desertification and occupy about one fourth of the land area. The desert plants are important components of a desert ecosystem, the biomass of the desert plants refers to the total amount of organic matters accumulated by a plant community within a certain time, and the productivity and the environmental quality of the desert ecosystem can be reflected.
In recent years, scholars at home and abroad have conducted a great deal of research on models for estimating aboveground biomass of desert plants. The method comprises the following steps of establishing an estimation model of biomass and branch biomass on haloxylon grounds in three ecological types of sandy soil, saline soil and gravels by Songtang ocean and the like with Guerbantong Gute deserts as research areas; alberto Burquez and the like establish a multi-species aboveground biomass estimation model of three habitat plant communities of Sonolan desert plains, droughts and hills; the Dang Xiaohong and the like research the biomass distribution pattern and the prediction model of five desert dominant bushes in the western Erdos of the inner Mongolia autonomous region.
The haloxylon ammodendron is an excellent sand-fixing tree species in northwest arid desert regions of China, is a national secondary protective plant, has developed root system, strong wind-proof sand-fixing capacity and strong drought-resisting and heat-resisting capacity, and is a desert plant suitable for climate in medium-temperature regions. Currently, researchers have studied population characteristics, soil characteristics and the like of the haloxylon ammodendron forest, but research on an estimation model of haloxylon biomass is few.
Disclosure of Invention
In order to solve the problems, the invention provides a method for estimating biomass on the ground of haloxylon ammodendron.
In order to achieve the purpose, the invention adopts the technical scheme that:
the estimation method of the biomass on the haloxylon ammodendron ground realizes the estimation of the biomass on the haloxylon persicum ground by constructing a ground biomass estimation model of a sandy soil habitat haloxylon persicum and a ground biomass estimation model of a gravel soil habitat haloxylon persicum; wherein, the above-ground biomass estimation model of the sandy soil habitat haloxylon and the above-ground biomass estimation model of the gravel soil habitat haloxylon are both power function equations, and are respectively W =0.138(DH)1.397And W =0.189(DH) 1.433。
The method utilizes the physiological indexes of the plants to establish a biomass estimation model, establishes the aboveground biomass estimation model of the haloxylon ammodendron in the sandy and gravelly soil habitat, has the estimation precisions of 82.825 percent and 83.688 percent respectively, has the average relative errors of 14.392 percent and 13.455 percent, has higher fitting precision, and can be applied to the estimation of the haloxylon ammodendron biomass.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
Examples
1.1 data Source
The natural protection area of the Gansu civil service and ancient city is taken as a research area, sample plot setting and sample collection are carried out in the protection area in 2019, and the shuttle with the habitat of sandy soil and gravel soil is selected for investigation according to the growth environment characteristics of the shuttle. Most of the haloxylon ammodendron in the protected area is planted manually and uniformly, the relationship between the haloxylon ammodendron vegetation and the environment is comprehensively considered, the opinion of local researchers is consulted, 10 typical areas (100 m multiplied by 100 m) where haloxylon ammodendron grows are selected in the protected area, 6 sandy soil habitats and 4 gravelly soil habitats are selected in the protected area, 5 parallel sample plots (20 m multiplied by 20 m) are established in each area, and 3-4 haloxylon ammodendron plants are randomly selected in the sample plots for investigation and sampling. The plant height (cm) and basal stem (cm) of the haloxylon ammodendron are measured on the spot, the plants are harvested and weighed in a flush manner to obtain the fresh weight (g), a part of samples are taken and taken back to a laboratory, the samples are dried to constant weight at 80 ℃, the water content (%) of the samples is calculated, and the plant biomass (g) is calculated according to the proportion.
1.2 model selection
The measurement models of the tree biomass are many, the commonly used parameters easy to measure comprise basal stems, plant heights, crown widths and the like, the haloxylon ammodendron is used as a desert plant, grows in a wind and sand environment for a long time, is influenced by external conditions such as weather, rainfall, wind erosion environment and the like, is in a shape of a tree with various aspects, has large crown width size change and is not suitable for estimationA factor. The data processing is carried out by using software SPSS 19.0 and Microsoft Excel 2010, information of 7 strains is randomly extracted from the data obtained from the two habitats respectively for model precision verification, and the rest data is used for establishing an estimation model. Performing dry weight of haloxylon ammodendron (DW), stem (D), plant height (H) and compound factor (D) of the dry weight of haloxylon ammodendron and the stem (D)2、D2H. DH, etc.), selecting appropriate factors as the independent variables of the model.
In order to ensure the estimation precision, three regression equations of linearity, multiple linearity and power function are selected to establish a haloxylon aboveground biomass estimation model, and the three models are commonly used for aboveground biomass estimation of shrubs and small trees[35-37]. The basic form of the model is as follows:
1.3 evaluation of the model
After the model is built, model evaluation is needed to check the model availability and select the optimal estimation model, and the model evaluation methods and indexes are many, and commonly used methods include goodness-of-fit inspection, residual analysis, decision coefficient analysis and the like. The evaluation indexes adopted by the invention are a correlation coefficient R, a residual square sum RSS and an adjusted judgment coefficient R2The average relative error absolute value RMA, the root mean square error RMSE and the estimated precision P are calculated according to the following formula:
in the formula, yiAs measured value, ŷ iTo estimate, ȳ iIs the average value of measured values; n is the number of samples, n-k-1 is the degree of freedom of the residual sum of squares, n-1 is the degree of freedom of the overall sum of squares, and m is the number of parameters in the regression model; t is tαIs the t distribution with confidence level α, α =0.05 in this study.
Residual squareAnd RSS represents the degree to which each measured value deviates from the model; adjusted decision coefficient R2Representing the degree of interpretation of the independent variable on the dependent variable, compared to the decision coefficient R2In other words, the influence of the number of variables on the judgment result is removed, and the goodness of fit of the regression model coefficient can be reflected more accurately; the average relative error absolute value RMA and the root mean square error RMSE can measure the deviation between an estimated value and an actually measured value; the estimation accuracy P can be used for checking the estimation effect of the model.
2 results and analysis
2.1 variable selection
As can be seen from the results of the correlation analysis of the variables of the shuttlecocks of different habitats (table 1), the dry weight of the shuttlecocks is very significantly correlated with the variables. The correlation coefficient of the dry weight of the sandy soil shuttle and the D is lower than that of the gravel soil; the correlation coefficient with H, DH is slightly lower than that of gravel but not very different (0.003 and 0.011, respectively); and D2、D2H、(D2H)2The correlation coefficient of the biomass of the sandy soil shuttles is higher than that of the gravelly soil, and the correlation coefficient of the biomass of the sandy soil shuttles and other variables is higher than that of the gravelly soil.
The correlation coefficient of the haloxylon ammodendron biomass growing in sandy soil and other variables is 0.748-0.976, and the correlation degree is as follows: d2H > D2>DH>(D2H)2>D>H; the correlation coefficient of the haloxylon ammodendron growing on the gravel soil is between 0.751 and 0.964, and the correlation degree is as follows: DH> D > D2H > D2 >(D2H)2 >H. The variable with the greatest degree of correlation with the dry weight of the haloxylon ammodendron in the two habitats is D2H and DH, and the correlation coefficient also in the prostate in another habitat. Taking into account, selection D2H and DH were used as independent variables for the biomass estimation model.
2.2 Biomass estimation model establishment
Table 2 is a biomass estimation model of haloxylon, in the form of linear, multiple linear, power function equations, respectively. The correlation coefficient of each regression equation is above 0.9, and the significance test is extremely significant, so that the feasibility of the estimation model can be preliminarily judged; the sum of the squared residuals of the equations is high, which may be due to the large number of samples and the large number of samples, resulting in a residual lifting.
TABLE 1 correlation of different habitat shuttle biomass with variables
The correlation coefficient of each biomass estimation model of the haloxylon ammodendron in the sandy habitat is between 0.947 and 0.978. Wherein the pre-estimation precision and the adjustment R of the linear equation 22Highest (87.348%, 0.953), RSS and RMSE are smallest; the correlation coefficient r of the multi-element linear equation 3 is highest (0.978), the RMA is minimum, and the equation 2 is better in performance in combination and is an optimal estimation model of the biomass on the sandy soil haloxylon ammodendron ground.
The correlation coefficient of each biomass estimation model of the gravel soil habitat haloxylon is between 0.919 and 0.986. The linear equation 6 has the highest prediction precision (84.931%), but other indexes are at the midstream level in the five equations; correlation coefficient R and adjustment R of power function equation 92The highest (0.986, 0.969) and the lowest RMA (RMA) are obtained, the prediction accuracy is only reduced by 1.243 percent compared with the equation 6, and the model is selected as the best estimation model of the biomass on the gravel soil shuttle ground.
TABLE 2 estimation model of biomass on haloxylon ground
2.3 model accuracy verification
The relative error can reflect the degree of confidence in the measurement. Model accuracy verification is performed by using the reserved shuttle data, an estimated value of the shuttle biomass is calculated according to a regression equation, and a correlation coefficient and a relative error between the estimated value and an actual measured value are calculated (table 3).
After verification, the estimated value and the measured value obtained according to the regression model have very significant correlation, and the correlation coefficient is between 0.889 and 0.969. The average relative error of the models 4-10 is small, the model fitting precision is high, and the estimation of the shuttle aboveground biomass has practicability; the average relative error of the models 1-3 is too large, the fitting precision is low, the estimation accuracy is difficult to guarantee, and the method is not suitable for estimating the biomass of the haloxylon ammodendron. This is probably because the difference of the water content of the haloxylon ammodendron at each sampling point of the sandy soil habitat is large, and the influence of the sampling time, the weather and the plant preservation mode.
In the two habitats, the correlation coefficient between the estimated value and the measured value of the sandy soil shuttle is between 0.889 and 0.938, and the correlation coefficient between the estimated value and the measured value of the sandy soil shuttle is between 0.926 and 0.969. The relative coefficient and the average relative error of the sandy soil habitat are higher than those of the gravelly soil, the fluctuation range of the relative error is larger, the relative error of an individual plant is far higher than the average level, an outlier appears, and in combination, the biomass estimation model of the gravelly soil haloxylon is higher in fitting precision.
TABLE 3 correlation coefficient and relative error between estimated and measured biomass values on shuttles
Note: the relative error range does not include outliers; significant correlation at 0.01 level.
2.4 optimal estimation model correction
The average relative error of the optimal estimation model of the biomass of the pebble soil haloxylon ammodendron is low (13.455%), the fitting precision is high, and the equation is feasible to be used for estimating the biomass of the haloxylon persicum. However, in a sandy habitat, due to the fact that the fitting accuracy of the models 1-3 is not high, the equation 4 is selected from the two power function models again to serve as an optimal estimation model, and except RMA, the performance of other indexes of the model is better than that of the equation 5. The best estimation models for sandy and gravel soil habitats were W =0.146(DH) respectively1.372And W =0.189(DH) 1.433All are power function equations with DH as argument.
3 conclusion
The biomass on the ground of haloxylon ammodendron, basal stem (D), plant height (H) and their composite variables (DH and D)2H, etc.) exhibit a very significant correlation. The invention utilizes variables DH, D2H, establishing an estimation model of biomass on the haloxylon ammodendron of the sandy and gravel soil habitat, comprising linearThe prediction precision is 77.987% -87.348%, and the fitting effect of a power function equation with DH as a variable is the best. The estimation model with the highest goodness of fit for the sandy soil habitat is W =0.146(DH)1.372The correlation coefficient R is 0.955, adjust R20.904, the estimated accuracy P is 82.825%, the correlation coefficient between the estimated value and the measured value is 0.926, and the average relative error is 14.392%; the estimation model with the highest goodness of fit for the gravelly soil habitat was W =0.189(DH)1.433The correlation coefficient R is 0.986, adjust R20.969, the estimated accuracy P is 83.688%, the correlation coefficient between the estimated value and the measured value is 0.969, and the average relative error is 13.455%.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (1)
1. A method for estimating biomass on a shed, comprising: the estimation of the aboveground biomass of the haloxylon ammodendron is realized by constructing an aboveground biomass estimation model of the haloxylon ammodendron in sandy soil and an aboveground biomass estimation model of the haloxylon ammodendron in gravel soil; wherein, the above-ground biomass estimation model of the sandy soil habitat haloxylon and the above-ground biomass estimation model of the gravel soil habitat haloxylon are both power function equations, and are respectively W =0.138(DH)1.397And W =0.189(DH) 1.433。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110105986.9A CN112651143A (en) | 2021-01-26 | 2021-01-26 | Estimation method for biomass on haloxylon ammodendron ground |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110105986.9A CN112651143A (en) | 2021-01-26 | 2021-01-26 | Estimation method for biomass on haloxylon ammodendron ground |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112651143A true CN112651143A (en) | 2021-04-13 |
Family
ID=75371130
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110105986.9A Pending CN112651143A (en) | 2021-01-26 | 2021-01-26 | Estimation method for biomass on haloxylon ammodendron ground |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112651143A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102217499A (en) * | 2011-05-31 | 2011-10-19 | 江苏大学 | Method for predicating biomass of overground part of Aegiceras corniculatum |
| CN206132052U (en) * | 2016-08-31 | 2017-04-26 | 新疆农业大学 | Estimate device of desolate and boundless bush geodyte volume fast |
| CN107132326A (en) * | 2017-05-04 | 2017-09-05 | 天津大学 | A kind of device of in-site detecting halomereid species and biomass |
| CN108427863A (en) * | 2018-05-17 | 2018-08-21 | 北京林业大学 | A kind of Bush biomass rating model |
| CN108876917A (en) * | 2018-06-25 | 2018-11-23 | 西南林业大学 | A kind of forest ground biomass remote sensing estimation universal model construction method |
| CN111368248A (en) * | 2020-03-17 | 2020-07-03 | 西南林业大学 | Estimation method for biomass of Yunnan pine seedlings |
| CN111814326A (en) * | 2020-07-02 | 2020-10-23 | 中国科学院东北地理与农业生态研究所 | Method for estimating aboveground biomass of swamp wetland reeds |
| CN112001654A (en) * | 2020-08-31 | 2020-11-27 | 上海市园林科学规划研究院 | Construction method of underground biomass model of greening tree species in urban relocation area |
-
2021
- 2021-01-26 CN CN202110105986.9A patent/CN112651143A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102217499A (en) * | 2011-05-31 | 2011-10-19 | 江苏大学 | Method for predicating biomass of overground part of Aegiceras corniculatum |
| CN206132052U (en) * | 2016-08-31 | 2017-04-26 | 新疆农业大学 | Estimate device of desolate and boundless bush geodyte volume fast |
| CN107132326A (en) * | 2017-05-04 | 2017-09-05 | 天津大学 | A kind of device of in-site detecting halomereid species and biomass |
| CN108427863A (en) * | 2018-05-17 | 2018-08-21 | 北京林业大学 | A kind of Bush biomass rating model |
| CN108876917A (en) * | 2018-06-25 | 2018-11-23 | 西南林业大学 | A kind of forest ground biomass remote sensing estimation universal model construction method |
| CN111368248A (en) * | 2020-03-17 | 2020-07-03 | 西南林业大学 | Estimation method for biomass of Yunnan pine seedlings |
| CN111814326A (en) * | 2020-07-02 | 2020-10-23 | 中国科学院东北地理与农业生态研究所 | Method for estimating aboveground biomass of swamp wetland reeds |
| CN112001654A (en) * | 2020-08-31 | 2020-11-27 | 上海市园林科学规划研究院 | Construction method of underground biomass model of greening tree species in urban relocation area |
Non-Patent Citations (2)
| Title |
|---|
| 王洋洋 等: "《利用智能手机图像无损快速估测荒漠灌木地上生物量——以梭梭为例》", 《江苏农业科学》 * |
| 许明 等: "《草地生物量遥感估算方法综述》", 《甘肃科技》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109829234B (en) | A kind of across scale Dynamic High-accuracy crop condition monitoring and yield estimation method based on high-definition remote sensing data and crop modeling | |
| Tchouto et al. | Diversity patterns in the flora of the Campo-Ma’an rain forest, Cameroon: do tree species tell it all? | |
| Freléchoux et al. | Stand structure, invasion, and growth dynamics of bog pine (Pinus uncinata var. rotundata) in relation to peat cutting and drainage in the Jura Mountains, Switzerland | |
| Chapman et al. | Net aerial production by Calluna vulgaris on lowland heath in Britain | |
| CN108764688A (en) | The wet stain of winter wheat of based on star multi-source precipitation data fusion does harm to remote-sensing monitoring method | |
| McEvoy et al. | Vegetative structure of an Appalachian oak forest in southwestern Virginia | |
| CN112837169A (en) | Gridding drought comprehensive monitoring, early warning and evaluating method in drought process | |
| CN114493011B (en) | Construction method of operation model considering under-film drip irrigation and hidden pipe salt removal collaborative regulation | |
| CN116665073A (en) | Corn yield remote sensing estimation method based on multi-source data | |
| Wallace et al. | Quantitative studies of roots of perennial plants in the Mojave Desert | |
| Turunen et al. | The influence of anthropogenic land use on Finnish peatland area and carbon stores 1950-2015. | |
| CN114792274A (en) | Layout method for artificial vegetation construction in arid region and mountain land | |
| Bila et al. | Fertility variation in Milletia sthuhlmannii, Brachystegia spiciformis, Brachystegia bohemii and Leucaena leucocephala and its effects on relatedness in seeds | |
| Pressland | Litter production and decomposition from an overstorey of Eucalyptus spp. on two catchments in the New England region of New South Wales | |
| Girma | Potential impact of climate and land use changes on the water resources of the Upper Blue Nile Basin | |
| CN112651143A (en) | Estimation method for biomass on haloxylon ammodendron ground | |
| Richardson et al. | Root-zone water storage and growth of Pinus radiata in the presence of a broom understorey | |
| Wright et al. | Evaporation from natural mountain grassland | |
| Azadi et al. | Qualitative land suitability assessment and estimating land production potential for main irrigated crops in northern of Fars province. | |
| De Groote et al. | ORCHIDEE-SRC v1. 0: an extension of the land surface model ORCHIDEE for simulating short rotation coppice poplar plantations | |
| TANG et al. | Models for estimating the aboveground biomass of Haloxylon ammodendron in Minqin, China. | |
| Kirsch | Breeding birds of the upper Mississippi River floodplain forest: One community in a changing forest, 1994 to 1997 | |
| Farrelly et al. | The use of site factors and site classification methods for the assessment of site quality and forest productivity in Ireland. | |
| Mummigatti | The effects of Japanese knotweed (Reynoutria japonica) on riparian lands in Otsego County, New York | |
| Lusk et al. | Seedling growth rates and light requirements of subtropical rainforest trees associated with basaltic and rhyolitic soils |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210413 |
|
| RJ01 | Rejection of invention patent application after publication |