CN101995380A - Method for monitoring soil petroleum pollution based on hyperspectral vegetation index - Google Patents
Method for monitoring soil petroleum pollution based on hyperspectral vegetation index Download PDFInfo
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- 239000002689 soil Substances 0.000 title claims abstract description 64
- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 14
- 239000003208 petroleum Substances 0.000 title abstract description 23
- 238000005070 sampling Methods 0.000 claims abstract description 15
- 238000005516 engineering process Methods 0.000 claims abstract description 8
- 239000003209 petroleum derivative Substances 0.000 claims abstract 11
- 238000005527 soil sampling Methods 0.000 claims abstract 3
- 230000010354 integration Effects 0.000 claims abstract 2
- 235000014676 Phragmites communis Nutrition 0.000 claims description 14
- 230000003595 spectral effect Effects 0.000 claims description 14
- 239000003129 oil well Substances 0.000 claims description 6
- 238000011155 quantitative monitoring Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims 3
- 238000004566 IR spectroscopy Methods 0.000 claims 1
- 230000003203 everyday effect Effects 0.000 claims 1
- 230000007613 environmental effect Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000011002 quantification Methods 0.000 abstract 1
- 239000003921 oil Substances 0.000 description 30
- 238000001228 spectrum Methods 0.000 description 29
- 229930195733 hydrocarbon Natural products 0.000 description 20
- 150000002430 hydrocarbons Chemical class 0.000 description 20
- 238000002310 reflectometry Methods 0.000 description 5
- 238000007811 spectroscopic assay Methods 0.000 description 3
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 230000006866 deterioration Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000010076 replication Effects 0.000 description 2
- 238000002798 spectrophotometry method Methods 0.000 description 2
- 239000002028 Biomass Substances 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000000243 photosynthetic effect Effects 0.000 description 1
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Abstract
一种基于高光谱植被指数监测土壤石油污染的方法属于环境监测领域，本发明通过以下技术方案来实现：1)建立土壤总石油烃含量高光谱预测模型，包括以下步骤：选择采样点；高光谱测定；土壤取样和总石油烃含量测定；计算植被指数；确定最佳预测模型；2)土壤总石油烃含量高光谱预测模型的集成。本发明的有益效果为：相对于传统的土壤、植被监测方法，本发明简单易行，可节约大量的人力、财力和时间，对植被破坏小；结合航空、航天等遥感技术，可实现土壤石油污染的定时、定位、定量、大面积监测。
A method for monitoring soil oil pollution based on hyperspectral vegetation index belongs to the field of environmental monitoring. The present invention is realized through the following technical solutions: 1) establishing a hyperspectral prediction model for total petroleum hydrocarbon content in soil, comprising the following steps: selecting sampling points; determination; soil sampling and determination of total petroleum hydrocarbon content; calculation of vegetation index; determination of the best prediction model; 2) integration of hyperspectral prediction models for soil total petroleum hydrocarbon content. The beneficial effects of the present invention are: compared with traditional soil and vegetation monitoring methods, the present invention is simple and easy to implement, can save a lot of manpower, financial resources and time, and has little damage to vegetation; combined with remote sensing technologies such as aviation and spaceflight, it can realize soil petroleum monitoring. Timing, positioning, quantification, and large-scale monitoring of pollution.
Description
Technical field
The invention belongs to the environmental monitoring field, be specifically related to high-spectrum remote-sensing in soil oil pollution Application in Monitoring.
Background technology
Be accompanied by the suitability for industrialized production and the utilization of oil, oil pollution has become a serious environmental problem.After oil entered the ecosystem, not only structure, the function to the ecosystem produced considerable influence, and petroleum pollution can finally enter human body, harm humans health by food chain enrichment step by step in animal and plant body.
In the oil total production of the whole world, about 80% by the land field produces.The crude oil that China produces also major part comes from onshore oil field.Therefore, how to carry out environmental monitoring that terrestrial ecosystems PetroChina Company Limited. the pollutes diffusion for the prevention oil pollution effectively, efficiently carry out the degraded and the repair of oil pollution, to administer oil pollution significant comprehensively.
At present, conventional soil, vegetation monitoring are generally carried out in the environmental monitoring of land oil pollution.But the monitoring of soil and vegetation often needs a large amount of samplings, and the assay determination of sample needs a large amount of instruments, and the mensuration program is also comparatively complicated, therefore is a consumption power, consumption wealth, work consuming time.Studies show that of past, oil pollution meeting influence physiological and biochemical indexs such as the leaf area index, biomass, vegetation cover degree, photosynthetic pigments of plant.And the variation of these indexs can utilize vegetation index to monitor effectively.Simultaneously, with respect to traditional broadband remote sensing technology, the high light spectrum image-forming spectrometer can reach nanoscale in the spectral resolution of visible light-near infrared region, therefore, can obtain research object spectral information in detail and accurately.Thereby be the calculating of the vegetation index space that provides more choices, the susceptibility and the accuracy of vegetation index monitoring are further improved.For these reasons, the present invention is directed to the reed is the ecosystem of sociales, utilizes the open-air vegetation high-spectral data that obtains, and calculates high spectrum vegetation index, has finally set up the high spectral prediction model of reed ecosystem soil oil pollution.
Summary of the invention
The purpose of this invention is to provide a kind of method, to overcome traditional soil, vegetation monitoring method consumption power, consumption wealth, deficiency consuming time based on the oil pollution of high spectrum vegetation index monitoring soil.
Purpose of the present invention is achieved through the following technical solutions:
1) set up the high spectral prediction model of soil total petroleum hydrocarbons content, may further comprise the steps:
(a) select sampled point: select 30 sampled points on the reed vegetation around the oil well, between 30-130m, sampled point does not suffer artificial interference to each sampling point apart from the distance of oil well, and vegetation is trampleed on, and sampled point does not exist non-oil pollution to coerce;
(b) high spectroscopic assay: adopt portable ground-object spectrum instrument through measuring the high-spectral data of reed vegetation on step (a) the selected sampled point, carry out sky ceiling unlimited during mensuration between when being determined at the 10-12 of every morning; Replication is 10 times on each sampled point, obtains 10 high curves of spectrum, and there are the abnormal curve of notable difference in deletion and other curves, calculate the mean value that each sampled point remains the high curve of spectrum again, obtain the high curve of spectrum of each sampled point reed vegetation;
(c) soil sample and total petroleum hydrocarbons content are measured: get the soil that the degree of depth is 0-30cm in each sample point, the heavy 450-550g of soil sample amount, adopt each sampled point soil total petroleum hydrocarbons content of infrared spectrophotometric determination then, the unit of soil total petroleum hydrocarbons content is mg/kg;
(d) calculate vegetation index:, calculate 44 kinds of vegetation indexes of each sampled point according to the high curve of spectrum data of step (b) gained reed vegetation;
(e) determine the optimum prediction model: adopt linearity, logarithm, inverse, secondary, three times, power, S type curve, exponential Function Model that the relation of 44 kinds of vegetation indexes of each sampled point and this sampled point soil total petroleum hydrocarbons content is carried out match respectively; Determine that the optimum prediction model is TPH=0.131/RES, wherein TPH is the soil total petroleum hydrocarbons content, and the unit of soil total petroleum hydrocarbons content is mg/kg; RES is a red limit slope, i.e. the maximal value of wavelength spectral reflectivity single order differential in the 680-750nm scope;
2) the high spectral prediction model of soil total petroleum hydrocarbons content is integrated: will be integrated in the accompanying software of ground-object spectrum instrument through step 1) gained forecast model, and realize the real-time positioning and the Quantitative Monitoring of soil oil pollution; In conjunction with the Aeronautics and Astronautics remote sensing technology, further realize the large tracts of land fast monitored of soil oil pollution.
Beneficial effect of the present invention is: with respect to traditional soil, vegetation monitoring method, the present invention is simple, can save great amount of manpower, financial resources and time, and is little to vegetation deterioration; In conjunction with remote sensing technologies such as Aeronautics and Astronautics, can realize timing, location, quantitative, the large tracts of land monitoring of soil oil pollution.
Description of drawings
Fig. 1 is the described a kind of graph of relation based on vegetation index red limit slope and soil total petroleum hydrocarbons content in the method for high spectrum vegetation index monitoring soil oil pollution of the embodiment of the invention.
Embodiment
The described a kind of method based on the oil pollution of high spectrum vegetation index monitoring soil of the embodiment of the invention may further comprise the steps:
1) set up the high spectral prediction model of soil total petroleum hydrocarbons content, may further comprise the steps:
(a) select sampled point: select 30 sampled points on the reed vegetation around the oil well, between 30-130m, sampled point does not suffer artificial interference to each sampling point apart from the distance of oil well, and vegetation is trampleed on, and sampled point does not exist non-oil pollution to coerce;
(b) high spectroscopic assay: adopt portable ground-object spectrum instrument through measuring the high-spectral data of reed vegetation on step (a) the selected sampled point, carry out sky ceiling unlimited during mensuration between when being determined at the 10-12 of every morning; Replication is 10 times on each sampled point, obtains 10 high curves of spectrum, and there are the abnormal curve of notable difference in deletion and other curves, calculate the mean value that each sampled point remains the high curve of spectrum again, obtain the high curve of spectrum of each sampled point reed vegetation;
(c) soil sample and total petroleum hydrocarbons content are measured: get the soil that the degree of depth is 0-30cm in each sample point, the heavy 450-550g of soil sample amount, adopt each sampled point soil total petroleum hydrocarbons content of infrared spectrophotometric determination then, the unit of soil total petroleum hydrocarbons content is mg/kg;
(d) calculate vegetation index:, calculate 44 kinds of vegetation indexes of each sampled point according to the high curve of spectrum data of step (b) gained reed vegetation;
(e) determine the optimum prediction model: adopt linearity, logarithm, inverse, secondary, three times, power, S type curve, exponential Function Model that the relation of 44 kinds of vegetation indexes of each sampled point and this sampled point soil total petroleum hydrocarbons content is carried out match respectively; Determine that the optimum prediction model is TPH=0.131/RES, wherein TPH is the soil total petroleum hydrocarbons content, and the unit of soil total petroleum hydrocarbons content is mg/kg; RES is a red limit slope, i.e. the maximal value of wavelength spectral reflectivity single order differential in the 680-750nm scope;
2) the high spectral prediction model of soil total petroleum hydrocarbons content is integrated: will be integrated in the accompanying software of ground-object spectrum instrument through step 1) gained forecast model, and realize the real-time positioning and the Quantitative Monitoring of soil oil pollution; In conjunction with the Aeronautics and Astronautics remote sensing technology, further realize the large tracts of land fast monitored of soil oil pollution.
In above-mentioned method based on the oil pollution of high spectrum vegetation index monitoring soil, during spectroscopic assay, the portable ground-object spectrum instrument of FieldSpec3 that portable ground-object spectrum instrument adopts U.S. ASD company (U.S. spectrometric instrument company) to produce.The portable ground-object spectrum instrument of FieldSpec3 is applicable to remote sensing survey, the crops monitoring, and forest research, industrial lighting is measured, the each side of oceanographic research and mineral prospecting.This instruments weight is light, can measure and observe reflection, transmission, the radiancy curve of spectrum in real time; Can show absolute reflectance in real time; Have advantages such as high s/n ratio, high reliability, high duplication.This instrument can be measured the spectrum of 350-2500nm wavelength coverage, and spectral resolution is 3-10nm.
In above-mentioned method based on the oil pollution of high spectrum vegetation index monitoring soil, when calculating vegetation index, the present invention has selected existing 44 kinds of vegetation indexes, and each vegetation index and computing formula thereof see Table 1.Because a lot of vegetation indexs calculate with the wide-band spectrum reflectivity at first, the spectral reflectivity with sensitive wave length in the corresponding wave band among the present invention replaces.
The vegetation index that uses among table 1 the present invention
The vegetation index that uses among continuous table 1 the present invention
The vegetation index that uses among continuous table 1 the present invention
The vegetation index that uses among continuous table 1 the present invention
In above-mentioned method based on the oil pollution of high spectrum vegetation index monitoring soil, when determining the optimum prediction model, adopt function models such as linearity, logarithm, inverse, secondary, three times, power, S type curve, index that the relation of each vegetation index and soil total petroleum hydrocarbons content has been carried out match respectively.Fitting result sees Table 2.
Each function model of table 2 is to the fitting result (n=30) of vegetation index
Continuous each function model of table 2 is to the fitting result (n=30) of vegetation index
There is negative value in the result of calculation of some vegetation index, therefore can't obtains the fitting result of this some function model of vegetation index, in table, represent with missing values symbol "-".
Index of correlation R in the table 2
2＞0.85 forecast model sees Table 3.Relatively each forecast model can determine that TPH=0.131/RES is the optimum prediction model, and its model as shown in Figure 1.Wherein TPH is the soil total petroleum hydrocarbons content, and the unit of soil total petroleum hydrocarbons content is mg/kg; RES is a red limit slope, i.e. the maximal value of spectral reflectivity single order differential in the 680nm-750nm wavelength coverage.The R of this forecast model
2Up to 0.948, the p value shows that also much smaller than 0.01 the degree of reliability of this model prediction is higher.From now on, this forecast model can be integrated in the accompanying software of ground-object spectrum instrument, realize the location of soil oil pollution, real-time, Quantitative Monitoring.In conjunction with remote sensing technologies such as Aeronautics and Astronautics, can further realize the large tracts of land fast monitored of soil oil pollution.This method is with respect to traditional soil, vegetation monitoring, and is simple, can save great amount of manpower, financial resources and time, little to vegetation deterioration.
Table 3 index of correlation R
2＞0.85 forecast model and check (n=30)
When adopting the present invention to monitor the soil oil pollution, because the difference of the ecosystem, soil suffers the vegetation index possibility of oil pollution degree different in the reflection ecosystem; Therefore at other ecosystems, can utilize other vegetation index (comprising the vegetation index that utilizes other sensitive wave lengths to calculate) to propose new spectral prediction model in conjunction with other function models.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680413A (en) * | 2012-05-25 | 2012-09-19 | 浙江大学 | Device and method for hyperspectral rapid detection on content of field soil organic matters by using panoramic annular belt |
| CN103146805A (en) * | 2013-03-07 | 2013-06-12 | 中国水产科学研究院黄海水产研究所 | Shellfish monitoring method for ocean oil spill pollution base on integration biomarker method |
| CN105527230A (en) * | 2015-12-04 | 2016-04-27 | 成都理工大学 | High vegetation coverage area remote sensing prospecting method based on near-infrared band 670- |
| CN109543654A (en) * | 2018-12-14 | 2019-03-29 | 常州大学 | A kind of construction method for the modified vegetation index reflecting crop growth situation |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107870150B (en) * | 2017-11-02 | 2019-03-29 | 北京师范大学 | Soil parameters EO-1 hyperion inversion method based on falling zone heavy metal-polluted soil |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4052666A (en) * | 1976-04-15 | 1977-10-04 | Nasa | Remote sensing of vegetation and soil using microwave ellipsometry |
| CN1614392A (en) * | 2004-11-30 | 2005-05-11 | 南京农业大学 | Collecting method for intelligent agricultural machinery loading high spectrum |
| CN1837787A (en) * | 2006-04-19 | 2006-09-27 | 南京大学 | A method for accurately measuring cotton biophysical parameters without damage |
| WO2009149384A1 (en) * | 2008-06-06 | 2009-12-10 | Monsanto Technology Llc | Generating agricultural information products using remote sensing |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4052666A (en) * | 1976-04-15 | 1977-10-04 | Nasa | Remote sensing of vegetation and soil using microwave ellipsometry |
| CN1614392A (en) * | 2004-11-30 | 2005-05-11 | 南京农业大学 | Collecting method for intelligent agricultural machinery loading high spectrum |
| CN1837787A (en) * | 2006-04-19 | 2006-09-27 | 南京大学 | A method for accurately measuring cotton biophysical parameters without damage |
| WO2009149384A1 (en) * | 2008-06-06 | 2009-12-10 | Monsanto Technology Llc | Generating agricultural information products using remote sensing |
Non-Patent Citations (3)
| Title |
|---|
| 《农业环境科学学报》 20100531 曹仕等 利用独立变量分析与高光谱植被指数模型监测成熟期水稻中砷污染 881-886 1 第29卷, 第5期 2 * |
| 《环境研究与监测》 20100630 马宏伟等 红外分光光度法测定土壤中石油烃总量的方法研究 47-48，72 1 第23卷, 第2期 2 * |
| 《遥感技术与应用》 19980331 方红亮等 高光谱遥感在植被监测中的研究综述 62-69 1 第13 卷, 第1 期 2 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680413A (en) * | 2012-05-25 | 2012-09-19 | 浙江大学 | Device and method for hyperspectral rapid detection on content of field soil organic matters by using panoramic annular belt |
| CN103146805A (en) * | 2013-03-07 | 2013-06-12 | 中国水产科学研究院黄海水产研究所 | Shellfish monitoring method for ocean oil spill pollution base on integration biomarker method |
| CN103146805B (en) * | 2013-03-07 | 2014-05-28 | 中国水产科学研究院黄海水产研究所 | Shellfish monitoring method for ocean oil spill pollution base on integration biomarker method |
| CN105527230A (en) * | 2015-12-04 | 2016-04-27 | 成都理工大学 | High vegetation coverage area remote sensing prospecting method based on near-infrared band 670- |
| CN109543654A (en) * | 2018-12-14 | 2019-03-29 | 常州大学 | A kind of construction method for the modified vegetation index reflecting crop growth situation |
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