CN113484247A - Shale oil-gas combustion extraction method based on multi-source time series remote sensing image - Google Patents
Shale oil-gas combustion extraction method based on multi-source time series remote sensing image Download PDFInfo
- Publication number
- CN113484247A CN113484247A CN202110766395.6A CN202110766395A CN113484247A CN 113484247 A CN113484247 A CN 113484247A CN 202110766395 A CN202110766395 A CN 202110766395A CN 113484247 A CN113484247 A CN 113484247A
- Authority
- CN
- China
- Prior art keywords
- shale oil
- gas combustion
- pixel
- reflectivity
- remote sensing
- 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.)
- Granted
Links
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 89
- 238000000605 extraction Methods 0.000 title claims abstract description 35
- 239000003079 shale oil Substances 0.000 claims abstract description 32
- 230000003287 optical effect Effects 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000002310 reflectometry Methods 0.000 claims description 25
- 238000010276 construction Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 239000007789 gas Substances 0.000 description 76
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 12
- 238000004519 manufacturing process Methods 0.000 description 5
- 239000003345 natural gas Substances 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 238000013022 venting Methods 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 150000002430 hydrocarbons Chemical class 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 239000004215 Carbon black (E152) Substances 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000008821 health effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/171—Systems in which incident light is modified in accordance with the properties of the material investigated with calorimetric detection, e.g. with thermal lens detection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3581—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/178—Methods for obtaining spatial resolution of the property being measured
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1789—Time resolved
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Toxicology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to a shale oil gas combustion extraction method based on a multi-source time sequence remote sensing image. The method has simple and easy execution steps, and has good shale oil-gas combustion effect in the extraction area and even in the whole world. The invention uses the optical image data with long time and large space coverage range, and realizes the accurate extraction of shale oil-gas combustion on a large space scale by utilizing an optimized and concise thermal anomaly index. The method is beneficial to compiling a detailed shale oil-gas combustion position which can be timely and effectively updated, and can provide scientific reference for shale oil-gas combustion management and evaluation of the potential influence of shale oil-gas combustion on the atmospheric environment in China.
Description
Technical Field
The invention relates to a shale oil-gas combustion extraction method based on multi-source time series remote sensing images. Belongs to the technical field of remote sensing geography application.
Technical Field
Over the past few decades, shale oil and gas production in the united states has grown dramatically with the development of unconventional oil and gas recovery techniques (including horizontal or directional drilling, hydraulic fracturing techniques) and has received widespread attention. Compared with traditional drilling techniques, unconventional oil and gas production techniques have the advantages of being more readily available (mainly on private land), shorter production times, lower costs and smaller scale, which make the united states a single world-wide country of oil/gas production. The shale revolution not only greatly reduces the oil/gas imports in the united states, but also creates economically growing engines, technological and innovative platforms, employment opportunities, new taxes and royalty revenues, and revitalizing the domestic industry, etc. However, as oil and gas are increasingly being produced in shale rich areas, the increasing burning of shale oil and gas makes the united states ascending five major burning countries worldwide, and it is estimated that an average of 102 billionths of cubic meters of natural gas are burned each year during 2013 and 2017.
Natural gas combustion and venting are common means of treating excess exhaust gas during shale oil and gas production. Gas burning is the process of burning natural gas at the wellhead using a dedicated flare, with venting meaning that the gas is released directly into the atmosphere, often prohibited or restricted under certain conditions. In general, combustion is preferred because methane is the major component of natural gas, a greenhouse gas that is more efficient than carbon dioxide, which is the major product of combustion. In 2018, the amount of U.S. natural gas reported to be vented and burned reaches 12.8 hundred million cubic feet per day, as stated by the U.S. energy information management agency. Therefore, accurate and effective detection of the spatiotemporal distribution of shale gas combustion is critical to scientific assessment and effective management of shale hydrocarbons in order to understand the environmental and health effects of venting and combustion, while helping to gauge the ecological environment effects of shale hydrocarbon recovery.
One of the main features of satellite remote sensing is its high detection capability, which makes it relatively easy and inexpensive to capture large areas of ground feature information. Currently, there are low resolution live Fire product data, such as VIIRS Active Fire (375m), MODIS Active Fire (1km), VIIRS Night Fire (750m) data, which have high temporal resolution but are far from sufficient for identifying very small fires like shale oil and gas combustion due to their low resolution. Therefore, Landsat-8OLI and Sentinel-2MSI optical satellite images have great advantages in large-scale monitoring of shale oil and gas combustion, have the spectral resolution of 10-30 meters and the short return visit period, and provide an opportunity for effectively monitoring the shale oil and gas combustion. The range of a shale oil and gas well site is from dozens of meters to 300 meters, and in an optical image with good imaging quality, near infrared and short wave infrared have higher reflectivity when shale oil and gas is combusted, and the reflectivity is obviously higher than surrounding ground object information, so that the shale oil and gas well site can be segmented through a certain algorithm. However, due to complex background noise (such as cloud, wave, reflection to water and banding effect) in cities, target pixels can be blurred, but based on the principle of shale oil-gas combustion position invariance, the combined use of the multi-source long-time sequence sensor can overcome weather limitation such as cloud rain.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the shale oil-gas combustion extraction method based on the multi-source time series remote sensing image is provided. The method jointly uses massive multi-source time sequence optical remote sensing data, the robustness of remote sensing image detection is enhanced due to high spatial and temporal resolution, the monitoring capability of shale oil-gas combustion is greatly improved, and the shale oil-gas combustion position can be rapidly, automatically and accurately identified.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a shale oil-gas combustion extraction method based on multi-source time series remote sensing images comprises the following steps:
the method comprises the following steps of firstly, preparing a multi-source remote sensing optical image, and respectively constructing a time-series waveband data set, wherein the multi-source remote sensing optical image comprises: landsat-8OLI optical images, and Sentinel-2MSI optical images;
step two, providing a high-temperature thermal anomaly index TAI ═ rhofar-SWIR-ρnear-SWIR)/ρNIRWhere ρ isfar-SWIRRepresenting the reflectivity, p, of the far-short wave infrared bandnear-SWIRepresenting the reflectivity, p, of the near-short wave infrared bandNIRRepresenting the reflectivity of the near infrared band;
thirdly, preliminarily extracting shale oil-gas combustion pixels
3.1, respectively extracting pixels with a high-temperature thermal anomaly index TAI of more than or equal to 0.15 aiming at the Landsat-8OLI optical image and the Sentinel-2MSI optical image;
3.2, further extracting rho on the basis of the extraction result of the step 3.1far-SWIR-ρnear-SWIR>ρfar-SWIR-ρNIRAnd ρfar-SWIRPixels of not less than 0.15;
3.3 extraction of rho for Landsat-8OLI optical images and Sentinel-2MSI optical imagesfar-SWIRRho is not less than 1near-SWIRThe pixel with oversaturated reflectivity is obtained;
3.4, if the pixel with the 3.2 extraction result exists in the eight fields of the pixel with the oversaturated reflectivity, adding the pixel into the 3.2 extraction result, and otherwise, rejecting the pixel as noise;
fourth step, for the result of step 3.2, the inequality ρ will be satisfiednear-SWIRLess than or equal to 0.05 or rhoNIREliminating the noise of the image by using the pixels less than or equal to 0.01 as the noise of the image, and removing cloud noise according to a QA wave band provided by Sentinel-2 and Landsat-8; taking the finally obtained pixel as a candidate pixel for shale oil-gas combustion, assigning the pixel to be 1, and assigning the rest pixels to be 0 to obtain binary images of candidate targets in different time sequences;
fifthly, aiming at the obtained binary images, combining all images in the same time phase, assigning the value of a pixel which is more than or equal to 1 to be 1, assigning the other values to be 0, performing mathematical form expansion of a single pixel to obtain a new binary image ordered in time phase, extracting the binary pixel, converting the binary pixel into a vector polygon, and calculating the mass center of the vector polygon to obtain a mass center point;
sixthly, removing a center of mass point in the construction land according to the construction land data from the GHS; establishing a buffer area according to the shale oil-gas well site point data, screening a center of mass point in the buffer area, determining the center of mass point as a single-time shale oil-gas combustion position point, and accordingly obtaining shale oil-gas combustion position points of all the years;
seventhly, accumulating time sequences of all the new binarized images obtained in the fifth step to obtain the shale oil-gas combustion observation frequency of each pixel, assigning the value of 1 or more pixels to be 1, and assigning the rest pixels to be 0 to obtain a final binarized image and converting the final binarized image into a vector polygon; (ii) a
Eighthly, selecting the vector polygon obtained in the seventh step based on shale oil-gas combustion position points to obtain an accurate shale oil-gas combustion range, counting the maximum combustion observation frequency and the non-cloud image observation frequency within the shale oil-gas combustion range one by one, and performing ratio operation to obtain the combustion rate of each shale oil-gas well site;
and ninthly, extracting shale oil-gas combustion positions with accumulated frequency greater than 15 times, and defining the shale oil-gas combustion positions as high-frequency shale oil-gas combustion.
The data sources adopted by the invention come from a plurality of sensor platforms, and the integration of the plurality of data sources is used for detecting the shale oil-gas combustion fine target, which belongs to the innovation of data application. The invention constructs a batch processing frame taking high-temperature thermal anomaly index as a core, and can automatically and accurately complete the identification of shale oil gas combustion and the determination of combustion rate under a larger scale, thereby being used for the evaluation of shale oil gas combustion on the large scale and providing a better theoretical basis for the exploitation research of shale oil gas in China.
The relevant data extraction and vector conversion processes are realized through Matlab, Arcpy programming and GEE platform, so that the manual participation is reduced, and the extraction efficiency is greatly improved.
In conclusion, the method has simple and easy execution steps and has better shale oil-gas combustion effect in the extraction area. At present, the information about the detailed position of shale oil-gas combustion is very little, and the method uses long-time optical image data with large space coverage and utilizes a high-temperature thermal anomaly index to realize the accurate extraction of shale oil-gas combustion on a larger space scale. The invention is helpful to compile a detailed shale oil-gas combustion position which can be timely and effectively updated, and can supplement a low-resolution product database. Meanwhile, scientific reference is provided for shale oil and gas management and evaluation of potential influence of shale oil and gas combustion on the ecological environment in the future.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a shale oil-gas combustion extraction method based on multi-source time series remote sensing images.
Fig. 2 is a response curve between spectral reflectance and wavelength.
FIG. 3 is a shale oil and gas sampling scatter plot.
FIG. 4 is a graph of the combustion rate spatial distribution for shale oil and gas combustion.
FIG. 5 is a high observed frequency shale oil and gas combustion profile from Landsat-8OLI extraction.
FIG. 6 is a high observed frequency shale oil and gas combustion profile of a Sentinel-2MSI extraction.
Detailed Description
The technical route and the operation steps of the present invention will be more clearly understood from the following detailed description of the present invention with reference to the accompanying drawings. The data used in the examples of the present invention are Landsat-8OLI and Sentinel-2MSI data covering the Texas region, USA. The data time span is between 3 months in 2013 and 5 months in 2020.
FIG. 1 is a flow chart of a shale oil-gas combustion extraction method based on multi-source time series remote sensing images, which comprises the following specific steps:
firstly, preparing a multi-source remote sensing optical image, and respectively constructing a time-series waveband data set. The method specifically comprises the following contents:
a. selecting a part of high-temperature thermal anomaly positions obtained through field investigation as sample points, obtaining AVIRIS images at the sample points, counting the wave band values of the images at the sampling points in ENVI software, and drawing response curves between the spectral reflectivity and the wavelength of different high-temperature thermal anomalies, as shown in FIG. 2.
b. Downloading Landsat-8OLI and Sentinel-2MSI data covering the range of a research area in batches based on GEE and Arcpy platforms, wherein the Landsat-8OLI images have 6862 scenes in total and the average cloud coverage rate is 33.91% from 3 months in 2013 to 5 months in 2020; the Sentinel-2MSI images had a total of 45468 scenes from 8 months 2015 to 5 months 2020 with an average cloud coverage of 37.55%. The spatial resolution of the near infrared and short wave infrared bands of Landsat and Sentinel were 30m and 20m, respectively.
And secondly, sampling and analyzing. Analysis shows that the near infrared band and the short wave infrared band have strong sensitivity to high temperature heat abnormity, so the invention provides a high temperature heat different field index TAI (rho ═ TAI) index according to the near infrared band and the short wave infrared bandfar-SWIR-ρnear-SWIR)/ρNIRWhere ρ isfar-SWIRRepresenting the reflectivity, p, of the far-short wave infrared bandnear-SWIRepresenting the reflectivity, p, of the near-short wave infrared bandNIRWhich represents the reflectivity in the near infrared band. 14818 unburnt shale oil-gas wells and 3980 shale oil-gas combustion positions obtained by field investigation are selected as sample points, and when the threshold value is set to be more than or equal to 0.15, the TAI (total internal pressure) can be obtainedShale oil and gas in the effective extraction area is combusted, and a scatter density chart is made by utilizing a scatter function of Matlab, as shown in figure 3.
Thirdly, preliminarily extracting shale oil-gas combustion pixels
3.1, respectively extracting pixels with a high-temperature thermal anomaly index TAI of more than or equal to 0.15 aiming at the Landsat-8OLI optical image and the Sentinel-2MSI optical image;
3.2, based on the Arcpy platform, further extracting rho on the basis of the extraction result in the step 3.1far-SWIR-ρnear-SWIR>ρfar-SWIR-ρNIRAnd ρfar-SWIRMore than or equal to 0.15 pixel, further refining the monitoring result and avoiding noise caused by low reflectivity;
3.3 extraction of rho for Landsat-8OLI optical images and Sentinel-2MSI optical imagesfar-SWIRRho is not less than 1near-SWIRThe pixel with oversaturated reflectivity is obtained;
and 3.4, if the pixel with the 3.2 extraction result exists in the eight fields of the pixel with the oversaturated reflectivity, adding the pixel into the extraction result of the 3.2, and otherwise, rejecting the pixel as noise.
Fourth step, for the result of step 3.2, the inequality ρ will be satisfiednear-SWIRLess than or equal to 0.05 or rhoNIRThe pixels less than or equal to 0.01 are regarded as the noise of the image (such as image packet loss, image edge abnormity and the like) to be removed, and the cloud noise is removed according to the QA wave band provided by Sentinel-2 and Landsat-8; and taking the finally obtained pixel as a candidate pixel for shale oil-gas combustion, assigning the pixel to be 1, and assigning the rest pixels to be 0 to obtain binary images of candidate targets in different time sequences.
And fifthly, removing noise. Firstly, combining binarized images of the same time phase in batches To generate New binarized images based on a Mosaic To New scanner tool of Arcpy, then carrying out mathematical form expansion of a single pixel by means of an Expand tool, extracting a grid with a Value of 1 by using an Extract by Value tool, converting the grid into a vector Polygon in batches by using a scanner To Polygon tool, then calculating the centroid of the Polygon by using a triangle Geometry tool, calculating the median coordinates of all centroid points positioned in the same high-frequency pixel boundary by using a Mean Center tool in ArcGIS, and calculating the following formula:
xiand yiRespectively representing the abscissa and ordinate of the ith centroid, and n representing the number of centroids. And outputting the geographical coordinates of each shale oil-gas combustion through calculation, and storing the geographical coordinates as a point file.
Sixthly, removing noise in the construction land by using an Erase tool according to the construction land data from the GHS; and according to shale oil gas well site data, a Buffer area of 250m is established by using a Buffer tool, and a center of mass point in the Buffer area is screened out by using a Clip tool, so that the shale oil gas combustion position in a single time phase can be determined, and shale oil gas combustion position points in all years can be obtained according to the shale oil gas well site data.
And seventhly, combining the New binarized images obtained in the fifth step into shale oil and gas combustion accumulated observation frequency images in batches based on a Mosaic To New scanner tool of Arcpy To obtain the shale oil and gas combustion observation frequency of each pixel, assigning the value of the pixel which is more than or equal To 1 To be 1, and assigning the rest To be 0 To obtain a final binarized image and converting the final binarized image into a vector polygon.
And eighthly, selecting the vector polygon obtained in the seventh step by using a Select by location tool in Arcpy aiming at the shale oil and gas combustion center point obtained in the seventh step, so as to obtain an accurate shale oil and gas combustion range. The method comprises the steps of traversing and counting the maximum value of a shale oil and gas combustion accumulation frequency image and the mean value of a non-cloud image observation frequency of a shale oil and gas combustion area by using a Zonal Statistics as Table tool, increasing a Field by using an Add Field to be the ratio of two values to obtain the combustion rate of shale oil and gas, and spatially connecting an attribute Table and a shale oil and gas combustion centroid point by using a Spatial Join tool, as shown in figure 4.
And ninthly, extracting shale oil-gas combustion accumulated frequency which is more than 15 by adopting a Con function based on Arcpy, defining the shale oil-gas combustion accumulated frequency as high-frequency shale oil-gas combustion, and evaluating the precision of the extracted shale oil-gas combustion by a visual identification method, wherein the method has high precision in Landsat-8OLI and Sentinel-2MSI, and is shown in fig. 5 and 6.
In addition to the above embodiments, the present invention may have other embodiments. All technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (7)
1. A shale oil-gas combustion extraction method based on multi-source time series remote sensing images comprises the following steps:
the method comprises the following steps of firstly, preparing a multi-source remote sensing optical image, and respectively constructing a time-series waveband data set, wherein the multi-source remote sensing optical image comprises: landsat-8OLI optical images, and Sentinel-2MSI optical images;
secondly, providing a high-temperature thermal anomaly index TAI = (C)ρ far-SWIR -ρ near-SWIR )/ ρ NIR Whereinρ far-SWIR Represents the reflectivity of the far-short wave infrared band,ρ near-SWI the reflectivity of the near short wave infrared band is shown,ρ NIR representing the reflectivity of the near infrared band;
thirdly, preliminarily extracting shale oil-gas combustion pixels
3.1, respectively extracting pixels with a high-temperature thermal anomaly index TAI of more than or equal to 0.15 aiming at the Landsat-8OLI optical image and the Sentinel-2MSI optical image;
3.2, further extracting on the basis of the extraction result in the step 3.1ρ far-SWIR - ρ near-SWIR > ρ far-SWIR - ρ NIR And isρ far-SWIR Pixels of not less than 0.15;
3.3 extraction of Landsat-8OLI optical image and Sentinel-2MSI optical imageρ far-SWIR Not less than 1 andρ near-SWIR the pixel with oversaturated reflectivity is obtained;
3.4, if the pixel with the 3.2 extraction result exists in the eight fields of the pixel with the oversaturated reflectivity, adding the pixel into the 3.2 extraction result, and otherwise, rejecting the pixel as noise;
fourth, for the result of step 3.2, the inequality will be satisfiedρ near-SWIR Less than or equal to 0.05 orρ NIR Eliminating the noise of the image by using the pixels less than or equal to 0.01 as the noise of the image, and removing cloud noise according to a QA wave band provided by Sentinel-2 and Landsat-8; taking the finally obtained pixel as a candidate pixel for shale oil-gas combustion, assigning the pixel to be 1, and assigning the rest pixels to be 0 to obtain binary images of candidate targets in different time sequences;
fifthly, aiming at the obtained binary images, combining all images in the same time phase, assigning the value of a pixel which is more than or equal to 1 to be 1, assigning the other values to be 0, performing mathematical form expansion of a single pixel to obtain a new binary image ordered in time phase, extracting the binary pixel, converting the binary pixel into a vector polygon, and calculating the mass center of the vector polygon to obtain a mass center point;
sixthly, removing a center of mass point in the construction land according to the construction land data from the GHS; establishing a buffer area according to shale oil-gas well site point data, screening a center of mass point in the buffer area, determining the center of mass point as a single-time shale oil-gas combustion position point, and obtaining shale oil-gas combustion position points of all years according to the center of mass point;
seventhly, accumulating time sequences of all the new binarized images obtained in the fifth step to obtain the shale oil-gas combustion observation frequency of each pixel, assigning the value of 1 or more pixels to be 1, and assigning the rest pixels to be 0 to obtain a final binarized image and converting the final binarized image into a vector polygon; (ii) a
Eighthly, selecting the vector polygon obtained in the seventh step based on shale oil-gas combustion position points to obtain an accurate shale oil-gas combustion range, counting the maximum combustion observation frequency and the non-cloud image observation frequency within the shale oil-gas combustion range one by one, and performing ratio operation to obtain the combustion rate of each shale oil-gas well site;
and ninthly, extracting shale oil-gas combustion positions with accumulated frequency greater than 15 times, and defining the shale oil-gas combustion positions as high-frequency shale oil-gas combustion.
2. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: for Landsat-8OLI optical images, TAI = (C =: (C))ρ 7 -ρ 6 )/ρ 5 ，ρ 7 Represents the reflectivity of the far-short wave infrared band,ρ 6 showing the reflectivity of a near short wave infrared band,ρ 5 which represents the reflectivity in the near infrared band.
3. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: for Sentiniel-2 MSI optical images, TAI = (C) ((C))ρ 12 - ρ 11 )/ρ 8a ，ρ 12 Represents the reflectivity of the far-short wave infrared band,ρ 11 showing the reflectivity of a near short wave infrared band,ρ 8a which represents the reflectivity in the near infrared band.
4. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: and fifthly, performing grid-To-Polygon conversion operation on each binary image by using a scanner To Polygon tool in ArcGIS software, only keeping the Polygon with the attribute value of 1, calculating the center of mass of the Polygon by using a scale Geometry tool, and keeping the center of mass point.
5. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: in the sixth step, the radius of the buffer zone is set to be 250 meters according to the position of the shale oil and gas well site.
6. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: and in the seventh step, accumulating all new binary images by using an Arcpy tool.
7. The shale oil-gas combustion extraction method based on multi-source time series remote sensing images according to claim 1, characterized in that: and in the eighth step, carrying out batch statistics on the combustion observation frequency and the cloud-free image frequency of the shale oil-gas range by using an Arcpy tool, and calculating the combustion rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110766395.6A CN113484247B (en) | 2021-07-07 | 2021-07-07 | Shale oil-gas combustion extraction method based on multi-source time series remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110766395.6A CN113484247B (en) | 2021-07-07 | 2021-07-07 | Shale oil-gas combustion extraction method based on multi-source time series remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113484247A true CN113484247A (en) | 2021-10-08 |
| CN113484247B CN113484247B (en) | 2022-03-25 |
Family
ID=77941605
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110766395.6A Active CN113484247B (en) | 2021-07-07 | 2021-07-07 | Shale oil-gas combustion extraction method based on multi-source time series remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113484247B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115791685A (en) * | 2022-11-21 | 2023-03-14 | 江苏省农业科学院 | Straw combustion extraction method based on time series remote sensing image |
| CN116721353A (en) * | 2023-08-07 | 2023-09-08 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000322556A (en) * | 1999-05-11 | 2000-11-24 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for time sequentially predicting image and storage medium storing time sequential image prediction program |
| US20140156806A1 (en) * | 2012-12-04 | 2014-06-05 | Marinexplore Inc. | Spatio-temporal data processing systems and methods |
| CN106682624A (en) * | 2016-12-29 | 2017-05-17 | 中国科学院深圳先进技术研究院 | Built-up area extraction method and apparatus based on time sequence remote sensing information |
| CN110532918A (en) * | 2019-08-21 | 2019-12-03 | 南京大学 | Method is determined based on the offshore wind electric field time-space attribute of time series remote sensing image |
| CN110570399A (en) * | 2019-08-16 | 2019-12-13 | 中国科学院地理科学与资源研究所 | oil palm tree age measuring and calculating method based on time series remote sensing data |
-
2021
- 2021-07-07 CN CN202110766395.6A patent/CN113484247B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000322556A (en) * | 1999-05-11 | 2000-11-24 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for time sequentially predicting image and storage medium storing time sequential image prediction program |
| US20140156806A1 (en) * | 2012-12-04 | 2014-06-05 | Marinexplore Inc. | Spatio-temporal data processing systems and methods |
| CN106682624A (en) * | 2016-12-29 | 2017-05-17 | 中国科学院深圳先进技术研究院 | Built-up area extraction method and apparatus based on time sequence remote sensing information |
| CN110570399A (en) * | 2019-08-16 | 2019-12-13 | 中国科学院地理科学与资源研究所 | oil palm tree age measuring and calculating method based on time series remote sensing data |
| CN110532918A (en) * | 2019-08-21 | 2019-12-03 | 南京大学 | Method is determined based on the offshore wind electric field time-space attribute of time series remote sensing image |
Non-Patent Citations (1)
| Title |
|---|
| WANG LIZHE 等: "DUK-SVD: dynamic dictionary updating for sparse representation of a long-time remote sensing image sequence", 《SOFT COMPUTING》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115791685A (en) * | 2022-11-21 | 2023-03-14 | 江苏省农业科学院 | Straw combustion extraction method based on time series remote sensing image |
| CN115791685B (en) * | 2022-11-21 | 2024-05-07 | 江苏省农业科学院 | Straw combustion extraction method based on time sequence remote sensing image |
| CN116721353A (en) * | 2023-08-07 | 2023-09-08 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
| CN116721353B (en) * | 2023-08-07 | 2023-11-03 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113484247B (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113484247B (en) | Shale oil-gas combustion extraction method based on multi-source time series remote sensing image | |
| CN111062368A (en) | City update region monitoring method based on Landsat time sequence remote sensing image | |
| Radeloff et al. | Need and vision for global medium-resolution Landsat and Sentinel-2 data products | |
| Mann et al. | Spatio-temporal forest cover dynamics along road networks in the Central Himalaya | |
| CN112365158A (en) | Remote sensing data-based mine greening monitoring and evaluating method | |
| CN115205709B (en) | Forest fire point identification method based on satellite remote sensing | |
| Manzo et al. | Integrated remote sensing for multi-temporal analysis of anthropic activities in the south-east of Mt. Vesuvius National Park | |
| Zhou et al. | Shadow pattern-enhanced building height extraction using very-high-resolution image | |
| Kennaway et al. | Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands | |
| Salman et al. | Detection of Spectral Reflective Changes for Temporal Resolution of Land Cover (LC) for Two Different Seasons in central Iraq | |
| Wang et al. | Remote sensing unveils the explosive growth of global offshore wind turbines | |
| CN113158770A (en) | Improved mining area change detection method of full convolution twin neural network | |
| CN117132894A (en) | Open-air coal mining area ecological damage area identification method based on time sequence remote sensing image | |
| Hu et al. | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images | |
| CN111008565A (en) | Burned area detection method based on short wave infrared and thermal infrared data feature fusion | |
| Dogon-Yaro et al. | Semi-automated approach for mapping urban trees from integrated aerial lidar point cloud and digital imagery datasets | |
| Kaur et al. | Satellite Image-based Land Use/Land Cover Dynamics and Forest Cover Change Analysis (1996-2016) in Odisha, India | |
| Sohail et al. | Spatio-temporal analysis of land use dynamics and its potential implications on land surface temperature in lahore district, punjab, pakistan | |
| Floyd et al. | Normalized Difference Vegetation Index (NDVI) Assessment of Vegetation Around Oben Gas Flow Station, Edo State, Nigeria | |
| Hill et al. | Use of high resolution digital multi-spectral imagery to assess the distribution of disease caused by Phytophthora cinnamomi on heathland at Anglesea, Victoria | |
| Ara et al. | Urban Area Growth Monitoring in Sylhet City Using Remote Sensing and Geographic Information System from 2002 to 2017 | |
| Karmakar et al. | Land Use/Land Cover Change and Environmental Impact Analysis of Ramgarh-Naudiha Region in Uttar Pradesh, India through Geospatial Technology | |
| Dogon-Yaro et al. | Extraction of urban trees from integrated airborne based digital image and LiDAR point cloud datasets-initial results | |
| Zheng et al. | UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern | |
| Hiew et al. | Land use classification and land use change analysis using satellite images in Lojing, Kelantan. |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |