CN114092837B - Remote sensing monitoring method and system for site environment based on long-time scale - Google Patents
Remote sensing monitoring method and system for site environment based on long-time scale Download PDFInfo
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Abstract
The invention relates to a remote sensing monitoring method and a remote sensing monitoring system for an ancient ruined site environment based on a long time scale, which are used for acquiring satellite shooting remote sensing data and extracting a time sequence of the ancient ruined site environment; extracting slowly-changing feature vectors by using a slow feature analysis method; selecting the slowest feature vector to obtain periodic features of multiple time scales; comparing the period of the periodic characteristic with the periodic rule of the climate mode, and determining the climate reason for generating the periodic characteristic; and removing all slowly-changed characteristic vectors from the time sequence, searching for a mutation point, and determining the reason causing the stability change. The invention explores the multilevel evolution law of the historic site environment by utilizing the historical dynamic transition of the long-time sequence historic site recorded in the high revisiting period of the satellite remote sensing. The randomness information due to sampling at a certain time point or several time points is avoided. The method considers two aspects of periodicity and non-periodicity of monitoring the change of the historic site environment, wherein the periodic rule can qualitatively estimate the future development trend of the historic site environment.
Description
Technical Field
The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing monitoring method and system for a historic site environment based on a long-time scale.
Background
The remote sensing monitoring of the ancient sites aims at the detection and verification of the archaeological sites, and is a necessary content aiming at the monitoring and evaluation of the archaeological environment on a macroscopic scale.
Since the 80 s in the 20 th century, with the development of the remote sensing earth observation science and technology, a great amount of remote sensing monitoring on archaeological sites is carried out at home and abroad by using remote sensing data.
The spectral resolution of remote sensing images is further developed, and thermal imaging, hyperspectral imaging, radar and laser radar imaging technologies are used for revealing the characteristics of the buried archaeological trails which are difficult to find by human eyes and standard photography. The thermal imaging technology is also researched and verified in the aspect of revealing the vestige of archaeological sites, such as rammed earth buildings, ash pits, ditches and the like. The hyperspectral imaging technology provides continuous spectrum sampling of the archaeological relics, helps to identify small category differences of earth surface coverage, differences of material components of ancient sites and the like, enhances archaeological weak information, and plays an important role in identifying types of ancient sites with different ages and components. The penetrability of radar remote sensing is a premise for detecting an archaeological target, and the longer the wavelength is, the stronger the radar electromagnetic wave penetrability is. Its electromagnetic wave penetration and depth are directly related to surface parameters, radar incidence angle and polarization mode, and secondly the surface penetration is optimal in dry sand areas. The laser radar remote sensing is a high and new technology developed in recent years, the laser pulse emitted by high frequency is used for rapidly acquiring three-dimensional dense points on the surface of an object in a non-contact active mode, and the method has unique advantages for discovering the trails left by human activities hidden in vegetation coverage areas.
Although a large amount of remote sensing monitoring on the archaeological site is carried out at home and abroad by using remote sensing data in the aspects of spatial resolution and spectral resolution, the high revisiting period of satellite remote sensing is rarely used for the remote sensing monitoring of the archaeological site. Historical dynamic changes of the historic site and the historic site are generally influenced by a plurality of factors, the influence is recorded by a high revisiting period of satellite remote sensing, the recorded historical dynamic changes belong to a non-stable sequence, have the characteristics of trend, periodicity and the like, have randomness, mutability and a multi-time scale structure, and have a multi-level evolution law.
At present, remote sensing monitoring of archaeological sites is mainly carried out aiming at site features and potential site information of the archaeological sites at a certain time point or a plurality of time points, and remote sensing long-time sequence information cannot be effectively utilized to monitor and evaluate the archaeological environment. The disadvantages of this approach are that some trending, periodic, and abrupt features are often ignored, and the randomness information due to sampling at a certain time point is enhanced.
In remote sensing monitoring research carried out by utilizing time series, wavelet analysis of a time-frequency multi-resolution function can reveal various change periods hidden in the time series. However, when the spectrum and various indicators calculated based on the spectrum (vegetation greenness, temperature, etc.) exhibit particularly strong annual changes or shorter periods of time variation, environmental signature modules in the site for long periods may be masked by seasonal variations in the surface coverage or the net solar radiation. That is, the fast changing features in the environment may be much larger than the slow changing features in the environment in the wavelet analysis of the time-frequency multi-resolution function, so that the slow changing features are ignored in the remote sensing monitoring.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a remote sensing monitoring method and a remote sensing monitoring system for an archaeological environment based on a long time scale, which effectively utilize the characteristics of a remote sensing high revisit period to monitor and evaluate the archaeological environment and avoid random information brought by sampling at a certain time point.
In order to achieve the purpose, the invention provides a remote sensing monitoring method for the historic site environment based on a long time scale, which comprises the following steps:
acquiring remote sensing data or indexes of the satellite-shot site month by month, quarter or year by year, and extracting a time sequence of the site environment;
extracting slowly-changing feature vectors in the time sequence by using a slow feature analysis method;
selecting the feature vector which changes most slowly from all slowly changing feature vectors to obtain the multi-time scale periodic features of the slowest feature vector;
comparing the period of the periodic characteristic with the periodic rule of the climate mode, and determining the climate reason for generating the periodic characteristic;
and removing all slowly-changed climate characteristic vectors from the time sequence, searching for a mutation point, and determining a man-made reason causing stability change aiming at the mutation point.
Furthermore, the time sequence of the site environment is a vegetation sequence, a temperature sequence or a ground surface evapotranspiration sequence.
Further, a vegetation index sequence is selected by focusing on the weak characteristics of the site, which are generated by the difference of obvious phenological laws of surface crops, in the environment; selecting a vegetation index sequence or a daytime temperature sequence under the influence of climate change in the site environment under the surface environment of natural vegetation or non-artificial crops; the method focuses on the selection of vegetation index sequences, temperature sequences and surface evapotranspiration sequences caused by artificial causes of land utilization mutation.
Further, acquiring the periodic characteristics of multiple time scales comprises: and performing wavelet analysis of a time-frequency multi-resolution function on the slowest feature vector to obtain periodic features of a plurality of different periods.
Further, comparing the period of the periodic feature with the periodic law of the climate mode, and determining the climate reason generating the periodic feature, including:
the periodic law of the climate modes includes: the early nino waves the south, with a period of 2-7 years; oscillating for two years, and the period is 2 years; the solar black sub-cycle, the cycle is 11 years; the wave in North Atlantic ocean has a period of 15-20 years.
Further, searching for a mutation point, and determining a cause causing the stability change for the mutation point includes: and removing all slowly-changed characteristic vectors from the time sequence, carrying out significance test to obtain a mutation point, comparing the mutation point with human activities and natural disaster records at the same time, and determining the reason causing the stability change. Further, a significance test was performed to select as a basis that the change in a certain event point was more than twice the standard deviation of the entire sequence.
Further, human activity interferes with changes in land use types.
In another aspect, a remote sensing and monitoring system for site environment based on long-time scale is provided, which includes:
the time sequence extraction module is used for acquiring remote sensing data or indexes of the satellite shooting site month by month, quarter or year by year and extracting the time sequence of the site environment;
the slow change characteristic vector extraction module is used for extracting the slow change characteristic vector in the time sequence by using a slow characteristic analysis method;
the periodic feature acquisition module is used for selecting the feature vector which changes the slowest from all the slowly-changing feature vectors and acquiring the multi-time scale periodic features of the slowest feature vector;
the climate reason analysis module is used for comparing the period of the periodic characteristics with the periodic rule of the climate mode to determine the climate reason generating the periodic characteristics;
and the mutation reason analysis module is used for removing all the slowly-changed characteristic vectors from the time sequence, searching mutation points and determining reasons causing stability change aiming at the mutation points.
Further, the periodic feature acquisition module: and performing wavelet analysis of a time-frequency multi-resolution function on the slowest feature vector to obtain periodic features of a plurality of different periods.
Further, the mutation reason analysis module eliminates all slow change characteristic vectors from the time sequence, conducts significance test to obtain mutation points, compares the mutation points with human activities and natural disaster records at the same time, and determines reasons causing stability change.
The technical scheme of the invention has the following beneficial technical effects:
(1) the method extracts the slow change characteristics (the characteristics of the time of year and the time of year) which are included in the remote sensing long-term monitoring; diagnosing "multiple time scale" periodic features of the "slowest" changing feature vector; evaluating a plurality of chronologic and chronologic environmental change modalities that change slowly, such as various climate change modalities; rapidly changing environmental change modalities, such as disasters and human activities, are evaluated. Finally, as an important supplement of the traditional archaeological surveying technology, the method can be used for more deeply knowing the annual and annual changes of the site environment with large scale, monitoring the stability and change reasons of the site environment and qualitatively estimating the future development trend of the system.
(2) The invention explores the multilevel evolution law of the historic site environment by utilizing the historical dynamic transition of the long-time sequence historic site recorded in the high revisiting period of the satellite remote sensing. The randomness information due to sampling at a certain time point or several time points is avoided.
(3) The meteorological research on various annual and dative environmental change periods is applied to monitoring the periodic and aperiodic changes of the site environment. Firstly, slowly changing characteristics (long time scale) in the site environment, such as climatic annual and chronologic changes, are monitored, and are usually periodic; secondly, rapid change characteristics (short time scale) in the site environment, such as human activity interference and disasters, are monitored, and the site environment is usually random and mutable. The method considers two aspects of periodicity and non-periodicity of monitoring the change of the site environment, wherein the influence of climate parameters on the site environment in the same period in the future can be predicted by mastering the periodic rule, if the site environment is influenced by Hercinor and southern surge, the period is 2-7 years; oscillating for nearly two years, with a period of 2 years; the solar black sub-cycle, the cycle is 11 years; the wave in North Atlantic ocean has a period of 15-20 years, and then qualitative estimation is carried out on the development trend of site environment.
(4) The invention has physical significance, considers the influence of the physical phenomenon that atmospheric changes have different expressions on different time scales and different periods on the site environment, and analyzes the remote correlation of the site remote sensing environment sequence and the climate annual and chronologic environment change module.
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FIG. 1 is a schematic diagram of a remote sensing monitoring process of a historic site environment based on a long time scale;
FIG. 2 is a schematic diagram of a remote sensing monitoring system for the historic site environment based on a long time scale.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
An embodiment of the present invention, as shown in fig. 1, provides a remote sensing and monitoring method for a historic site environment based on a long-time scale, which includes the following steps:
(1) and acquiring monthly and yearly remote sensing data of the satellite-shot site, and extracting a time sequence of the site environment.
Time series such as vegetation series, temperature series, surface evapotranspiration series. The vegetation sequence can be selected by emphasizing weak site characteristics in an environment with less artificial interference. Selecting a vegetation index sequence by focusing on the weak characteristics of the historic site generated by the obvious difference of the phenological laws of the surface crops in the environment; the ambient is masked by the cyclic variations in climate since the nighttime temperature sequence is more significantly disturbed by the temperature of the solar radiation. Therefore, the vegetation index sequence or the daytime temperature sequence is selected by paying attention to the influence of climate change on the site environment under the surface environment of natural vegetation or non-artificial crops; the method focuses on the selection of vegetation index sequences, temperature sequences and surface evapotranspiration sequences caused by artificial causes of land utilization mutation.
The method comprises the following steps of acquiring a long-time scale spectrum/spectrum index sequence by using satellite remote sensing data with a high revisiting period, and monitoring the historic site environment in two aspects: firstly, slowly changing characteristics (long time scale) in the site environment, such as climatic annual and chronologic changes, are monitored, and are usually periodic; secondly, rapid change characteristics (short time scale) in the site environment, such as human activity interference and disasters, are monitored, and the site environment is usually random and mutable.
(2) And extracting slowly-changing feature vectors in the time sequence by using a slow feature analysis method.
And (3) extracting a plurality of slowly-changing annual and annual environmental change combination vectors in the remote sensing monitoring time sequence by using a Slow Feature Analysis (SFA) method. The basic idea of the SFA algorithm is to perform eigenvalue decomposition on the vector matrix, which is an effective method for extracting slowly varying external drive information from non-stationary time series. The method is widely applied to computer vision and used for identifying slowly changing objects in images, such as roadblocks in traffic flows and limb actions of people, and the result after successful identification can be applied to automatic driving, intelligent transportation and customer analysis, and a directly-called program package (see package: rSFA) is arranged in open source software R during operation. The specific process is as follows:
1) the input data are data sets such as a vegetation sequence NDVI (t), a temperature sequence LST (t) or a surface evapotranspiration sequence E (t), wherein t is t1, … and tn, and is monthly, quarterly or yearly data.
2) Converting the input data into an m-dimensional matrix, here exemplified by a vegetation sequence NDVI, x (t) { NDVI } 1 (t),…,NDVI m (t) }, wherein t ═ t 1 ,…,t N (ii) a N-m + 1. Annotating the yearly data may set m to 13.
3) Reconstructing the m-dimensional matrix into a k-dimensional matrix:
H(t)＝{h 1 (t),…,h K (t)}
＝{NDVI 1 (t),…,NDVI m (t),NDVI 1 2 (t)…,NDVI 1 (t)NDVI m (t),...,
NDVI m-1 2 (t)，NDVI m (t)，NDVI 2 m (t)}
wherein K is m + m (m + 1)/2; t is t 1 ,…,t N 。
4) Normalization processing is carried out, the following constraints are met, zero mean value is ensured, latent variables with information content and all slow characteristics are not related to each other.
H*(t)＝{h* 1 (t),..h* K (t)}
<h* j >＝0
<h* j h* j T >＝1
<h* i h* j T >＝0
5) The normalized H x (t) is decomposed orthogonally to yield: z (t) ═ z 1 (t),…,z K (t) }, calculating the first derivative of the slow feature over time, Z '(t) ═ Z' 1 (t),…,z’ K (t) to find the slow eigenvector W with the smallest Slowness (Slowness) 1 I.e. linear mapping matrix of slow features, to obtain slow features y 1 (t)。
y 1 (t)＝W 1 ·Z(t)
(3) Selecting the most slowly varying eigenvector y of all slowly varying eigenvectors 1 And (t) acquiring the multi-time scale periodic features of the slowest feature vector.
And selecting the slowest characteristic vector from the extracted multiple annual and chronologic environmental change combination vectors with slow change, and performing wavelet analysis with time-frequency multi-resolution function to reveal multiple change periods hidden in the time sequence. The wavelet transform obtains frequency components and knows the time position of the frequency through the time-frequency localization characteristics of the time-frequency window of the transform basis function, namely the expansion and the translation of the transform basis function. Therefore, the number of environment change modes and the periodic characteristics of multiple time scales contained in the historic site environment are judged, and relevant program packages are contained in Matlab during operation.
(4) And comparing the period of the periodic characteristic with the periodic rule of the climate mode, and determining the climate reason for generating the periodic characteristic.
And performing multi-time scale periodic characteristic remote correlation analysis. By revealing various annual and chronologic environmental change periods hidden in a time sequence, the non-stationary sequence and the multi-level evolution law of various annual and chronologic climate modes in the climate system are combined. Such as Erlenno and Tanso in south, with a period of 2-7 years; shaking QBO for two years with a period of 2 years; the solar black sub-cycle, the cycle is 11 years; NAO is billed in North Atlantic, the period is 15-20 years, period comparison is carried out, and the influence of climate change on the site environment is explained. For example, obtaining two cycle signatures, with 4 and 11 years cycle, respectively, indicates that the climatic causes for the cycle signature are Hercino and southern billowed ENSO and Sun Black, respectively.
(5) And removing all slowly-changing characteristic vectors from the time sequence, searching for a mutation point, and determining a reason causing stability change aiming at the mutation point.
On the basis of removing the periodic influence of the environment, the rapidly changing characteristics (short time scale) in the site environment are evaluated. In particular, since evaluating rapidly changing features in the site environment is expected not to be affected by large scale environmental modal changes, more attention is paid to changes in detail in the true sense. Therefore, on the basis of long-time sequence original remote sensing data, the quantized various annual and annual environmental change periods are removed, and the influence of large-scale climate phenomena on long-time sequence remote sensing parameters (vegetation indexes, land surface temperatures and the like) is removed. Further, randomness and mutability such as human activity interference, disasters and the like in a long-time sequence are accurately analyzed.
And removing all slowly-changing characteristic vectors from the time sequence, carrying out significance test, selecting a standard deviation of which the change of a certain event point is more than twice of that of the whole sequence as a basis to obtain a mutation point, and comparing the mutation point with human activities and natural disaster records at the same time to determine the reason causing stability change.
Human activities interfere with changes in the type of land use, such as planting trees, landfilling, felling, planting crops, and the like.
The invention provides a remote sensing monitoring system for the historic site environment based on a long time scale, which is combined with the graph 2 and comprises a time sequence extraction module, a slow change feature vector extraction module, a periodic feature acquisition module, a climate reason analysis module and a mutation reason analysis module.
And the time sequence extraction module is used for acquiring monthly or yearly remote sensing data of the satellite shooting site and extracting the time sequence of the site environment.
And the slow change characteristic vector extraction module is used for extracting the slow change characteristic vector in the time sequence by using a slow characteristic analysis method.
And the periodic feature acquisition module is used for selecting the feature vector which changes the slowest from all the slowly-changing feature vectors and acquiring the multi-time scale periodic features of the slowest feature vector.
Further, the periodic feature acquisition module: and performing wavelet analysis of a time-frequency multi-resolution function on the slowest feature vector to obtain periodic features of a plurality of different periods.
And the climate reason analysis module is used for comparing the period of the periodic characteristics with the periodic rule of the climate mode and determining the climate reason for generating the periodic characteristics.
And the mutation reason analysis module is used for removing all slowly-changed characteristic vectors from the time sequence, searching mutation points and determining reasons causing stability change aiming at the mutation points.
Further, the mutation reason analysis module eliminates all slow change characteristic vectors from the time sequence, conducts significance test to obtain mutation points, compares the mutation points with human activities and natural disaster records at the same time, and determines reasons causing stability change.
In summary, the invention relates to a remote sensing monitoring method and system for the historic site environment based on a long time scale, which obtains the monthly or yearly remote sensing data of the historic site shot by a satellite and extracts the time sequence of the historic site environment; extracting slowly-changing feature vectors in the time sequence by using a slow feature analysis method; selecting the slowest feature vector to obtain periodic features of multiple time scales; comparing the period of the periodic characteristic with the periodic rule of the climate mode, and determining the climate reason for generating the periodic characteristic; and removing all slowly-changed characteristic vectors from the time sequence, searching for a mutation point, and determining the reason causing the stability change. The invention explores the multilevel evolution law of the historic site environment by utilizing the historical dynamic transition of the long-time sequence historic site recorded in the high revisiting period of the satellite remote sensing. The randomness information due to sampling at a certain time point or several time points is avoided. The periodicity and the non-periodicity of the monitoring of the historic site environment change are considered, wherein the periodicity rule can qualitatively estimate the future development trend of the historic site environment.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A remote sensing monitoring method for the historic site environment based on a long-time scale is characterized by comprising the following steps:
acquiring remote sensing data or indexes of the satellite-shot site monthly, quarterly or yearly, and extracting a time sequence of the site environment;
extracting slowly-changing feature vectors in the time sequence by using a slow feature analysis method;
selecting the feature vector which changes most slowly from all slowly changing feature vectors to obtain the multi-time scale periodic features of the slowest feature vector;
comparing the period of the periodic characteristics with the periodic rule of the climate mode, and determining the climate reason for generating the periodic characteristics;
and removing all slowly-changed climate characteristic vectors from the time sequence, searching for a mutation point, and determining a man-made reason causing stability change aiming at the mutation point.
2. The remote sensing monitoring method for the historic site environment based on the long-time scale as claimed in claim 1, wherein the time sequence of the historic site environment is a vegetation sequence, a temperature sequence or a surface evapotranspiration sequence.
3. The remote sensing monitoring method for the historic site environment based on the long-time scale is characterized in that a vegetation index sequence is selected by focusing on the fact that the environment generates the historic site weak characteristics due to the difference of the apparent phenological laws of the surface crops; selecting a vegetation index sequence or a daytime temperature sequence under the influence of climate change in the site environment under the surface environment of natural vegetation or non-artificial crops; the method focuses on the selection of vegetation index sequences, temperature sequences and surface evapotranspiration sequences caused by artificial causes of land utilization mutation.
4. The remote sensing monitoring method for the historic site environment based on the long-time scale as claimed in claim 1 or 2, wherein the obtaining of the periodic characteristics of the multi-time scale comprises: and performing wavelet analysis of a time-frequency multi-resolution function on the slowest feature vector to obtain periodic features of a plurality of different periods.
5. The remote sensing monitoring method for the historic site environment based on the long-time scale as claimed in claim 1 or 2, wherein the period of the periodic feature is compared with the periodic rule of the climate mode to determine the climate reason for generating the periodic feature, and the method comprises the following steps:
the periodic law of the climate modes includes: the early nino waves the south, with a period of 2-7 years; oscillating for nearly two years, with a period of 2 years; the solar black sub-cycle, the cycle is 11 years; the wave in North Atlantic ocean has a period of 15-20 years.
6. The remote sensing monitoring method for the historic site environment based on the long time scale as claimed in claim 1 or 2, wherein the searching of the mutation point, and the determination of the reason causing the stability change aiming at the mutation point comprises the following steps: removing all slowly-changing characteristic vectors from the time sequence, carrying out significance test to obtain mutation points, comparing the mutation points with human activities and natural disaster records at the same time, and determining reasons causing stability change; further, a significance test was performed to select as a basis that the change in a certain event point was more than twice the standard deviation of the entire sequence.
7. The remote sensing monitoring method for the historic site environment based on the long time scale is characterized in that human activity interference is change of land utilization type.
8. A remote sensing monitoring system for site environment based on long-time scale is characterized by comprising:
the time sequence extraction module is used for acquiring remote sensing data or indexes of the satellite shooting site month by month, quarter or year by year and extracting the time sequence of the site environment;
the slow change characteristic vector extraction module is used for extracting the slow change characteristic vector in the time sequence by using a slow characteristic analysis method;
the periodic feature acquisition module is used for selecting the feature vector which changes the slowest from all the slowly-changing feature vectors and acquiring the multi-time scale periodic features of the slowest feature vector;
the climate reason analysis module is used for comparing the period of the periodic characteristics with the periodic rule of the climate mode and determining the climate reason generating the periodic characteristics;
and the mutation reason analysis module is used for removing all slowly-changed characteristic vectors from the time sequence, searching mutation points and determining artificial reasons causing stability change aiming at the mutation points.
9. The remote sensing and monitoring system for the historic site environment based on the long time scale is characterized in that the periodic feature acquisition module is used for: and performing wavelet analysis of a time-frequency multi-resolution function on the slowest feature vector to obtain periodic features of a plurality of different periods.
10. The remote sensing and monitoring system for the historic site environment based on the long time scale according to claim 8 or 9, wherein the mutation reason analysis module eliminates all slow change feature vectors from the time sequence, performs significance test to obtain mutation points, compares the mutation points with human activities and natural disaster records at the same time, and determines the reason causing stability change.
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