CN107833205B - Quercus serrata fruiting old and small year phenomenon research method based on remote sensing image - Google Patents

Quercus serrata fruiting old and small year phenomenon research method based on remote sensing image Download PDF

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CN107833205B
CN107833205B CN201710996323.4A CN201710996323A CN107833205B CN 107833205 B CN107833205 B CN 107833205B CN 201710996323 A CN201710996323 A CN 201710996323A CN 107833205 B CN107833205 B CN 107833205B
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姚忠
辛在军
吴永明
游海林
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Abstract

A method for researching a Quercus serrata fruiting and chronological phenomenon based on remote sensing images is characterized in that a hyperspectral remote sensing image is used as a data source, a ground Quercus serrata fruit survey is combined, a multivariate linear regression algorithm is applied, a Quercus serrata fruit yield estimation model based on a reflection spectrum of a crown layer of a Quercus serrata growing season is built, the yield of Quercus serrata fruits is predicted in a large area, and the spatial distribution heterogeneity of the Quercus serrata fruiting and chronological phenomenon is quantitatively analyzed by the aid of G statistics.

Description

Quercus serrata fruiting old and small year phenomenon research method based on remote sensing image
Technical Field
The invention relates to a research method of Quercus serrata fruiting old and small year phenomenon based on remote sensing image, belonging to the technical field of remote sensing application.
Background
Quercus serrata (Quercus serrata) is a common forest deciduous tree which grows in most provinces of China, and the fruit of Quercus serrata is called acorn, is an important food for forest animals such as squirrel and wild boar, and can also be used as a raw material in the food industry. Quercus serrata fructification has the phenomenon of more than one year (called big year) and less than one year (called small year), namely the phenomenon of fruiting in small and large years, and has important influence on forest animal population dynamics and spatial distribution. According to the traditional method for researching tree fruiting and old-year phenomena, multiple data or simulation models are used for field investigation, the field investigation is time-consuming and labor-consuming and is limited to a small range, and the models are often too complex and difficult to verify due to more parameters. Spatial information technologies such as remote sensing and geographic information systems provide a brand new research mode and technical means for large-scale research of phenomena of tree fruiting and bearing years.
Disclosure of Invention
The invention aims to solve the problem of the traditional method for researching the Quercus serrata fruiting old and small phenomena, and provides a method for researching the Quercus serrata fruiting old and small phenomena based on remote sensing images.
The technical scheme of the invention is as follows:
a research method of Quercus serrata fruiting old and small phenomenon based on remote sensing images comprises the following steps:
(1) in a target area with Quercus serrata as a dominant species of a forest community, a plurality of Quercus serrata trees aged more than 10 years are selected for observation samples, each crown is ensured to be not smaller than the spatial resolution of a hyperspectral remote sensing image, the geographic coordinates of each Quercus serrata sample are recorded by using a GPS (global positioning system), 5-8 fruit collectors are arranged around each Quercus serrata, and the fruit yield (fruiting amount) of each Quercus serrata sample every 3 years (research years) continuously is obtained.
(2) And acquiring hyperspectral remote sensing images of the same target area in multiple stages of Quercus serrata growth period (4-7 months) every year in the research year, and preprocessing the hyperspectral remote sensing images by taking ground control points acquired in advance in the same area as a reference to obtain a real reflectivity image.
(3) And (3) acquiring the spectral reflectivity of each sample based on the coordinates of the sample Quercus serrata by using the reflectivity image obtained in the step (2), and generating a plurality of spectral transformation forms.
(4) Performing correlation analysis on the multiple spectral transformation forms of the sample obtained in the step (3) and the annual consolidation quantity of the sample Quercus serrata obtained in the step (1), obtaining a quantitative relation between the Quercus serrata consolidation quantity of the target area sample and remote sensing data of the annual growth period, and establishing an estimation model of Quercus serrata fruit yield: y ispi＝ai+biX；
Wherein i represents a year during the 3 year study year, p represents the pth sample Quercus serrata of the target region, and YpiRepresents the amount of fruiting in year i of the p th sample Quercus serrata obtained in step (1), aiAnd biLinear fit coefficients for study year i.
(5) And (4) carrying out object-based supervision and classification on the remote sensing image successfully establishing the quantitative relation in the step (4) by using eCogination software, and obtaining the spatial distribution of all oaks in the remote sensing image of the target area.
(6) And (5) inversely analyzing the fruit production condition of all oaks in the corresponding year in the target area obtained in the step (5) by using a remote sensing image according to the estimation model of the Quercus serrata fruit yield obtained in the step (4).
(7) And (3) analyzing the spatial distribution characteristics of the Quercus serrata fruiting and growth year phenomenon by utilizing a G coefficient in a local spatial statistical method on the basis of all Quercus serrata fruiting conditions in the target region obtained in the step (6):
Figure BDA0001442586790000021
wherein x isiAnd xjTwo Quercus serrata points representing target regions at positions i and j, n representing total number of Quercus serrata pointsij(d) The distance hierarchical connectivity matrix represents whether two positions i and j are within a range of a distance d, if so, the value is 1, and if not, the value is 0; the spatial heterogeneity of Quercus serrata within the target region in years and between years can be reflected by plotting the G value, so as to research and analyze the phenomenon of Quercus serrata in large and small years.
The method analyzes the spatial synchronism of Quercus serrata fruiting in a target area by normalizing the value of G and utilizing the maximum value of normalized G and the value of distance d where the maximum value of normalized G appears, so as to obtain the spatial mechanism of the phenomenon of Quercus serrata fruiting in small and large years, wherein the calculation of the normalized G value is shown in the formula Gi(d) ' the following:
Figure BDA0001442586790000031
wherein the content of the first and second substances,
Figure BDA0001442586790000032
Figure BDA0001442586790000033
the various spectral transformations include raw spectra, first order differential spectra, spectral dimensional features based on Principal Component Analysis (PCA) and Partial Least Squares (PLS), and variables based on various Vegetation Indices (VI), spectral positions (e.g., red, blue, and yellow), and spectral areas.
In a target area Quercus serrata spatial distribution diagram obtained by performing object-based supervised classification by using eCognion software and a standard nearest neighbor classifier (the stationary nearest neighbor classifier), each remote sensing image is segmented into objects classified into Quercus serrata, and the objects are regarded as each Quercus serrata.
The growth period of Quercus serrata is 4-7 months; the study year was 3 years.
The method has the advantages that the method takes the hyperspectral remote sensing image as a data source, combines the investigation of the Quercus serrata fruit on the ground, applies a multivariate linear regression algorithm, establishes a Quercus serrata fruit yield estimation model based on the reflection spectrum of the crown layer of the Quercus serrata growing season, carries out large-area prediction on the yield of the Quercus serrata fruit, and carries out quantitative analysis on the spatial distribution heterogeneity of the Quercus serrata fruiting small-year phenomenon by using G statistics. Compared with the traditional method for researching the tree fructification and old-year phenomenon, the method is time-saving and labor-saving, and is more accurate and practical.
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FIG. 1 is a flow chart of the practice of the present invention.
Detailed Description
A specific embodiment of the present invention is shown in fig. 1.
As shown in fig. 1, an embodiment of the present invention includes the following steps:
(1) selecting Quercus serrata trees which are reasonably distributed and the number of which meets the statistical verification in a target area as ground sampling points:
in specific implementation, in a forest land range of a target region with oaks as a dominant species of a community, a plurality of oaks with the age of more than 10 years are uniformly selected for observation samples, each crown is ensured to be not less than the spatial resolution of a hyperspectral remote sensing image, the geographic coordinates of each sample oaks are recorded by using a GPS, 5-8 fruit collectors are arranged around each oaks, and the fruit yield (fruiting amount) of each sample oaks every 3 consecutive years (minimum period of a big-small year phenomenon) is obtained.
(2) And acquiring hyperspectral remote sensing images of the same target area in multiple stages of Quercus serrata growth period (4-7 months) every year in the research year, and preprocessing the hyperspectral remote sensing images by taking ground control points acquired in advance in the same area as a reference to obtain a real reflectivity image.
In specific implementation, the hyperspectral remote sensing image can be set and utilized by self, such as an Airborne hyperspectral aerial remote sensing Imaging system (Airborner Imaging Spectrometer for Applications) adopting Finland SPECIM in the embodiment
The data obtained had a spatial resolution of 1.5 m and 72 bands (407- & 898 nm).
(3) And (3) acquiring the spectral reflectivity of each sample based on the coordinates of the sample Quercus serrata by using the reflectivity image obtained in the step (2), and generating a plurality of spectral transformation forms, such as original spectra, first-order differential spectra, spectral dimensional characteristics based on Principal Component Analysis (PCA) and Partial Least Squares (PLS), variables based on various Vegetation Indexes (VI), spectral positions (such as red edges, blue edges and yellow edges) and spectral areas, and the like.
(4) Performing correlation analysis on the multiple spectral transformation forms of the sample obtained in the step (3) and the annual consolidation quantity of the sample Quercus serrata obtained in the step (1), obtaining a quantitative relation between the Quercus serrata consolidation quantity of the target area sample and remote sensing data of the annual growth period, and establishing an estimation model of Quercus serrata fruit yield:
Ypi＝ai+biX
wherein i represents a year during the 3 year study year; p represents the pth sample of the target region Quercus serrata; y ispiRepresents the fruit set of the p th sample Quercus serrata obtained in the step (1) in the i year during the study year; a is aiAnd biLinear fit coefficients for study year i.
In specific implementation, the spectral transformation form most sensitive to the amount of Quercus serrata fruiting is screened by analyzing the correlation between the generated multiple spectral transformation forms and the amount of Quercus serrata fruiting, a multivariate linear regression model is used for establishing an estimation model of Quercus serrata fruit yield, the estimation model of Quercus serrata fruit yield is established by using spectral dimensional characteristic parameters extracted by a Partial Least Squares (PLS) analysis method according to the embodiment, and a fitting equation and an evaluation index (correlation coefficient or determination coefficient) are obtained as shown in Table 1:
TABLE 1
Figure BDA0001442586790000051
(5) And (4) carrying out object-based supervision and classification on the remote sensing image successfully establishing the quantitative relation in the step (4) by using eCogination software, and obtaining the spatial distribution of all Quercus serrata fructification in the remote sensing image of the target area.
(6) And (5) inversely analyzing the fruit production condition of all oaks in the corresponding year in the target area obtained in the step (5) by using a remote sensing image according to the estimation model of the Quercus serrata fruit yield obtained in the step (4).
(7) And (3) analyzing the spatial distribution characteristics of the Quercus serrata fruiting and growth year phenomenon by utilizing a G coefficient in a local spatial statistical method on the basis of all Quercus serrata fruiting conditions in the target region obtained in the step (6):
Figure BDA0001442586790000061
wherein x isiAnd xjTwo Quercus serrata points representing target regions at positions i and j, n representing total number of Quercus serrata pointsij(d) The distance hierarchical connectivity matrix represents whether two positions i and j are within a distance d range or not, if the two positions i and j are within the distance d range, a value is 1, otherwise the value is 0, and the spatial heterogeneity of the target area Quercus serrata within a year and between years can be reflected by charting a G value, so that the phenomenon of Quercus serrata within the year and between years can be researched and analyzed.
In particular, the spatial synchronization of Quercus serrata fructification in a target region can be analyzed by normalizing the G value to enhance the explanatory power thereof and by using the maximum value of each Quercus serrata standardized G and the distance d value at which the maximum value of the standardized G appears, so as to explore the spatial mechanism of the phenomenon of Quercus serrata fructification in small and large years, wherein the formula G is calculated by normalizing the G valuei(d) ' the following:
Figure BDA0001442586790000062
Figure BDA0001442586790000063
Figure BDA0001442586790000064
in specific implementation, the method can adopt a computer software technology to realize an automatic operation process, and can also adopt a modularized mode to provide a corresponding system.

Claims (4)

1. A method for researching Quercus serrata fruiting and climacteric phenomenon based on remote sensing images is characterized by comprising the following steps:
(1) selecting a plurality of oaks with the age of more than 10 years for observation samples in a target area with oaks as a dominant species of a forest community, ensuring that each crown is not smaller than the spatial resolution of a hyperspectral remote sensing image, recording the geographic coordinates of each sample oaks with a GPS (global positioning system), arranging 5-8 fruit collectors around each oaks, and obtaining the annual fruit yield of the sample oaks in a continuous research year;
(2) acquiring hyperspectral remote sensing images of the same target area in multiple stages of Quercus serrata growth period every year in a research year, and preprocessing the hyperspectral remote sensing images by taking ground control points acquired in advance in the same area as a reference to obtain a real reflectivity image;
(3) acquiring the spectral reflectivity of each sample based on the coordinates of the sample Quercus serrata by using the reflectivity image obtained in the step (2), and generating a plurality of spectral transformation forms;
(4) performing correlation analysis on the sample multispectral transformation form obtained in the step (3) and the fruiting amount of the sample Quercus serrata in the same year obtained in the step (1), obtaining a quantitative relation between the fruiting amount of the sample Quercus serrata in a target area and remote sensing data in the growth period of the same year, and establishing an estimation model of Quercus serrata fruit yield: y ispi＝ai+biX；
Wherein i represents a year during the 3 year study year, p represents the pth sample Quercus serrata of the target region, and YpiRepresents the amount of fruiting in year i of the p th sample Quercus serrata obtained in step one, aiAnd biLinear fit coefficients for study year i;
(5) carrying out object-based supervision and classification on the remote sensing image successfully establishing the quantitative relation in the step (4) to obtain the spatial distribution of all oaks in the remote sensing image of the target area;
(6) according to the estimation model of the Quercus serrata fruit yield obtained in the step (4), carrying out inversion analysis on the fruit production conditions of all Quercus serrata in the target area obtained in the step (5) in the corresponding year by using a remote sensing image;
(7) and (3) analyzing the spatial distribution characteristics of the Quercus serrata fruiting and growth year phenomenon by utilizing a G coefficient in a local spatial statistical method based on the Quercus serrata fruiting condition in the target region obtained in the step (6):
Figure FDA0002784932750000021
wherein x isiAnd xjTwo Quercus serrata points representing target regions at positions i and j, n representing total number of Quercus serrata pointsij(d) The distance hierarchical connectivity matrix represents whether two positions i and j are within a range of a distance d, if so, the value is 1, and if not, the value is 0;
the method analyzes the spatial synchronism of Quercus serrata results in a target area by normalizing the G value and utilizing the maximum value of normalized G and the value of distance d where the maximum value of normalized G appears, so as to obtain the spatial mechanism of the phenomenon of big and small years of Quercus serrata results, wherein the calculation of the normalized G value is shown in the formula Gi(d) ' the following:
Figure FDA0002784932750000022
wherein the content of the first and second substances,
Figure FDA0002784932750000023
Figure FDA0002784932750000024
2. the method for researching Quercus serrata fruiting old and young phenomenon based on remote sensing image of claim 1, wherein the plurality of spectral transformation forms comprise original spectrum, first order differential spectrum, spectral dimensional characteristics based on principal component analysis and partial least square method, and variables based on various vegetation indexes, spectral positions and spectral areas.
3. The method for researching Quercus serrata fruiting year and year phenomena based on remote sensing images as claimed in claim 1, wherein the object-based supervised classification adopts eCogination software, an object classified into Quercus serrata is segmented from each remote sensing image in a target region Quercus serrata spatial distribution map obtained by the object-based supervised classification, and each object classified into Quercus serrata is considered as each Quercus serrata.
4. The method for researching the Quercus serrata fruiting old and young phenomenon based on remote sensing image according to claim 1, wherein the growth period of Quercus serrata is 4-7 months.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002360070A (en) * 2001-06-12 2002-12-17 Kansai Electric Power Co Inc:The Evaluation method of plant vitality
CN101435873A (en) * 2008-12-24 2009-05-20 中国中医科学院中药研究所 Remote sense monitoring method of medicinal plant resource based on concomitant species and community classification
CN103500344A (en) * 2013-09-02 2014-01-08 中国测绘科学研究院 Method and module for extracting and interpreting information of remote-sensing image
CN105893977A (en) * 2016-04-25 2016-08-24 福州大学 Rice mapping method based on self-adaptive feature selection

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002360070A (en) * 2001-06-12 2002-12-17 Kansai Electric Power Co Inc:The Evaluation method of plant vitality
CN101435873A (en) * 2008-12-24 2009-05-20 中国中医科学院中药研究所 Remote sense monitoring method of medicinal plant resource based on concomitant species and community classification
CN103500344A (en) * 2013-09-02 2014-01-08 中国测绘科学研究院 Method and module for extracting and interpreting information of remote-sensing image
CN105893977A (en) * 2016-04-25 2016-08-24 福州大学 Rice mapping method based on self-adaptive feature selection

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Spatial Heterogeneity and Temporal Stability of Throughfall under Individual Quercus brantii Trees;Omid Fathizadeh et al.;《ResearchGate》;20140131;第1-14页 *
基于像斑直方图G统计量的遥感影像分类;李亮 等;《国土资源遥感》;20161231;第28卷(第4期);第71-76页 *
应用G统计量分析2006年浙江省肺结核的空间异质性;应倩 等;《中国预防医学杂志》;20120630;第46卷(第6期);第524-526页 *

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