CN110321402B - Method for predicting potential distribution of arbor forest in mountainous area - Google Patents
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Abstract
A prediction method for potential distribution of arbor forests in mountainous areas is characterized in that an arbor forest distribution model is constructed based on TVDI temperature vegetation drought index, SMMI soil humidity monitoring index and limiting factor, and based on VTCI condition vegetation temperature index, SMMI soil humidity monitoring index and limiting factor, and the model is selected; extracting arbor forest distribution indexes, and respectively generating arbor forest distribution index graphs; randomly generating sampling points in the research area range, selecting the sampling points in an arbor forest according to the remote sensing image, counting the sampling points to generate a scatter diagram, and manually debugging the scatter diagram by visually interpreting and referring to the remote sensing image to obtain a threshold value; and deducting the regions where the normalized soil index, the altitude, the slope direction and the gradient are not suitable for the growth of the arbor forest from the arbor forest distribution index map to obtain an arbor forest distribution region, and finally deducting the existing arbor forest distribution region to obtain the arbor forest potential distribution region.
Description
Technical Field
The invention relates to a method for predicting potential distribution of mountainous arbor forests, and belongs to the technical field of predicting potential distribution of mountainous arbor forests based on Remote Sensing (RS) and Geographic Information System (GIS).
Background
The mountain natural forest is located between an accumulated snow glacier and a river water system, has important ecological hydrological effects of controlling energy balance and water circulation, protecting permanent accumulated snow and glacier safety, intercepting and adjusting rainfall and ice and snow melting water in the mountain, has important ecological functions of conserving water sources, purifying water quality, maintaining water and soil, preventing wind and fixing sand and the like, and is responsible for adjusting ecological balance. Along with the increase of the demand of economic construction on forest material products and the increase of the interference intensity of various economic activities on a forest ecological system, the natural updating problem of natural forests in mountain areas is increasingly prominent. How to scientifically define the suitable growing area of the arbor forest in the mountainous area is a problem which needs to be solved urgently at present.
Traditional forest potential distribution prediction is based on meteorological data, and simulates potential distribution areas of the forest by analyzing precipitation, air temperature and solar radiation thresholds appropriate for the existing forest distribution. Due to the sparsity, the non-uniformity and the discreteness of the distribution of the meteorological sites, the method has the following defects: 1. spatial interpolation is needed to be carried out on meteorological sites when meteorological data of a research area are required to be obtained, so that the accuracy of the meteorological data is reduced, and the prediction precision of potential distribution of a forest land is influenced; 2. in the area without the weather station, if the weather data is required to be acquired, the weather station needs to be arranged for actual measurement, so that the cost is high, the period is long, and the timeliness is poor.
The method for simulating potential distribution of Tianshan Yunshilin model constructed by extracting terrain humidity index, backlight degree, altitude, slope direction, gradient and precipitation data from the Tianshan Yunshilin model based on meteorological data and terrain data by using remote sensing and geographic information system technology is provided in the year 2016 (literature: dingcheng, lixia, zhang Boangxiang, etc.. The potential distribution area of the Tianshan Yunshilin in the middle of the Tianshan based on the habitat index is predicted [ J ]. The application ecology newspaper, 2016,27 (8): 2401-2408). The method improves the prediction accuracy to a certain extent, but is still limited by sparsity, nonuniformity and discreteness of meteorological site distribution.
Disclosure of Invention
The invention provides a method for predicting potential distribution of arbor forests in mountainous areas based on remote sensing images, aiming at overcoming the problem that the prediction accuracy of the potential distribution of forests is limited by the distribution of meteorological sites.
A prediction method of mountain area arbor forest potential distribution comprises the following steps;
respectively extracting a TVDI temperature vegetation drought index, a VTCI condition vegetation temperature index, an SMMI soil humidity monitoring index and an NDSI normalized soil index from remote sensing image data through a GIS (geographic information system) technology and an RS remote sensing technology, and extracting elevation, slope and gradient data from DEM data, wherein the TVDI temperature vegetation drought index, the VTCI condition vegetation temperature index and the SMMI soil humidity monitoring index are used as appropriate factors for predicting potential distribution of the arbor forest, and the NDSI normalized soil index, the elevation, the slope and the gradient are used as limiting factors; constructing a arbor forest distribution model based on the TVDI temperature vegetation drought index, the SMMI soil humidity monitoring index and the limiting factor respectively, and the VTCI condition vegetation temperature index, the SMMI soil humidity monitoring index and the limiting factor respectively, and selecting the model; extracting arbor forest distribution indexes, and respectively generating arbor forest distribution index maps;
randomly generating sampling points in a research area range, selecting sampling points in an arbor forest according to a remote sensing image, respectively extracting arbor forest distribution indexes based on TVDI temperature vegetation drought indexes, arbor forest distribution indexes based on VTCI condition vegetation temperature indexes, normalized soil indexes, elevations, slopes and slopes corresponding to the sampling points, counting the sampling points to generate a scatter diagram, and manually debugging the scatter diagram by visually interpreting and referring to the remote sensing image to obtain a threshold value;
predicting a potential arbor forest distribution area, respectively extracting arbor forest distribution indexes, normalized soil indexes, altitudes, slopes and gradient factors by using a determined threshold, and outputting vector diagrams for generating an arbor forest distribution index distribution diagram, a normalized soil index distribution diagram, an altitude distribution diagram, a slope distribution diagram and a gradient distribution diagram;
and deducting the regions where the normalized soil index, the altitude, the slope direction and the gradient are not suitable for the growth of the arbor forest from the arbor forest distribution index map to obtain an arbor forest distribution region, and finally deducting the existing arbor forest distribution region to obtain the arbor forest potential distribution region.
The invention has the beneficial effects that: according to the method, the arbor forest distribution model is constructed by extracting the relevant index factors from the remote sensing image, the spatial distribution area of the arbor forest in the mountainous area can be simulated and generated rapidly, the afforestation position is provided for the management department of forest in the mountainous area rapidly and accurately, and a scientific basis is provided for updating natural forests in the mountainous area.
Meteorological data of a meteorological site cannot accurately reflect meteorological conditions of the whole mountainous area, so that the method for predicting potential distribution of arbor forests through the meteorological data has limitation. The method overcomes the limitation, the remote sensing image data can cover the whole mountain area, accurate data can be extracted aiming at each point, the data acquisition way is faster, and the prediction precision is improved.
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A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein the accompanying drawings are included to provide a further understanding of the invention and form a part of the specification, and wherein the illustrated embodiments of the invention and the description thereof are intended to illustrate and not to limit the invention, as illustrated in the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the present invention.
The invention is further illustrated with reference to the following figures and examples.
Detailed Description
Obviously, many modifications and variations of the present invention based on the gist of the present invention will be apparent to those skilled in the art.
It will be understood by those within the art that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art.
The following examples are further illustrative in combination for ease of understanding the embodiments and are not intended to limit the invention.
Example 1: as shown in fig. 1, a method for predicting potential distribution of arbor forest in mountainous area includes the following steps; respectively extracting a TVDI (total volatile differential) temperature vegetation drought index, a VTCI (virtual terrestrial digital interface) condition vegetation temperature index, a SMMI (national geographic information System) soil humidity monitoring index and an NDSI (normalized soil index) from remote sensing image data by a GIS (geographic information system) technology and an RS (remote sensing system) technology, and extracting elevation, slope and slope data from DEM data, wherein the TVDI temperature vegetation drought index, the VTCI condition vegetation temperature index and the SMMI soil humidity monitoring index are used as appropriate factors for predicting potential distribution of arbor forests, and the NDSI normalized soil index, the elevation, the slope and the slope are used as limiting factors; constructing a arbor forest distribution model based on the TVDI temperature vegetation drought index, the SMMI soil humidity monitoring index and the limiting factor respectively, and the VTCI condition vegetation temperature index, the SMMI soil humidity monitoring index and the limiting factor respectively, and selecting the model; extracting arbor forest distribution indexes, and respectively generating arbor forest distribution index graphs;
randomly generating sampling points in a research area range, selecting sampling points in an arbor forest according to a remote sensing image, respectively extracting arbor forest distribution indexes based on TVDI temperature vegetation drought indexes, arbor forest distribution indexes based on VTCI condition vegetation temperature indexes, normalized soil indexes, elevations, slopes and slopes corresponding to the sampling points, counting the sampling points to generate a scatter diagram, and manually debugging the scatter diagram by visually interpreting and referring to the remote sensing image to obtain a threshold value;
predicting potential distribution areas of the arbor forest, respectively extracting arbor forest distribution indexes, normalized soil indexes, altitudes, slope directions and gradient factors by using the determined threshold values, and outputting vector diagrams such as an arbor forest distribution index distribution map, a normalized soil index distribution map, an altitude distribution map, a slope direction distribution map and a gradient distribution map;
and deducting the regions where the normalized soil index, the altitude, the slope direction and the gradient are not suitable for the growth of the arbor forest from the arbor forest distribution index map to obtain an arbor forest distribution region, and finally deducting the existing arbor forest distribution region to obtain the arbor forest potential distribution region.
The method comprises the following steps of constructing a model based on the TVDI temperature vegetation drought index, the SMMI soil humidity monitoring index and the limiting factor:
step 1, collecting data from geospatial data cloud websites (b)http:// www. Gsclone. Cn /) download Landsat 8Remote sensing image data and DEM image data.
And 2, preprocessing data, namely preprocessing the remote sensing image by using an ENVI remote sensing image processing platform, such as geometric correction, radiometric calibration, atmospheric correction, terrain correction and the like.
Step 3, extracting the temperature vegetation drought index, firstly using ENVI according to a mathematical model:
extracting surface temperature, T, from remote sensing image s Is the surface temperature of any picture element,
for the radiance value of any pixel,
landsat 8K 1 ＝774.8853W/(m 2 *sr*μm)、K 2 ＝1321.0789K。
Secondly, applying an ENVI basis mathematical model:
NDVI＝(NIR-RED)/(NIR+RED) (2)
NDVI (normalized vegetation index) is extracted from the remote sensing image, NIR is a near infrared band, and RED is an infrared band.
And finally, applying an ENVI basis mathematical model:
TVDI＝(T s -T s_min )/(T s_max -T s_min ) (3)
extracting temperature vegetation drought index, T, from remote sensing image s Is the surface temperature, T, of any pixel s_min The minimum earth surface temperature, namely the wet edge, is obtained by fitting the earth surface temperature and the vegetation index, and the calculation formula is as follows:
T s_min ＝a 1 +b 1 *NDVI；
T s_max the highest surface temperature corresponds to the dry edge, and the calculation formula is as follows:
T s_min ＝a 2 +b 2 *NDVI，
a 1 、b 1 are the coefficients of the wet-edge fit equation,
a 2 、b 2 are the coefficients of the dry edge fitting equation.
Step 4, extracting a soil humidity monitoring index, and applying an ENVI according to a mathematical model:
extracting SMMI (soil moisture monitoring index) from the remote sensing image and generating an SMMI distribution map, wherein: NIR is the near infrared band and SWIR is the short wave infrared band.
Step 5, extracting the normalized soil index, and applying an ENVI according to a mathematical model:
and (3) extracting NDSI (normalized soil index) from the remote sensing image and generating an NDSI distribution map.
In the formula: NIR is the near infrared band and SWIR is the short wave infrared band.
And 6, extracting terrain factors, extracting the terrain factors such as altitude, slope direction and gradient from the DEM image by using a 3D analysis tool of ArcGIS, and generating a corresponding distribution map.
And 7, constructing a model, namely constructing a arbor forest distribution index comprehensive model by using a GIS space modeling technology based on the factors extracted in the steps 3, 4, 5 and 6.
T d ＝{T(TVDI，SMMI)}\{E up ，A s ，S d ，NDSI i，j } (6)
In the formula (I), the compound is shown in the specification,
T d is a comprehensive model of arbor forest distribution index,
t is a arbor distribution exponential function constructed by TDVI and SMMI,
E up is not suitable for arbor growthThe altitude threshold of (a) is set,
A s is a slope threshold value which is not suitable for the growth of the arbor,
S d is a slope threshold value which is not suitable for the growth of trees,
NDSI i，j and the normalized soil index interval value is the normalized soil index interval value of the bare rock, the gravel and the soil containing the gravel.
Arbor distribution index function is as follows:
in the formula:
to investigate the average value of the region TVDI,
a is a constant.
And 8, extracting the arbor forest distribution index, and applying an ArcGIS 10.2 grid calculator tool according to the function determined in the step 7 based on the TVDI and SMMI determined in the steps 2 and 3:
and extracting the arbor forest distribution index and generating an arbor forest distribution index map.
And 9, determining a threshold, randomly generating sampling points in the research area range, and selecting the sampling points in the arbor forest according to the remote sensing image. And (3) respectively extracting the arbor forest distribution index, the normalized soil index, the altitude, the slope direction and the slope corresponding to each sample point by using an ArcGIS space analysis tool, counting the sample points by using Origin8.0, generating a scatter diagram, and manually debugging the scatter diagram by visually judging and referring to the remote sensing image to obtain an optimal threshold value.
And step 10, predicting potential distribution areas of the arbor forest, respectively extracting factors such as arbor forest distribution indexes, normalized soil indexes, altitudes, slope directions and the like by using the ArcGIS according to the threshold determined in the step 9, and generating a distribution map. And deducting areas unsuitable for arbor forest growth, such as normalized soil indexes, altitudes, slope directions, slopes and the like from the arbor forest distribution index map by using an ArcGIS analysis tool to obtain an arbor forest distribution area, and finally deducting the existing arbor forest distribution area to obtain an arbor forest potential distribution area.
And 11, verifying the precision, wherein the precision verification adopts two steps, namely a step of quantizing the precision by adopting a sampling method and a step of comparing the habitat.
Step 1), adopting a sampling method to quantize precision: arranging sample points in a research area range by applying ArcGIS, wherein the specification is 500m multiplied by 500m; respectively extracting sampling points positioned inside and outside the existing forest land by referring to the remote sensing image, and distributing attributes of additional arbor forests and non-arbor forests; and respectively counting the number of sampling points with the attribute of being the arbor forest in the arbor forest distribution area and the number of sampling points with the attribute of being the non-arbor forest in the arbor forest potential distribution area by adopting an ArcGIS space superposition analysis function.
The precision verification formula of the numerator sampling method is as follows:
Pi＝Ni/N (8)
in the formula: p is the precision of the image to be measured,
pi is a number of the product of the number,
ni is the number of the i-type pattern points,
n is the total number of the sampling points,
t is 1.96.
Step 2), adopting a habitat comparison method to perform the steps of: by collecting soil temperature and humidity data of the existing arbor forest distribution area, arbor forest potential distribution area and arbor forest non-distribution area and carrying out correlation analysis, the habitat difference of 3 distribution types is compared and analyzed by using the One-way ANOVA function of IBM SPSS Statistics.
Example 2: as shown in fig. 1, a method for predicting potential distribution of arbor forest in mountainous area includes the following steps;
step 1, collecting data from geospatial data cloud websites (b)http: v/www. Gscloud. Cn /) download Landsat 8Remote sensing image data and DEM image data.
And 2, preprocessing data, namely preprocessing geometric correction, radiometric calibration, atmospheric correction, terrain correction and the like on the remote sensing image by using ENVI.
Step 3, extracting the temperature vegetation drought index, firstly using ENVI according to a mathematical model:
extracting surface temperature, T, from remote sensing image s Is the surface temperature of any pixel,
k of Landsat8, the radiance value of any pixel 1 ＝774.8853W/(m 2 *sr*μm)、K 2 ＝1321.0789K。
Secondly, applying an ENVI basis mathematical model:
NDVI＝(NIR-RED)/(NIR+RED) (2)
NDVI (normalized vegetation index) is extracted from the remote sensing image, NIR is a near infrared band, and RED is an infrared band.
Finally, applying ENVI according to a mathematical model:
TVDI＝(T s -T s _ min )/(T s_max -T s_min ) (3)
extracting temperature vegetation drought index, T, from remote sensing image s_min The minimum earth surface temperature, namely the wet edge, is obtained by fitting the earth surface temperature and the vegetation index, and the calculation formula is as follows:
T s_min ＝a 1 +b 1 *NDyI；
T s_max the highest surface temperature corresponds to the dry edge, and the calculation formula is as follows:
T s_min ＝a 2 +b 2 *NDVI，
a 1 、b 1 are the coefficients of the wet-edge fit equation,
a 2 、b 2 are the coefficients of the dry edge fitting equation.
Step 4, extracting the vegetation temperature index under the condition: applying ENVI according to a mathematical model:
VTCI＝(T s_max -T s )/(T s_max -T s_min ) (4)
extracting conditional vegetation temperature index, T, from remote sensing images s_min The minimum earth surface temperature, namely the wet edge, is obtained by fitting the earth surface temperature and the vegetation index, and the calculation formula is as follows:
T s_min ＝a 1 +b 1 *NDVI；
T s_max the highest surface temperature corresponds to the dry edge, and the calculation formula is as follows:
T s_min ＝a 2 +b 2 *NDVI，
a 1 、b 1 are the coefficients of the wet-edge fit equation,
a 2 、b 2 are the coefficients of the dry edge fitting equation.
T s The extraction modes of (surface temperature) and NDVI (normalized vegetation index) are the same as those in step 3.
Step 5, extracting a soil humidity monitoring index, and applying an ENVI according to a mathematical model:
extracting SMMI (soil moisture monitoring index) from the remote sensing image and generating an SMMI distribution graph, wherein: NIR is the near infrared band and SWIR is the short wave infrared band.
Step 6, extracting the normalized soil index, and applying an ENVI according to a mathematical model:
and (3) extracting NDSI (normalized soil index) from the remote sensing image and generating an NDSI distribution map.
In the formula: NIR is the near infrared band and SWIR is the short wave infrared band.
And 7, extracting the terrain factors such as the altitude, the slope direction, the gradient and the like from the DEM image by using a 3D analysis tool of ArcGIS, and generating a corresponding distribution map.
And 8, constructing a model based on TVDI, SMMI, NDSI and topographic factors, and constructing a arbor forest distribution index comprehensive model by using a GIS spatial modeling technology:
T d_TVDI ＝{T TVDI (TVDI，SMMI)}\{E up ，A s ，S d ，NDSI i，j } (7)
in the formula, T d_TVDI Is a comprehensive model of arbor forest distribution index,
T TVDI for the arbor distribution index function constructed with TDVI and SMMI,
E up is an altitude threshold value which is not suitable for the growth of trees,
A s is a slope threshold value which is not suitable for the growth of the arbor,
S d is a slope threshold value which is not suitable for arbor growth,
NDSI i，j the normalized soil index interval value of the soil containing bare rock, gravel and gravel is shown.
Arbor distribution index function is as follows:
in the formula:
to investigate the average value of the region TVDI,
mean value of SMMI for the study areaAnd a is a constant.
And 9, constructing a model based on VTGI, SMMI, NDSI and the terrain factor, and constructing a arbor forest distribution index comprehensive model by using a GIS space modeling technology.
T d _ VTCI ＝{T VTCI (VTCI，SMMI)}\{E up ，A s ，S d ，NDSI i，j } (9)
In the formula, T d_VTCI Is a comprehensive model of arbor forest distribution index,
T VTCI for the arbor distribution index function constructed with VTCI and SMMI,
E up is an altitude threshold value which is not suitable for the growth of trees,
A s is a slope threshold value which is not suitable for the growth of the arbors,
S d is a slope threshold value which is not suitable for arbor growth,
NDSI i，j the normalized soil index interval value of the soil containing bare rock, gravel and gravel is shown.
Arbor distribution index function is as follows:
in the formula:
as an average value of the VTCI of the study area,
b is a constant value for the mean value of the study area SMMI.
And step 10, extracting the arbor forest distribution index by using an ArcGIS 10.2 grid calculator tool according to the function (8) and the function (10) determined in the step 8 and the step 9 respectively, and generating an arbor forest distribution index map respectively.
And 11, determining a threshold value, randomly generating sampling points in the research area range, and selecting the sampling points in the arbor forest according to the remote sensing image. Respectively extracting the arbor forest distribution index based on TVDI, the arbor forest distribution index based on VTCI, the normalized soil index, the altitude, the slope direction and the slope grade corresponding to each sampling point by using an ArcGIS space analysis tool, counting the various points by using 0rigin 8.0, generating a scatter diagram, and manually debugging by visually judging and referring to a remote sensing image on the scatter diagram to obtain an optimal threshold value.
And step 12, predicting potential distribution areas of the arbor forest, respectively extracting factors such as arbor forest distribution indexes, normalized soil indexes, altitudes, slope directions and the like by using the ArcGIS according to the threshold determined in the step 11, and generating a distribution map. And deducting regions unsuitable for the growth of the arbor forest, such as the normalized soil index, the altitude, the slope direction, the gradient and the like from the arbor forest distribution index map by using an ArcGIS analysis tool to obtain an arbor forest distribution region, and finally deducting the conventional arbor forest distribution region to obtain the arbor forest potential distribution region. Accordingly, the arbor forest potential distribution area based on TVDI prediction and the arbor forest potential distribution area based on VTCI prediction are obtained respectively.
And step 13, precision verification, wherein the precision verification adopts two modes, namely, a sampling method is adopted to quantify the precision, and a habitat comparison method is adopted.
Step 1), a number sampling method: arranging sample points in a research area range by applying ArcGIS, wherein the specification is 500m multiplied by 500m; respectively extracting sampling points positioned inside and outside the existing forest land by referring to the remote sensing image, and distributing attributes of additional arbor forests and non-arbor forests; and respectively counting the number of sampling points with the attribute of being the arbor forest in the arbor forest distribution area and the number of sampling points with the attribute of being the non-arbor forest in the arbor forest potential distribution area by adopting an ArcGIS space superposition analysis function.
The precision verification formula of the numerator sampling method is as follows:
Pi＝Ni/N (11)
in the formula: p is precision, pi is number of finished products, ni is number of i-type samples, N is total number of samples, and t is 1.96.
Step 2), habitat comparison method: by collecting soil temperature and humidity data of the existing arbor forest distribution area, arbor forest potential distribution area and arbor forest non-distribution area and carrying out correlation analysis, the habitat difference of 3 distribution types is compared and analyzed by using the One-way ANOVA function of IBM SPSS Statistics.
By using the two methods, the accuracy of the arbor forest potential distribution area based on TVDI prediction and the accuracy of the arbor forest potential distribution area based on VTCI prediction are verified respectively, and an optimal model is selected.
As described above, although the embodiments of the present invention have been described in detail, it will be apparent to those skilled in the art that many modifications are possible without substantially departing from the spirit and scope of the present invention. Therefore, all such modifications are also included in the scope of the present invention.
Claims (4)
1. A prediction method of potential distribution of mountain arbor forest is characterized by comprising the following steps;
respectively extracting a TVDI temperature vegetation drought index, a VTCI condition vegetation temperature index, an SMMI soil humidity monitoring index and an NDSI normalized soil index from remote sensing image data through a GIS (geographic information system) technology and an RS remote sensing technology, and extracting elevation, slope and gradient data from DEM data, wherein the TVDI temperature vegetation drought index, the VTCI condition vegetation temperature index and the SMMI soil humidity monitoring index are used as appropriate factors for predicting potential distribution of the arbor forest, and the NDSI normalized soil index, the elevation, the slope and the gradient are used as limiting factors; constructing a arbor forest distribution model based on the TVDI temperature vegetation drought index, the SMMI soil humidity monitoring index and the limiting factor respectively, and the VTCI condition vegetation temperature index, the SMMI soil humidity monitoring index and the limiting factor respectively, and selecting the model; extracting arbor forest distribution indexes, and respectively generating arbor forest distribution index maps;
randomly generating sampling points in a research area range, selecting sampling points in an arbor forest according to a remote sensing image, respectively extracting arbor forest distribution indexes based on TVDI temperature vegetation drought indexes, arbor forest distribution indexes based on VTCI condition vegetation temperature indexes, normalized soil indexes, elevations, slopes and slopes corresponding to the sampling points, counting the sampling points to generate a scatter diagram, and manually debugging the scatter diagram by visually interpreting and referring to the remote sensing image to obtain a threshold value;
predicting a potential distribution area of the arbor forest, respectively extracting arbor forest distribution indexes, normalized soil indexes, altitude, slope directions and gradient factors by using a determined threshold, and outputting vector diagrams for generating an arbor forest distribution index distribution map, a normalized soil index distribution map, an altitude distribution map, a slope direction distribution map and a gradient distribution map;
and deducting the regions where the normalized soil index, the altitude, the slope direction and the slope are not suitable for the growth of the arbor forest from the arbor forest distribution index map to obtain an arbor forest distribution region, and finally deducting the conventional arbor forest distribution region to obtain an arbor forest potential distribution region.
2. The method for predicting the potential distribution of the arbor forest in the mountainous area as claimed in claim 1, comprising the steps of:
step 1, collecting data, namely remotely sensing image data and DEM image data from a geographic space data cloud website;
step 2, data preprocessing, namely preprocessing geometric correction, radiometric calibration, atmospheric correction and terrain correction of the remote sensing image by using an ENVI remote sensing image processing platform;
step 3, extracting the temperature vegetation drought index, firstly applying an ENVI according to a mathematical model:
extracting surface temperature T from remote sensing image s Is the surface temperature of any pixel,
for the radiance value of any pixel,
landsat 8K 1 ＝774.8853W/(m 2 *sr*μm)、K 2 ＝1321.0789K；
Secondly, applying an ENVI basis mathematical model:
NDVI＝(NIR-RED)/(NIR+RED) (2)
NDVI (normalized vegetation index) is extracted from the remote sensing image, NIR is a near infrared band, and RED is an infrared band;
and finally, applying an ENVI basis mathematical model:
TVDI＝(T s -T s_min )/(T s_max -T s_min ) (3)
extracting temperature vegetation drought index, T, from remote sensing images s Is the surface temperature of any picture element,
T s_min is the minimum earth surface temperature, namely the wet edge, is obtained by fitting the earth surface temperature and the vegetation index,
the calculation formula is as follows:
T s_min ＝a 1 +b 1 *NDVI；
T s_max the highest surface temperature corresponds to the dry edge, and the calculation formula is as follows:
T s_min ＝a 2 +b 2 *NDVI，
a 1 、b 1 are the coefficients of a wet-edge fitting equation,
a 2 、b 2 is the coefficient of the dry edge fitting equation;
step 4, extracting a soil humidity monitoring index, and applying an ENVI to the soil humidity monitoring index according to a mathematical model:
extracting the SMMI soil humidity monitoring index from the remote sensing image and generating an SMMI distribution graph, wherein: NIR is the near infrared band, SWIR is the short wave infrared band;
step 5, extracting the normalized soil index, and applying an ENVI according to a mathematical model:
extracting an NDSI normalized soil index from the remote sensing image and generating an NDSI distribution map;
in the formula: NIR is the near infrared band, SWIR is the short wave infrared band;
step 6, extracting terrain factors, extracting elevation, slope and gradient terrain factors from the DEM image by using a 3D analysis tool of ArcGIS, and generating a corresponding distribution map;
step 7, constructing a model, namely constructing a arbor forest distribution index comprehensive model by using a GIS space modeling technology based on the factors extracted in the steps 3, 4, 5 and 6;
T d ＝{T(TVDI,SMMI)}\{E up ,A s ,S d ,NDSI i,j } (6)
in the formula (I), the compound is shown in the specification,
T d is a comprehensive model of arbor forest distribution index,
t is an arbor distribution exponential function constructed by TDVI and SMMI,
E up is an altitude threshold value which is not suitable for the growth of trees,
A s is a slope threshold value which is not suitable for the growth of the arbors,
S d is a gradient threshold value which is not suitable for arbor growth,
NDSI i,j the normalized soil index interval value is the normalized soil index interval value of the bare rock, the gravel and the soil containing the gravel;
the arbor distribution index function is as follows:
in the formula:
to investigate the average value of the region TVDI,
a is a constant;
step 8, extracting the arbor forest distribution index, and based on the TVDI and SMMI determined in the steps 2 and 3, applying an ArcGIS 10.2 grid calculator tool according to the function determined in the step 7:
extracting arbor forest distribution indexes and generating an arbor forest distribution index graph;
step 9, determining a threshold value, randomly generating sampling points in the research area range, and selecting the sampling points in the arbor forest according to the remote sensing image; respectively extracting arbor forest distribution indexes, normalized soil indexes, altitudes, slope directions and slopes corresponding to all the sampling points by using an ArcGIS space analysis tool, counting the sampling points by using Origin8.0, generating a scatter diagram, and manually debugging the scatter diagram by visually judging and referring to a remote sensing image to obtain an optimal threshold value;
step 10, predicting potential arbor forest distribution areas, respectively extracting arbor forest distribution indexes, normalized soil indexes, altitudes, slope directions and gradient factors by using ArcGIS according to the threshold determined in the step 9, and generating a distribution map; and deducting the areas where the normalized soil index, the elevation, the slope direction and the gradient are not suitable for the growth of the arbor forest from the arbor forest distribution index map by using an ArcGIS analysis tool to obtain an arbor forest distribution area, and finally deducting the existing arbor forest distribution area to obtain the arbor forest potential distribution area.
3. The method according to claim 2, further comprising the steps of; and (3) quantizing the precision by adopting a sampling method: arranging sample points in a research area range by applying ArcGIS, wherein the specification is 500m multiplied by 500m; respectively extracting sampling points positioned inside and outside the existing forest land by referring to the remote sensing image, and distributing attributes of additional arbor forests and non-arbor forests; respectively counting the number of sampling points with the attributes of arbor forests in the arbor forest distribution area and the number of sampling points with the attributes of non-arbor forests in the arbor forest potential distribution area by adopting an ArcGIS space superposition analysis function;
the accuracy verification formula of the integer sampling method is as follows:
Pi＝Ni/N (8)
in the formula: p is the precision of the image to be measured,
pi is a number of the product of the number,
ni is the number of i-type patterns,
n is the total number of the sampling points,
t is 1.96.
4. The method as claimed in claim 2, further comprising the steps of; adopting a habitat comparison method to perform the following steps: by collecting soil temperature and humidity data of the existing arbor forest distribution area, arbor forest potential distribution area and arbor forest non-distribution area and carrying out correlation analysis, the habitat difference of 3 distribution types is compared and analyzed by using the One-way ANOVA function of IBM SPSS statics.
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| CN107036968A (en) * | 2016-12-27 | 2017-08-11 | 西安科技大学 | A kind of soil moisture method of real-time |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107036968A (en) * | 2016-12-27 | 2017-08-11 | 西安科技大学 | A kind of soil moisture method of real-time |
Non-Patent Citations (3)
| Title |
|---|
| 基于Landsat数字影像的阿尔泰山乔木林空间分布;张景路等;《南京林业大学学报(自然科学版)》;20180715(第04期);全文 * |
| 基于TVDI的土壤湿度时空分布及影响因子分析;郭瑞宁等;《灌溉排水学报》;20171205(第04期);全文 * |
| 基于生境指数的天山中部云杉林潜在分布区预测;丁程锋等;《应用生态学报》;20160831(第08期);全文 * |
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