CN109559053B - Vegetation reachability measurement method based on path distance - Google Patents

Vegetation reachability measurement method based on path distance Download PDF

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CN109559053B
CN109559053B CN201811471623.1A CN201811471623A CN109559053B CN 109559053 B CN109559053 B CN 109559053B CN 201811471623 A CN201811471623 A CN 201811471623A CN 109559053 B CN109559053 B CN 109559053B
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vegetation
landscape
factor
reachability
index
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CN109559053A (en
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孟庆岩
孙云晓
张凯
陈旭
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Sanya Zhongke Remote Sensing Research Institute
Tianjin Chengjian University
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Sanya Zhongke Remote Sensing Research Institute
Tianjin Chengjian University
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y02A30/60—Planning or developing urban green infrastructure

Abstract

The invention discloses a vegetation accessibility measuring method based on path distance, which comprises the following steps: step 1) extracting vegetation and impermeable water based on multispectral remote sensing images, and respectively obtaining a vegetation reference map only containing vegetation distribution and a impermeable surface reference map only containing impermeable surface distribution; step 2) screening to obtain a representative vegetation landscape index by adopting a method combining statistical test and correlation analysis aiming at a vegetation reference map; step 3) concentrating the representative vegetation landscape index into a vegetation landscape structure measuring factor based on a factor analysis method; step 4) calculating an reachable range based on the path distance by combining a research area road network aiming at the watertight surface reference map; and 5) calculating reachability measurement factors in the range of the path distance, determining the weight of each reachability measurement factor by adopting a analytic hierarchy process, and carrying out weighted calculation on each reachability measurement factor to obtain a vegetation reachability distribution diagram.

Description

Vegetation reachability measurement method based on path distance
Technical Field
The invention relates to a vegetation accessibility measurement method based on path distance, in particular to a path distance-based accessibility range calculation method and a vegetation accessibility comprehensive measurement method considering vegetation quantity, landscape structure and growth condition.
Background
Vegetation is an important component of the urban ecological system and is important for beautifying cities, improving urban environment quality and optimizing the urban ecological system. With the aggravation of urban environment problems, improvement of urban environment by greening is an effective and common measure.
Reachability refers to a quantitative representation of the desires and capabilities of a resident to overcome resistance to distance and travel time to a service or event. The spatial distribution of vegetation is often unbalanced due to factors such as design concepts, historical reasons and the like. In the past two decades, vegetation accessibility has been widely recognized as an important indicator of environmental fairness, and a great deal of research has been devoted to how to measure the accessibility of vegetation and the impact of lack of reachable vegetation on the health of residents.
Although vegetation availability is increasingly being studied, there is no theories by the various nationists as to how to measure vegetation availability. Most students measure accessibility by using a Geographic Information System (GIS), and the most commonly used measurement indexes are vegetation areas and vegetation indexes, and commonly used measurement methods include a container measurement method, a walking distance method and a nuclear density estimation method. Yin Haiwei and the like measure the space accessibility of parks in Shanghai city by taking the distance to the nearest park as a measure index of park accessibility; li Shi dividing the study area into grids, measuring the resistance accumulated value between each grid and the most accessible urban green space nearby by using a Cost Weighted tool in ArcGIS, and taking the resistance accumulated value as the urban green space accessibility measurement result of Xiamen city; appariio et al found that when the green space accessibility of the Montreal city center was calculated using Euclidean distance and Manhattan distance, the results of the two had a strong correlation, but the calculation results of the two methods were quite different in suburban areas.
In general, most research has focused on measuring the number of vegetation and evaluating vegetation accessibility therewith. However, the number is only one of measures of vegetation quality, and the size, quality and landscape structure of urban vegetation, especially parks, are measures affecting the accessibility of vegetation, and the measurement result of the accessibility of vegetation obtained by using a single measure index is not very scientific and practical.
Aiming at the problems, the invention is based on remote sensing vegetation information extraction, tries to establish a set of vegetation reachability measurement method based on path distance, in particular to a path distance-based reachability range calculation method and a vegetation reachability comprehensive measurement method considering the number of vegetation, landscape structure and growth condition, which are used for solving the problems of unpractical reachability range definition, unscientific reachability measurement factor and the like in vegetation reachability measurement and providing technical reference for effectively measuring vegetation reachability.
Disclosure of Invention
Aiming at the problems that the accessibility range is defined in the current vegetation accessibility measurement, the accessibility measurement factor is not scientific, the automation degree of the measurement method is low, and the like, the invention provides a vegetation accessibility measurement method and technical flow based on path distance.
The aim of the invention is achieved by the following technical steps:
step 1) extracting vegetation and impermeable water based on multispectral remote sensing images, and respectively obtaining a vegetation reference map only containing vegetation distribution and a impermeable surface reference map only containing impermeable surface distribution;
step 2) screening to obtain a representative vegetation landscape index by adopting a method combining statistical test and correlation analysis aiming at a vegetation reference map;
step 3) concentrating the representative vegetation landscape index into a vegetation landscape structure measuring factor based on a factor analysis method;
step 4) aiming at the reference diagram of the impervious surface, combining with a road network of a research area, and adopting calculation of an reachable range based on the path distance;
and 5) calculating reachability measurement factors in the range of the path distance, determining the weight of each reachability measurement factor by adopting a analytic hierarchy process, and carrying out weighted calculation on each reachability measurement factor to obtain a vegetation reachability distribution diagram.
Further, the specific method of the step 1) is as follows:
a) Classifying the multispectral remote sensing images based on a maximum likelihood classification method, wherein the target categories are vegetation, impermeable water, water body and bare soil respectively; b) Combining with satellite map images, adopting a layered sample point method to verify classification accuracy; c) If the classification precision reaches the target precision, performing the step d); if the classification precision does not reach the target precision, carrying out manual classification post-processing until the classification precision reaches the target precision; d) Masking the classification result map to obtain a vegetation reference map containing only vegetation distribution and a watertight surface reference map containing only watertight surface distribution, respectively.
Further, the specific method of the step 2) is as follows:
a) Dividing a vegetation reference map into grid units with equal sizes, and extracting a plurality of representative grid units from the grid units by adopting a layered random sampling method; b) Calculating the landscape index of each representative grid unit by using Fragstats4.2 software; c) Carrying out statistical test on each landscape index, namely testing basic description statistics (maximum value, minimum value, mean value and standard deviation) of each landscape index, and eliminating the landscape index with abnormal behavior in the statistics; d) And carrying out correlation analysis on the rest landscape indexes after the statistical test, namely calculating Spearman correlation coefficients among all the landscape indexes, and eliminating the landscape indexes which are obviously correlated to obtain the representative vegetation landscape indexes.
Further, the specific method of the step 3) is as follows:
a) Performing factor analysis on the representative landscape index obtained in the step 2) to obtain a factor load matrix, and extracting main components with characteristic root values larger than 1; b) Removing landscape indexes with weak correlation with the extracted main components; c) Carrying out factor analysis on the residual landscape indexes in the step b) again to obtain a new factor load matrix, and extracting main components with characteristic root values larger than 1, namely vegetation landscape structure measurement factors; d) And c) calculating to obtain a factor scoring matrix of each landscape index according to the factor loading matrix in c), wherein the value in the factor scoring matrix is the coefficient of the vegetation landscape structure measuring factor calculated by the landscape index.
Further, the specific method of the step 4) is as follows:
a) Dividing the watertight surface reference graph into reachability calculation units (each unit corresponds to different reachability ranges) by adopting a grid method; b) Adding road network data, and calculating the reachable range of 2km along the road network by adopting a service area analysis method and taking the impervious surface pixel at the center of each reachability calculation unit as a starting point; c) And b) taking the reachable range obtained in the step b) as a boundary, and extracting vegetation distribution in each reachable range from the vegetation reference map.
Further, the specific method in the step 5) is as follows:
a) Calculating vegetation reachability measurement factors for the vegetation distribution in each reachability range in the step 4), including: vegetation area (representing vegetation quantity), vegetation landscape structure measuring factors (the calculating method is shown in step 3), vegetation landscape structure representing and vegetation NDVI average value (representing vegetation growth condition); b) Determining the weight of each reachability measurement factor by adopting an analytic hierarchy process; c) Multiplying each vegetation reachability measurement factor in a) by a corresponding weight to obtain a vegetation reachability comprehensive measurement result.
Drawings
FIG. 1 is a plot type distribution diagram;
FIG. 2 is a representative grid cell location;
the reachability calculation unit of FIG. 3 corresponds to the calculation starting point
FIG. 4 is a schematic diagram of the reach;
fig. 5 is a graph of a reachability integrated metric.
Detailed Description
The invention 'a vegetation accessibility measuring method based on path distance' is further described below with reference to the accompanying drawings.
The path distance-based reach calculation method and the vegetation accessibility comprehensive measurement method considering the vegetation quantity, the landscape structure and the growth condition are important innovations of vegetation accessibility measurement. The method for calculating the reachable range based on the path distance has the advantages of being high in practicability of the starting point calculation method, accurate in reachable range calculation and practical, meanwhile, the algorithm is high in running speed and high in operability, has practical value compared with the method for calculating the reachable range based on the linear distance, and provides a feasible method for calculating the reachable range in vegetation reachability measurement; the comprehensive measurement method for the vegetation accessibility considering the vegetation quantity, the landscape structure and the growth condition fuses the theory and the method of the landscape ecology into the vegetation accessibility measurement, comprehensively considers the influence of the vegetation quantity, the landscape structure and the growth condition on different degrees of accessibility, and compared with a common accessibility measurement model only based on the vegetation quantity, the measurement method is more scientific and practical and can provide reference for subsequent research and application.
Vegetation reference map and impervious surface reference map manufacture
And a vegetation reference map and a watertight surface reference map are manufactured by adopting a method combining automatic classification and visual interpretation. According to the purpose of the invention and the spectrum and space characteristics of the multispectral remote sensing image, four types of target features are determined as follows: vegetation, impermeable surface, water body and bare soil, and the target precision is 90%. Firstly, four types of target object classification samples are selected, and classification is implemented by adopting a maximum likelihood classification method, so that an initial classification result is obtained. And then, selecting layering verification sampling points for the initial classification result, wherein the number of vegetation, impermeable water, water and bare soil sampling points is N1, N2, N3 and N4 respectively. And comparing the ground object types of the selected sample points on the satellite map image and the initial classification result one by one, and manufacturing an confusion matrix. Then, performing manual post-classification treatment on the target ground objects which do not reach the target precision until the classification precision of various target ground objects reaches the target precision, and obtaining a final classification result graph (figure 1). Finally, masking the final classification map to obtain a vegetation reference map (non-vegetation area is non-valued) containing only vegetation distribution and a watertight surface reference map (non-watertight area is non-valued) containing only watertight surface distribution.
(II) representative Vegetation View index screening
Landscape pattern information is highly concentrated by landscape indexes, but the problems of redundancy repetition and undefined ecological significance exist among indexes. The aim of the step is to screen out the representative vegetation landscape index from the tedious landscape index, and avoid the conditions of index redundancy, repetition and abnormality.
First, the vegetation reference map is divided into n×n grid cells, and a hierarchical random sampling method is used to select a replacement tabular grid cell from the n×n grid cells (fig. 2). The implementation method of the hierarchical random sampling method comprises the following steps: according to the vegetation area percentage in each grid unit, the layer boundary is determined by adopting an accumulated equivalent frequency square root method, the layering number is P layers, and a% of samples are respectively extracted in each layer to obtain a representative grid unit. Then, according to the purpose of the present invention, 16 landscape indexes are selected. The respective landscape indices of the respective representative grid cells are calculated. Then, the basic descriptive statistics (maximum, minimum, mean and standard deviation) of each landscape index are checked to determine if the landscape index has abnormal behavior, and if the standard deviation is too large, the index is rejected. Finally, calculating Spearman correlation coefficients between every two landscape indexes, if the absolute value of the correlation coefficients is larger than 0.90 and the correlation coefficients are obvious on the level of 0.01, then obvious correlation relations exist among the indexes, and representative vegetation landscape indexes are obtained after rejection.
The 16 indexes selected are respectively: the landscape type AREA Percentage (PLAND), the maximum plaque index (LPI), the Edge Density (ED), the average plaque AREA (area_mn), the AREA weighted average plaque AREA (area_am), the plaque AREA standard deviation (area_sd), the landscape element Plaque Density (PD), the Landscape SHAPE Index (LSI), the SHAPE index mean (shape_mn), the AREA weighted average SHAPE index (shape_am), the plaque SHAPE standard deviation (shape_sd), the AREA weighted average plaque fraction dimension (frac_am), the average nearest distance (enn_mn), the landscape separation degree (SPLIT), the Aggregation Index (AI), the aggregation index (concentration). Each landscape index expression is the same as Fragstats4.2. The Spearman correlation coefficient calculation formula is:n is the level pair number of the two variables that are subjected to correlation analysis, i.e., the sample content. d, d i Is the difference between the same pair of ranks (i=1, 2,3 … …, n). The screened representative vegetation landscape indexes are respectively as follows: edge density index (ED), plaque AREA standard deviation (area_sd), plaque SHAPE index mean (shape_mn), plaque SHAPE standard deviation (shape_sd), AREA weighted average plaque partition dimension (frac_am), and plaque aggregation index (coesifon).
(III) vegetation landscape structure measurement factor construction
The purpose of this step is to concentrate the representative vegetation landscape index into a few metric factors, further removing redundant information between landscape indices.
First, factor analysis is carried out on a representative vegetation landscape index to obtain a factor load matrix, and main components with characteristic root values larger than 1 are selected from analysis results. The objective of factor analysis is to reduce the dimension, the mathematical expression of which is: x=a×f+e, X is the original variable, a is the factor loading matrix, F is the common factor, and E is the variance effect of the common factor on the data. Then, the correlation between the selected principal component in the factor load matrix and each landscape index is checked,the landscape index which has smaller correlation with the selected principal component is eliminated. And then, carrying out factor analysis on the residual landscape indexes again to obtain a new factor load matrix, extracting main components with characteristic root values larger than 1, wherein the extracted main components are vegetation landscape structure measurement factors. Finally, according to the new factor load matrix, calculating to obtain each landscape index factor scoring matrix, wherein a calculation formula is F=S×X+E, and S is the factor scoring matrix. The value in the factor score matrix is multiplied by the corresponding landscape index, namely the value of the vegetation landscape structure measurement factor (selected main component). The vegetation landscape structure measurement factors constructed by the invention are totally two (F 1 And F 2 ) The calculation formulas are respectively as follows:
F 1 ＝0.591×ED-0.178×AREA_SD+0.485×SHAPE_SD+0.597×FRAC_AM
-0.165×COHESION
F 2 ＝-0.193×ED+0.647×AREA_SD+0.465×SHAPE_SD+0.157×FRAC_AM
+0.551×COHESION
(IV) Path distance-based reach calculation
First, the impervious surface reference map is divided into m×m grid units, i.e., reachability calculation units (each grid unit eventually has a vegetation reachability metric value representing the vegetation reachability level of that grid unit) (fig. 3 (a)). Then, road network data are superimposed, and an achievable range of 2km along the road network is calculated with the water-impermeable surface pixel at the center of each reachability calculation unit as the starting point (fig. 4). The most central impervious surface pixel refers to a pixel whose euclidean distance accumulation with all other impervious surface pixels in the reachability calculation unit is smallest (fig. 3 (b)). And finally, extracting vegetation distribution in each reachable range from the vegetation reference map by taking each reachable range as a boundary.
(V) comprehensive measurement method for vegetation accessibility
In the previous step, the reachable range corresponding to each reachable computing unit and the vegetation distribution in the reachable range are obtained, and in the step, the reachable range reachability computing factors of the vegetation are computed firstly, including: comprising the following steps: the vegetation area (representing the vegetation quantity), the vegetation landscape structure measuring factor (the calculating method is shown in step 3), the vegetation landscape structure and the vegetation NDVI mean value (representing the vegetation growth condition). And then determining the weight of each vegetation accessibility measuring factor by adopting an analytic hierarchy process. The relative importance of each metric factor when applying the analytic hierarchy process is as follows: the vegetation area is larger than the vegetation landscape structure measurement factor is larger than the vegetation NDVI average value. Finally, the vegetation reachability measurement factors in the reachable ranges are multiplied by the corresponding weights and added to obtain a vegetation reachability comprehensive measurement result (figure 5).

Claims (6)

1. A path distance-based vegetation reachability metric method, the metric method comprising the steps of:
step 1) extracting vegetation and impermeable water based on multispectral remote sensing images, and respectively obtaining a vegetation reference map only containing vegetation distribution and a impermeable surface reference map only containing impermeable surface distribution;
step 2) screening to obtain a representative vegetation landscape index by adopting a method combining statistical test and correlation analysis aiming at a vegetation reference map; the statistical test is to test the basic description statistics of each landscape index, including maximum value, minimum value, mean value and standard deviation, and reject the landscape index with abnormal behavior in the statistics; calculating Spearman correlation coefficients between every two landscape indexes through correlation analysis, eliminating the landscape indexes which are obviously correlated, and obtaining a representative vegetation landscape index after eliminating;
step 3) concentrating the representative vegetation landscape index into a vegetation landscape structure measuring factor based on a factor analysis method; the factor analysis method is to perform factor analysis on the representative vegetation landscape index, obtain a factor load matrix through a mathematical expression of the factor analysis, and extract main components with characteristic root values larger than 1;
step 4) calculating an reachable range based on the path distance by combining a research area road network aiming at the watertight surface reference map;
and 5) calculating reachability measurement factors in the range of the path distance, determining the weight of each reachability measurement factor by adopting a analytic hierarchy process, and carrying out weighted calculation on each reachability measurement factor to obtain a vegetation reachability distribution diagram.
2. The method according to claim 1, wherein the specific method of step 1) is as follows:
a) Classifying the multispectral remote sensing images based on a maximum likelihood classification method, wherein the target categories are vegetation, impermeable water, water body and bare soil respectively; b) Combining with satellite map images, adopting a layered sample point method to verify classification accuracy; c) If the classification precision reaches the target precision, performing the step d); if the classification precision does not reach the target precision, carrying out manual classification post-processing until the classification precision reaches the target precision; d) Masking the classification result map to obtain a vegetation reference map containing only vegetation distribution and a watertight surface reference map containing only watertight surface distribution, respectively.
3. The method according to claim 1, wherein the specific method of step 2) is as follows:
a) Dividing a vegetation reference map into grid units with equal sizes, and extracting a plurality of representative grid units from the grid units by adopting a layered random sampling method; b) Calculating the landscape index of each representative grid unit by using Fragstats4.2 software; c) Carrying out statistical test on each landscape index, namely carrying out test on basic description statistics of each landscape index, including maximum value, minimum value, mean value and standard deviation, and eliminating the landscape index with abnormal behavior in the statistics; d) And carrying out correlation analysis on the rest landscape indexes after the statistical test, namely calculating Spearman correlation coefficients between every two landscape indexes, and if the absolute value of the correlation coefficients is larger than 0.90 and the indexes are obvious on the level of 0.01, eliminating the obvious correlation landscape indexes to obtain the representative vegetation landscape indexes.
4. The method according to claim 1, wherein the specific method of step 3) is as follows:
a) Performing factor analysis on the representative landscape index obtained in the step 2) to obtain a factor load matrix, extracting main components with characteristic root values larger than 1, wherein the mathematical expression of the factor analysis is as follows: x=a×f+e, X is the original variable, a is the factor loading matrix, F is the common factor, E is the variance effect of the common factor on the data; b) Checking the correlation between the selected main component and each landscape index in the factor load matrix, and eliminating the landscape index with weak correlation with the extracted main component; c) Carrying out factor analysis on the residual landscape indexes in the step b) again to obtain a new factor load matrix, and extracting main components with characteristic root values larger than 1, namely vegetation landscape structure measurement factors; d) And c) calculating to obtain a factor scoring matrix of each landscape index according to the factor loading matrix in c), wherein the value in the factor scoring matrix is the coefficient of the vegetation landscape structure measuring factor calculated by the landscape index.
5. The method according to claim 1, wherein the step 4) proposes a path distance-based reach calculation method, which specifically comprises:
a) Dividing the watertight surface reference graph into reachability calculation units by adopting a grid method, wherein each unit corresponds to different reachability ranges; b) Adding road network data, calculating an achievable range of 2km along the road network by taking the water-impermeable surface pixel at the most center of each reachability calculation unit as a starting point; c) And b) taking the reachable range obtained in the step b) as a boundary, and extracting vegetation distribution in each reachable range from the vegetation reference map.
6. The method of claim 1, wherein the step 5) proposes a comprehensive measure of vegetation accessibility considering the number of vegetation, landscape architecture and growth status, and the specific calculation method is:
a) Calculating vegetation reachability measurement factors for the vegetation distribution in each reachability range in the step 4), including: (1) the vegetation area is used for representing the vegetation quantity; (2) the vegetation landscape structure measurement factor is used for representing a vegetation landscape structure; (3) the vegetation NDVI average value is used for representing vegetation growth conditions; b) The weight of each reachability measurement factor is determined by adopting an analytic hierarchy process, and the relative importance of each measurement factor is as follows: the vegetation area is larger than the vegetation landscape structure measurement factor is larger than the vegetation NDVI average value; c) Multiplying each vegetation reachability measurement factor in a) by a corresponding weight to obtain a vegetation reachability comprehensive measurement result.
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