CN106650015A - Landscape scale deduction method of urban forest leaf area index - Google Patents
Landscape scale deduction method of urban forest leaf area index Download PDFInfo
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Abstract
The invention relates to a landscape scale deduction method of an urban forest leaf area index, and aims at solving the problem that in an existing urban forest management process, urban forest leaf area index data on a landscape scale is difficult to acquire. The method comprises the first step of field measurement of the urban forest leaf area index; the second step of acquisition of an urban forest vegetation index; the third step of construction of a scale deduction model of the urban forest leaf area index. The landscape scale deduction method of the urban forest leaf area index is applicable to the management of an urban forest ecosystem.
Description
Technical field
The present invention relates to a kind of urban forests leaf area index landscape scale deduction method.
Background technology
Urban forests as the important component part in urban ecological system, with many Ecosystem Services such as carbon sequestration
Oxygen release, absorption atmosphere pollution, reduction tropical island effect, protection bio-diversity etc., it is the weight that urban ecological system is well run
Ensure.Urban forests leaf area is a very important factor for affecting urban forests ecological functions to play.Urban forests
Some important ecological processes between air such as rising, carbon exchange and energy exchange, it is all subject to urban forests blade face
Long-pending impact.Therefore urban forests leaf area index how is quickly obtained so as to understand urban forests present situation further to greatest extent
The ecological functions of raising urban forests be a problem for being badly in need of solving at present.
However, current urban forests leaf area index data are in short supply, it is primarily due to based on sample investigation
The acquisition modes of traditional cities Forest Leaf Area Index waste time and energy.And the discontinuous number of point-like for being all based on sample prescription for obtaining
According to, it is impossible to urban forests leaf area index is evaluated on urban landscape yardstick, the space of urban forests leaf area index
The disappearance of data can significantly affect urban forests ecological functions spatial analysis research, and urban forests leaf area index is in sky
Between on how to present and its heterogeneous rule is the major issue for meriting attention.In a word, still lack at present and obtained in landscape scale
Take the rapid and effective method of urban forests leaf area index, it is impossible to meet the reality of China's urban forests management and city good for habitation's construction
Border demand.
The content of the invention
The invention aims to solve during existing urban forests management, urban forests leaf area refers in landscape scale
Number data are difficult to the problem for obtaining, and provide a kind of urban forests leaf area index landscape scale deduction method.
A kind of urban forests leaf area index landscape scale deduction method of the present invention is specifically carried out according to the following steps：
First, the field survey of urban forests leaf area index：In summer, ground using ArcGIS10.3 spatial analysis software
Study carefully area carries out the laying of urban forests sample prescription by the way of stratified random sampling；After the completion of the setting of forest sample prescription, using GPS systems
System is positioned to the position of each forest sample prescription, then using TRAC Vegetation canopies analyzer to the city in each forest sample prescription
City's Forest Leaf Area Index is measured；The forest sample prescription includes road woods, attached woods, forest for landscape and recreation, production forest and life
State public welfare forest；
2nd, urban forests vegetation index is obtained：Pass through TM/ETM image remote sensing in the same time point for laying forest sample prescription
Image collection data source, to data source image cutting-out, geometric correction and atmospheric correction are carried out, and research is then extracted from data source
Various vegetation indexs in area, then carry out calculating inverting using corresponding computing formula in ENVI 4.6, obtain urban forests
The spatial data of vegetation index, by the position of each forest sample prescription city forest cover index in forest sample prescription is extracted, as
Build the data of model；
3rd, build urban forests leaf area index yardstick and deduce model：According in each forest sample prescription that step one is obtained
Urban forests leaf area index and various vegetation indexs for obtaining of step 2；On forest sample size, initially with correlation
Property analyze in various vegetation indexs and each forest sample prescription urban forests leaf area index between qualitative relationships, obtain optimum
Vegetation index；Then using multiple regression quantitative analysis optimum vegetation index and the urban forests leaf area in each forest sample prescription
Relation between index, builds urban forests leaf area index yardstick and deduces model；And each forest obtained by step one
Urban forests leaf area index in sample prescription is deduced model and is verified to urban forests leaf area index yardstick.
Beneficial effects of the present invention：
The present invention carries out position (GPS) positioning of sample prescription using high-precision difference GPS system, so as to effectively accurately extract
The vegetation index of sample prescription place TM image picture elements, is to build yardstick and deduce model to offer precise data support.
Method that can be with integrated use remote sensing, geographic information system technology in combination with sample prescription on-site inspection of the invention is directed to
Urban forests leaf area index is quickly rebuild in landscape scale；Quicklook specify that urban forests leaf area index
Spatial framework situation.For clear and definite urban forests, the rational deployment in landscape scale provides data reference to present study,
Making rational planning for important directive significance to urban forests.The inventive method simplicity, fast reconstruction speed, low cost and management
Easily, in can be widely applied to the management of urban forest ecosystem.
Description of the drawings
Fig. 1 is urban forests leaf area index estimation models curve of the embodiment one based on NDVI；
Fig. 2 is the leaf area index estimation models precision test of embodiment one.
Specific embodiment
Specific embodiment one：A kind of urban forests leaf area index landscape scale deduction method of present embodiment is concrete
It is to carry out according to the following steps：
First, the field survey of urban forests leaf area index：In summer, ground using ArcGIS10.3 spatial analysis software
Study carefully area carries out the laying of urban forests sample prescription by the way of stratified random sampling；After the completion of the setting of forest sample prescription, using GPS systems
System is positioned to the position of each forest sample prescription, then using TRAC Vegetation canopies analyzer to the city in each forest sample prescription
City's Forest Leaf Area Index is measured；The forest sample prescription includes road woods, attached woods, forest for landscape and recreation, production forest and life
State public welfare forest；
2nd, urban forests vegetation index is obtained：Pass through TM/ETM image remote sensing in the same time point for laying forest sample prescription
Image collection data source, to data source image cutting-out, geometric correction and atmospheric correction are carried out, and research is then extracted from data source
Various vegetation indexs in area, then carry out calculating inverting using corresponding computing formula in ENVI 4.6, obtain urban forests
The spatial data of vegetation index, by the position of each forest sample prescription city forest cover index in forest sample prescription is extracted, as
Build the data of model；
3rd, build urban forests leaf area index yardstick and deduce model：According in each forest sample prescription that step one is obtained
Urban forests leaf area index and various vegetation indexs for obtaining of step 2；On forest sample size, initially with correlation
Property analyze in various vegetation indexs and each forest sample prescription urban forests leaf area index between qualitative relationships, obtain optimum
Vegetation index；Then using multiple regression quantitative analysis optimum vegetation index and the urban forests leaf area in each forest sample prescription
Relation between index, builds urban forests leaf area index yardstick and deduces model；And each forest obtained by step one
Urban forests leaf area index in sample prescription is deduced model and is verified to urban forests leaf area index yardstick.
Specific embodiment two：Present embodiment from unlike specific embodiment one：Forest sample described in step one
The area of side is 30m × 30m.Other steps and parameter are identical with specific embodiment one.
Specific embodiment three：Present embodiment from unlike specific embodiment one or two：It is gloomy described in step one
The quantity of woods sample prescription is more than 40.Other steps and parameter are identical with specific embodiment one or two.
Specific embodiment four：Unlike one of present embodiment and specific embodiment one to three：Institute in step 2
Various vegetation indexs are stated for ratio vegetation index SR, green normalized differential vegetation index GNDVI and normalized differential vegetation index NDVI.Its
His step and parameter are identical with one of specific embodiment one to three.
Specific embodiment five：Unlike one of present embodiment and specific embodiment one to four：Institute in step 2
Corresponding computing formula is stated for NDVI=(b4-b3)/(b4+b3), SR=(b4/b3), GNDVI=(b4-b2)/(b4+b2), it is above-mentioned
B2, b3 and b4 are band value in formula.Other steps and parameter are identical with one of specific embodiment one to four.
Beneficial effects of the present invention are verified by following examples：
Embodiment one：A kind of urban forests leaf area index landscape scale deduction method of the present embodiment is specifically by following
Step is carried out：
First, the field survey of urban forests leaf area index：In summer, ground using ArcGIS10.3 spatial analysis software
Study carefully area carries out the laying of urban forests sample prescription by the way of stratified random sampling；After the completion of the setting of forest sample prescription, using GPS systems
System is positioned to the position of each forest sample prescription, then using TRAC Vegetation canopies analyzer to the city in each forest sample prescription
City's Forest Leaf Area Index is measured；The forest sample prescription includes road woods, attached woods, forest for landscape and recreation, production forest and life
State public welfare forest；
2nd, urban forests vegetation index is obtained：Pass through TM/ETM image remote sensing in the same time point for laying forest sample prescription
Image collection data source, to data source image cutting-out, geometric correction and atmospheric correction are carried out, and research is then extracted from data source
Various vegetation indexs in area, then carry out calculating inverting using corresponding computing formula in ENVI 4.6, obtain urban forests
The spatial data of vegetation index, by the position of each forest sample prescription city forest cover index in forest sample prescription is extracted, as
Build the data of model；
3rd, build urban forests leaf area index yardstick and deduce model：According in each forest sample prescription that step one is obtained
Urban forests leaf area index and various vegetation indexs for obtaining of step 2；On forest sample size, initially with correlation
Property analyze in various vegetation indexs and each forest sample prescription urban forests leaf area index between qualitative relationships, obtain optimum
Vegetation index；Then using multiple regression quantitative analysis optimum vegetation index and the urban forests leaf area in each forest sample prescription
Relation between index, builds urban forests leaf area index yardstick and deduces model；And each forest obtained by step one
Urban forests leaf area index in sample prescription is deduced model and is verified to urban forests leaf area index yardstick.
The present embodiment is by taking Changchun as an example, to further illustrate practicing for the present invention：
1. Selecting research area-Changchun；
It is survey region that the present embodiment selects main city zone in the ring of Changchun 4.
2. sample prescription setting
The present embodiment sets the urban forests sample prescription of 69 30m × 30m, and in 2011 to urban forests leaf area index
Investigated.Wherein 49 are used to build urban forests leaf area index estimation models, another 20 essences for being used to verify the model
Exactness.
3. model construction and checking
The present embodiment selects three kinds of vegetation indexs (normalized differential vegetation index, ratios with TM remote sensing images in 2011 as data source
Value vegetation index, greening normalized differential vegetation index) it is used for model construction, correlation analysis show as shown in table 1：Normalization vegetation
Index is best with the correlation of leaf area index, is most suitable for structure leaf area index estimation models and obtains Fig. 1.Model proof list
It is bright：The leaf area index precision of forecasting model of this research and establishment as shown in Figure 2 (transverse and longitudinal coordinate is identical in Fig. 2), can be applied very well
In practice.
The vegetation index of table 1. and urban forests leaf area index correlation analysis
| Vegetation index | LAI |
| Normalized differential vegetation index (NDVI) | 0.711** |
| Ratio vegetation index (SR) | 0.699** |
| Green normalized differential vegetation index (GNDVI) | 0.634** |
4. urban forests leaf area index in landscape scale is obtained
The present embodiment deduces model using the urban forests leaf area index yardstick for having built, with reference to the landscape based on remote sensing
Optimum urban forests vegetation index is NDVI on yardstick, empty so as to clearly study urban forests leaf area index in area's landscape scale
Between general layout.
Claims (5)
1. a kind of a kind of urban forests leaf area index landscape scale deduction method, it is characterised in that urban forests leaf area index
Landscape scale deduction method is specifically carried out according to the following steps：
First, the field survey of urban forests leaf area index：In summer, using ArcGIS10.3 spatial analysis software in research area
The laying of urban forests sample prescription is carried out by the way of stratified random sampling；After the completion of the setting of forest sample prescription, using GPS system pair
The position of each forest sample prescription is positioned, then gloomy to the city in each forest sample prescription using TRAC Vegetation canopies analyzer
Woods leaf area index is measured；The forest sample prescription includes that road woods, attached woods, forest for landscape and recreation, production forest and ecology are public
Beneficial woods；
2nd, urban forests vegetation index is obtained：Pass through TM/ETM image remote sensing images in the same time point for laying forest sample prescription
Gathered data source, to data source image cutting-out, geometric correction and atmospheric correction are carried out, and are then extracted from data source in research area
Various vegetation indexs, then calculating inverting is carried out in ENVI 4.6 using corresponding computing formula, obtain urban forests vegetation
The spatial data of index, extracts city forest cover index in forest sample prescription, as structure by the position of each forest sample prescription
The data of model；
3rd, build urban forests leaf area index yardstick and deduce model：According to the city in each forest sample prescription that step one is obtained
Various vegetation indexs that city's Forest Leaf Area Index and step 2 are obtained；On forest sample size, initially with correlation point
The qualitative relationships between the urban forests leaf area index in various vegetation indexs and each forest sample prescription are analysed, optimum vegetation is obtained
Index；Then using multiple regression quantitative analysis optimum vegetation index and the urban forests leaf area index in each forest sample prescription
Between relation, build urban forests leaf area index yardstick and deduce model；And each forest sample prescription obtained by step one
Interior urban forests leaf area index is deduced model and is verified to urban forests leaf area index yardstick.
2. a kind of urban forests leaf area index landscape scale deduction method according to claim 1, it is characterised in that step
The area of forest sample prescription described in rapid one is 30m × 30m.
3. a kind of urban forests leaf area index landscape scale deduction method according to claim 1, it is characterised in that step
The quantity of forest sample prescription described in rapid one is more than 40.
4. a kind of urban forests leaf area index landscape scale deduction method according to claim 1, it is characterised in that step
Various vegetation indexs described in rapid two are that ratio vegetation index SR, green normalized differential vegetation index GNDVI and normalization vegetation refer to
Number NDVI.
5. a kind of urban forests leaf area index landscape scale deduction method according to claim 1, it is characterised in that step
Corresponding computing formula described in rapid two is NDVI=(b4-b3)/(b4+b3), SR=(b4/b3), GNDVI=(b4-b2)/(b4+
B2), b2, b3 and b4 are band value in above-mentioned formula.
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Cited By (4)
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| CN107766799A (en) * | 2017-09-28 | 2018-03-06 | 中国地质大学（武汉） | The analysis method and system of multi- source Remote Sensing Data data source remittance landscape based on scale effect |
| CN108009384A (en) * | 2017-12-26 | 2018-05-08 | 中国科学院东北地理与农业生态研究所 | A kind of urban forests organic C storage landscape scale deduction method |
| CN111062628A (en) * | 2019-12-20 | 2020-04-24 | 上海市园林科学规划研究院 | Forest asset quality grading evaluation method |
| CN112668448A (en) * | 2020-12-24 | 2021-04-16 | 中国科学院地理科学与资源研究所 | Ecological process change analysis method, device, medium and terminal equipment |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107766799A (en) * | 2017-09-28 | 2018-03-06 | 中国地质大学（武汉） | The analysis method and system of multi- source Remote Sensing Data data source remittance landscape based on scale effect |
| CN107766799B (en) * | 2017-09-28 | 2019-12-17 | 中国地质大学（武汉） | Method and system for analyzing source-sink landscape of multi-source remote sensing data based on scale effect |
| CN108009384A (en) * | 2017-12-26 | 2018-05-08 | 中国科学院东北地理与农业生态研究所 | A kind of urban forests organic C storage landscape scale deduction method |
| CN111062628A (en) * | 2019-12-20 | 2020-04-24 | 上海市园林科学规划研究院 | Forest asset quality grading evaluation method |
| CN111062628B (en) * | 2019-12-20 | 2023-04-18 | 上海市园林科学规划研究院 | Forest asset quality grading evaluation method |
| CN112668448A (en) * | 2020-12-24 | 2021-04-16 | 中国科学院地理科学与资源研究所 | Ecological process change analysis method, device, medium and terminal equipment |
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