CN115860238A - Green land type optimal configuration method for relieving urban heat island effect - Google Patents
Green land type optimal configuration method for relieving urban heat island effect Download PDFInfo
- Publication number
- CN115860238A CN115860238A CN202211592247.8A CN202211592247A CN115860238A CN 115860238 A CN115860238 A CN 115860238A CN 202211592247 A CN202211592247 A CN 202211592247A CN 115860238 A CN115860238 A CN 115860238A
- Authority
- CN
- China
- Prior art keywords
- area
- configuration
- green land
- type
- partition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a green land type optimal configuration method for relieving urban heat island effect, which comprises the following steps: step 1: inverting the earth surface temperature by adopting a single window algorithm; step 2: adopting a WUDAPT tool to perform local climate zoning on a city; and step 3: calculating the area and the average temperature of each subarea; and 4, step 4: determining a green land type configuration range; and 5: determining the vegetation type of green land planting and the green land configuration cost per unit area; step 6: constructing a linear interval programming model, and calculating the green land type optimization configuration under the set cooling effect by taking the lowest cost as a target; the method has the advantages that the urban green land type can be optimally configured, the lowest cost target under the set cooling effect is achieved, a method is provided for urban green land type configuration, and reference is provided for planners.
Description
Technical Field
The invention relates to the technical field of green space type optimal configuration, in particular to a green space type optimal configuration method for relieving urban heat island effect.
Background
The urban heat island effect refers to a temperature difference phenomenon that the urban temperature is higher than that of surrounding rural or suburban areas, local Climate Zones (LCZ) can describe the forms and functions of the urban and rural areas by adopting a standard, objective and quantitative scheme aiming at the ambiguity of the urban and rural boundaries and the uncertainty problem of the division, and provide a unified frame and a standard for calculating the urban heat island strength;
in view of the foregoing, there is a need for improvements in existing urban greenbelt types that can accommodate the current need to mitigate the effects of urban heat islands.
Disclosure of Invention
The invention aims to solve the problems and designs a green land type optimal configuration method for relieving the urban heat island effect.
The technical scheme of the invention is that the green land type optimal configuration method for relieving the urban heat island effect comprises the following steps:
step 1: inverting the earth surface temperature by adopting a single window algorithm;
step 2: adopting a WUDAPT tool to perform local climate zoning on a city;
and step 3: calculating the area and the average temperature of each subarea;
and 4, step 4: determining a green space type configuration range;
and 5: determining the vegetation type of green land planting and the green land configuration cost per unit area;
step 6: and (3) constructing a linear interval programming model, and calculating the green space type optimal configuration under the set cooling effect by taking the lowest cost as a target.
Further supplementing the technical scheme, the step 1 comprises the following working steps:
step (1): carrying out remote sensing image data preprocessing, downloading Landsat remote sensing image data in Earth Explorer official network, carrying out radiometric calibration by using a thermal infrared band, carrying out atmospheric correction processing, and embedding and cutting according to a research area to obtain a preprocessed image;
step (2): calculating radiance temperature and earth surface emissivity which needs to be calibrated by multispectral radiometric calibration and calculate vegetation normalized index (NDVI) and vegetation coverage;
and (3): and performing surface temperature inversion operation, calculating the atmospheric moisture content and atmospheric transmittance, searching the atmospheric average action temperature data, and performing surface temperature inversion by using the Band Math function in the ENVI software to obtain the surface temperature of the research area.
Further supplementing the technical scheme, the step 2 comprises the following working steps:
the method comprises the following steps: preprocessing and resampling the remote sensing image;
step two: using Google earth software to outline the training sample; training area vectorization is carried out by using Google earth software, a representative area of each local climate partition type is represented as a training sample by using a polygon, and each local climate partition type comprises 20-30 training samples. Selecting each training sample through Google earth software, street view and field observation investigation;
step three: loading the preprocessed remote sensing image and the selected training area into SAGA GIS software, and classifying the local climate area of the research area by using a random forest classification method according to the similarity of the training sample and the rest research areas to preliminarily generate a local climate subarea map;
step four: and exporting the generated map to a KML file, loading the KML file into Google earth for verification, resampling the training area of the inconsistent area, and repeating the operation until the constructed map is consistent with the actual situation.
And (2) further supplementing the technical scheme, the operation of preprocessing the remote sensing image in the second step is the same as that in the step (1).
Further supplementing the technical scheme, the step one resampling is to resample the image from the resolution of 30m to 100m in SAGA GIS software to obtain the spectral signal of the local scale urban feature.
Further supplementing the technical scheme, the step 3 comprises the following working steps:
step (1): in ArcGIS software, calculating the area of each local climate zone by using a zone statistical tool to obtain the areas of dense forests, sparse forests, shrubs, short plants and sand zones in natural types;
step (2): and extracting the earth surface temperature of each partition by using a grid surface turning and cutting tool of ArcGIS software, and calculating the average earth surface temperature of each partition by using a grid calculator and a partition statistical tool.
The technical scheme is further supplemented, the patent aims to convert sparse forests, shrubs, short plants and sand areas into dense forests so as to reduce the heat island effect, so that the configuration range of the green land type is divided into the above areas, wherein the shrub areas and the short plants have the functions of bearing ecology and society, the sand areas need to identify farmlands, and the range identification needs to be carried out by referring to land utilization data.
The technical scheme is further supplemented, the determination of the type of vegetation planted in the green land requires that the regional climate is determined firstly, the selection of the planted tree species is carried out according to the climate and the local natural geographic conditions, and the selection of the planted tree species is carried out by referring to the local existing vegetation type if necessary;
and determining the unit area cost of planting the tree species by referring to the related data and expert consultation, wherein the unit area cost comprises nursery stock cost, machine ploughing cost, maintenance cost, labor cost and the like.
Further supplementing the technical solution, said step 6 includes the steps of:
step (1): in order to achieve the effect of relieving the heat island effect, a cooling target is set;
step (2): the objective function is set with the lowest cost as the target:
constraint conditions are as follows:
T 0 -T L ≥T ± #(2)
in the formula:
a i the unit area cost of the ith natural partition is converted into the dense forest partition;
x i the unit area of the ith natural partition is converted into a dense forest partition;
b j the average earth surface temperature of the j-th subarea which is not converted into the dense forest subarea;
c i the average surface temperature of the ith natural partition is obtained;
S i the area of the i-th natural partition;
x j the area of the j-th zone which is not converted into the dense forest zone;
T 0 the average surface temperature when the green land is not converted in the research area;
T L average surface temperature after green field change for the study area;
And (3): and finally, carrying out model solution, inputting a formula and data in Lingo software, carrying out model solution, and solving the optimal configuration of the green land type at the lowest cost.
The method has the advantages that the urban green land type can be optimally configured, the lowest cost target under the set cooling effect is achieved, a method is provided for urban green land type configuration, and reference is provided for planners.
Drawings
FIG. 1 is a technical roadmap for green space type optimization configuration to mitigate urban heat island effects;
FIG. 2 is a plot of a surface temperature inversion of an embodiment of the present invention;
fig. 3 is a view of a local climate zone according to an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
In order to make the technical solution more clear to those skilled in the art, the technical solution of the present invention will be described in detail below with reference to fig. 1 to 3:
referring to fig. 1, the technical scheme adopted by the invention is as follows: a green land type optimal configuration method for relieving urban heat island effect comprises the following steps:
step 1: taking Shenzhen Futian region as an embodiment, inverting the earth surface temperature by adopting a single-window algorithm, and specifically operating as follows:
preprocessing remote sensing image data, downloading a Landsat8OLI/TIRS remote sensing image in an Earth Explorer official network, selecting the remote sensing image which needs to be clearly visible and a research region is not blocked by clouds, wherein the cloud amount is less than 5%, accordingly, selecting the remote sensing image of 10-21.2022-year-old, the row number is 122/44, carrying out radiometric calibration and atmospheric correction on the selected remote sensing image in ENVI software, and then carrying out tailoring according to Shenzhen Shandong region vector boundary data;
and calculating the ground emissivity, wherein the ground emissivity needs to be calculated through multispectral radiometric calibration, vegetation normalization index (NDVI) calculation, vegetation coverage calculation and ground emissivity calculation. For the calculation of the NDVI, an NDVI tool is directly selected from ENVI software, and the calculation is carried out to determine whether the numerical value is between (-1,1) in a statistics-space statistics check; for vegetation coverage calculation, the NDVI data calculated in the past is substituted, and the calculation is carried out by using a Band Math tool, and the calculated value is checked according to the steps and is normal between (0,1); and finally, calculating the earth surface emissivity.
Performing surface temperature inversion operation: inputting the imaging time and the central longitude and latitude of the remote sensing image in the NASA official network, searching the atmospheric transmittance, inquiring the weather of the day, calculating the weather as the near-ground temperature to obtain the average action temperature of the atmosphere, and performing surface temperature inversion by using the Band Math function in the ENVI software to obtain the surface temperature of the embodiment, wherein the surface temperature inversion of the embodiment is shown in figure 2.
Step 2: the WUDAPT tool is adopted for local climate zoning, and the specific operation path is as follows:
downloading the Landsat8OLI/TIRS remote sensing image from the Earth Explorer official network, selecting the remote sensing image which needs to be clearly visible and is not shielded by cloud in a research area, wherein the cloud amount is less than 5%, accordingly, selecting the remote sensing images of 10 months, 14 days and 21 days in 2022, wherein the row numbers are 121/44 and 122/44 respectively, and the preprocessing operation is the same as the step 1; resampling the image from 30m resolution to 100m in SAGAGIS software;
referring to the partitioning step of adopting a World city Database and an Access Portal Tools (World Urban Database and Access portals Tools, WUDAPPT, https:// www.wudapt.org /), training area vectorization is carried out by using Google earth software, a representative area of each local climate partition type is represented as a training sample by using a polygon, and each local climate partition type comprises 20-30 training samples. Selecting each training sample through Google earth software, streetscape pictures and field observation investigation;
loading the preprocessed remote sensing image and the selected training area into SAGA GIS software, and classifying the local climate areas of the embodiment area by using a random forest classification method according to the similarity of the training sample and the rest research areas to preliminarily generate a local climate subarea map;
and exporting the generated map to a KML file, loading the generated map to Google earth for verification, resampling the training area of the inconsistent area, and repeating the operations until the constructed map is consistent with the actual situation, wherein the local climate partition of the embodiment is shown in figure 3.
And 3, step 3: determining the area of each subarea and the average earth surface temperature, wherein the specific operation path comprises the following steps:
in ArcGIS software, calculating the area of each local climate zone in the embodiment by using a zone statistical tool to obtain the areas of dense forests, sparse forests, shrubs, short plants and sand zones in natural types;
and extracting the surface temperature of each partition by using the grid surface turning and cutting functions in the ArcGIS, and calculating the average surface temperature of each partition by using a grid calculator and a partition statistical tool.
TABLE 1 example plot area for each zone and average surface temperature
Step 4, determining a green space type configuration range, wherein the specific operation path is as follows:
the purpose of the patent is to convert sparse woods, shrubs, short plants and sand partitions into dense woods to reduce the heat island effect, so that the green land type configuration range is divided on the partitions, wherein the shrub partitions and the short plants are required to bear ecological functions and social functions, the sand part is required to identify farmlands, the range identification is required to be carried out by referring to land utilization data, the region of the embodiment has no farmlands according to the land utilization type data of the embodiment, and therefore the green land type configuration range is used according to the area of each partition in the embodiment.
the embodiment is characterized in that the area is subtropical monsoon climate, the main planting vegetation type in the dense forest subarea is south subtropical evergreen broad-leaved forest, and the embodiment is characterized in that the area is planted with eucalyptus group vegetation, so fast-growing eucalyptus is selected for planting;
and determining the planting cost of planting the fast-growing eucalyptus by referring to the related data and expert consultation.
TABLE 2 planting 1 mu of fast-growing eucalyptus cost list for the first year (unit: yuan)
And 6, constructing an interval planning model, and calculating a green space type optimal configuration interval under the set cooling effect by taking the lowest cost as a target. The specific operation path is as follows:
scene 1, with the constraint that the overall cooling effect of the embodiment is at 0.2 ℃, solving is carried out in Lingo software:
table 3 embodiment Green land type optimization configuration table under overall cooling of 0.2 DEG C
And (2) under the constraint that the overall cooling effect of the embodiment is 0.35 ℃, solving in Lingo software:
table 4 table for optimizing green space type at total cooling 0.35 deg.c
The technical solutions described above only represent the preferred technical solutions of the present invention, and some possible modifications to some parts of the technical solutions by those skilled in the art all represent the principles of the present invention, and fall within the protection scope of the present invention.
Claims (9)
1. A green land type optimal configuration method for relieving urban heat island effect is characterized by comprising the following steps:
step 1: inverting the earth surface temperature by adopting a single window algorithm;
step 2: adopting a WUDAPT tool to perform local climate zoning on a city;
and step 3: calculating the area and the average temperature of each subarea;
and 4, step 4: determining a green land type configuration range;
and 5: determining the vegetation type of green land planting and the green land configuration cost per unit area;
step 6: and constructing a linear interval programming model, and calculating the green space type optimal configuration under the set cooling effect by taking the lowest cost as a target.
2. The method for optimizing the configuration of the type of greenbelt for alleviating the urban heat island effect according to claim 1, wherein the step 1 comprises the following working steps:
step (1): preprocessing remote sensing image data, downloading Landsat remote sensing image data in an Earth Explorer official network, performing radiometric calibration by using a thermal infrared band, performing atmospheric correction, and embedding and cutting according to a research area to obtain a preprocessed image;
step (2): calculating radiance temperature and ground emissivity, wherein the ground emissivity needs to be calibrated through multispectral radiometric calibration, and vegetation normalization index (NDVI) and vegetation coverage are calculated;
and (3): and performing surface temperature inversion operation, calculating the atmospheric moisture content and atmospheric transmittance, searching the atmospheric average action temperature data, and performing surface temperature inversion by using the Band Math function in the ENVI software to obtain the surface temperature of the research area.
3. The method for optimizing the configuration of the greenbelt type for alleviating the urban heat island effect according to claim 2, wherein the step 2 comprises the following working steps:
the method comprises the following steps: preprocessing and resampling the remote sensing image;
step two: using Google earth software to outline the training sample;
step three: using SAGA GIS software, and adopting a random forest algorithm to perform regional climate partition classification;
step four: and exporting the generated map out of a KML file, loading the generated map into Google earth for verification, resampling the training area of the inconsistent area, and repeating the operation until the constructed map is consistent with the actual situation.
4. The method for optimally configuring the greenbelt types for alleviating the urban heat island effect according to claim 3, wherein the preprocessing operation on the remote sensing images in the second step is the same as that in the step (1).
5. The method for green space type optimal configuration for alleviating urban heat island effect according to claim 4, wherein the step-resampling is to resample the image from 30m resolution to 100m in SAGAGIS software to obtain the spectral signal of the local scale urban feature.
6. The method for optimizing the configuration of the type of greenbelt for alleviating the urban heat island effect according to claim 1, wherein the step 3 comprises the following working steps:
step (1): in ArcGIS software, calculating the area of each local climate zone by using a zone statistical tool to obtain the areas of dense forests, sparse forests, shrubs, short plants and sand zones in natural types;
step (2): and extracting the earth surface temperature of each partition by using a grid surface turning and cutting tool of ArcGIS software, and calculating the average earth surface temperature of each partition by using a grid calculator and a partition statistical tool.
7. The method for optimizing the configuration of the green land type for alleviating urban heat island effect according to claim 1, wherein the step 4 green land type configuration range is the conversion of sparse forest, shrub, short plant and sand area into dense forest.
8. The method of claim 7, wherein the step 5 comprises determining the type of vegetation planted in the green area by determining the climate of the area under study, and selecting the species of trees to be planted according to the climate and the local natural geographical conditions.
9. The method for optimizing the configuration of the greenbelt type for alleviating the urban heat island effect according to claim 1, wherein the step 6 comprises the following steps:
step (1): in order to achieve the effect of relieving the heat island effect, a cooling target is set;
step (2): the objective function is set with the lowest cost as the target:
constraint conditions are as follows:
in the formula:
a i the unit area cost of the ith natural partition is converted into the dense forest partition;
x i the unit area of the ith natural partition is converted into a dense forest partition;
b j the average earth surface temperature of the j-th subarea which is not converted into the dense forest subarea;
c i the average surface temperature of the ith natural partition is obtained;
S i the area of the i-th natural partition;
x j the area of the j-th zone which is not converted into the dense forest zone;
T 0 the average surface temperature when the green land is not converted for the research area;
T L average surface temperature after green field change for the study area;
And (3): and finally, carrying out model solution, inputting a formula and data in Lingo software, carrying out model solution, and solving the optimal configuration of the green land type at the lowest cost.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211592247.8A CN115860238A (en) | 2022-12-09 | 2022-12-09 | Green land type optimal configuration method for relieving urban heat island effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211592247.8A CN115860238A (en) | 2022-12-09 | 2022-12-09 | Green land type optimal configuration method for relieving urban heat island effect |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115860238A true CN115860238A (en) | 2023-03-28 |
Family
ID=85672230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211592247.8A Pending CN115860238A (en) | 2022-12-09 | 2022-12-09 | Green land type optimal configuration method for relieving urban heat island effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115860238A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116150859A (en) * | 2023-04-20 | 2023-05-23 | 广东工业大学 | Wetland park cooling effect and building energy consumption prediction system, method and device |
-
2022
- 2022-12-09 CN CN202211592247.8A patent/CN115860238A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116150859A (en) * | 2023-04-20 | 2023-05-23 | 广东工业大学 | Wetland park cooling effect and building energy consumption prediction system, method and device |
| CN116150859B (en) * | 2023-04-20 | 2023-08-22 | 广东工业大学 | Wetland park cooling effect and building energy consumption prediction system, method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kabano et al. | Evidence of urban heat island impacts on the vegetation growing season length in a tropical city | |
| Cunha et al. | Very early prediction of wine yield based on satellite data from VEGETATION | |
| CN108458978B (en) | Sensitive waveband and waveband combination optimal tree species multispectral remote sensing identification method | |
| Gomarasca et al. | One century of land use changes in the metropolitan area of Milan (Italy) | |
| CN107239756A (en) | Density of population analysis system based on high score satellite remote sensing date combination type of ground objects | |
| CN113869370A (en) | Tree species refined classification method based on deep learning algorithm and time sequence sentinel image | |
| CN115860238A (en) | Green land type optimal configuration method for relieving urban heat island effect | |
| CN113205014B (en) | Time sequence data farmland extraction method based on image sharpening | |
| CN116543316B (en) | Method for identifying turf in paddy field by utilizing multi-time-phase high-resolution satellite image | |
| CN116665073A (en) | Corn yield remote sensing estimation method based on multi-source data | |
| Han et al. | Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery | |
| Yildiz et al. | Monitoring urban growth by using segmentation-classification of multispectral Landsat images in Izmit, Turkey | |
| CN112036313A (en) | Tobacco planting area detection method, device and equipment and readable storage medium | |
| CN117611993B (en) | Method for estimating vegetation classification based on remote sensing actual evapotranspiration | |
| Bhattarai et al. | Evaluating land use dynamics and forest cover change in Nepal’s Bara district (1973–2003) | |
| CN111461992A (en) | Ultrahigh-resolution remote sensing image tea distribution automatic extraction method and device based on deep learning | |
| CN115223062B (en) | Eucalyptus artificial forest area stand accumulation amount time difference correction method based on UAV data | |
| CN115830464A (en) | Plateau mountain agricultural greenhouse automatic extraction method based on multi-source data | |
| CN115527108A (en) | Method for rapidly identifying water and soil loss artificial disturbance plots based on multi-temporal Sentinel-2 | |
| Wan et al. | Contribution degree of different surface factors in urban interior to urban thermal environment | |
| Prata Shimomura et al. | LCZ in Metropolitan Regions: Surface Temperature in urban and rural areas | |
| CN115063707A (en) | Migration area loss compensation evaluation method based on oblique photogrammetry technology | |
| CN115271343A (en) | Method and system for monitoring and adjusting decision of crop planting structure in water-deficient area | |
| Aryastana et al. | Irrigation Water Management by Using Remote Sensing and GIS Technology to Maintain the Sustainability of Tourism Potential in Bali | |
| CN113868826A (en) | Method for measuring farmland production capacity gravity center change based on grid scale |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |