Urban difficult site space identification method, medium and equipment

Abstract

The invention relates to the technical field of quantitative identification of difficult sites, in particular to an urban difficult site space identification method, a medium and equipment, and the method comprises the steps: dividing urban difficult sites into two types: natural urban difficult sites and artificial urban difficult sites according to different cause types and site types; the natural city difficult site and the artificial city difficult site type are respectively integrated with the existing land utilization classification data, and the natural city difficult site is identified by using the remote sensing image of the existing land utilization classification data. Identifying the artificial urban difficult site according to whether the construction land transfer is needed or not in combination with the remote sensing image; and the NDVilt is determined; 0.2 low-coverage grassland is supplemented as a newly divided natural urban difficult site; and combining the identified natural urban difficult site range and the artificial urban difficult site range to obtain a complete urban difficult site range. The method can be specifically applied to urban difficult site space identification in different scale ranges.

Description

Technical Field

[0001] The present invention relates to the technical field of quantitative identification of difficult sites, and particularly relates to a method, medium and device for spatially identifying urban difficult sites. Background Art

[0002] Urban difficult sites refer to the general term of site spaces in the urban regional environment that cannot meet the site conditions required for the normal growth of the main species of zonal vegetation. Existing research on urban difficult sites focuses on research based on understanding the nature of the difficult sites of the plots, such as the confirmation of the definition, the prevention and control of vegetation diseases and pests in urban difficult sites, and the screening of vegetation. There is a lack of more in-depth research. This is because the current identification of urban difficult sites is more determined by on-site manual identification or experience accumulation, and there is a lack of a method that can directly identify urban difficult sites in space. In the "Methods and Practices of Ecological Landscape Construction in Urban Difficult Sites" edited by Zhang Lang, the types of urban difficult sites are divided into three major categories and 10 sub-categories, including natural type, degraded type and artificial type, comprehensively covering the existing types of urban difficult sites, but it is difficult to quantitatively identify the spatial distribution range of urban difficult sites.

[0003] In addition, the current land use classification standard divides space into 12 first-level categories and 73 second-level categories, including cultivated land, garden land, forest land, grassland, commercial service land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water area and water conservancy facilities land, and other land. However, there is no accurate category for urban difficult sites, and their scope cannot be directly retrieved from space.

[0004] Therefore, the present invention proposes a new classification method, enabling the direct identification of the scope of urban difficult sites in space. Summary of the Invention

[0005] To solve the defect that the existing definition and classification of urban difficult sites cannot quantitatively delimit the scope in space, the present invention provides a method, medium and device for spatially identifying urban difficult sites. This method combines the "Standard for Classification of Urban Land and Planning Construction Land" (GB50137-2011), the "Land Administration Law of the People's Republic of China (Revised in 2019)" and the definition of urban difficult sites, and proposes a new classification method for urban difficult sites that is convenient for spatial identification by referring to the existing classification methods of urban difficult sites and urban ecological land. Based on this classification method, using the existing land use data and remote sensing image data, by analyzing and overlaying the two types of data and comparing the transfer of land use data between adjacent years, the scope of urban difficult sites can be delimited in space.

[0006] The first aspect of the present invention provides a method for spatially identifying urban difficult sites, including the following steps:

[0007] According to different formation types and site types, urban difficult sites are divided into two major categories: natural urban difficult sites and artificial urban difficult sites;

[0008] Integrate the types of natural urban difficult sites and artificial urban difficult sites with the existing land use classification data respectively. Use the remote sensing images of the existing land use classification data to identify natural urban difficult sites, and identify artificial urban difficult sites in combination with remote sensing images according to whether construction land transfer is required;

[0009] Furthermore, invert NDVI through remote sensing image data, and supplement low-coverage grasslands with NDVI < 0.2 as newly divided natural urban difficult sites;

[0010] Merge the identified ranges of natural urban difficult sites and artificial urban difficult sites to obtain the complete range of urban difficult sites.

[0011] Natural urban difficult sites: Ecological land where the site conditions are dominated by natural factors such as climate and geology, causing obstacles to plant growth. Refer to "Methods and Practices of Ecological Landscape Construction in Urban Difficult Sites" edited by Zhang Lang.

[0012] Artificial urban difficult sites: Ecological land where the site conditions are dominated by human interference factors such as engineering construction, land use type conversion, and pollutant emissions, causing obstacles to plant growth or severely damaging the ecosystem function.

[0013] Construction land transfer: Whether the land use type has been affected by human construction activities and has become construction land, and it is repaired on the basis of construction land. In the present invention, it specifically refers to the situation where it is currently identified as green space but was previously construction land.

[0014] Preferably, the types of natural urban difficult sites include saline-alkali land, waste grassland, sandy land, bare land and bare rock gravel land, glaciers and permanent snow cover, damaged wetlands or water areas.

[0015] Preferably, the types of artificial urban difficult sites include industrial relocation sites, controlled idle lands, landfills, and building greening spaces.

[0016] Preferably, the existing land use classification data comes from the National Science Data Center for Glaciers, Permafrost and Deserts. This data classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water bodies, desert land, snow mountain glacier land and wetlands. The unused land, desert land, snow mountain glacier land and wetlands within the urban area are considered natural urban difficult sites.

[0017] Preferably, the method for identifying natural-type urban difficult sites is as follows: Use the existing land use classification data to identify and overlay the natural-type urban difficult site types. Further, invert the NDVI through remote sensing image data, and supplement the low-coverage grassland with NDVI < 0.2 as natural-type urban difficult sites. The two parts are overlaid to form the scope of natural-type urban difficult sites.

[0018] Preferably, invert the NDVI according to the Landsat 8 remote sensing image data to obtain the low-coverage grassland data with NDVI < 0.2.

[0019] In the present invention, the NDVI is inverted using the 30m Landsat 8 remote sensing image data extracted by the United States Geological Survey.

[0020] Preferably, the method for identifying artificial-type urban difficult sites is as follows: Use the land use transfer data of two consecutive times for delineation. Screen the plots that are currently identified as green land but were formerly construction land, and they are identified as artificial-type urban difficult sites. The land use data used in the method for identifying artificial-type urban difficult sites comes from the National Data Center for Glaciology, Permafrost and Desert Science. This data classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land, and wetland. The cultivated land, forest land, grassland, and shrub land in the data classification are reclassified as green land. The construction land and water body in the data classification are still identified as construction land and water body. The remaining categories are classified as natural-type urban difficult sites. Finally, the original nine land use types in the data are reclassified into four categories: green land, construction land, natural-type urban difficult sites, and water body. Calculate the transfer trend and transfer volume of construction land and green land through the land use transfer matrix. The part transferred from construction land to green land is identified as artificial-type difficult sites.

[0021] Land use transfer data: Refers to the data situation of plots that are currently identified as green land but were formerly construction land.

[0022] The second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the urban difficult site spatial identification method described above is implemented.

[0023] The third aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the urban difficult site spatial identification method described above is implemented.

[0024] The fourth aspect of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the urban difficult site spatial identification method described above is implemented.

[0025] The overall idea of the present invention is as follows: As an important component of the urban spatial pattern and an important source of urban greening space, the research on urban difficult sites has substantial significance. The reason for the lack of relevant research is that it is impossible to identify them spatially, and subsequent research cannot be advanced. The present invention proposes a classification method for urban difficult sites. According to the definition of urban difficult sites and the current "Urban Land Classification and Planning Construction Land Standard" (GB50137-2011), "Land Administration Law of the People's Republic of China (Revised in 2019)", the types of urban difficult sites are determined. And according to different causes and site types, urban difficult sites are divided into natural urban difficult sites and artificial urban difficult sites. By integrating with the existing land use classification data (the selected land use data comes from the National Science Data Center for Glaciers, Permafrost and Deserts (http: / / www.ncdc.ac.cn)), the two major categories of natural and artificial urban difficult sites can be directly identified spatially.

[0026] The present invention divides urban difficult sites into two major categories, natural and artificial, according to the cause type and site type, and then integrates them with the existing land use classification data (the selected land use data comes from the National Science Data Center for Glaciers, Permafrost and Deserts (http: / / www.ncdc.ac.cn)). The natural urban difficult sites and artificial urban difficult sites are respectively matched into the nine major categories in the existing land use classification data. Then, the natural urban difficult sites can be directly identified and superimposed based on the remote sensing identification images in the existing database. Further, the NDVI is inverted through the remote sensing image data, and the low-coverage grassland with NDVI < 0.2 is supplemented as a natural urban difficult site. According to whether it is necessary to transfer construction land as the standard and combining with remote sensing images to identify artificial urban difficult sites, the scopes of the identified natural urban difficult sites and artificial urban difficult sites are merged to obtain the complete scope of urban difficult sites, so that the scope of urban difficult sites can be directly identified spatially.

[0027] The present invention has the following advantages and beneficial effects:

[0028] The present invention solves the limitation that the existing concept of urban difficult sites is difficult to directly use for quantitatively identifying the spatial scope of urban difficult sites, and can be specifically applied in the spatial identification of urban difficult sites in different scale ranges.

[0029] The present invention re-proposes a classification method for urban difficult sites. According to the cause type and site type, urban difficult sites are divided into two major categories, natural and artificial. After integrating with the existing land use classification data, spatial identification can be directly carried out, ensuring the comprehensiveness of the scope of urban difficult sites and the accuracy of spatial identification, and having important guiding significance for urban land use planning. Description of the Drawings

[0030] Figure 1 The land use data of a certain city in 2020 extracted for the urban built-up area in Example 2, reclassified into four categories of green land, unused land, construction land, and water bodies using ArcGIS (left) situation map and the NDVI data (right) map obtained after vegetation inversion of the Landsat 8 remote sensing image in this area;

[0031] Figure 2 The map of the natural-type urban difficult sites in the urban built-up area of a certain city in 2020 in Example 2;

[0032] Figure 3 The schematic diagram of the transfer situation from 2020 to 2021 calculated using the land use transfer matrix method in the intersect module of ArcGIS for the reclassified data of 2020 and 2021 in Example 2;

[0033] Figure 4 The schematic diagram of the final urban difficult sites in a certain city in 2020 in Example 2. Specific implementation manners

[0034] To better understand the present invention, the following embodiments are further descriptions of the present invention, but the content of the present invention is not limited to the following embodiments only.

[0035] Embodiment 1

[0036] Table 1 shows the current classification standard for urban difficult sites, and the classification standard is from "Methods and Practices of Ecological Landscape Construction in Urban Difficult Sites" edited by Zhang Lang.

[0037] Table 1 Current classification standard for urban difficult sites

[0038]

[0039]

[0040] The above classification divides urban difficult sites into three major categories: natural type, degraded type, and artificial type. If spatial recognition is directly carried out on these three categories, certain recognition repetitions may occur. For example, the recognition methods for urban relocation sites in the degraded type and the artificial type are both through the part where construction land is transformed into green land, and they will be uniformly recognized as construction land in space, which will lead to the repetition of the two types.

[0041] To solve the above problems, the present invention proposes a new method for spatial recognition of urban difficult sites, including the following steps:

[0042] First, according to the definition and causes of difficult urban sites, the major categories of difficult urban sites are clarified as two types: natural and artificial. Secondly, a comparison is made based on the descriptions of land types in the "Standard for Classification of Urban Land Use and Planning Construction Land" (GB 50137-2011) and the "Land Administration Law of the People's Republic of China (Revised in 2019)", and referring to the urban ecological site types in existing research, the included types in the secondary categories of difficult urban sites are determined. According to the cause types and site types, difficult urban sites are divided into two major categories: artificial and natural, as shown in Table 2:

[0043] Table 2 Classification of Optimized Difficult Urban Sites

[0044]

[0045] Second, according to the different causes of natural and artificial types, different identification methods are selected:

[0046] The distribution of natural-type difficult urban site types is in the major categories of the existing land use classification standard. The existing land use classification data is used for identification and overlay. In this embodiment, the land use data selected comes from the National Science Data Center for Glaciers, Permafrost and Deserts (http: / / www.ncdc.ac.cn). This data set uses Landsat remote sensing images to provide data on China's land cover types over 37 years. By extracting stable samples and collecting training samples through visual interpretation samples, temporal indicators are constructed and the results are obtained through a random forest classifier. The data quality is good. This data classifies land use into 9 categories, namely cultivated land, forest land, grassland, shrub land, construction land, unused land, water body, desert land, snow mountain glacier land and wetland. The unused land, desert land, snow mountain glacier land and wetland within the urban scope are considered natural-type difficult urban sites.

[0047] Specifically, the type definitions of natural urban difficult sites are compared with the definitions of the nine types of land use classification in the data set. Unused land refers to land other than agricultural land and construction land, mainly including wasteland, saline-alkali land, swamp, sandy land, bare land, bare rock, etc. It includes two secondary land types: unused land and other unused land. Unused land is divided into: ① Wasteland. Tree density <10%, soil on the surface, weeds growing, excluding saline-alkali land, swamp and bare land. ② Saline-alkali land. Land with salt and alkali accumulation on the surface, only natural salt-tolerant plants grow. ③ Sandy land. Land with sand covering the surface and basically no vegetation, including deserts, excluding beaches in water systems. ④ Bare land. Land with soil on the surface and basically no vegetation cover. ⑤ Bare rock and gravel land. Land with rock or gravel on the surface, with a coverage area of ​​>50%. Other unused land is divided into: ① Other land. Other water areas not included in agricultural land and construction land. ② River surface. ③ The land below the normal water level of a naturally formed or artificially excavated river. ③ The surface of a lake. The land below the normal water level of a naturally formed waterlogged area. ④ Reed land. Land where reeds grow, including reed land on tidal flats. ⑤ Glaciers and permanent snow. Land whose surface is covered with ice and snow all year round. The overlap degree of site types with the classification of natural urban difficult sites is high. The sandy land in the unused land is similar to the desert land type, but the land use data used in the present invention separately classifies the desert land, and the desert land also conforms to the classification of natural urban difficult sites. Therefore, the unused land and desert land, snow-capped mountain glacier land and wetland within the urban area are regarded as natural urban difficult sites. If the desert land, snow-capped mountain glacier land and wetland are not identified within the urban built-up area, the unused land part is regarded as natural urban difficult site. If all three can be identified, they are all regarded as natural urban difficult sites. It depends on the differences in land use types in different urban built-up areas. Through practical verification, the urban built-up area almost does not contain desert land, snow-capped mountain glacier land and wetland, so the unused land range is mainly extracted.

[0048] In order to prevent the omission of some natural urban difficult sites, the NDVI was inverted using the 30m Landsat 8 remote sensing image data extracted by the United States Geological Survey (https: / / earthexplorer.usgs.gov / ), and low-coverage grasslands with NDVI < 0.2 were added as natural urban difficult sites. The two parts were superimposed to form the range of natural urban difficult sites.

[0049]

[0050] Where: NIR is the near infrared band reflectance value, R is the infrared band reflectance value

[0051] In the third step, referring to the description of artificial urban difficult sites, determine the plots that are based on construction land and have become idle or have a negative impact on the surrounding environment after restoration or control, and are mainly used for greening or identified as green space by surface remote sensing after ecological restoration. Therefore, for the remote sensing identification of such land, the transfer of land use data at two consecutive times can be used to delimit it. Screen the plots that were construction land but are currently identified as green space by remote sensing, that is, identify them as artificial urban difficult sites. The land use data used still comes from the National Data Center for Glaciology, Cryopedology and Desertology (http: / / www.ncdc.ac.cn). Reclassify cultivated land, forest land, grassland, and shrub land as green space, calculate the transfer trend and transfer volume between construction land and green space through the land use transfer matrix, and the part transferred from construction land to green space is identified as artificial difficult sites.

[0052] Specifically, in a broad sense, urban green space refers to various green spaces within the urban planning area, which is the general term for the land covered by vegetation, open spaces, and water bodies within the urban planning area. In this invention, the identification method for artificial urban difficult sites is that the areas currently identified as green space were once construction land, and there is no need for further targeted analysis of each type within the green space. Therefore, considering the difference in image interpretation and facilitating the analysis of the overall spatial pattern, the four categories of cultivated land, forest land, grassland, and shrub land in the data classification are reclassified as green space, and the construction land and water bodies in the data classification are still identified as construction land and water bodies. The remaining categories are classified as natural urban difficult sites as described above. Finally, the nine land use types in the original data are reclassified into four categories: green space, construction land, natural urban difficult sites, and water bodies, and the overall transfer trend is tested and analyzed. Calculate the transfer trend and transfer volume between construction land and green space through the land use transfer matrix. The land use transfer matrix is based on the exploration of the land use change information mining method of the transfer matrix. Its calculation method is shown in Table 3 below.

[0053] Table 3 Land Use Transfer Matrix

[0054]

[0055] In the table: the rows represent the land use types at time point T1, and the columns represent the land use types at time point T2. P ij represents the percentage of the area where land type i is converted to land type j during the period from T1 to T2 in the total land area; P ii represents the percentage of the area where the i-th land use type remains unchanged during the period from T1 to T2. P i+ represents the total area percentage of land type i at time point T1. P +j represents the total area percentage of the land use types at time point T2. P i+ -P iiis the percentage of the reduction in the area of land use type i during the period from T1 to T2; P +j -P jj is the percentage of the increase in the area of land use type j during the period from T1 to T2.

[0056] Import the land use data of 2020 and 2021 obtained from the National Data Center for Glaciology, Cryopedology and Desert Research into ArcGIS 10.7 software, and use the "Clip" function to clip the data according to the research area; use the "Reclassify" tool to reclassify the land use classification into four categories: green land, construction land, unused land and water body; use the "Raster to Polygon" tool to convert the raster data into vector data; use the "Intersect" tool to conduct a superposition analysis of the land use transfer between the reclassified data in 2020 and 2021. The part transferred from construction land to green land is identified as the artificial type of difficult-to-plant sites in 2020.

[0057] After the previous steps are completed, open the attribute table of the data in ArcGIS 10.7, add area calculation to the category of "construction land - green land", and the area transferred from construction land to green land from 2020 to 2021 can be obtained. This area is the area of artificial type of difficult-to-plant urban sites in 2020.

[0058] In the fourth step, merge the identified natural and artificial types of difficult-to-plant urban site ranges to obtain the complete difficult-to-plant urban site range.

[0059] Example 2

[0060] As Figure 1 shown, Figure 1 The left figure is the land use data of a certain city in 2020 extracted from the urban built-up area range of the city in 2020. After extracting the urban built-up area range, the land use types within the built-up area of the city in 2020 are seven types: cultivated land, forest land, grassland, shrub land, unused land, construction land and water body, excluding desert land, snow mountain glacier land and wetland. Therefore, it is reclassified into four categories in ArcGIS: green land, unused land (natural type of difficult-to-plant urban sites after overlaying low-coverage grassland), construction land and water body.

[0061] Figure 1 The right figure is the NDVI data obtained by vegetation inversion of the Landsat 8 remote sensing image within this range, and the value range is between -1 and 1.

[0062] Use the extract by attribute module in ArcGIS to extract unused land and grassland; extract the part where the NDVI value is less than 0.2 and overlaps with the grassland, and use the extract by mask method to screen out the low-coverage grassland with NDVI < 0.2, and conduct a mosaic to new raster operation with the unused land to synthesize the overall natural type of difficult-to-plant urban site range in the built-up area of a certain city in 2020, as Figure 2 shown.

[0063] The extraction of artificial urban difficult sites in a certain city in 2020 requires the land use classification data of the city in 2020 and 2021. First, reclassify the land use data from 2020 to 2021 into four categories: green land, unused land, construction land, and water bodies. The part transferred from construction land to green land is the artificial urban difficult site. Then, use the land use transfer matrix method to calculate the transfer situation from 2020 to 2021 for the reclassified data of 2020 and 2021 in the Intersect module of ArcGIS, as Figure 3 shown.

[0064] Use the extraction by attributes in ArcGIS to extract the part where construction land is converted into green land, and merge it with the natural urban difficult site layer. Finally, obtain the scope of urban difficult sites in a certain city in 2020, as Figure 4 shown.

[0065] Example 3

[0066] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the urban difficult site space recognition method described above.

[0067] Example 4

[0068] A non-transitory computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the urban difficult site space recognition method described above.

[0069] Example 5

[0070] A computer program product includes a computer program. When the computer program is executed by a processor, it implements the urban difficult site space recognition method described above.

[0071] The above is the preferred implementation manner of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and changes can be made, and these improvements and changes are also regarded as the protection scope of the present invention.

Claims

1. A method for identifying difficult urban sites, characterized in that: The following steps are involved: According to different causes and site types, urban difficult sites are divided into two categories: natural urban difficult sites and artificial urban difficult sites. Integrate the natural urban difficult sites and artificial urban difficult sites with the existing land use classification data, use the remote sensing images of the existing land use classification data to identify the natural urban difficult sites, and use the remote sensing images to identify the artificial urban difficult sites based on whether the construction land needs to be transferred. Furthermore, NDVI was inverted through remote sensing image data, and low-coverage grasslands with NDVI < 0.2 were added as newly classified natural urban difficult sites; The identified natural urban difficult sites and artificial urban difficult sites are merged to obtain the complete urban difficult site range.

2. The method for identifying difficult urban sites according to claim 1, characterized in that: Natural urban difficult site types include saline-alkali land, wasteland, sandy land, bare land and bare rock and gravel land, glaciers and permanent snow, and damaged wetlands or waters.

3. The method for identifying difficult urban sites according to claim 1, characterized in that: Artificial urban difficult site types include industrial relocation sites, controlled idle land, landfills, and building green spaces.

4. The method for identifying difficult urban sites according to claim 1, characterized in that: The existing land use classification data comes from the National Glacier, Frozen Soil and Desert Science Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water bodies, desert land, snow-capped mountain and glacier land, and wetland. Unused land, desert land, snow-capped mountain and glacier land, and wetland within the urban area are considered as natural urban difficult sites.

5. The method for identifying difficult urban sites according to claim 4, characterized in that: The identification method of natural urban difficult sites is as follows: use the existing land use classification data to identify and superimpose the types of natural urban difficult sites, further invert NDVI through remote sensing image data, and add low-coverage grasslands with NDVI < 0.2 as natural urban difficult sites. The two parts are superimposed to form the scope of natural urban difficult sites.

6. The method for identifying difficult urban sites according to claim 1, characterized in that: The NDVI was inverted based on the Landsat 8 remote sensing image data, and the low-coverage grassland data with NDVI < 0.2 was obtained.

7. The method for identifying difficult urban sites according to claim 1, characterized in that: The identification method of artificial urban difficult sites is as follows: using the land use transfer data of two consecutive times to delineate, screening the plots currently identified as green land by remote sensing but once used as construction land, that is, identifying them as artificial urban difficult sites; Specifically, the land use data used in the identification method of artificial urban difficult sites comes from the National Glacier, Frozen Soil and Desert Science Data Center, which classifies land use into cultivated land, forest land, grassland, shrub land, construction land, unused land, water bodies, desert land, snow-capped mountain and glacier land, and wetland. The cultivated land, forest land, grassland, and shrub land in the data classification are reclassified as green land, the construction land and water bodies in the data classification are still identified as construction land and water bodies, and the remaining categories are classified as natural urban difficult sites. Finally, the nine land use types in the original data are reclassified into four categories: green land, construction land, natural urban difficult sites, and water bodies. The transfer trend and transfer amount of construction land and green land are calculated through the land use transfer matrix, and the part transferred from construction land to green land is identified as artificial difficult site.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for identifying difficult urban sites as described in any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying difficult urban sites as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying difficult urban sites as described in any one of claims 1 to 7 is implemented.

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