From da877c7eb90ac7e6402ce6e62298727ff33dbd7e Mon Sep 17 00:00:00 2001 From: "willeke.acampo" Date: Sun, 14 Jan 2024 20:08:22 +0100 Subject: [PATCH] update: tree-visilibity docs --- .../methods/04_tree_visibility/README.md | 101 +++++++++++------- .../04_tree_visibility/img/buffer_count.png | Bin 0 -> 65332 bytes .../img/graphical_abstract.tif | Bin 0 -> 900106 bytes .../img/visual_exposure.png | Bin 0 -> 395840 bytes .../visual_exposure_graphical_abstract.png | Bin 0 -> 601491 bytes .../04_tree_visibility/img/visual_impact.png | Bin 0 -> 326149 bytes .../04_tree_visibility/installation.md | 96 +++++++++++++++++ .../data_AR5_private_public_green_space.xlsx | Bin 0 -> 9464 bytes .../data_SSB_private_public_green_space.xlsx | Bin 0 -> 10639 bytes .../tbl/data_building_footprints.xlsx | Bin 0 -> 9453 bytes .../tbl/viewshed_parameters.xlsx | Bin 0 -> 9953 bytes 11 files changed, 158 insertions(+), 39 deletions(-) create mode 100644 docs/source/methods/04_tree_visibility/img/buffer_count.png create mode 100644 docs/source/methods/04_tree_visibility/img/graphical_abstract.tif create mode 100644 docs/source/methods/04_tree_visibility/img/visual_exposure.png create mode 100644 docs/source/methods/04_tree_visibility/img/visual_exposure_graphical_abstract.png create mode 100644 docs/source/methods/04_tree_visibility/img/visual_impact.png create mode 100644 docs/source/methods/04_tree_visibility/installation.md create mode 100644 docs/source/methods/04_tree_visibility/tbl/data_AR5_private_public_green_space.xlsx create mode 100644 docs/source/methods/04_tree_visibility/tbl/data_SSB_private_public_green_space.xlsx create mode 100644 docs/source/methods/04_tree_visibility/tbl/data_building_footprints.xlsx create mode 100644 docs/source/methods/04_tree_visibility/tbl/viewshed_parameters.xlsx diff --git a/docs/source/methods/04_tree_visibility/README.md b/docs/source/methods/04_tree_visibility/README.md index 833b451..3017b50 100644 --- a/docs/source/methods/04_tree_visibility/README.md +++ b/docs/source/methods/04_tree_visibility/README.md @@ -1,29 +1,27 @@ # Mapping of trees visibility and accessibiltiy -This method appendix documents the methods for the task “Tree Visibility and Impact Mod-elling”. -The resulting products of the task are: (i) a map that shows the visual exposure of tree crowns within the municipalities study area, (ii) a map for a case study site in Oslo, which shows the public and private visibility value per tree, and (iii) three indicators for the 3-30-300 rule calculated per district (grunnkrets). +This the documentation page related to the task: **Tree Visibility** and **Impact Modelling**. -**The 3-30-300 green space rule** - -The 3-30-300 green space proposed by Cecil Konijnendijk, is an evidence-based guideline for guideline for creating healthier and more sustainable cities through the strategic use of urban green spaces. (Konijnendijk, C.C. 2023). The rule outlies three key objectives: - -**Rule 3:** all residents should be able to see at least 3 trees from their home. +The resulting products of the task are: +1. a map that shows the **visual exposure** of tree crowns within the municipalities study area +--> see layer "trekronesynlighet" in the urban tree atlases +2. a map for a case study site in Oslo, which shows the **public** and **private visibility value per tree** +--> see figure X in NINA report +3. three indicators for the **3-30-300 rule** calculated per district (grunnkrets). +--> see figure X, X, X and X in the NINA rapport -**Rule 30:** all neighbourhoods should have 30% tree canopy cover. - -**Rule 300:** all residents should be able to access a green area within 300 m from their home. +## Installation +Installation instructions can be found here: [installation](installation.md) -## Materials and Methods +## Data -### Data - -The following data sources were used within this project. See Appendix X for a detailed de-scription of each dataset. +The following data sources were used within this project task. **Building footprints** -The FKB-Buildings vector map (Kartverket, 2020) was used to extract building footprints from the map layer ‘fkb_bygning_omrade’. In addition, we classified these footprints into res-idential and non-residential buildings using the bygningstype attribute (Appendix X). +The FKB-Buildings vector map (Kartverket, 2020) was used to extract building footprints from the map layer ‘fkb_bygning_omrade’. In addition, we classified these footprints into residential and non-residential buildings using the bygningstype attribute (EXCEL TABLE). **Water bodies** @@ -31,61 +29,86 @@ Water bodies were derived from the land resource map FKB-AR5 (NIBIO, 2020), cate **Digital surface model, digital terrain difference model (DSM-DTM)** -The Digital Surface Model (DSM) and Digital Terrain Model (DTM) are created from laser point cloud data. The DSM includes all points in the cloud, while the DTM only includes points classified as bare earth. The DSM-DTM is created by subtracting the DTM from the DSM, and it shows the height of all objects above the ground. The DSM-DTM model is down sampled to 1-meter resolution to improve processing speed. +The Digital Surface Model (DSM) and Digital Terrain Model (DTM) are created from the laser point cloud data delivered by the municipalities. The DSM includes all points in the cloud, while the DTM only includes points classified as bare earth. The DSM-DTM is created by subtracting the DTM from the DSM, and it shows the height of all objects above the ground. The DSM-DTM model is down sampled to 1-meter resolution to improve processing speed. **Tree crown polygons and binary raster** -Vector map of tree crown polygons detected from municipal laser data (see Tree Detection). -The crown polygons were converted into a binary raster of 1x1 m resolution, where 1 repre-sents pixels covered by tree crown and NA represent pixels without tree crown coverage. +Vector map of tree crown polygons detected from municipal laser data created in task 1 ([see urban-tree-detection](../01_tree_detection/README.md)). The crown polygons were converted into a binary raster of 1 meter resolution, where 1 represents pixels covered by tree crown and NA represent pixels without tree crown coverage. **Open Space** -Within this project open space is defined as any space within the study area which is not a building footprint, a tree canopy or a water surface as recommended by (REF – visual im-pact appendix). Areas smaller than 1 m² were excluded from the open space dataset. The open space dataset was classified into public and private open space using a land resource map FKB-AR5 (NIBIO, 2020) and a land use map (Statistics Norway, 2022). The classes transport, open land, green areas, wetlands, forest and unclassified were classified as open space. Private space was classified as any open space that is not public (Appendix X). +Here open space is defined as any space within the study area which is not a building footprint, a tree canopy or a water surface as recommended by Cimburova et al. (2023). Areas smaller than 1 m² were excluded from the open space dataset. The open space dataset was classified into public and private open space using a land resource map FKB-AR5 (NIBIO, 2020) and a land use map (Statistics Norway, 2022). The classes transport, open land, green areas, wetlands, forest and unclassified were classified as open space. Private space was classified as any open space that is not public (EXCEL TABLE) **Green space** -Green space is defined by Statistics Norway (Statistisk sentralbyrå, SSB) as urban nature areas, nærturterreng, larger than 200 000 m2 and urban recreation areas ,leke- rekreasjon-sareal, between 5 000 – 200 000 m2 (REF SSB). The World Health Organization (WHO) de-fines green space as public green spaces of at least 10 000 m2 (Konijnendijk, C. C. 2023). Here we use the definition by SSB. +Green space is defined by Statistics Norway (Statistisk sentralbyrå, SSB) as urban nature areas, nærturterreng, larger than 200 000 m2 and urban recreation areas ,leke- rekreasjon-sareal, between 5 000 – 200 000 m2 ([SSB definition](https://www.ssb.no/natur-og-miljo/areal/statistikk/rekreasjonsareal-og-naerturterreng)). The World Health Organization (WHO) defines green space as public green spaces of at least 10 000 m2 (Konijnendijk, C. C. 2023). Here we use the definition by SSB. -A land resource map FKB-AR5 (NIBIO, 2020) and a land use map (Statistics Norway, 2022) are combined, and the different land use classes are translated to green space or no-green space. The classes forest, open land, wetland, bare rock, gravel- and stone, park, sport are-as, together with lakes and ponds smaller than 1 000 m2 are classified as green space (Ap-pendix X). +A land resource map FKB-AR5 (NIBIO, 2020) and a land use map (Statistics Norway, 2022) are combined, and the different land use classes are translated to green space or no-green space. The classes forest, open land, wetland, bare rock, gravel- and stone, park, sport areas, together with lakes and ponds smaller than 1 000 m2 are classified as green space (EXCEL TABLE). -## Viewshed exposure modelling +## Methods -Visual exposure to urban trees was determined using the r.viewshed.exposure tool in GRASS GIS (Cimburova and Blumentrath (2022) for all treecrown municipalities. The DSM-DTM and the tree crown binary raster served as inputs for the analysis. The default methods settings, which define a good balance between accuracy and processing time, were applied to all municipalities (Table X). The viewshed parametrisation function was set to distance decay, which means that the pixel’s weight decreases the further you move away from the tree. The resulting visual exposure raster quantifies the level of visual exposure to urban trees, ranging from 0 (no exposure) to high expo-sure values. +### Viewshed exposure modelling +**github-repo**: [r.viewshed.exposure](https://github.com/OSGeo/grass-addons/tree/grass8/src/raster/r.viewshed.exposure) -**FIGURE HERE** +Visual exposure to urban trees was determined using the r.viewshed.exposure tool in GRASS GIS developed by Cimburova and Blumentrath (2022) for all treecrown municipalities. The DSM-DTM and the tree crown binary raster served as inputs for the analysis. The default methods settings, which define a good balance between accuracy and processing time, were applied to all municipalities (Table X). The viewshed parametrisation function was set to distance decay, which means that the pixel’s weight decreases the further you move away from the tree. The resulting visual exposure raster quantifies the level of visual exposure to urban trees, ranging from 0 (no exposure) to high exposure values. -## Visual Impact Modelling +
+ visual exposure modelling +
Processing workflow of the r.viewshed.exposure tool in GRASS GIS, along with its accuracy & performance assessment. Figure is taken from Cimburova and Blumentrath (2022).
+
-The individual tree visibility of trees located in public and private spaces were modelled us-ing the r.viewshed.impact tool (Cimburovam, 2023). The viewshed impact tool differs from the viewshed exposure tool as it uses an additional exposure weight layer and results in an individual tree visibility attribute value. The DSM-DTM raster was used as elevation data and the tree crown binary raster as the input source. The model was run two times, with first the public open space and second the private open space binary raster as exposure weight lay-er. The viewshed parametrisation function, radius and sampling density are set to the same values as for the visual exposure modelling. This resulted in a public tree visibility value and a private tree visibility value for each tree located in in the Ulven study area. +### Visual Impact Modelling +**github-repo**: [r.viewshed.impact](https://github.com/zofie-cimburova/r.viewshed.impact) -**FIGURE HERE** +The individual tree visibility of trees located in public and private spaces were modelled us-ing the r.viewshed.impact tool (Cimburovam, 2023). The viewshed impact tool differs from the viewshed exposure tool as it uses an additional exposure weight layer and results in an individual tree visibility attribute value. The DSM-DTM raster was used as elevation data and the tree crown binary raster as the input source. The model was run two times, with first the public open space and second the private open space binary raster as exposure weight lay-er. The viewshed parametrisation function, radius and sampling density are set to the same values as for the visual exposure modelling. This resulted in a public tree visibility value and a private tree visibility value for each tree located in in the Ulven study area. +
+ visual impact modelling +
Processing workflow of the r.viewshed.impact tool in GRASS GIS. Figure is taken from Cimburova et al. (2023).
+
-**TABLE WITH PARAMETERS** +### Calculating measures for the 3-30-300 rule +Within this study we calculated the three indicators of the 3-30-300 rule for each district (grunnkrets) within the study area. -## Calculating measures for the 3-30-300 rule +:::{note} +**The 3-30-300 green space rule** -Within this study we calculated the three indicators of the 3-30-300 rule for each district (grunnkrets) within the study area. Code is documented on GitHub (link to notebook). +The 3-30-300 green space proposed by Cecil Konijnendijk, is an evidence-based guideline for guideline for creating healthier and more sustainable cities through the strategic use of urban green spaces. (Konijnendijk, C.C. 2023). The rule outlies three key objectives: -### Tree visibility count for residential buildings per district (rule 3) +**Rule 3:** all residents should be able to see at least 3 trees from their home. -As an indicator for rule 3, all residents should be able to see at least 3 trees from their home, we tested two different measures: (i) tree buffer count and (ii) viewshed count per district. +**Rule 30:** all neighbourhoods should have 30% tree canopy cover. -Tree crown buffer count -The buffer count is a measure of the amount of green space accessible to residents of a given area. The buffer count is calculated by counting the number of green spaces within a specified buffer distance of each residential address (Nieuwenhuijsen et al., 2022). This study calculated the number of tree canopies that intersect with a buffer distance of 15 me-ters of residential buildings. The output is a new attribute named “count_crown_buffer” as-signed to each district polygon. +**Rule 300:** all residents should be able to access a green area within 300 m from their home. +::: + +**Rule 3: Tree visibility count for residential buildings per district** +As an indicator for rule 3, we used a tree buffer count to estimate the number of trees visible from each residential building. The tree buffer count is calculated by counting the number of tree crowns within a specified buffer distance of each residential address (Nieuwenhuijsen et al., 2022). This study calculated the number of tree canopies that intersect with a buffer distance of 15 meters of residential buildings. The output is a new attribute assigned to each district polygon. +
+ buffer count +
Workflow diagram illustrating the calculation of tree crown buffer count per district. .
-**WORKFLOW HERE** +**Rule 30: Tree crown area percentage per district (rule 30)** +The total crown area percentage is calculated per district as an indicator for rule 30, all neighbourhoods should have 30% tree canopy cover. +**Rule 300: Green area proximity for residential buildings per district (rule 300)** +We calculated the percentage of residential buildings per district that are located within a 300 m euclidean distance of green space as an indicator for rule 300. First, the *Euclidean distance* from each residential building to a green space is calculated in meters. Then, we count the number of residential houses per district that are located within 300 m of green spaces. -### Tree crown area percentage per district (rule 30) -The total crown area percentage is calculated per district as an indicator for rule 30, all neighbourhoods should have 30% tree canopy cover. +### **References** +- Cimburova, Z. and Blumentrath, S., 2022. Viewshed-based modelling of visual exposure to urban greenery – An efficient GIS tool for practical planning applications. Landscape and Urban Planning, Volume 222,104395. https://doi.org/10.1016/j.landurbplan.2022.104395 +- Cimburova, Z., Blumentrath, S., Barton, D.N., 2023. Making trees visible: A GIS method and tool for modelling visibility in the valuation of urban trees. Urban Forestry & Urban Greening 81, 127839. https://doi.org/10.1016/j.ufug.2023.127839 +- Konijnendijk, C.C. 2023. Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. J. For. Res. 34, 821–830. https://doi.org/10.1007/s11676-022-01523-z +- FKB-Bygg (Kartverket, 2020) +- FKB-AR5 (NIBIO, 2020) +- SSB arealbruk(Statistics Norway, 2022) -### Green area proximity for residential buildings per district (rule 300) -We calculated the percentage of residential buildings per district that are located within a 300 m distance of green space as an indicator for rule 300. First, the distance from each residential building to a green space is calculated in meters. This value is added to the green_area_proximity attribute in the residential buildings’ dataset. Then, we count the number of residential houses per district that are located within 300m of green areas. 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GIS and GRASS Addonns installation + +## Computer specifications + +The tools r.viewshed.exposure and r.viewshed.impact were run on a server with the following specifications (Table 1). + +Table 1. Computer specifications. +||Visual Exposure|Visual Impact| +|---|---|---| +|Server|HPE ProLiant DL360 Gen 10|HPE ProLiant DL360 Gen 10| +|Processor|2 Intel ® Xeon ® Gold 6134 CPU @ 3.20 GHz Central Processing Units|2 Intel ® Xeon ® Gold 6134 CPU @ 3.20 GHz Central Processing Units| +|CPU cores|40 cores|40 cores| +|Random Access Memory|370 GB|377 GB| +|SSD|960 GB|960 GB| +|Fie System|Ubuntu 18.04.5 LTS|Ubuntu 18.04.5 LTS| + + +## Install GRASS GIS with Docker + +Pull official [OSGEO GRASS](https://grass.osgeo.org/download/docker/#GRASS-GIS-current) +`docker pull osgeo/grass-gis:releasebranch_8_3-ubuntu_wxgui` + +Enter container from **Command Line Interface (CLI)** +`docker run -it osgeo/grass-gis:releasebranch_8_3-ubuntu_wxgui` + +Enter Container from **Visual Studio Code** +- CTRL + ALT + P +- add the .devcontainer.json to your .devcontainer folder + + +## Open the GRASS GIS Command Line Interface (CLI) and Graphical User Interface (GUI) + +Inside your container start GRASS +```bash +Grass +g.gui wxpython +``` + +*Common Error:* “Unable to access the X Display” +- Stop containers +- outside container check whom has access to xhost `xhost` +- Prune docker `docker system prune` +- Recreate your grass container + +## Install GRASS GIS Addons + +**Using GRASS GUI** +- Settings > Addons Extension +- Search for extension +- Install +- Check that extension is installed + +**Using GRASS CLI** +`g.extension.install source=path/to/extension, destination=/path/to/volume/extension` + +## Create new GRASS database +GRASS GIS Database - GRASS GIS Manual (osgeo.org) +- New database > your grassdata directory +- Name your location folder UTMxx or WGSxx +- Set your EPSG code + - 25832 (UTM32) + - 25833 (UTM33) +- Apply transformation. +- Finish + +## Import data into your GRASS Mapset +- Import dsm_dtm +- Import trecrown binary raster +- ensure that 0 is set to NA + +## Set Processing extent + +**Using GUI** +- Settings +- Computational region +- Set region [g.region] +- Set elevation model as g.region + +**Using CLI** +`gs.run_command('g.region', raster='%municipality%_dsmdtm_1m_utm32_flt@%MAPSET%’)` + + +## Run r.viewshed.exposure tool + +**Using GUI** +- Tools +- Search for `r.viewshed.exposure` +- Fill in parameters + +**Using CLI** +`python3 /user/.grass8/addons/scripts/r.viewshed.exposure.py input=%municipality%_dsmdtm_1m_utmXX_flt@%MAPSET% out-put=%municipality%_visual_exposure_1m_utmXX source=%municipality%_treecrown_1m_utmXX_int@%MAPSET% range=100 func-tion=Distance_decay sample_density=25 seed=1 memory=200000 nprocs=40` + +## Export rasters to GeoTIFF + +Set 0 to NA (for better visualisation) +File > Export raster map diff --git a/docs/source/methods/04_tree_visibility/tbl/data_AR5_private_public_green_space.xlsx b/docs/source/methods/04_tree_visibility/tbl/data_AR5_private_public_green_space.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..e8b6c598a2e9422c1e88ca4b1eb5ad4884d6a906 GIT binary patch literal 9464 zcmeHtgv+#u7&=0|44EuM4^v8_NQ!N|N1_wkT*m4kg9B)q3t13M}R z)~;ntA;8hyyT{N*N*3>|uXmVDL?OvVW?~ZkG98fms=iZ$SLU0PVi~zLF^PEK>bu!q zZTzL4M(h&(A|cpZ$Fhp{BEDdM@i^Y<7~zDi4YQB&=_eGb7KWEln?DIWQsyPEDvmrW(eH_~V{P*_^!09IiitW? 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