• Home
  • About

GIScience News Blog

News of Heidelberg University’s GIScience Research Group.

Feed on
Posts
Comments
« New paper: Opaque voxel-based tree models for virtual laser scanning in forestry applications
MapSwipe is App of the Day in Apple AppStore »

ohsome Region of the Month - August

Aug 24th, 2021 by Sarah Heidekorn

Hello and welcome back to the ohsome Region of the Month-blog post series where you can read about potential use cases of the ohsome API and maybe even get inspired to send some requests of your own. If you are new to the series you should definitely take a look at former blog posts, for example our last one on the length of coastlines or this one which is about forest-tags too and was done in a jupyter notebook. This weeks blogpost looks at different “tree-related” tags in OpenStreetMap and their development in different countries over time (2008-2021). The tags of interest are “natural=wood“, “landuse=forest” and “landcover=trees“. Let’s begin!

Data

For sending requests to the ohsome API you need input boundaries first, be it a GeoJSON (bpolys) or the coordinates of a region of interest (bboxes). As usual, we sent a “bpolys-request” and got our data from osm-boundaries. For convenience, you can find a GeoJSON-file with all the boundary data used in this analysis in a snippet here as well as the requests sent themselves.

Requests

Since there was a need for more than one filter-condition, several requests were sent. The conditions used are listed below:

  • filter1 = geometry:polygon and natural=wood
  • filter2 = geometry:polygon and landuse=forest
  • filter3 = geometry:polygon and landcover=trees
  • time = 2008-01-01/2021-07-01/P1M

Data exploration

First of all, one can observe that different tags dominate in each region and furthermore it is important to note that according to the OpenStreetMap wiki “landuse=forest” is often times used for maintained forest areas, but some mappers use it on any woodland so there is no clear definition. On the other hand there is the “natural=wood“-tag which is usually used for forests with limited to no forestry management so in these regions there might either be a certain form of landuse dominating or the tagging behaviours are locally different from each other. The third tag looked at in this analysis is “landcover=trees” which describes an area physically covered by trees independently of the degree of human involvement.  Unfortunately, we could only find information on imports and tagging conventions for Canada and Japanbut based on those one can assume that for Canada, “natural=wood” would be the dominating tag, and that for Japan “natural=wood” and “landuse=forest” display low/no and high maintenance woodlands as described earlier. For all the countries looked at within the analysis the “landcover=trees“-tag has occurred at some point but only in few countries the use of it increased considerably, so it seems there might be a development in some countries while others focus on the other two tags.

Below you can see several graphics displaying the development of tree-related tags between 2008 and 2021

When looking at Finland, there is a first strong increase of the “natural=wood“-tag between March and May 2011, however since then there has been a an increasing trend in area with a subsequent stagnation and a light decrease beginning in the second half of 2020. The “landuse=forest“-tag on the other hand starts with only slowly rising values until the time span between May and October 2019 when there is a strong jump in numbers. Ever since “landuse=forest” held the highest object-numbers for Finland. The “landcover=trees“-tag occurred first in December 2012 and increased until today yet in much lower manner (see additional graphic in the snippet).

Within Paraguay, “natural=wood” emerges as the dominant tag, but with a rather changeable trend, as there are always phases of alternating increase and stagnation, as well as a decline in values between May 2017 and December 2019. In contrast, “landuse=forest” shows an increase without collapses, but with area values at a considerably lower level. In addition, a data jump occurs between September 2019 and January 2020. The “landcover=trees” day proves to be the day with the lowest values and reaches its maximum in March 2019 and records a strong decrease in the following month. As a result, values start to rise again.

For Canada the dominating tag is, as was expected, “natural=wood” with notably strong increases during the first half of 2017 followed by a minor but notable collapse. Since then the rise was more moderate. The other two tags show much lower values, but “landuse=forest” is still higher in numbers than “landcover=trees“. The main jump in data is between February and May 2018 and again between December 2019 and March 2020. The “landcover=trees“-tag doesn’t occur until December 2015 and the tag is seldom used.

New Zealand barely uses the “landcover=trees“-tag, which only occurred in December 2019, yet there is a notable increase of it since May 2021. The tag “natural=wood” dominates and displays a strong rise in values from January to July 2013 after which it stays more or less on the same level with a minor decline in the values between July 2019 and September 2020. It is similar with “landuse=forest” although in this case a strong increase only takes place between March and July 2013 and again between May and June 2014. Subsequent to that these values too stay on about the same level.

For Morocco, “natural=wood” initially turns out to be the most represented tag. This was the case until October 2012 and since then the tag “landuse=forest” has been at the top. The values for “landcover=trees” are generally quite low in this case as well, however towards the end of the period under consideration there is an increase to the previous maximum value, which was reached from August 18.

Looking at the tag development for the area of Italy, “landuse=forest” is the most represented and shows a quite strong increase right at the beginning until May 2008. Subsequently, the trend flattens out until about January 2010. At this point, a phase of high increase of the area begins until September 2012. After this, the trend flattens out again. The tag “natural=wood” shows a more or less steady and moderate trend until October 2014. Since then, the values have tended to stagnate.  The last tag “landcover=trees” has been more strongly represented since February 2011, but, as in all cases, is relatively poorly represented. Since June 2013, no major in- or decreases have been recorded here.

At last, for Japan “natural=wood” shows by far the highest count values as well as a strong increase between March 2010 and January 2011, when they peaked too. Regarding the information on tagging conventions we were able to find and under the premise that everything was tagged according to them of course, it appears that in Japan most (tagged) woodland is low to no maintenance area.  Current values are getting a little closer to this maximum after declining subsequent to the peak. The “landuse=forest“-tag does not display any notable increases after January 2011 and values are beginning to slightly decrease by January 2019. The first occurrence of “landcover=trees” is in January 2017 but in comparison to the other tags the values are quiet low.

Finally, it can be summarised that “landcover=trees” has only very low values in all cases, whereas Paraguay and Italy in particular can show comparatively high values when considered individually. Moreover, it should not go unmentioned that “landcover=forest” and “natural=wood” alternate as the dominant tag depending on the region considered. In many cases, Italy being the exception, however, “natural=wood” is usually more strongly represented at first and is only surpassed by “landuse=forest” later in the course.

Thanks for reading this ohsome Region of the Month blogpost, we hope you liked it. Stay tuned for more content from this series in the future!

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

  • ohsome general idea

  • ohsome general architecture

  • how to become ohsome blog series

  • how spatial joins queries work in the OpenStreetMap History Database OSHDB

Tags: become-ohsome, heigit, intrinsic quality analysis, ohsome, ohsome example, OSM, OSM History Analytics, visualization

Posted in Services, Software

Comments are closed.

  • About

    GIScience News Blog
    News of Heidelberg University’s GIScience Research Group.
    There are 1,642 Posts and 0 Comments so far.

  • Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org
  • Recent Posts

    • Understanding spatiotemporal trip purposes of urban micro-mobility from the lens of dockless e-scooter sharing
    • Audiobeitrag: Das Heidelberg Institute for Geoinformation Technology (HeiGIT) im Campus Radio
    • 3DGeo contributions to ISPRS Congress 2022 now online
    • Recent feature additions to Ohsome Quality analysT
    • Job Offer: Senior Science Manager — Innovation & Research Manager GIScience (m, f, d), 100%, permanent
  • Tags

    3D 3DGEO Big Spatial Data CAP4Access Citizen Science Climate Change Conference crisis mapping Crowdsourcing data quality deep learning disaster DisasterMapping GeoNet.MRN GIScience heigit HOT humanitarian humanitarian mapping Humanitarian OpenStreetMap team intrinsic quality analysis landuse laser scanning Lidar machine-learning Mapathon MapSwipe MissingMaps Missing Maps ohsome ohsome example Open data openrouteservice OpenStreetMap OSM OSM History Analytics Quality quality analysis remote sensing routing social media spatial analysis Teaching VGI Workshop
  • Archives

    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
    • October 2014
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • May 2014
    • April 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • November 2013
    • October 2013
    • September 2013
    • August 2013
    • July 2013
    • June 2013
    • May 2013
    • April 2013
  •  

    August 2021
    M T W T F S S
    « Jul   Sep »
     1
    2345678
    9101112131415
    16171819202122
    23242526272829
    3031  
  • Recent Comments

    GIScience News Blog CC by-nc-sa Some Rights Reserved.

    Free WordPress Themes | Fresh WordPress Themes