Pioneering Easy-to-Use Forestry Data with Forest Explorer

Tracking #: 2598-3812

Guillermo Vega Gorgojo
José M. Giménez-García
Cristóbal Ordóñez
Felipe Bravo

Responsible editor: 
Christoph Schlieder

Submission type: 
Tool/System Report
Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it — any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.
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Minor Revision

Solicited Reviews:
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Review #1
By Martin Tomko submitted on 26/Oct/2020
Review Comment:

I am satisfied with the revisions carried out by the authors to the manuscript, and I see it fit for acceptance as a Tools and Systems Report. I see this paper as a meaningful contribution to the discussion of Linked Data tools and their application to different use cases, rather than a ground-breaking scientific contribution. Yet, I think this does not diminish its value for the community.

Review #2
By Erwin Folmer submitted on 31/Oct/2020
Minor Revision
Review Comment:

The revised version of this paper has made some positive additions to the original paper, particularly where the responses of the user survey are seemingly guiding the planned integrations and additions to the current tool. What I am still missing from the discussion section of this paper is how easily this tool can be applied to other (spatial) linked datasets/use cases of where this tool has been or can be applied elsewhere. We tried the code in practice but were unsuccessful for various reasons, including the fact that the code was documented in Spanish. I welcome the planned application of this tool to the Portuguese Cross-Forest dataset, but like my previous comment suggest, I would like to see discussion about broader use cases or datasets; particularly because the authors seem to suggest that there are plans to incorporate other (linked) datasets (e.g cadastral) into the tool over time. I think this is where the tool is most interesting for the wider SWJ public.
In summary, the current tool itself is less special in terms of general applicability than I initially hoped. When the tool is only applicable to this specific forestry data, it then becomes less interesting for the SWJ reader. Nevertheless, the paper is describing an interesting and well documented use case and is overall well done. As such, my main question I have about this paper is whether it is really a ‘Tools and Systems Report’ or whether there are better ways to publish it as a use case within SWJ? Of course, broadening the scope of the tool and adding some reflection on the challenges and successes that incorporating other (linked) data sources into the tool will bring is also welcome and would support the placement of this paper into this category.

Review #3
Anonymous submitted on 12/Jan/2021
Review Comment:

The new version of the manuscript is improved and the the authors also submitted a very clarifying and detailed cover letter. I am convinced that the work described in this manuscript is good enough to be published (in terms of quality and impact).
The manuscript is also very well written and the technologies described are well documented in the revised version, while the limitations are now discussed more clearly.