Analysis of Free and open Land Cover maps for agricultural land use planning at the local level
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Number of Parts | 351 | |
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License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
Identifiers | 10.5446/68938 (DOI) | |
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Production Year | 2022 |
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00:00
PlanningLocal ringMathematical analysisOpen setFreewareCivil engineeringFederation of Bosnia and HerzegovinaProcess modelingEuclidean vectorPhysical systemExecution unitAbelian categoryPermanentPolygonAreaObservational studyCategory of beingSocial classTexture mappingClique-widthSatelliteForestGraph (mathematics)Element (mathematics)Projective planeCovering spaceLevel (video gaming)Process (computing)Social classError messageResultantMatrix (mathematics)NeuroinformatikVector spaceSurfaceMereologySynchronizationForestOpen setExecution unitPoint (geometry)Product (business)Connected spaceAreaObservational studyFigurate numberSubject indexingFrequency2 (number)InformationStudent's t-testPolygonSoftwareFocus (optics)Procedural programmingSign (mathematics)MappingComputer animation
04:37
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Transcript: English(auto-generated)
00:00
Thank you, Marco, for the introduction. Welcome everyone. My name is Stefan Jovanovic and I will present the work done with the chief of Agroecology department within the Agricultural Institute of Republic of Srpska and with Gorica Bratish PhD student from Politecnico di Milano. This one doesn't work. Okay, just few information about Agricultural Institute. It is founded in
00:24
1947 and within 9th department there is a unit that deals with GIS. The story of GIS started 10 years ago with one FAO project supported by Italy. Thank you, Italy, one second. Practically after post-war period
00:42
people needed to create land cover and land use map in order to protect agricultural land. So starting with this project we developed projects and documentation for the country level and afterwards for the local level. The goal of these projects and this documentation, as I said before, is
01:06
to create policies that will protect agricultural land and one of the crucial elements was land cover, land use map layer. So in this research that I'm presenting today
01:22
we tested three and open layers with respect to the one that are proprietary, the one that we created as the Institute and the study area is Lakhdashi municipality which is placed in the northern part of Bosnia. As you can see here some details about the surface and
01:42
population in this region. So the land cover map on the left side is the one that we have created according to FAO classification that is modified for Bosnia as a country. Within this classification we have two main categories, non-agriculture and agricultural, which is our focus,
02:04
then nine main classes and then individual classes. Also on the other side, you can see corina land cover, which is free and open data that we examined during this research. Practically in this methodology we used free and open JS software, QGIS, where we unified
02:24
vectors represented with these two land cover maps. We did synchronization with respect to land cover, land use, the one that we created and in Python we did the reclassification in order to find easier way to create error matrix and after creation of error matrix
02:45
we did the computation of land cover indexes. So on the figure you can see the proportions of the classes generated with these two maps. As you can see corina land cover
03:00
has mapped significantly bigger area of agricultural land, which is first issue. Regarding procedure, sorry, producer accuracy for certain classes, cultivated dominates show the best results. The issue that we found that some of the classes, agricultural classes, were not recognized within corina, which for us is quite the big deal and
03:27
the class with biggest portion of omission error was cultivated dominates. Regarding user accuracy, the best one was forest built up and orchard and
03:41
regarding omission error for classes, pastures and built-up converted in the surface is around 350 hectares. In conclusion I left this at the end. The overall accuracy was 71%. The issue as I mentioned are the missing classes within corina land cover and
04:03
what we found out for certain classes we can use for this kind of documentation corina land cover as a good starting point. Also in our country a lot of private companies are trying to deal with jazz and somehow with their connections to the politics they are making this corina a proprietary data and they are trying to sell product like this,
04:26
which is not correct and I want to stress that so we have to rely on data that are based on science not just to sell something that is for our process fine or not. Thank you for the attention.