QGIS 3.4 Data Management & Analysis Expert
Budget: ₹1,500 – ₹12,500 INR
I'm in need of a skilled professional adept at handling QGIS 3.4 software.
Key tasks include:
---- Georeference the picture of the map above. Start with the QGIS project gdms0000_Burial_Mounds_Uedem_V001.qgz. Georeference the picture of the map by means of the QGIS Georeferencer together with the layer DTK10, the NRW topographic map in 1:10000, already imported from the NRW WMS server and added in the QGIS project. Use crossing forest trails, crossroads, road junctions and other features you can identify on DTK10 as land marks (aka ground control points, GCP) with known coordinates (can be read from the QGIS map canvas). Use EPSG:25832. Add the georeferenced map to the QGIS project.
-----Create a hillshade model from the DTM layer. Plot your georeferenced map partly transparent on top of the hillshade model. Compare. What do you observe? How good is the georeferenced map section showing the burial mounds?
-----Use the DTM (not the hillshapde model!) and measure the typical mound heights relative to their direct environment/neighborhood (not the absolute height above sealevel!). What is their typical elvation in the landscape?
----Study the hillshade model in direction East-North-East of the burial mounds area and search for weakly visible reectangular structures which are not paths. What do you observe? Do you have a guess about the origin of these patterns? Choose at least one of the structures, digitize it with a polygon and save it as a geopackage.
The last work is
The Soil Moisture Index (SMI) is a product from the Drought Monitor of Umweltforschungszentrum Leipzig (UFZ). It classifies the soil moisture in soil moisture index classes (drought classes) according to the long-term local soil moisture distribution. The particular soil moisture value to a soil moisture index is not fixed but depends on the history of the local soil moisture distribution over time. Example: A soil moisture of 10% (volumetric) might be classified as very dry at a usually wet location with a higher mean moisture over the last decades but classified as moderate at another location with lower mean moisture.
-------1) Download the historical Soil Moisture Index data from the topsoil (up to 25 cm depth) and total soil moisture (up to 1.8m) datasets from the official UFZ Drought Monitor Website.
--------2) Find a way to import the data in QGIS to use the temporal controller
--------3) Genarate a video of the monthly snapshots of the soil moisture index from 2010 to 2018 for the SMI_topsoil and another video for the SMI_totalsoil. Make sure to use an appropiate simbology (you can find an appropiate color scale an explanation of the SMI in the Drought Monitor Website).
The ideal candidate must have extensive experience with these tasks and the noted data types in the QGIS 3.4 environment. A solid background in geoinformatics and a deep understanding of geographical data is a must for a successful project outcome.
Key tasks include:
---- Georeference the picture of the map above. Start with the QGIS project gdms0000_Burial_Mounds_Uedem_V001.qgz. Georeference the picture of the map by means of the QGIS Georeferencer together with the layer DTK10, the NRW topographic map in 1:10000, already imported from the NRW WMS server and added in the QGIS project. Use crossing forest trails, crossroads, road junctions and other features you can identify on DTK10 as land marks (aka ground control points, GCP) with known coordinates (can be read from the QGIS map canvas). Use EPSG:25832. Add the georeferenced map to the QGIS project.
-----Create a hillshade model from the DTM layer. Plot your georeferenced map partly transparent on top of the hillshade model. Compare. What do you observe? How good is the georeferenced map section showing the burial mounds?
-----Use the DTM (not the hillshapde model!) and measure the typical mound heights relative to their direct environment/neighborhood (not the absolute height above sealevel!). What is their typical elvation in the landscape?
----Study the hillshade model in direction East-North-East of the burial mounds area and search for weakly visible reectangular structures which are not paths. What do you observe? Do you have a guess about the origin of these patterns? Choose at least one of the structures, digitize it with a polygon and save it as a geopackage.
The last work is
The Soil Moisture Index (SMI) is a product from the Drought Monitor of Umweltforschungszentrum Leipzig (UFZ). It classifies the soil moisture in soil moisture index classes (drought classes) according to the long-term local soil moisture distribution. The particular soil moisture value to a soil moisture index is not fixed but depends on the history of the local soil moisture distribution over time. Example: A soil moisture of 10% (volumetric) might be classified as very dry at a usually wet location with a higher mean moisture over the last decades but classified as moderate at another location with lower mean moisture.
-------1) Download the historical Soil Moisture Index data from the topsoil (up to 25 cm depth) and total soil moisture (up to 1.8m) datasets from the official UFZ Drought Monitor Website.
--------2) Find a way to import the data in QGIS to use the temporal controller
--------3) Genarate a video of the monthly snapshots of the soil moisture index from 2010 to 2018 for the SMI_topsoil and another video for the SMI_totalsoil. Make sure to use an appropiate simbology (you can find an appropiate color scale an explanation of the SMI in the Drought Monitor Website).
The ideal candidate must have extensive experience with these tasks and the noted data types in the QGIS 3.4 environment. A solid background in geoinformatics and a deep understanding of geographical data is a must for a successful project outcome.