In order to check the loss of area from gazing land and barren land, alternative source income opportunities to the community dwellers may be provided. Rapid population growth demanded more land for cultivation, more trees for domestic fuelwood consumption and more area for settlement had been responsible for drastic change in the land use/land cover change in the last 3 decades in the Beressa watershed. The % share of grazing land and barren land has been decreased to 10 and 10.1 % respectively during 1999 that further reduced to 6.1 and 5.2 % during 2015 as against the 12.4 and 13.1 % during 1984 respectively in the Beressa watershed. In this program, plantation of indigenous tree species other than eucalyptus (which affect the ecology) was encouraged. This increase could be possible due to the involvement of local communities to plant trees around their homestead and farm lands. Between 19, forest cover and water body decreased 5 ha/year and 0.03 ha/year respectively but increased and 7.1 ha/year between 19 respectively. The result of classified image indicated that in the last 3 decades, farm land and settlement area increased 71.6 ha/year and 16.8 ha/year respectively. ArcGIS10.2.2 and ERDAS Imagine14 have been used for image processing to produce 6 land uses/land cover classes in the study area. Retrospective analysis of land use/land cover dynamics and its driving force has been undertaken using satellite images of Landsat5 TM 1984, Landsat5 TM 1999, and Landsat8 TM 2015 with 30 m spacial resolution for Beressa watershed of Ethiopia. Via E.Analysing the trend of land use/land cover change and its cause and consequence on human livelihoods as well as on the environment is a matter of concern for sustainable development and management of natural resource. (1) IASMA Research and Innovation Centre: Environment and Natural Resources Area Keywords: GRASS – GIS – Digital Elevation Model – Winkler – Huglin – GladstonesĪuthors: R. The input of the cadastral data can occur also by means of a comma separated values (.csv) sheet, allowing the characterisation of hundreds of vineyards in few minutes. The procedure is written in php and can be adapted to every region and purpose, modifying the vector and the raster layers. The data are automatically stored in the ‘vineyards’ table of the database and result immediately available on the web. Moreover three bioclimatic indices (Winkler, Huglin, and Gladstones) are automatically calculated, based on modelling of 10-years of meteorological data from 64 weather station distributed over the Province, and the elevation of the site. In the following step GRASS GIS performs the query of all the available raster maps (digital elevation model, slope, aspect, etc.) within the limits of the vineyard and returns the correspondent mean values.
#MODIFY TABLE GRASS GIS SOFTWARE#
office (The Digital Terrain Model at 10 m resolution (PAT –S.I.A.T.) was used in the open source GIS software GRASS 6.4 to derive the slope and aspect maps (r.slope.aspect function), whereas the cumulated global radiation, and mean insolation (sun hours) during the vegetative period (1st April – 31th October) were calculated at 20 m resolution using the r.sun command. After sending the request an automatic procedure starts, which extracts the geometry of the vineyard from the vector cadastral map of the Autonomous Province of Trento, provided by the PAT – S.I.A.T. The required fields are the cadastral codes of the zone as well as of the parcels, which composes it.
In the present work we developed an easy to use and open-source application, accessible on the web, exploiting the functionalities of GRASS-GIS in the analysis of geospatial data and PostgreSQL/PostGIS as geodatabase, allowing a rapid characterisation of the sites.Įach vineyard is identified through the compilation of a simple form on the web.
There are several commercial and open-source GIS-applications available and also the geodata are continuously increasing in amount, spatial resolution, frequency, but their use remains matter of specialists! Many of these features could be derived from Digital Elevation Models (DEM), using Geographic Information Systems (GIS). The physical factors that influence the grape ripening include elevation, slope, aspect, potential global radiation, sun hours and soil type of the vineyards.
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