The increasing availability of high-resolution satellite and airborne remote sensing data will promote the multi-sectoral data use by international, national and local public institutions. Data about land cover and land use (LC-LU) provides valuable information that has a number of different applications. Therefore, the main objective of the SENTISIMULAT Project is to develop LC-LU algorithm particularly for Latvian user needs. To ensure that LC-LU algorithm account for the potential user needs, the project team interviewed numerous representatives of public bodies, non-governmental organisations and academic institutions from Latvia in the summer of 2015.
In Latvia, the institutions that could benefit from LC-LU data use the most are those from the following fields: agriculture, forestry, regional planning, research and nature protection. Until now, the project team has carried out the interviews with: Rural Support Service (RSS); Latvian Geospatial Information Agency (LGIA); Institute for Agrarian Economics (IAE); Ministry of Environmental and Regional Development (MERD); Latvian Environment, Geology and Meteorology Centre (LEGMC); Forest Research Institute “Silava”; Faculty of Geography and Earth Science, University of Latvia (FGES); Latvia’s State Forests (LSF); Riga Planning Region (RPR); Vidzeme Planning Region (VPR); Cēsis Municipality (CM).
The satellite data applications in various fields in Latvia:
|
|
|
|
!["Agriculture"]("/assets/upload/userfiles/images/Agriculture_white.jpg")
!["Forestry"]("/assets/upload/userfiles/images/Forestry_white.jpg")
!["Wetlands"]("/assets/upload/userfiles/images/Wetlands_white.jpg")
!["Landscapes"]("/assets/upload/userfiles/images/Landscapes_white.jpg")
The following needs identified by the user needs analysis can be satisfied by Sentinel-2 data: detection of abandoned agricultural lands; development of risk maps of potential burning areas; identification of potential beaver dam areas; detection and assessment of drained lands; built environment – urban sprawl monitoring; detection of illegal building; landscape monitoring; assessment of degraded territories; detection of land management; assessment of forest watercourses, hogweed stands in forests, agricultural lands and urban areas; assessment of forest calamities and insect attacks and damages; flood and snow cover data; data on forest and peat bog fires; data on deforestation; vegetation stress data; data on land surface moisture regimes; data for control of drainage conditions.
The project team took note of user needs, which will serve as the basis for the LC-LU classification algorithm that will be developed as part of the SENTISIMULAT project.
