. . . "use the pre-processed imagery to design and implement a flowchart to create an agro-environmental stratification, that is optimized for a specified purpose."@en . . "use the pre-processed imagery to design and implement a flowchart to create an agro-environmental stratification, that is optimized for a specified purpose."@en . . . "explain how agro-ecosystems may be mapped and monitored using a selection of hyper-temporal RS-based indices related to vegetation greenness and vigour, evapotranspiration, and rainfall."@en . . "explain how agro-ecosystems may be mapped and monitored using a selection of hyper-temporal RS-based indices related to vegetation greenness and vigour, evapotranspiration, and rainfall."@en . . . "identify and evaluate impacts of choices made during the design of the flowchart."@en . . "identify and evaluate impacts of choices made during the design of the flowchart."@en . . . "create, document, and defend, a “ready-to-go” spatial-temporal stratification and dataset that underpins the individual Agro-Ecosystems related research interests of the participant."@en . . "create, document, and defend, a “ready-to-go” spatial-temporal stratification and dataset that underpins the individual Agro-Ecosystems related research interests of the participant."@en . . . "obtain and pre-process image time series of selected indices, and evaluate how to incorporate their metadata in the pre-processing chain."@en . . "obtain and pre-process image time series of selected indices, and evaluate how to incorporate their metadata in the pre-processing chain."@en . . "https://ltb.itc.utwente.nl/page/792/concept/152736" . . "Land cover change"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152760" . . "Monitoring"@en . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152779" . . "Hyper-temporal"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152827" . . "Agro-ecological stratification"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152859" . . "NDVI"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Master’s Programme Geo-Information Science and Earth Observation (M-GEO) is a two-year academic curriculum at MSc level, taught fully in English, dedicated to understanding the earth’s systems from a geographic and spatial perspective. The field of Geo-information Science and Earth Observation has, in recent years, witnessed fast scientific and technological developments. As a result, geographic information has become a vital asset to society and part of our daily life. The ubiquitous production and availability of spatial data require cloud computing and new technology to turn the increasing volume of ‘big data’ to good use. The growing range of global challenges, from climate change and resource depletion to environmental pollution and pandemic diseases, that our society and in particular the more vulnerable populations on our planet are facing, increases the demand for academic professionals who have the ability, attitudes and skills to design solutions that are sustainable, transdisciplinary and innovative with positive societal impacts. Our education focuses on addressing these global problems by means of advanced geo-information and earth observation applications."@en . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . . . "ArcGIS"@en . . . "ENVI"@en . . . "Erdas"@en . . . "IDL"@en . . . "ILWIS"@en . . . "Course"@en . "201800311" . "NRS_0007" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "f2f" . "The course gradually changes from acquiring a general overview of the use/functionality of RS-imagery (spatial-temporal) to address food/water security aspects, to commonly used indices to monitor and assess that, and to tools and skills developments to obtain-extract-derive-interpret specific spatial-temporal data. It concludes with an individual self-defined task. That task will be assessed. The task must connect to the participant's interests, to a food/water security issue, and to a probable MSc research topic that the participant contemplates pursuing. Ideally, the task consists of prior academic/analytical work as required to underpin an MSc-research proposal."@en . "Gradually this course will move to the requirement that students have experience with Notebooks and Python script to assess and process data at different DIAS-systems. ,All participants must have passed successfully both M-GEO core-modules (RS and GIS), or do possess an equal level RS/GIS skills and knowledge."@en . "6"^^ . "4" . "2B " . "2023-04-23T22:00:00Z"^^ . "How will we meet the challenge of producing more food to feed a growing population while sustaining the natural resources that agriculture depends upon? Achieving this requires informed decision making, which will heavily depend upon spatial and temporal information derived through the use of remotely sensed data-streams.\n\nThis course provides students with the skills to select, use and interpret state of the art hyper-temporal remote sensing imagery, including both optical and SAR sensors. These skills will be applied to map, monitor, evaluate and explain the performance of the agro-ecosystems. Hyper-temporal remote sensing is also applicable for monitoring urban and natural environments, and to study/assess processes related to e.g. bio-diversity and disasters.\nStudents will learn when to use and how to process hyper-temporal remote sensing images (SPOT-Vegetation, MODIS, PROBA-V, Sentinel-1, 2, and 3, etc.), data mining and probability techniques to:\n\nmap and monitor different aspects of agro-ecosystems using remote sensing indices such as NDVI, LSWI and LAI, to address e.g. “what food or feed crops are produced where and when?”\ndetect anomalies and/or changes in land use and land cover over time, to address e.g. “where are changes in crop production happening and why?”\nfeed into early warning systems by detecting anomalies in vegetation, temperature, precipitation and soil moisture, to address e.g. “where and when do droughts, floods, heat/cold waves, fires and pest and diseases affect agriculture?”\nAfter completing this course, the student will have an additional/improved skill-set as required for a wide range of specialized advisory work, like:\n\nPreparation of inventories for land cover and land use mapping.\nCreation of maps and legends with info on crop calendars and crop management practiced, plus an analysis on production constraints and impacts by perils (yield gaps).\nProviding timely and accurate spatial information that feeds into early warning systems and index based insurance programs.\nQuantified yield gap assessments for land use planning, specifications of advice for extension services, work agenda specifications by research stations, and policy-making considerations."@en . "Spatio-temporal Analysis of Remote Sensing Data for Food and Water Security"@en . "Spatio-temp. Analysis RS for food&water"@en . "Spatio-temp. A