. . . . "ITC Bok"@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. Analysis RS for food&water"@en . "https://ltb.itc.utwente.nl/page/792/concept/152779" . . "Hyper-tem