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DTSTART;TZID=Europe/Berlin:20240611T113000
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DTSTAMP:20240529T110104Z
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UID:10000255-1718105400-1718110800@whatworksclimate.solutions
SUMMARY:Innovations in Climate Evaluation and Synthesis: Leveraging Remote Sensing and Machine Learning for Climate Solutions
DESCRIPTION:Increased availability of and access to satellite and remotely sensed data have paved the way for innovations in methods and tools for climate evaluation. These have allowed us to observe\, measure\, and evaluate the impact of climate change on humans and ecosystems at a relatively low cost. High-resolution and frequent observations from satellites have facilitated the creation of wide ranging and precise proxy indicators (complementary to traditional on-the-ground measures) to observe environmental outcomes such as carbon emissions\, land use changes\, and vegetation health. The volume of available data has enabled the training of robust machine learning models\, allowing for more accurate classification\, detection\, and prediction tasks in geospatial analysis. \nIn this presentation\, we explore the pivotal role of these innovations in enhancing climate evaluation methodologies and driving informed\, evidence-based decision-making in climate policy. In doing so\, we share an interface called “Remote Sensing Indicators for Development”\, a resource compiling existing literature\, empirical applications\, and guidance on the use of remote sensing and machine learning in evidence generation and synthesis. We showcase the multifaceted ways in which researchers may use this resource in their own work – to further their understanding of the distinctions and the comparability between various remotely sensed indices\, including their advantages and disadvantages\, and to determine which indices may be used for ML-based prediction in regions where data is lacking. This presentation fills a critical knowledge gap by offering a comprehensive resource that explores the spectrum of applications that leverage remote sensing indicators and machine learning in climate evaluation.
URL:https://whatworksclimate.solutions/presentation/innovations-in-climate-evaluation-and-synthesis-leveraging-remote-sensing-and-machine-learning-for-climate-solutions/
LOCATION:H 0104 (Elinor Ostrom Hall)
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240611T093000
DTEND;TZID=Europe/Berlin:20240611T110000
DTSTAMP:20240531T075143Z
CREATED:20240508T105901Z
LAST-MODIFIED:20240531T075143Z
UID:10000126-1718098200-1718103600@whatworksclimate.solutions
SUMMARY:Geospatial Impact Evaluation of Agricultural Intensification Program in Niger
DESCRIPTION:Climate change is exacerbating food insecurity around the world\, particularly in drought-prone areas that are already highly vulnerable\, such as the Sahel region of West Africa. To improve food security and resilience\, the government of Niger\, with funding from the West African Development Bank (BOAD)\, implemented a multi-faceted agricultural production intensification program in 2011 (PIPA/SA). We employ geospatial data (spectral indices from Landsat-7 imagery) and a synthetic difference-in-differences (SDID) design to measure the impact of this program (irrigation and land rehabilitation efforts) on agricultural production\, water availability\, siltation\, and desertification (using proxies for “level of greenness” such as NDVI\, SAVI\, etc.). Determining the impact of this program will inform the Government of Niger’s future policy and programming related to climate change mitigation/adaptation and food intensification\, with implications for human health\, nutrition\, livelihoods\, and economic development more broadly. \nThis project contributes to the evidence base on climate and nutrition interventions\, and provides more evidence on the benefits and application of geospatial/remote sensing data in climate-related impact evaluation.
URL:https://whatworksclimate.solutions/presentation/geospatial-impact-evaluation-of-agricultural-intensification-program-in-niger/
LOCATION:H 0112
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