Mike Mahoney, PhD

A book and blog focused on: 1. Where can I find geospatial data? 2. How do I know if I can trust that data? 3. What societal issues are intertwined with geospatial analysis? Copyright, location privacy, ethics, and more? https://ow.ly/LLyA50T29PM Spatial Reserves.

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A book and blog focused on: 1. Where can I find geospatial data? 2. How do I know if I can trust that data? 3. What societal issues are intertwined with geospatial analysis? Copyright, location privacy, ethics, and more? https://ow.ly/Ib1g50T29YM Spatial Reserves.

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Are you curious about causal inference analysis? If so, check out Esri's newest ArcGIS lab, where you will learn how to perform causal inference analysis in ArcGIS Pro. Learn more: https://ow.ly/U7SN50SQrpw

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💥 New Ep of #TheBoundingBox 🎙️ Talking to Joseph Kerski Phd GISP, educator extraordinaire! We talk #geospatial in education, challenges, integration in other fields, and so much more! GIS and Geography are integral to so many facets of education and industries, it's an exciting time to be a student... I hope! I'm excited for them! #gis #geography #geodev https://lnkd.in/g-j7hJux

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Remote sensing learning materials 👇🏾 Minesat Community offers a curated library of remote sensing knowledge for all! From basic 101 materials to executive-level insights and conference information, explore a wealth of information to support the democratization of remote sensing knowledge. See: https://lnkd.in/dngHieY7 #remotesensing #geophysics #geospatial

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A new #researcharticle, "Cost-effectiveness of remote sensing technology for spruce budworm monitoring in Maine, USA" by Allison Foster, Parinaz Rahimzadeh-Bajgiran, Adam Daigneault, and Aaron Weiskittel, was published today at Forests Monitor (#FM). Forest pests are a major disturbance factor in forest ecosystems, which can result in tree mortality and loss of ecosystem services, leading to further negative impacts on the forest economy. Spruce budworm (Choristoneura fumiferana (Clem.); SBW) is a native forest pest in the northeastern USA and Canada, including the state of Maine, which defoliates balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea spp.) trees with cyclical outbreaks every 30-60 years. SBW is typically monitored via ground sampling techniques such as pheromone traps and overwintering second instar larvae (L2) branch sampling. Remote sensing data can also provide information about defoliation patterns across the landscape and forest susceptibility to outbreaks. This study presents a cost-effectiveness analysis comparing remote sensing data, ground sampling techniques, and an integrated monitoring approach, combining remote sensing change detection with field sampling. Over a 10-year project period, Sentinel-2 imagery emerged as the most cost-effective option, ranging from US$33 to US$63/square kilometer (sq km), offering wide spatial coverage and moderate resolution suitable for the identification of defoliation patterns. PlanetScope imagery ranged from US$77 to US$241/sq km, and unmanned aerial vehicle (UAV) imagery had the greatest variation, from US$9,220 to US$58,481/sq km. Labor costs are the most influential in our study, ranging from 30% of total costs for remote sensing approaches to 80% for field sampling. The integrated monitoring approach proposed in this study presents a synergistic strategy for effective and timely SBW monitoring, ranging from US$144 to US$213/sq km. Utilizing this integrated approach leverages both remote sensing and L2 branch surveys to enhance the accuracy and timeliness of monitoring efforts, leading to more effective management strategies for mitigating pest outbreaks for landowners. Our research highlights the importance of adaptive monitoring strategies and integrating remote sensing for forest pest detection. 👉Read the whole paper here (open access): https://lnkd.in/dm_V4Kxp

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🌍 Tackling mapping at large geographical area: Data Management Challenges in Remote Sensing 🌍 Working with large spatial data presents unique challenges, especially when mapping ecosystems. While many might assume that the most complex aspect is the data modeling itself, I’ve found that data management is equally—if not more—demanding. To manage the vast volumes of inference data generated from my mapping models, I rely on Google Cloud Storage. This cloud-based solution enables me to store, organize, and access extensive datasets seamlessly, which is crucial for handling high-resolution mapping across large geographical areas. Using cloud storage has also facilitated data sharing and collaboration, allowing me to work effectively with other researchers and stakeholders, even when dealing with terabytes of imagery and model outputs. This experience has underscored the importance of efficient data management in ensuring that high-resolution environmental mapping projects are not only feasible but also scalable. 

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Unexplained heat wave hotspots, #AGU24, impacts of climate change on renewable energy, Doomsday glacier melting, harnessing power of geospatial data, new MS in Climate, updated theory of best-preserved dinosaurs, plus more research and education news from Columbia Climate School and State of the Planet! ➡️ Explore our latest newsletter: https://lnkd.in/eid3hGkW 📷 Regions where observed heat waves exceed trends from climate models. Boxed areas with the darkest red colors are the most extreme; lesser reds and oranges exceed models, but not by as much. Yellows roughly match models, while greens and blues are below what models would project. (Adapted from Kornhuber et al., PNAS 2024)

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Why do we take time to map actual field boundaries? 🤔 Working with our farming partners, we’ve noticed Farm Service Agency (FSA) maps don’t always match how growers farm their land. For example, FSA maps might include old farm roads or cut fields in half. This can cause a mismatch between what’s on paper and what’s really happening on the field. That’s why we work with farmers to map the actual footprint of their operation. dMRV on actual field boundaries = accurate climate-smart reporting

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In case you missed it, the geodatabase team published an article last week about some of the major new features released in ArcGIS Pro 3.4 - https://lnkd.in/gGVVq8rf It includes some of the items I touched on (triggering fields, templated rules, etc) but also contains a few interesting new features that I didn't cover!

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Introducing Catalog Layers in ArcGIS Online. New with the June 2024 release, this layer type provides a way to browse layers by regions or topics, preview data, and add layers to a map. https://ow.ly/jcqQ50SwE60

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The NC CASC has a new blog page! Check out the first installment, by Dustin O'Brien, a CAST (Climate Adaptation Scientist of Tomorrow) participant from this summer: https://lnkd.in/gV3tDpwc #cast #climatescience #climateadaptation #adaptationscience #climateadaptationscience #studentblog #blog #scienceblog #indigenous #water #waterrights #waterresources #tribal

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The future is now. Check out this free #Esri tutorial on the use of an #AI Assistant to explore the pickleball market #GIS #ArcGIS #GeoAI https://ow.ly/z6m930sIfGH

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https://lnkd.in/eiXK4haT My new presentation on resources and strategies for using #gis that I gave to UCAR - The University Corporation for Atmospheric Research yesterday but I hope is helpful to many.

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New in #MATLAB R2024b: Learn how to crop raster data to an Area of Interest (AOI) for focused geospatial analysis. #GIS #DataScience https://spr.ly/6046qKvXp

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Publishing workflows to ArcGIS Enterprise: The Publish data from an enterprise geodatabase tutorial, now available in the tutorial gallery, explores data-sharing approaches to ArcGIS Enterprise. https://ow.ly/OiQr50SgSmN

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Are you curious about causal inference analysis? If so, check out Esri's newest ArcGIS lab, where you will learn how to perform causal inference analysis in ArcGIS Pro. Learn more ➡️ https://ow.ly/Bg2B50SVs9P

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If you have not heard, the Locate Press book Collecting Field Data with QGIS and Mergin Maps is out! Read the blog below to learn more!

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