Add allometrics preprint

mikemahoney218 · May 13, 2024 · c528c77 · c528c77
1 parent 2d4c2b2
commit c528c77
Showing 1 changed file with 5 additions and 3 deletions.
diff --git a/papers/index.qmd b/papers/index.qmd
@@ -4,14 +4,16 @@
     <h2>Papers</h2>
     Click "[PDF]" to download each paper.
     <hr>
+    <h3>2024</h3>
+    <p>Johnson, L. K., <strong>Mahoney, M. J.</strong>, Domke, G. M., and Beier, C. M. In Review. New allometric models for the USA create a step-change in forest carbon estimation, modeling, and mapping. In review at <em>Remote Sensing of Environment</em>. <a href="https://arxiv.org/abs/2405.04507">https://arxiv.org/abs/2405.04507</a> <a href='https://arxiv.org/pdf/2405.04507'>[PDF]</a></p>
     <h3>2023</h3>
-    <p><strong>Mahoney, M. J.</strong> In Review. waywiser: Ergonomic methods for assessing spatial models. <a href="https://arxiv.org/abs/2303.11312">https://arxiv.org/abs/2303.11312</a> <a href='https://arxiv.org/pdf/2303.11312'>[PDF]</a></p>
-    <p><strong>Mahoney, M. J.</strong>, Johnson, L. K., Silge, J., Frick, H., Kuhn, M., and Beier, C. M. In Review. Assessing the performance of spatial cross-validation approaches for models of spatially structured data. <a href="https://arxiv.org/abs/2303.07334">https://arxiv.org/abs/2303.07334</a> <a href='https://arxiv.org/pdf/2303.07334'>[PDF]</a></p>
+    <p><strong>Mahoney, M. J.</strong> Preprint. waywiser: Ergonomic methods for assessing spatial models. <a href="https://arxiv.org/abs/2303.11312">https://arxiv.org/abs/2303.11312</a> <a href='https://arxiv.org/pdf/2303.11312'>[PDF]</a></p>
+    <p><strong>Mahoney, M. J.</strong>, Johnson, L. K., Silge, J., Frick, H., Kuhn, M., and Beier, C. M. Preprint. Assessing the performance of spatial cross-validation approaches for models of spatially structured data. <a href="https://arxiv.org/abs/2303.07334">https://arxiv.org/abs/2303.07334</a> <a href='https://arxiv.org/pdf/2303.07334'>[PDF]</a></p>
     <p>Johnson, L. K., <strong>Mahoney, M. J.</strong>, Desrochers, M. L., and Beier, C. M. 2023. Mapping historical forest biomass for stock-change assessments at parcel to landscape scales. <em>Forest Ecology and Management</em>, 546, 121348. <a href="https://doi.org/10.1016/j.foreco.2023.121348">https://doi.org/10.1016/j.foreco.2023.121348</a> <a href='historical_agb_2023.pdf'>[PDF]</a></p>
     <p><strong>Mahoney, M. J.</strong>, Johnson, L. K., and Beier, C. M. 2023. AI for shrubland identification and mapping. In Sun Z, Cristea N, Rivas P (eds.), _Artificial Intelligence in Earth Science_, 295-316. Elsevier. ISBN 978-0-323-91737-7. <a href="https://doi.org/10.1016/B978-0-323-91737-7.00010-4">https://doi.org/10.1016/B978-0-323-91737-7.00010-4</a> <a href='shrub_book_2023.pdf'>[PDF]</a></p>
     <h3>2022</h3>
     <p><strong>Mahoney, M. J.</strong>, Johnson, L. K., Guinan, A. Z., and Beier, C. M. 2022. Classification and mapping of low‑statured ’shrubland’ cover types in post‑agricultural landscapes of the US Northeast. <em>The International Journal of Remote Sensing</em>, 43(19‑24), 7117‑7138. <a href="https://doi.org/10.1080/01431161.2022.2155086">https://doi.org/10.1080/01431161.2022.2155086</a> <a href='shrubland_pub_2022.pdf'>[PDF]</a></p>
-    <p>Johnson, L. K., <strong>Mahoney, M. J.</strong>, Bevilacqua, E., Stehman, S. V., Domke, G. M., and Beier, C. M. In Review. Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages. <em>International Journal of Applied Earth Observation and Geoinformation</em> 114: 103059. <a href="https://doi.org/10.1016/j.jag.2022.103059">https://doi.org/10.1016/j.jag.2022.103059</a> <a href='lidar_agb_2022.pdf'>[PDF]</a></p>
+    <p>Johnson, L. K., <strong>Mahoney, M. J.</strong>, Bevilacqua, E., Stehman, S. V., Domke, G. M., and Beier, C. M. 2022. Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages. <em>International Journal of Applied Earth Observation and Geoinformation</em> 114: 103059. <a href="https://doi.org/10.1016/j.jag.2022.103059">https://doi.org/10.1016/j.jag.2022.103059</a> <a href='lidar_agb_2022.pdf'>[PDF]</a></p>
     <p><strong>Mahoney, M. J.</strong>, Johnson, L. K., Bevilacqua, E., and Beier, C. M. 2022. Ground noise filtering produces inferior models of forest aboveground biomass. <em>GIScience and Remote Sensing</em> 59(1): 1266-1280. <a href="https://doi.org/10.1080/15481603.2022.2103069">https://doi.org/10.1080/15481603.2022.2103069</a> <a href='groundfiltering_2022.pdf'>[PDF]</a></p>
     <p><strong>Mahoney, M. J.</strong>, Beier, CM, and Ackerman, AC. 2022. unifir: A Unifying API for Interacting with Unity from R. <em>Journal of Open Source Software</em> 7(73): 4388. <a href="https://doi.org/10.21105/joss.04388">https://doi.org/10.21105/joss.04388</a> <a href='unifir_2022.pdf'>[PDF]</a></p>
     <p>Tamiminia, H., Salehi, B., Mahdianpari, M., Beier, C. M., Johnson, L. K., Phoenix, D. B., and, <strong>Mahoney, M. J.</strong> 2022. Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis. <em>Geocarto International</em>. <a href="https://doi.org/10.1080/10106049.2022.2071475">https://doi.org/10.1080/10106049.2022.2071475</a></p>