{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:22:52Z","timestamp":1760235772708,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.<\/jats:p>","DOI":"10.3390\/rs13193893","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2676-6277","authenticated-orcid":false,"given":"John","family":"Hogland","sequence":"first","affiliation":[{"name":"Rocky Mountain Research Station, U.S. Forest Service, Missoula, MT 59801, USA"}]},{"given":"David L. R.","family":"Affleck","sequence":"additional","affiliation":[{"name":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s40725-019-00101-7","article-title":"Risk Management and Analytics in Wildfire Response","volume":"5","author":"Thompson","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hogland, J., Dunn, C.J., and Johnston, J.D. (2021). 21st Century Planning Techniques for Creating Frie-Resilient forest in the American West. Forests, 12.","DOI":"10.3390\/f12081084"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hogland, J., Affleck, D.L.R., Anderson, N., Seielstad, C., Dobrowski, S., Graham, J., and Smith, R. (2020). Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA. 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