Browsing by Author "Powell, Scott L."

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Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments
(Frontiers Media SA, 2022-10) Wood, David J. A.; Stoy, Paul C.; Powell, Scott L.; Beever, Erik A.
Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.
Effect of thematic map misclassification on landscape multi-metric assessment
(2015-05) Kleindl, William; Powell, Scott L.; Hauer, F. Richard
Advancements in remote sensing and computational tools have increased our awareness of large-scale environmental problems, thereby creating a need for monitoring, assessment, and management at these scales. Over the last decade, several watershed and regional multi-metric indices have been developed to assist decision-makers with planning actions of these scales. However, these tools use remote-sensing products that are subject to land-cover misclassification, and these errors are rarely incorporated in the assessment results. Here, we examined the sensitivity of a landscape-scale multi-metric index (MMI) to error from thematic land-cover misclassification and the implications of this uncertainty for resource management decisions. Through a case study, we used a simplified floodplain MMI assessment tool, whose metrics were derived from Landsat thematic maps, to initially provide results that were naive to thematic misclassification error. Using a Monte Carlo simulation model, we then incorporated map misclassification error into our MMI, resulting in four important conclusions: (1) each metric had a different sensitivity to error; (2) within each metric, the bias between the error-naive metric scores and simulated scores that incorporate potential error varied in magnitude and direction depending on the underlying land cover at each assessment site; (3) collectively, when the metrics were combined into a multi-metric index, the effects were attenuated; and (4) the index bias indicated that our naive assessment model may overestimate floodplain condition of sites with limited human impacts and, to a lesser extent, either over- or underestimated floodplain condition of sites with mixed land use.
Hyperspectral Detection of a Subsurface CO2 Leak in the Presence of Water Stressed Vegetation
(ublic Library of Science, 2014) Bellante, Gabriel J.; Powell, Scott L.; Lawrence, Rick L.; Repasky, Kevin S.; Dougher, Tracy
Remote sensing of vegetation stress has been posed as a possible large area monitoring tool for surface CO2 leakage from geologic carbon sequestration (GCS) sites since vegetation is adversely affected by elevated CO2 levels in soil. However, the extent to which remote sensing could be used for CO2 leak detection depends on the spectral separability of the plant stress signal caused by various factors, including elevated soil CO2 and water stress. This distinction is crucial to determining the seasonality and appropriateness of remote GCS site monitoring. A greenhouse experiment tested the degree to which plants stressed by elevated soil CO2 could be distinguished from plants that were water stressed. A randomized block design assigned Alfalfa plants (Medicago sativa) to one of four possible treatment groups: 1) a CO2 injection group; 2) a water stress group; 3) an interaction group that was subjected to both water stress and CO2 injection; or 4) a group that received adequate water and no CO2 injection. Single date classification trees were developed to identify individual spectral bands that were significant in distinguishing between CO2 and water stress agents, in addition to a random forest classifier that was used to further understand and validate predictive accuracies. Overall peak classification accuracy was 90% (Kappa of 0.87) for the classification tree analysis and 83% (Kappa of 0.77) for the random forest classifier, demonstrating that vegetation stressed from an underground CO2 leak could be accurately discerned from healthy vegetation and areas of co-occurring water stressed vegetation at certain times. Plants appear to hit a stress threshold, however, that would render detection of a CO2 leak unlikely during severe drought conditions. Our findings suggest that early detection of a CO2 leak with an aerial or ground-based hyperspectral imaging system is possible and could be an important GCS monitoring tool.
Multitemporal Hyperspectral Characterization of Wheat Infested by Wheat Stem Sawfly, Cephus cinctus Norton
(MDPI AG, 2024-09) Ermatinger, Lochlin S.; Powell, Scott L.; Peterson, Robert K.D.; Weaver, David K.
Wheat (Triticum aestivum L.) production in the Northern Great Plains of North America has been challenged by wheat stem sawfly (WSS), Cephus cinctus Norton, for a century. Damaging WSS populations have increased, highlighting the need for reliable surveys. Remote sensing (RS) can be used to correlate reflectance measurements with nuanced phenomena like cryptic insect infestations within plants, yet little has been done with WSS. To evaluate interactions between WSS-infested wheat and spectral reflectance, we grew wheat plants in a controlled environment, experimentally infested them with WSS and recorded weekly hyperspectral measurements (350–2500 nm) of the canopies from prior to the introduction of WSS to full senescence. To assess the relationships between WSS infestation and wheat reflectance, we employed sparse multiway partial least squares regression (N-PLS), which models multidimensional covariance structures inherent in multitemporal hyperspectral datasets. Multitemporal hyperspectral measurements of wheat canopies modeled with sparse N-PLS accurately estimated the proportion of WSS-infested stems (R2 = 0.683, RMSE = 13.5%). The shortwave-infrared (1289–1380 nm) and near-infrared (942–979 nm) spectral regions were the most important in estimating infestation, likely due to internal feeding that decreases plant-water content. Measurements from all time points were important, suggesting aerial RS of WSS in the field should incorporate the visible through shortwave spectra collected from the beginning of WSS emergence at least weekly until the crop reaches senescence.
Risk Assessment for the Establishment of Vespa mandarinia (Hymenoptera: Vespidae) in the Pacific Northwest, United States
(Oxford University Press, 2021-07) Norderud, Erik D.; Powell, Scott L.; Peterson, Robert K. D.
The recent introduction of the Asian giant hornet, Vespa mandarinia Smith, in the United States in late 2019 has raised concerns about its establishment in the Pacific Northwest and its potential deleterious effects on honey bees, Apis spp., and their pollination services in the region. Therefore, we conducted a risk assessment of the establishment of V. mandarinia in Washington, Oregon, Montana, and Idaho on a county-by-county basis. Our highly conservative tier-1 qualitative and semiquantitative risk assessment relied on the biological requirements and ecological relationships of V. mandarinia in the environments of the Pacific Northwest. Our risk characterization was based on climate and habitat suitability estimates for V. mandarinia queens to overwinter and colonize nests, density and distribution of apiaries, and locations of major human-mediated introduction pathways that may increase establishment of the hornet in the counties. Our results suggest that 32 counties in the region could be at low risk, 120 at medium risk, and 23 at high risk of establishment. Many of the western counties in the region were estimated to be at the highest risk of establishment mainly because of their suitable climate for queens to overwinter, dense forest biomass for nest colonization, and proximity to major port and freight hubs in the region. By design, our tier-1 risk assessment most likely overestimates the risk of establishment, but considering its negative effects, these counties should be prioritized in ongoing monitoring and eradication efforts of V. mandarinia.
Sentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheat
(Elsevier BV, 2023-11) Fordyce, Simon I.; Carr, Patrick M.; Jones, Clain; Eberly, Jed O.; Sigler, W. Adam; Ewing, Stephanie; Powell, Scott L.
The thickness or depth of fine-textured soil (zf) dominates water storage capacity and exerts a control on nutrient leaching in semi-arid agroecosystems. At small pixel sizes (< 1 m; ‘fine resolution’), the normalized difference vegetation index (NDVI) of cereal crops during senescence (Zadoks Growth Stages [ZGS] 90–93) offers a promising alternative to destructive sampling of zf using soil pits. However, it is unclear whether correlations between zf and NDVI exist (a) at larger pixel sizes (1–10 m; ‘intermediate resolution’) and (b) across field boundaries. The relationship of zf to NDVI of wheat (Triticum aestivum L.) was tested using images from a combination of multispectral sensors and fields in central Montana. NDVI was derived for one field using sensors of fine and intermediate spatial resolution and for three fields using intermediate resolution sensors only. Among images acquired during crop senescence, zf was correlated with NDVI (p < 0.05) independent of sensor (p = 0.22) and field (p = 0.94). The zf relationship to NDVI was highly dependent on acquisition day (p < 0.05), but only when pre-senescence (ZGS ≤ 89) images were included in the analysis. Results indicate that cereal crop NDVI of intermediate resolution can be used to characterize zf across field boundaries if image acquisition occurs during crop senescence. Based on these findings, an empirical index was derived from multi-temporal Sentinel-2 imagery to estimate zf on fields in and beyond the study area.
The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring
(Public Library of Science, 2022-03) Stoy, Paul C.; Khan, Anam M.; Wipf, Aaron; Silverman, Nick; Powell, Scott L.
Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.