How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?

Ecological Indicators - Tập 139 - Trang 108935 - 2022
Cara Applestein1, Matthew J. Germino1
1U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, 230 N Collins Road, Building 4, Boise, ID 83702, United States

Tài liệu tham khảo

Allred, 2021, Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty, Methods Ecol. Evol., 12, 841, 10.1111/2041-210X.13564 Applestein, 2018, Appropriate sample sizes for monitoring burned pastures in sagebrush steppe: how many plots are enough, and can one size fit all?, Rangeland Ecol. Manage., 71, 721, 10.1016/j.rama.2018.05.003 Applestein, C., Germino, M.J. (2021). Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires. Fire Ecology, 17(1), 1–1. https://doi.org/10.1186/s42408-021-00091-7. Barnard, 2019, Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology, Restor. Ecol., 27, 1053, 10.1111/rec.12938 Bradley, 2018, Invasive species risk assessments need more consistent spatial abundance data, Ecosphere, 9, 10.1002/ecs2.2302 Briske, 2006, A unified framework for assessment and application of ecological thresholds, Rangeland Ecol. Manage., 59, 225, 10.2111/05-115R.1 Beever, 2019, Social–ecological mismatches create conservation challenges in introduced species management, Front. Ecol. Environ., 17, 117, 10.1002/fee.2000 Booth, 2006, Point sampling digital imagery with ‘SamplePoint’, Environ. Monit. Assess., 123, 97, 10.1007/s10661-005-9164-7 Boswell, 2017, Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis, AIMS Environ. Sci., 4, 1, 10.3934/environsci.2017.1.1 Costello, 2011, Twelve-year mapping and change analysis of eelgrass (Zostera marina) areal abundance in Massachusetts (USA) identifies statewide declines, Estuaries Coasts, 34, 232, 10.1007/s12237-010-9371-5 Dark, 2007, The modifiable areal unit problem (MAUP) in physical geography, Prog. Phys. Geogr., 31, 471, 10.1177/0309133307083294 Davies, 2012, Trajectories of change in sagebrush steppe vegetation communities in relation to multiple wildfires, Ecol. Appl., 22, 1562, 10.1890/10-2089.1 Devendra, D., Pastick, N.J., Parajuli, S., Wylie, B.K. (2021). Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 – 2019): U.S. Geological Survey data release, https://doi.org/10.5066/P9XT1BV2. Elmore, 2003, Regional patterns of plant community response to changes in water: Owens Valley, California, Ecol. Appl., 13, 443, 10.1890/1051-0761(2003)013[0443:RPOPCR]2.0.CO;2 Fotheringham, 1991, The modifiable areal unit problem in multivariate statistical analysis, Environ. Plann., 23, 1025, 10.1068/a231025 Germino, 2018, Thresholds and hotspots for shrub restoration following a heterogeneous megafire, Landscape Ecol., 33, 1177, 10.1007/s10980-018-0662-8 Hansen, 2012, A review of large area monitoring of land cover change using Landsat data, Remote Sens. Environ., 122, 66, 10.1016/j.rse.2011.08.024 Jin, X.M., Zhang, Y.K., Schaepman, M.E., Clevers, J.G., Su, Z., Cheng, J., Jiang, J., van Genderen, J. (2008) Impact of elevation and aspect on the spatial distribution of vegetation in the Qilian mountain area with remote sensing data. In: XXIth ISPRS Congress, Beijing, 3 July 2008 - 11 July 2008. Int. Soc. Photogrammetry Remote Sens., 1385–1390. https://doi.org/10.5167/uzh-77426. Krivoruchko, 2019, Evaluation of empirical Bayesian kriging, Spatial Statistics, 32, 10.1016/j.spasta.2019.100368 Lechner, 2012, Investigating species–environment relationships at multiple scales: Differentiating between intrinsic scale and the modifiable areal unit problem, Ecol. Complexity, 11, 91, 10.1016/j.ecocom.2012.04.002 Ludwig, 2007, Assessing landscape health by scaling with remote sensing: when is it not enough?, Landscape Ecol., 22, 163, 10.1007/s10980-006-9038-6 Mansour, 2012, Remote sensing based indicators of vegetation species for assessing rangeland degradation: opportunities and challenges, Afr. J. Agric. Res., 7, 3261 McNellie, 2021, Extending vegetation site data and ensemble models to predict patterns of foliage cover and species richness for plant functional groups, Landscape Ecol., 36, 1391, 10.1007/s10980-021-01221-x Miller, 2005, Incorporating spatial dependence in predictive vegetation models: residual interpolation methods, Professional Geogr., 57, 169, 10.1111/j.0033-0124.2005.00470.x Mitchell, 2017, Relative importance of abiotic, biotic, and disturbance drivers of plant community structure in the sagebrush steppe, Ecol. Appl., 27, 756, 10.1002/eap.1479 Mkrtchyan, 2004, Spatial interpolation of field data on plant abundance. In Natural Forests in the Temperate Zone of Europe-Values and Utilisation, 13 Myneni, 1995, Optical remote sensing of vegetation: modeling, caveats, and algorithms, Remote Sens. Environ., 51, 169, 10.1016/0034-4257(94)00073-V Passey, H.B., Hugie, V.K., Williams, E.W., Ball, D.E., 1982. Relationships between soil, plant community, and climate on rangelands of the Intermountain West. United States Department of Agriculture Economic Research Service Technical Bulletin 53: 1689–1699. Pilliod, 2013, Performance of quantitative vegetation sampling methods across gradients of cover in Great Basin plant communities, Rangeland Ecol. Manage., 66, 634, 10.2111/REM-D-13-00063.1 Porensky, 2018, Plant community responses to historical wildfire in a shrubland–grassland ecotone reveal hybrid disturbance response, Ecosphere, 9, 10.1002/ecs2.2363 Pyke, 2002, Rangeland health attributes and indicators for qualitative assessment, J. Range Manag., 55, 584, 10.2307/4004002 Räsänen, 2019, Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes, Remote Sens. Environ., 230 Rigge, M., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B., Postma, K., Danielson, P., Case, A., Xian, G. (2021). Trends in rangelands fractional components across the western US from 1985–2018. Remote Sensing, 13, 813. https://doi.org/10.3390/rs13040813. Sant, 2014, Assessment of sagebrush cover using remote sensing at multiple spatial and temporal scales, Ecol. Ind., 1, 297, 10.1016/j.ecolind.2014.03.014 Smith, 1994, 221 Smith, W.K., Dannenberg, M.P., Yan, D., Herrmann, S., Barnes, M.L., Barron-Gafford, G.A., Biederman, J.A., Ferrenberg, S., Fox, A.M., Hudson, A., & Knowles, J.F. (2019). Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities. Remote Sens. Environ., 233, 111401. https://doi.org/10.1016/j.rse.2019.111401. Spanhove, 2012, Can remote sensing estimate fine-scale quality indicators of natural habitats?, Ecol. Ind., 1, 403, 10.1016/j.ecolind.2012.01.025 Svoray, 2013, Ecological sustainability in rangelands: the contribution of remote sensing, Int. J. Remote Sens., 34, 6216, 10.1080/01431161.2013.793867 Shriver, 2019, Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance, Ecol. Lett., 22, 1357, 10.1111/ele.13291 Valley, 2016, Case Study. Spatial and temporal variation of aquatic plant abundance: Quantifying change, J. Aquatic Plant Manage., 54, 95 Wilson, 2011, Scaling up: Linking field data and remote sensing with a hierarchical model, Int. J. Geogr. Inf. Sci., 25, 509, 10.1080/13658816.2010.522779 Wu, 2002, Empirical patterns of the effects of changing scale on landscape metrics, Landscape Ecol., 17, 761, 10.1023/A:1022995922992 Xu, 2018, The feasibility of satellite remote sensing and spatial interpolation to estimate cover crop biomass and nitrogen uptake in a small watershed, J. Soil Water Conserv., 73, 682, 10.2489/jswc.73.6.682 Yu, 2014, Meta-discoveries from a synthesis of satellite-based land-cover mapping research, Int. J. Remote Sens., 35, 4573, 10.1080/01431161.2014.930206