Ninth International Conference on Permafrost ... - IARC Research
Ninth International Conference on Permafrost ... - IARC Research
Ninth International Conference on Permafrost ... - IARC Research
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Modeling and M<strong>on</strong>itoring Ecosystem Performance of Boreal Forests in theYuk<strong>on</strong> River BasinBruce K. WylieASRC <strong>Research</strong> and Technology Soluti<strong>on</strong>s, c<strong>on</strong>tractor to U.S. Geological Survey (USGS)Earth Resources Observati<strong>on</strong> and Science (EROS) CenterLi ZhangSAIC, c<strong>on</strong>tractor to USGS EROSNorman BlissASRC <strong>Research</strong> and Technology Soluti<strong>on</strong>s, c<strong>on</strong>tractor to U.S. Geological Survey (USGS)Earth Resources Observati<strong>on</strong> and Science (EROS) CenterLei JiASRC <strong>Research</strong> and Technology Soluti<strong>on</strong>s, c<strong>on</strong>tractor to U.S. Geological Survey (USGS)Earth Resources Observati<strong>on</strong> and Science (EROS) CenterLarry TieszenU.S. Geological Survey, Earth Resources Observati<strong>on</strong> and Science Center, Sioux Falls, South DakotaW.M. JollyU.S. Department of Agriculture Forest Service, Fire Sciences Laboratory, Missoula, M<strong>on</strong>tanaIntroducti<strong>on</strong>We emphasize the ability to quantify ecosystem processes,not simply changes in land cover, across the entire period ofthe remote sensing archive (Wylie et al., in press). The methodbuilds up<strong>on</strong> remotely sensed measurements of vegetati<strong>on</strong>greenness for each growing seas<strong>on</strong>. However, a time seriesof greenness often reflects annual climate variati<strong>on</strong>s intemperature and precipitati<strong>on</strong>. Our method seeks to removethe influence of climate so that changes in underlyingecological c<strong>on</strong>diti<strong>on</strong>s are identified and quantified. Wedefine an “expected ecosystem performance” to representthe greenness resp<strong>on</strong>se expected in a particular year giventhat year’s climate. We distinguish “performance anomalies”as cases where the ecosystem resp<strong>on</strong>se is significantlydifferent from the expected ecosystem performance.Performance anomaly maps and anomaly trends providevaluable informati<strong>on</strong> <strong>on</strong> the ecosystem for land managersand policy makers at 1-km resoluti<strong>on</strong>. The results offer aprototype to assess the entire Yuk<strong>on</strong> River Basin, a taskslated for completi<strong>on</strong> with our Canadian counterparts for theentire archival period (1984 to current) at 1 km and at 250 m(2000 to current).MethodsA regressi<strong>on</strong> tree model was developed to predict growingseas<strong>on</strong> Normalized Difference Vegetati<strong>on</strong> Index (NDVI),or expected ecosystem performance, from nearly 14,000random pixels, which represents a range of annual climaticc<strong>on</strong>diti<strong>on</strong>s and numerous site c<strong>on</strong>diti<strong>on</strong>s. We used 1-kmAdvanced Very High Resoluti<strong>on</strong> Radiometer NDVI 7-daycomposites integrated from April through the first weekof October as a proxy for ecosystem performance. Usingspatial climatic data and site potential informati<strong>on</strong>, annualmaps of expected growing seas<strong>on</strong> NDVI from 1996 to 2004were c<strong>on</strong>structed from this model.Site potential is the historical performance related toelevati<strong>on</strong>, slope, aspect, soils, and other factors. Dry yearshave lower expected ecosystem performance, and wetyears have higher expected ecosystem performance. Areasthat do not perform within a normal range determined bythe regressi<strong>on</strong> tree model’s expected error were identifiedas ecosystem performance anomalies. These anomalies areareas that resp<strong>on</strong>ded to climatic c<strong>on</strong>diti<strong>on</strong>s differently fromareas with similar expected ecosystem performance.The anomalies were validated using Composite BurnIndex data from selected fires and Landsat spectral indicesacross a burned-to-unburned gradient. Linear time seriestrends in the performance anomaly were mapped based <strong>on</strong>the significance and sign (positive or negative) of the slope.ResultsExpected ecosystem performanceRegressi<strong>on</strong> tree models predicted expected performancefrom site potential, climate data, and land cover (R 2 = 0.84)and showed little bias. Withheld test locati<strong>on</strong>s had similarmean standard error of regressi<strong>on</strong> values as those of themodel development dataset. Regressi<strong>on</strong> tree committeemodels were used, wherein each regressi<strong>on</strong> tree modelpossessed five different regressi<strong>on</strong> trees, each trying toimprove predicti<strong>on</strong>s made by the previous regressi<strong>on</strong> treemodel. This resulted in over 500 different piecewise multipleregressi<strong>on</strong>s being employed.Ecosystem performance anomalySignificant ecosystem performance anomalies weredetermined at 90% c<strong>on</strong>fidence intervals of the expectedecosystem performance model. Underperforming anomaliescorrelated with recent fires. Composite burn index (Eptinget al. 2005) from selected fires validated the ecosystemperformance results. Landsat spectral indices were also353