Remote sensing grassland phenology in the greater Yellowstone ecosystem : biophysical correlates, land use effects and patch dynamics

124 Introduction . 125 Expected Results Based on Current Ecological Understanding . 129 Methods. 130 Study Area . 130 Overview of Methods to Identify Natural Grasslands . 131 Summary of Methods to Identify Forage-phenology. 131 Rate of Grassland Development . 133 Mapping Patches and Patterns . 134 Evaluating Results . 135 vii TABLE OF CONTENTS CONTINUED Results . 135 Summary of Results . 135 Spring Green-up . 135 Landscape Pattern of Green Grassland Patches . 136 Discussion . 139 Patterns of Grassland Patches Relative to Herbivore Space Use . 139 Applicability to Other Species . 141 Limitations and Recommendations for Future Applications . 142 Literature Cited . 143 5. CONCLUSIONS AND FUTURE RESEARCH . 149 Direction for Future Research . 149 Conclusions . 151 Literature Cited . 154 REFERENCES CITED . 155 viii LIST OF TABLES Table Page 2.1. Biophysical predictors of LSP, their definitions and ecological relevance of hypothesized relationships . 32 2.2. Modeling results. Biophysical drivers explain the timing of LSP events across space (study-area) and through time (2001-2009). RF predictors explain spatial-variation in the timing (SOS and EOS) or magnitude (MAX and INDVI) of LSP for an average year . 49 3.1. Land use characteristics of study-area . 92 3.2. Land use map validation comparison with other data sources and time-periods. Percentages indicate the degree to which the land use map produced by the present study agreed with other land use maps at 250 randomly identified points within the study-area . 93 3.3. LSP Biophysical model prediction performance for an average year (2001 – 2009). Errors were calculated by applying biophysical models to an independent dataset (n=260) and subtracting predicted LSP from actual. Root-mean-squared-errors and 95% confidence intervals are reported based on distribution of 260 errors (see Chapter 2 this dissertation for complete description of methods) . 94 3.4. Comparison of LSP observed under residential land use versus predicted wildland condition using 477 study-sites . 95 3.5. Comparison of LSP observed under agricultural land use versus predicted wildland condition using 353 study-sites . 95 4.1. Distribution of patch sizes by study panel throughout a typical year. X-axis shows size classes in hectares and Y-axis frequency . 138 ix LIST OF FIGURES Figure Page 2.1. Study-area and grassland pixels used for modeling the relationship between land surface phenology (LSP) and their biophysical drivers in the Upper Yellowstone River Basin . 17 2.2. NDVI time-series and LSP metrics for an example grassland pixel for year 2005. Blue dots are raw NDVI observations, red-circles indicate the quality (small circles low, large circles high) of observations and hence weight that is assigned for Savitksy-Golay fits in TIMESAT software (J önsson and Eklundh 2002, 2004). The brown line shows the Savitsky-Golay fitted continuous curve. A) Start of season (SOS) is identified by a brown dot and corresponds to the day of year when NDVI surpasses half of its annual amplitude. B) End of season (EOS) is identified by a brown dot and corresponds to the day of year when NDVI drops below half of its annual amplitude. C) Peak greenness (MAX) refers to the maximum annual NDVI value fit by Savitsky-Golay. D) Total integrated annual NDVI (INDVI) refers to the area under the growing season curve bounded by SOS and EOS on the x-axis and NDVI on the y-axis . 21 2.3. Average spatial and inter-annual patterns in the start of the grassland growing season (SOS) in the Upper Yellowstone River, 2001 – 2009. Boxplot shows data for 360 grassland pixels. (See Figure 2.1 for elevation ranges of shaded-relief) . 35 2.4. Average spatial and inter-annual patterns of the end of the grassland growing season (EOS) in the Upper Yellowstone River, 2001 – 2009. Boxplot shows data for 360 grassland pixels. (See Figure 2.1 for elevation ranges of shaded-relief) . 36 2.5. Average spatial and inter-annual temporal patterns of MAX in the Upper Yellowstone River, 2001 – 2009. Boxplot shows data for 360 grassland pixels. (See Figure 2.1 for elevation ranges of shaded-relief) . 37 2.6. Average spatial and inter-annual temporal patterns of INDVI in the Upper Yellowstone River, 2001 – 2009. Boxplot shows data for 360 grassland pixels. (See Figure 2.1 for elevation ranges of shaded-relief) . 39 x LIST OF FIGURES CONTINUED Figure Page 2.7. Decision-tree for biophysical model of SOS constructed with 100 grassland points over 9 years of study . 40 2.8. Relationship between date of spring snowmelt and grassland start of the growing season (SOS) in Upper Yellowstone River Basin, 2001 – 2009. Red points show years and locations for which snow-melt occurred prior to March 31 . The strength of the relationship increases the later in the year that snow-melt occurs . 41 2.9. Decision-tree for biophysical EOS model constructed with 100 grassland points over 9 years of study . 42 2.10. Relationship between vapor-pressure deficit (VPD) and grassland end of the growing season (EOS) in Upper Yellowstone River Basin, 2001 – 2009. The growing season ends earliest for locations and years that have high VPD at the time of EOS. 43 2.11. Decision-tree for biophysical MAX model constructed with 100 grassland points over 9 years of study . 44 2.12. Decision-tree for biophysical INDVI model constructed with 100 grassland points over 9 years of study . 45 2.13. Relationship between cumulative growing season precipitation and total annual grassland productivity (INDVI) in the Upper Yellowstone River Basin, 2001 – 2009. Locations and years that receive more precipitation are generally more productive . 46 2.14. Spatial pattern of EOS model residuals for an average year across 360 natural grassland pixels. The size of points represents the size of residuals when comparing observed EOS to biophysically predicted EOS. 47 3.1. Map of study-area including location of land use, ownership and distribution of public and undeveloped grasslands. The “checkerboard” pattern of land ownership is an artifact of the Public-land Survey System and railroad land grants . 85 xi LIST OF FIGURES CONTINUED Figure Page 3.2. Empirical cumulative distribution functions of MAX under agricultural land use (black line) versus predicted wildland condition (red line) (n=353). Half of ag. sites are higher and outside of common model wildland biophysical prediction errors (Table 3.5) . 96 3.3. (A) Comparison of biophysically predicted average (2001 – 2009) timing of the start of the growing season (SOS), versus (B) observed SOS under human land use using 1-kilometer data. Study results find few differences between predicted wildland and land use SOS . 98 3.4. (A) Comparison of biophysically predicted average (2001 – 2009) timing of the end of the growing season (EOS), versus, (B) observed EOS under human land use using 1-kilometer data. Under human land use the growing season is lengthened in the fall by over 40 days on average across land use types . 99 3.5. (A) Comparison of biophysical