# Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Data

#### 2.2. Polar Coordinate Transformation and Phenology Metrics

#### 2.3. Dimensional Reduction and Phenological Classification

## 3. Results and Discussion

#### 3.1. Phenology Metrics

#### 3.2. Dimensional Reduction

#### 3.3. Phenological Classification

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- White, M.A.; Thornton, P.E.; Running, S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles
**1997**, 11, 217–234. [Google Scholar] [CrossRef] - Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ.
**2003**, 84, 471–475. [Google Scholar] [CrossRef] - Kennedy, R.E.; Yang, Z.; Cohen, W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sens. Environ.
**2010**, 114, 2897–2910. [Google Scholar] [CrossRef] - Morisette, J.T.; Richardson, A.D.; Knapp, A.K.; Fisher, J.I.; Graham, E.A.; Abatzoglou, J.; Wilson, B.E.; Breshears, D.D.; Henebry, G.M.; Hanes, J.M. Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century. Front. Ecol. Environ.
**2009**, 7, 253–260. [Google Scholar] [CrossRef][Green Version] - Liang, L.; Schwartz, M.D.; Fei, S. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens. Environ.
**2011**, 115, 143–157. [Google Scholar] [CrossRef] - Rodriguez-Galiano, V.F.; Dash, J.; Atkinson, P.M. Intercomparison of satellite sensor land surface phenology and ground phenology in Europe. Geophys. Res. Lett.
**2015**, 42, 2253–2260. [Google Scholar] [CrossRef][Green Version] - Hargrove, W.W.; Spruce, J.P.; Gasser, G.E.; Hoffman, F.M. Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogramm. Eng. Remote Sens.
**2009**, 75, 1150–1156. [Google Scholar] - Kennedy, R.E.; Townsend, P.A.; Gross, J.E.; Cohen, W.B.; Bolstad, P.; Wang, Y.Q.; Adams, P. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens. Environ.
**2009**, 113, 1382–1396. [Google Scholar] [CrossRef] - Cleland, E.E.; Chuine, I.; Menzel, A.; Mooney, H.A.; Schwartz, M.D. Shifting plant phenology in response to global change. Trends Ecol. Evol.
**2007**, 22, 357–365. [Google Scholar] [CrossRef] - Polgar, C.A.; Primack, R.B. Leaf-out phenology of temperate woody plants: From trees to ecosystems. New Phytol.
**2011**, 191, 926–941. [Google Scholar] [CrossRef] - Norman, S.P.; Hargrove, W.W.; Christie, W.M. Spring and autumn phenological variability across environmental gradients of Great Smoky Mountains National Park, USA. Remote Sens.
**2017**, 9, 407. [Google Scholar] [CrossRef][Green Version] - Van Leeuwen, J.D.W. Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data. Sensors
**2008**, 8, 2017–2042. [Google Scholar] [CrossRef][Green Version] - Kleynhans, W.; Olivier, J.C.; Wessels, K.J.; Salmon, B.P.; van den Bergh, F.; Steenkamp, K. Detecting Land Cover Change Using an Extended Kalman Filter on MODIS NDVI Time-Series Data. IEEE Geosci. Remote Sens. Lett.
**2011**, 8, 507–511. [Google Scholar] [CrossRef][Green Version] - De Beurs, K.M.; Townsend, P.A. Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens. Environ.
**2008**, 112, 3983–3990. [Google Scholar] [CrossRef] - Hicke, J.A.; Allen, C.D.; Desai, A.R.; Dietze, M.C.; Hall, R.J.; Hogg, E.H.; Kashian, D.M.; Moore, D.; Raffa, K.F.; Sturrock, R.N.; et al. Effects of biotic disturbances on forest carbon cycling in the United States and Canada, Global Change Biology. Glob. Chang. Biol.
**2012**, 18, 7–34. [Google Scholar] [CrossRef] - Spruce, J.P.; Hicke, J.A.; Hargrove, W.W.; Grulke, N.E.; Meddens, A.J.H. Use of MODIS NDVI Products to Map Tree Mortality Levels in Forests Affected by Mountain Pine Beetle Outbreaks. Forests
**2019**, 10, 811. [Google Scholar] [CrossRef][Green Version] - Van Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; Daniels, L.D.; Franklin, J.F.; Fule, P.Z.; Harmon, M.E.; Larson, A.J.; Smith, J.M.; Taylor, A.H.; et al. Widespread Increase of Tree Mortality Rates in the Western United States. Science
**2009**, 323, 521–524. [Google Scholar] [CrossRef][Green Version] - White, M.A.; Hoffman, F.M.; Hargrove, W.W.; Nemani, R.R. A global framework for monitoring phenological responses to climate change. Geophys. Res. Lett.
**2005**, 32, 4. [Google Scholar] [CrossRef][Green Version] - Gu, Y.; Brown, J.F.; Miura, T.; Van Leeuwen, W.J.D.; Reed, B.C. Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data. Remote Sens.
**2010**, 2, 526–544. [Google Scholar] [CrossRef][Green Version] - Zhang, Y.; Hepner, G.F.; Dennison, P.E. Delineation of Phenoregions in Geographically Diverse Regions Using k-means++ Clustering: A Case Study in the Upper Colorado River Basin. Gisci. Remote Sens.
**2012**, 49, 163–181. [Google Scholar] [CrossRef] - Kumar, J.; Mills, R.T.; Hoffman, F.M.; Hargrove, W.W. Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets. Procedia Comput. Sci.
**2011**, 4, 1602–1611. [Google Scholar] [CrossRef][Green Version] - Mills, R.T.; Kumar, J.; Hoffman, F.M.; Hargrove, W.W.; Spruce, J.P.; Norman, S.P. Identification and Visualization of Dominant Patterns and Anomalies in Remotely Sensed Vegetation Phenology Using a Parallel Tool for Principal Components Analysis. Procedia Comput. Sci.
**2013**, 18, 2396–2405. [Google Scholar] [CrossRef][Green Version] - Bolton, D.K.; Gray, J.M.; Melaas, E.K.; Moon, M.; Eklundh, L.; Friedl, M.A. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sens. Environ.
**2020**, 240. [Google Scholar] [CrossRef] - Morellato, L.P.C.; Alberti, L.F.; Hudson, I.L. Applications of Circular Statistics in Plant Phenology: A Case Studies Approach. In Phenological Research: Methods for Environmental and Climate Change Analysis; Hudson, I.L., Keatley, M.R., Eds.; Springer: Dordrecht, The Netherlands, 2010; pp. 339–359. [Google Scholar] [CrossRef]
- Brooks, B.J.; Lee, D.C.; Pomara, L.Y.; Hargrove, W.W.; Desai, A.R. Quantifying Seasonal Patterns in Disparate Environmental Variables Using the PolarMetrics R Package. In Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, USA, 18–21 November 2017; pp. 296–302. [Google Scholar] [CrossRef]
- Spruce, J.P.; Gasser, G.E.; Hargrove, W.W. MODIS NDVI Data, Smoothed and Gap-Filled, for the Conterminous US: 2000–2015; ORNL DAAC: Oak Ridge, TN, USA, 2016. [Google Scholar] [CrossRef]
- Prados, D.; Ryan, R.E.; Ross, K.W. Remote Sensing Time Series Product Tool. In Proceedings of the American Geophysical Union, Fall Meeting 2006, San Francisco, CA, USA, 11–15 December 2006. abstract id. IN33B-1341. [Google Scholar]
- McKellip, R.D.; Spruce, J.P.; Smoot, J.C.; Gasser, G.E.; Ryan, R.E.; Holekamp, K.; Ross, K. Time Series Product Tool (TSPT) Version 2.0. Available online: https://www.techbriefs.com/component/content/article/ntb/tech-briefs/information-sciences/20965 (accessed on 5 April 2020).
- McKellip, R.D.; Ross, K.W.; Spruce, J.P.; Smoot, J.C.; Ryan, R.E.; Gasser, G.E.; Prados, D.L.; Vaughan, R.D. Phenological Parameters Estimation Tool. Available online: https://www.techbriefs.com/component/content/article/ntb/tech-briefs/software/8481 (accessed on 5 April 2020).
- Burgan, R.; Hardy, C.; Ohlen, D.; Fosnight, G.; Treder, R. Ground sample data for the Conterminous U.S. Land Cover Characteristics Database; General Technical Report RMRS-GTR-41; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Ogden, UT, USA, 1999; Volume 13. [Google Scholar] [CrossRef]
- Melendez-Pastor, I.; Navarro-Pedreno, J.; Koch, M.; Gomez, I.; Hernandez, E.I. Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area. Remote Sens.
**2010**, 2, 2072–4292. [Google Scholar] [CrossRef] - R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org (accessed on 26 May 2020).
- Sokal, R.R. The Principles and Practice of Numerical Taxonomy. Taxon
**1963**, 12, 190–199. [Google Scholar] [CrossRef] - Hartigan, J.A. Clustering Algorithms, 99th ed.; John Wiley & Sons, Inc.: New York, NY, USA, 1975; ISBN 047135645X. [Google Scholar]
- Forgy, E. Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classifications. Biometrics
**1965**, 21, 768–780. [Google Scholar] - MacQueen, J.B. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability; University of California Press: Berkeley, CA, USA, 1967. [Google Scholar]
- Hartigan, J.A.; Wong, M.A. Algorithm AS 136: A K-Means Clustering Algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.)
**1979**, 28, 100–108. [Google Scholar] [CrossRef] - Lloyd, S. Least squares quantization in PCM. IEEE Trans. Inf. Theory
**1982**, 28, 129–137. [Google Scholar] [CrossRef] - Mills, R.T.; Hoffman, F.M.; Kumar, J.; Hargrove, W.W. Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats. Procedia Comput. Sci.
**2011**, 4, 1612–1621. [Google Scholar] [CrossRef][Green Version] - SAS Institute Inc. SAS/STAT® 14.2 User’s Guide; SAS Institute Inc.: Cary, NC, USA, 2016. [Google Scholar]

**Figure 1.**Visualizations of NDVI data from one MODIS pixel extracted from Great Smoky Mountains National Park, North Carolina. (

**a**) Time series showing evergreen decline caused by hemlock tree mortality within the pixel. (

**b**) The same data plotted radially in a polar graph. (

**c**) Cumulative NDVI as a function of time. The 15% and 80% milestones define the start and end of the specified growing season within a phenology-centered year. (

**d**) The phenological offset of each cell from the start of the calendar year is used to rotate and standardize the measurement of phenological completion milestones and growing season measures.

**Figure 2.**Example land surface phenology metric maps for the phenological year 2016 across North America, based on polar coordinate transformed NDVI time-series data. (

**a**) GSmid, the middle of the growing season, illustrates regional variability in the timing of the phenology year. (

**b**) LOS, the length of the growing season. Note short anthropogenic growing seasons of agricultural landscapes across the Corn Belt and Mississippi River valley. (

**c**) Mean_NDVI_grw, the mean growing season greenness, is a proxy for vegetation productivity. (

**d**) AVgrw, the strength of seasonality, distinguishes between evergreen and deciduous vegetation. These variables and others in Table 1 collectively characterize vegetation similarity and difference at regional and landscape scales. For example, dense and productive evergreen vegetation in the Pacific Northwest displays a long growing season centered late in the calendar year, with high mean greenness. Some of these features are shared, while some contrast with other forested systems such as the boreal forest in eastern Canada, Appalachian deciduous forests, and southeastern US mixed conifer/hardwood forests.

**Figure 3.**Distributions of cluster centroids (k = 500) resulting from cluster analysis, with respect to (

**a**) factors 1 and 2 (timing factors) and (

**b**) factors 3 and 4 (productivity and seasonality factors). The distributions in the margins compare the factor score data to their representative cluster centroids. The more uniform distribution of centroids was chosen intentionally to disperse phenoclass representation across factor-space. The prominent circular distribution of centroids in (

**a**) is a result of the relationship between factors 1 and 2, which are sine and cosine complements of each other. That is, the factor 1 dimension represents a subset of sine, cosine phenology timing variables that mirrors another subset of sine, cosine variables represented by the factor 2 dimension (Table 2). While the fixed relationship of sine and cosine for input dates plots points exclusively on the periphery of a circle, clustered output values can result in internal points as well. The direction of these points indicates seasonal dates, and the magnitude indicates strength of that seasonality. (

**b**) Factor 3 and factor 4 centroid values indicate average NDVI, seasonal amplitude, and variability.

**Figure 4.**RGB composite based on three of the four factors from Table 2 for the phenological year 2016 (Red, Green, and Blue are associated with phenological Seasonality, Productivity, and Timing respectively). (

**a**) Continental scale. (

**b**) Central Texas is shown with ecoregion boundaries to illustrate local variation. The combined factors provide a nuanced description of phenology and collectively are a robust indicator of gradients in phenological diversity at landscape, regional, and continental scales. Hue and intensity in this image together indicate overall phenological distinctiveness and similarity. For example, the Southeastern US, Atlantic coast, and Pacific Northwest share a relatively low seasonality and high greenness (in the RGB spectrum, yellow results from high R and G values). Likewise, the largely agricultural Corn Belt and Mississippi River Valley are shown to be phenologically similar, as are the forested Appalachian and Great Lakes regions. Color similarity results from both shared low and high values; for example, red in parts of the Colorado Plateau and northern Great Plains results from both low factor 3 (low productivity) values and high factor 4 (weak seasonality). In (

**b**) Land uses are evident, such as urban areas, as are landscape-scale ecological gradients such as between river floodplains and uplands.

**Figure 5.**Phenological variability over time as indicated by changing factor scores and phenological class frequencies. (

**a**) Standard deviation in factor score values among phenological years, 2000–2017. The RGB color composite indicates variability in three different factors. Lighter tones indicate more active year-to-year dynamics, and dominance of a given color indicates more variability in that factor. (

**b**) Mean year of occurrence of 500 different phenoclasses, weighted by their frequency within years, shows continental trends over time in phenological traits. The x-axis gives the phenological year, where 9 is the center year among years 1 through 17. Line endpoints relative to year 9 give the frequency-weighted mean year of phenoclass occurrence. Y-axes give the phenoclass centroid values for the three factors shown in the map. Factor one is sine-transformed because its factor loadings are on the day-of-year variables. Broadly, phenological variability over time was highest in the center of the continent and at the highest elevations, and included continental trends towards phenoclasses with higher factor 1, lower factor 3, and more extreme factor 4 values.

**Figure 6.**An historic drought in 2011 in Texas and surrounding states resulted in depressed vegetation productivity, a delayed growing season, and other observable vegetation phenology impacts. RGB multitemporal false color images of (

**a**) factor 1 and (

**b**) factor 3 show both regionally coherent drought impacts and strong local variability (see Table 2: factor 1 is correlated with growing season timing variables, and factor 3 with greenness/productivity variables). Each color band represents a different year. Gray tones indicate similar phenology values for all years 2010–2012 (lighter grays indicate higher values). Purple color in the large central domain indicates that values in 2010 and 2012 were high relative to 2011. In this region, lower factor 1 values correspond to a later growing season. (

**c**) A radial NDVI plot for a single MODIS pixel (yellow cross on maps) reflects reduced greenness and a delayed growing season in 2011 (MGS = Middle of growing season); seasonality impacts are also evident. (

**d**) shows the 2011 drought response as the percentage of pixels in the view frame that changed their phenoclass membership from the preceding year.

Type | Variable Name | Descriptive Name | Units | Polar Description |
---|---|---|---|---|

Timing Variables | GSbegin | Beginning of growing season | Days | Number of days (or radial angle) corresponding to 15% of cumulative annual NDVI |

GSmid_early | Middle of early growing season | Days | Number of days (or radial angle) corresponding to 32.5% of cumulative annual NDVI | |

GSmid | Middle of entire growing season | Days | Number of days (or radial angle) corresponding to 50% of cumulative annual NDVI | |

GSmid_late | Middle of late growing season | Days | Number of days (or radial angle) corresponding to 65% of cumulative annual NDVI | |

GSend | End of growing season | Days | Number of days (or radial angle) corresponding to 80% of cumulative annual NDVI | |

Greenness & Seasonality Variables | LOS | Length of growing season | Days | Number of days between early and late growing season thresholds |

mean_NDVI_grw | Average growing season greenness | NDVI | Average NDVI during the growing season (GSbegin to GSend) | |

std_NDVI_grw | Variability in growing season greenness | NDVI | Standard deviation of NDVI during the growing season | |

AVearly | Magnitude of early growing season seasonality | NDVI | Length of the average vector during early growing season (GSbegin to GSmid) | |

AVgrw | Magnitude of entire growing season seasonality | NDVI | Length of the average vector during entire growing season (GSbegin to GSend) | |

AVlate | Magnitude of late growing season seasonality | NDVI | Length of the average vector during late growing season (GSmid to GSend) | |

Theta (Offset) ^{1} | Offset between calendar year and start of phenological year | Days | Number of days between the beginning of the calendar year (1 January) and the start of the phenological year (defined by when the average minimum in NDVI occurs) |

^{1}Offset was not a variable used in analysis but was a timing point used to define the start of the phenological year within which all PCT variables were measured.

**Table 2.**Factor loadings from factor analysis using varimax rotation. Coefficients smaller than $\left|0.2\right|$ are not shown. Variable definitions are given in Table 1.

Factor 1 | Factor 2 | Factor 3 | Factor 4 | ||
---|---|---|---|---|---|

Timing Variables | GSbegin sin | −0.892 | 0.311 | 0.244 | |

GSbegin cos | 0.283 | 0.911 | |||

GSmid_early sin | 0.959 | ||||

GSmid_eary cos | 0.927 | −0.245 | 0.202 | ||

GSmid sin | 0.675 | 0.702 | |||

GSmid cos | 0.672 | −0.662 | 0.241 | ||

GSmid_late sin | 0.936 | −0.231 | 0.215 | ||

GSmid_late cos | −0.939 | 0.304 | |||

GSend sin | 0.568 | −0.689 | 0.392 | ||

GSend cos | −0.764 | −0.579 | |||

Greenness & Seasonality Variables | LOS | 0.898 | |||

mean_NDVI_grw | −0.209 | 0.964 | |||

std_NDVI_grw | 0.321 | −0.836 | |||

AVearly | 0.960 | ||||

AVgrw | −0.247 | 0.838 | −0.457 | ||

AVlate | −0.274 | 0.859 | −0.367 | ||

Factor 1 | Factor 2 | Factor 3 | Factor 4 | ||

Proportional Variance | 0.294 | 0.282 | 0.231 | 0.145 | |

Cumulative Variance | 0.294 | 0.576 | 0.807 | 0.953 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Brooks, B.-G.J.; Lee, D.C.; Pomara, L.Y.; Hargrove, W.W. Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology. *Forests* **2020**, *11*, 606.
https://doi.org/10.3390/f11060606

**AMA Style**

Brooks B-GJ, Lee DC, Pomara LY, Hargrove WW. Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology. *Forests*. 2020; 11(6):606.
https://doi.org/10.3390/f11060606

**Chicago/Turabian Style**

Brooks, Bjorn-Gustaf J., Danny C. Lee, Lars Y. Pomara, and William W. Hargrove. 2020. "Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology" *Forests* 11, no. 6: 606.
https://doi.org/10.3390/f11060606