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Chapter 8: Vegetation Index Analysis

Learning Objectives

After completing this chapter, you will be able to: - Interpret vegetation index maps to identify stress zones, healthy areas, and transitional regions - Establish NDVI threshold ranges for different turf types and management zones - Conduct temporal change detection by comparing index maps across weekly and seasonal timeframes - Generate health scoring reports that translate spectral data into superintendent-actionable language

Key Concepts

  1. Stress mapping from vegetation indices
  2. Chlorophyll concentration detection via NDRE
  3. Turf health scoring systems
  4. NDVI threshold calibration by turf type
  5. Temporal analysis and trend detection
  6. Week-over-week change detection
  7. Seasonal baseline establishment
  8. Heat maps and color ramp interpretation
  9. Zone-based aggregation (green-by-green scoring)
  10. Statistical outlier identification
  11. False positive and false negative management
  12. Ground-truthing spectral observations
  13. Reporting templates for superintendents
  14. Alert thresholds and trigger points
  15. Integrating multiple indices for comprehensive assessment

Summary

Raw vegetation index maps are data, not insight. A green-to-red NDVI heat map covering 18 greens contains thousands of data points, but without contextual interpretation — what constitutes "normal" for Green #7 in mid-July, how this week's values compare to the three-week trend, whether a low-NDVI zone corresponds to a known shade pattern or an emerging stress event — the map remains an expensive image rather than a management tool. This chapter bridges the gap between spectral data and agronomic decision-making.

Threshold calibration is the critical first step in operationalizing vegetation index analysis. NDVI values above 0.80 typically indicate vigorous, healthy turf; values between 0.65-0.80 warrant monitoring; values below 0.65 demand investigation. However, these thresholds vary significantly by grass species, mowing height, time of season, and local conditions. A bentgrass green at 0.72 in August may be performing well under heat stress, while the same reading on a bermudagrass green in June signals a problem. Establishing course-specific baselines through the first season of data collection is essential before automated alerting becomes reliable.

Temporal change detection transforms single-point-in-time snapshots into trend analysis. A green showing NDVI of 0.70 in isolation provides limited information. That same green showing a decline from 0.82 to 0.76 to 0.70 over three consecutive weeks reveals an accelerating stress trajectory that demands immediate attention — likely 7-14 days before visible symptoms would trigger a traditional visual inspection response. This predictive window is where precision turf analytics delivers its highest value.

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