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Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients. [ABSTRACT FROM AUTHOR]

We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes ('phenoclasses') based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics. [ABSTRACT FROM AUTHOR]

Extensive bark beetle outbreaks have recently occurred in western North American forests, resulting in overstory tree mortality across millions of hectares. Annual aerial surveys are currently used to operationally monitor bark beetle induced tree mortality, though this method is subjective and can exclude some forest areas. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data offer a potential alternative means to develop regional tree mortality maps. Accurate methods using such data could aid natural resource managers in surveys of forests with frequent overstory mortality, helping to prioritize forest treatment and restoration activities. This paper discusses a study to test the potential of using MODIS data to detect tree mortality. We developed and tested an approach to use 250-m resolution MODIS Normalized Difference Vegetation Index (NDVI) data products collected during a mountain pine beetle (MPB) outbreak and related tree mortality event in the northern Rocky Mountains of Colorado, USA. The 94 km2 study area is predominantly lodgepole pine forest with most of the MPB-caused mortality occurring between 2003 and 2008. We used a 2.4-m forest conditions map from 2008 aerial multispectral imagery to calculate percentage of mortality within 240-m pixels for use as reference data. Using either daily or 16-day products, MODIS NDVI change products were calculated for 2008 versus either 2000 or 2003 baselines. MODIS change products were used as predictors in linear regression analysis to assess correlation between MODIS data and the aerial percent forest mortality map. Depending on the MODIS product, linear regression analyses yielded r2 values ranging from 0.362 to 0.544 without outliers removed and from 0.406 to 0.570 with extreme outliers removed. Daily MODIS NDVI products from 2003 and 2008 were used with exponential regression to improve the r2 to 0.593. The project showed some MODIS NDVI data potential for mapping percent tree mortality in forests subjected to regional bark beetle outbreaks and severe drought. [ABSTRACT FROM AUTHOR]