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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]

Efforts to monitor the broad-scale impacts of drought on forests often come up short. Drought is a direct stressor of forests as well as a driver of secondary disturbance agents, making a full accounting of drought impacts challenging. General impacts can be inferred from moisture deficits quantified using precipitation and temperature measurements. However, derived meteorological indices may not meaningfully capture drought impacts because drought responses can differ substantially among species, sites and regions. Meteorology-based approaches also require the characterization of current moisture conditions relative to some specified time and place, but defining baseline conditions over large, ecologically diverse regions can be as difficult as quantifying the moisture deficit itself. In contrast, remote sensing approaches attempt to observe immediate, secondary, and longer-term changes in vegetation response, yet they too are no panacea. Remote sensing methods integrate responses across entire mixed-vegetation pixels and rarely distinguish the effects of drought on a single species, nor can they disentangle drought effects from those caused by various other disturbance agents. Establishment of suitable baselines from remote sensing may be even more challenging than with meteorological data. Here we review broad-scale drought monitoring methods, and suggest that an integrated data-mining approach may hold the most promise for enhancing our ability to resolve drought impacts on forests. A big-data approach that integrates meteorological and remotely sensed data streams, together with other datasets such as vegetation type, wildfire occurrence and pest activity, can clarify direct drought effects while filtering indirect drought effects and consequences. This strategy leverages the strengths of meteorology-based and remote sensing approaches with the aid of ancillary data, such that they complement each other and lead toward a better understanding of drought impacts. [ABSTRACT FROM AUTHOR]