The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that: • A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. • Regional estimates of total biomass agree well with those from plot data alone.• The system tracks biomass densities up to 450-500 Mg ha −1 with little bias, but begins underestimating true biomass as densities increase further. • Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway. • Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data. • In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth. Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.
Many studies have used thermal data from remote sensing to characterize how land use and surface properties modify the climate of cities. However, relatively few studies have examined the impact of elevated temperature on ecophysiological processes in urban areas. In this paper, we use time series of Landsat data to characterize and quantify how geographic variation in Boston's surface urban heat island (SUHI) affects the growing season of vegetation in and around the city, and explore how the quality and character of vegetation patches in Boston affect local heat island intensity. Results from this analysis show strong coupling between Boston's SUHI and vegetation phenology at the scale of both individual landscape units and for the region as a whole, with significant detectable signatures in both surface temperature and growing season length extending 15 km from Boston's urban core. On average, land surface temperatures were about 7°C warmer and the growing season was 18-22 days longer in Boston relative to adjacent rural areas. Within Boston's urban core, patterns of temperature and timing of phenology in areas with higher vegetation amounts (e.g., parks) were similar to those in adjacent rural areas, suggesting that vegetation patches provide an important ecosystem service that offsets the urban heat island at local scales. Local relationships between phenology and temperature were affected by the intensity of urban land use surrounding vegetation patches and possibly by the presence of exotic tree species that are common in urban areas. Results from this analysis show how species composition, land cover configuration, and vegetation patch sizes jointly influence the nature and magnitude of coupling between vegetation phenology and SUHIs, and demonstrate that urban vegetation provides a significant ecosystem service in cities by decreasing the local intensity of SUHIs.
Recent years have seen growing appreciation that rapidly intensifying “flash droughts” are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility-based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the Contiguous United States (CONUS) for the period 1979-2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture “flash droughts” are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.
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