An empirical, integrated forest biomass monitoring system

Abstract

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.

A land systems science approach to assessing forest governance and characterizing the emergence of social forestry in the Western Cascades of Oregon

“…Mapping actual patterns of timber harvest Our geospatial analysis linked time-series satellite data with forest inventory data to track patterns of timber harvest at scales commensurate with timber management decision-making. Methods to build the maps used here are described in detail in Kennedy et al (2018) and are briefly summarized here.…”

“…The latter provides imagery used in statistical modeling to create consistent maps over time. The former allows mapping of forest disturbance , which can then be matched with expert interpretation and machine learning to ascribe labels to each forest disturbance (Kennedy et al , 2018. For this study, we focused on disturbances labeled as 'clearcuts,' here defined as full removal of all trees from a site, and 'partial harvest,' here defined as any incomplete removal of trees from a site.…”

“…It allows flexible mapping of any metric that can be calculated from FIA plot measurements. Here we calculated aboveground forest biomass per cubic meter as per Kennedy et al (2018), then converted to board feet by dividing by 423.776. We created maps of standing forest volume for every year between 1991 and 2010.…”

Tree growth declines and mortality were associated with a parasitic plant during warm and dry climatic conditions in a temperate coniferous forest ecosystem

“…We present (a) tree basal area and (b) percentage of basal area attributed to western hemlock (Tsuga heterophylla) for the T. T. Munger Research Natural Area within the Trout Creek Division of the Wind River Experimental Forest, Washington, United States. Basal area data were extracted from the 2012 gradient nearest neighbor vegetation mapping product (Davis et al, 2015;Kennedy et al, 2018) data, we defined a measurement interval as beginning with a tree measurement year and ending the year before the subsequent tree measurement year (e.g., tree measurement interval of 1991-1998 has a climate measurement interval of 1991-1997). All manipulations of raster data, including the preparation of climate data used the raster package version 2.6-7 (Hijmans & van Etten, 2011) in the R statistical programming environment version 3.5.1 (R Development Core Team, 2016).…”

A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

“…This has facilitated the development of many approaches that utilize Landsat time-series imagery to characterize forest change [22,23]. Information from Landsat time-series has been widely used in many studies to estimate forest biomass and other structure attributes across large areas (e.g., [15,16,19,20,[24][25][26][27]). At the conceptual level, Kennedy, Ohmann, Gregory, Roberts, Yang, Bell, Kane, Hughes, Cohen, Powell, Neeti, Larrue, Hooper, Kane, Miller, Perkins, Braaten and Seidl [24] developed a comprehensive forest biomass monitoring framework that is based on the analysis of Landsat time-series.…”