Integrating LIDAR and forest inventories to fill the trees outside forests data gap

Abstract

Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin

“…<10% cover) from 8.1% of the study area in 2000 to just 1.3% in 2016 ( figure 7); as the 2016 land cover map may have failed to capture areas of recent PJ encroachment. Surveying and improvements in remote sensing of trees outside of forests could provide a more complete picture to carbon accounting efforts (Johnson et al 2015).…”

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

“…For example, Skowronski et al [217] used FIA data and lidar in New Jersey to map forest structure and fire fuel loads. Johnson et al [218,219] used lidar in concert with FIA plots to make high-resolution maps of forest carbon in Maryland. Sheridan et al [220] summarized lidar data on and in the vicinity of FIA plots and modeled and mapped tree volume and biomass in Oregon.…”

An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection

“…For example, Sankey et al (2017) [31] integrated LiDAR and hyperspectral images to classify plants and measure 3-dimensional canopy structures at the level of individual species by the decision tree method. Johnson et al (2015) combined LiDAR data with forest inventory data to estimate above-ground biomass accurately [32]. Furthermore, with the development of remote sensing methods, Kennedy et al (2012) created the Recovery Indicator (RI) [33], which reflected the relative magnitude of ecosystem recovery by the ratio of spectral values in the year of disturbance to those five years later.…”