ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests

Taking Stock of Circumboreal Forest Carbon With Ground Measurements, Airborne and Spaceborne LiDAR

The boreal forest accounts for one-third of global forests, but remains largely inaccessible to ground-based measurements and monitoring. It contains large quantities of carbon in its vegetation and soils, and research suggests that it will be subject to increasingly severe climate-driven disturbance. We employ a suite of ground-, airborne- and space-based measurement techniques to derive the first satellite LiDAR-based estimates of aboveground carbon for the entire circumboreal forest biome. Incorporating these inventory techniques with uncertainty analysis, we estimate total aboveground carbon of 38 +/- 3.1 Pg. This boreal forest carbon is mostly concentrated from 50 to 55degN in eastern Canada and from 55 to 60degN in eastern Eurasia. Both of these regions are expected to warm >3 C by 2100, and monitoring the effects of warming on these stocks is important to understanding its future carbon balance. Our maps establish a baseline for future quantification of circumboreal carbon and the described technique should provide a robust method for future monitoring of the spatial and temporal changes of the aboveground carbon content

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas

THE UNCERTAINTY OF SPACEBORNE OBSERVATION OF VEGETATION STRUCTURE IN THE TAIGA-TUNDRA ECOTONE: A CASE STUDY IN NORTHERN SIBERIA

The ability to characterize vegetation structure in the taiga-tundra ecotone (TTE) at fine spatial scales is critical given its heterogeneity and the central role of its patterns on ecological processes in the high northern latitudes and global change scenarios. This research focuses on quantifying the uncertainty of TTE forest structure observations from remote sensing at fine spatial scales. I first quantify the uncertainty of forest biomass estimates from current airborne and spaceborne active remote sensing systems and a planned spaceborne LiDAR (ICESat-2) across sparse forest gradients. At plot-scales, current spaceborne models of biomass either explain less than a third of model variation or have biomass estimate uncertainties ranging from 50-100%. Simulations of returns from the planned ICESat-2 for a similar gradient show the uncertainty of near-term estimates vary according to the ground length along which returns are collected. The 50m length optimized the resolution of forest structure, for which there is a trade-off between horizontal precision of the measurement and vertical structure detail. At this scale biomass error ranges from 20-50%, which precludes identifying actual differences in aboveground live biomass density at 10 Mg•ha-1 intervals. These broad plot-scale uncertainties in structure from current and planned sensors provided the basis for examining a data integration technique with multiple sensors to measure the structure of sparse TTE forests. Spaceborne estimates of canopy height used complementary surface elevation measurements from passive optical and LiDAR to provide a means for directly measuring TTE forest height from spaceborne sensors. This spaceborne approach to estimating forest height was deployed to assess the spaceborne potential for examining the patterns of TTE forest structure explained with a conceptual biogeographic model linking TTE patterns and its dynamics. A patch-based analysis was used to scale estimates of TTE forest structure from multiple sensors and provided a means to simultaneously examine the horizontal and vertical structure of groups of TTE trees. The uncertainty of forest patch height estimates provides focus for improving spaceborne depictions of TTE structure patterns associated with recent change that may explain the variability of this change and the vulnerability of TTE forest structure

Spaceborne Potential for Examining Taiga-Tundra Ecotone Form and Vulnerability

In the taiga-tundra ecotone (TTE), site-dependent forest structure characteristics can influence the subtle and heterogeneous structural changes that occur across the broad circumpolar extent. Such changes may be related to ecotone form, described by the horizontal and vertical patterns of forest structure (e.g., tree cover, density and height) within TTE forest patches, driven by local site conditions, and linked to ecotone dynamics. The unique circumstance of subtle, variable and widespread vegetation change warrants the application of spaceborne data including high-resolution (less than 5m) spaceborne imagery (HRSI) across broad scales for examining TTE form and predicting dynamics. This study analyzes forest structure at the patch-scale in the TTE to provide a means to examine both vertical and horizontal components of ecotone form. We demonstrate the potential of spaceborne data for integrating forest height and density to assess TTE form at the scale of forest patches across the circumpolar biome by (1) mapping forest patches in study sites along the TTE in northern Siberia with a multi-resolution suite of spaceborne data, and (2) examining the uncertainty of forest patch height from this suite of data across sites of primarily diffuse TTE forms. Results demonstrate the opportunities for improving patch-scale spaceborne estimates of forest height, the vertical component of TTE form, with HRSI. The distribution of relative maximum height uncertainty based on prediction intervals is centered at approximately 40%, constraining the use of height for discerning differences in forest patches. We discuss this uncertainty in light of a conceptual model of general ecotone forms, and highlight how the uncertainty of spaceborne estimates of height can contribute to the uncertainty in identifying TTE forms. A focus on reducing the uncertainty of height estimates in forest patches may improve depiction of TTE form, which may help explain variable forest responses in the TTE to climate change and the vulnerability of portions of the TTE to forest structure change. structural changes

The Use of Sun Elevation Angle for Stereogrammetric Boreal Forest Height in Open Canopies

Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; less than 5 m spatial resolution) yields fine-scaled digital surface models (DSMs) of elevation. These DSMs may represent elevations that range from the ground to the vegetation canopy surface, are produced from stereoscopic image pairs (stereo pairs) that have a variety of acquisition characteristics, and have been coupled with lidar data of forest structure and ground surface elevation to examine forest height. This work explores surface elevations from HRSI DSMs derived from two types of acquisitions in open canopy forests. We (1) apply an automated mass-production stereogrammetry workflow to along-track HRSI stereo pairs, (2) identify multiple spatially coincident DSMs whose stereo pairs were acquired under different solar geometry, (3) vertically co-register these DSMs using coincident spaceborne lidar footprints (from ICESat-GLAS) as reference, and(4) examine differences in surface elevations between the reference lidar and the co-registered HRSI DSMs associated with two general types of acquisitions (DSM types) from different sun elevation angles. We find that these DSM types, distinguished by sun elevation angle at the time of stereo pair acquisition, are associated with different surface elevations estimated from automated stereogrammetry in open canopy forests. For DSM values with corresponding reference ground surface elevation from spaceborne lidar footprints in open canopy northern Siberian Larix forests with slopes less than10, our results show that HRSI DSM acquired with sun elevation angles greater than 35deg and less than 25deg (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors less than 0.68 m relative to lidar. The latter, particularly those with angles less than 10deg, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (6.0 m, sigma = 1.4 m) was consistent with the distribution of plot-level mean tree heights (6.5m, sigma = 1.2 m). We conclude that the variation in sun elevation angle at time of stereo pair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register rand difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests

Exploring the Relationship between Forest Canopy Height and Canopy Density from Spaceborne LiDAR Observations

Forest structure is a useful proxy for carbon stocks, ecosystem function and species diversity, but it is not well characterised globally. However, Earth observing sensors, operating in various modes, can provide information on different components of forests enabling improved understanding of their structure and variations thereof. The Ice, Cloud and Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS), providing LiDAR footprints from 2003 to 2009 with close to global coverage, can be used to capture elements of forest structure. Here, we evaluate a simple allometric model that relates global forest canopy height (RH100) and canopy density measurements to explain spatial patterns of forest structural properties. The GLA14 data product (version 34) was applied across subdivisions of the World Wildlife Federation ecoregions and their statistical properties were investigated. The allometric model was found to correspond to the ICESat GLAS metrics (median mean squared error, MSE: 0.028; inter-quartile range of MSE: 0.022–0.035). The relationship between canopy height and density was found to vary across biomes, realms and ecoregions, with denser forest regions displaying a greater increase in canopy density values with canopy height, compared to sparser or temperate forests. Furthermore, the single parameter of the allometric model corresponded with the maximum canopy density and maximum height values across the globe. The combination of the single parameter of the allometric model, maximum canopy density and maximum canopy height values have potential application in frameworks that target the retrieval of above-ground biomass and can inform on both species and niche diversity, highlighting areas for conservation, and potentially enabling the characterisation of biophysical drivers of forest structure

Integrating Remote Sensing and Ecosystem Models for Terrestrial Vegetation Analysis: Phenology, Biomass, and Stand Age

Terrestrial vegetation plays an important role in global carbon cycling and climate change by assimilating carbon into biomass during the growing season and releasing it due to natural or anthropogenic disturbances. Remote sensing and ecosystem models can help us extend our studies of vegetation phenology, aboveground biomass, and disturbances from field sites to regional or global scales. Nonetheless, remote sensing-derived variables may differ in fundamental and important ways from ground measurements. With the growth of remote sensing as a key tool in geoscience research, comparisons to ground data and intercomparisons among satellite products are needed. Here I conduct three separate but related analyses and show promising comparisons of key ecosystem states and processes derived from remote sensing and theoretical modeling to those observed on the ground. First, I show that the Moderate Resolution Imaging Spectroradiometer (MODIS) greenup product is significantly correlated with the earliest ground phenology event for North America. Spring greenup indices from different satellites demonstrate similar variability along latitudes, but the number of ground phenology observations in summer, fall, and winter is too limited to interpret the remote sensing-derived phenology products. Second, I estimate aboveground biomass (AGB) for California and show that it agrees with inventory-based regional biomass assessments. In this approach, I present a new remote sensing-based approach for mapping live forest AGB based on a simple parametric model that combines high-resolution estimates of Leaf Area Index derived from Landsat and canopy maximum height from the space-borne Geoscience Laser Altimeter System (GLAS) sensor. Third, I built a theoretical model to estimate stand age in primary forests by coupling a carbon accumulation function to the probability density of disturbance occurrences, and then ran the model with satellite-derived AGB and net primary production. The validated remote sensing data, integrated with ecosystem models, are particularly useful for large-region vegetation research in areas with sparse field measurements, and will help us to explore the long-term vegetation dynamics

Estimating Canopy Fuel Parameters with In-Situ and Remote Sensing Data

Crown fires, the fastest spreading of all forest fires, can occur in any forest type throughout the United States and the world. The occurrence of crown fires has become increasingly frequent and severe in recent years. The overall aim of this study is to estimate the forest canopy fuel parameters including crown base height (CBH) and crown bulk density (CBD), and to investigate the potential of using airborne lidar data in east Texas. The specific objectives are to: (1) propose allometric estimators of CBD and CBH and compare the results of using those estimators to those produced by the CrownMass/FMAPlus software at tree and stand levels for 50 loblolly pine plots in eastern Texas, (2) develop a methodology for using airborne light detection and ranging (lidar) to estimate CBD and CBH canopy fuel parameters and to simulate fire behavior using estimated forest canopy parameters as FARSITE inputs, and (3) investigate the use of spaceborne ICEsat /GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System) lidar for estimating canopy fuel parameters. According to our results from the first study, the calculated average CBD values, across all 50 plots, were 0.18 kg/m³ and 0.07 kg/m³, respectively, for the allometric equation proposed herein and the CrownMass program. Lorey’s mean height approach was used in this study to calculate CBH at plot level. The average height values of CBH obtained from Lorey’s height approach was 10.6 m and from the CrownMass program was 9.1 m. The results obtained for the two methods are relatively close to each other; with the estimate of CBH being 1.16 times larger than the CrownMass value. According to the results from the second study, the CBD and CBH were successfully predicted using airborne lidar data with R² values of 0.748 and 0.976, respectively. The third study demonstrated that canopy fuel parameters can be successfully estimated using GLAS waveform data; an R² value of 0.84 was obtained. With these approaches, we are providing practical methods for quantifying these parameters and making them directly available to fire managers. The accuracy of these parameters is very important for realistic predictions of wildfire initiation and growth