Showing papers by "Douglas C. Morton published in 2019"

The Global Fire Atlas of individual fire size, duration, speed and direction

Abstract: . Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642 , and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

Abstract: We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducibl...

The gathering firestorm in southern Amazonia

Abstract: Amazon wildfires could emit 17.0 Pg of CO2 by 2050, and avoiding new deforestation could cut associated net emissions by half. Wildfires, exacerbated by extreme weather events and land use, threaten to change the Amazon from a net carbon sink to a net carbon source. Here, we develop and apply a coupled ecosystem-fire model to quantify how greenhouse gas–driven drying and warming would affect wildfires and associated CO2 emissions in the southern Brazilian Amazon. Regional climate projections suggest that Amazon fire regimes will intensify under both low- and high-emission scenarios. Our results indicate that projected climatic changes will double the area burned by wildfires, affecting up to 16% of the region’s forests by 2050. Although these fires could emit as much as 17.0 Pg of CO2 equivalent to the atmosphere, avoiding new deforestation could cut total net fire emissions in half and help prevent fires from escaping into protected areas and indigenous lands. Aggressive efforts to eliminate ignition sources and suppress wildfires will be critical to conserve southern Amazon forests.

Soy expansion in Brazil's Cerrado

Abstract: The Cerrado biome is Brazil's breadbasket and a major provider of ecosystem services, though these dual roles are increasingly at odds, in part because there are few mechanisms to protect remaining vegetation from large‐scale agricultural expansion. We assessed Cerrado conversion to soy using over 580,000 property boundaries, covering 77% of the biome that is eligible for commercial land use, and using microwatersheds, to cover 100% of eligible areas. Soy expansion accounted for 22% of conversion during 2003–14. Only 15% of clearing exceeded restrictions on private properties under the Forest Code (FC). However, 51% of soy farms have violated the FC, five times the rate of other farms. As a leading cause of both Cerrado conversion and FC violations, the soy sector has environmental and economic incentives to shift production to already cleared land. We used suitability maps to identify potential pathways for soy expansion across both old and new cropland frontiers.

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Abstract: Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping.

Abstract: Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that LiDAR data and forest characteristics are often strongly correlated, it is possible to make use of the former to model, predict, and map forest variables over regions of interest. This entails dealing with the high-dimensional (~102) spatially dependent LiDAR outcomes over a large number of locations (~105-106). With this in mind, we develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model, and embed it in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of forest variables with associated uncertainty over a large region of boreal forests in interior Alaska.

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Abstract: Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

Global Fire Atlas with Characteristics of Individual Fires, 2003-2016

Abstract: The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product. The algorithm identified 13.3 million individual fires (>=21 ha or 0.21 km2; the size of one MODIS pixel) over the 2003-2016 study period.

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Abstract: . Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.