气候变化领域集成服务门户(CCPortal): eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects

CCPortal

DOI	10.1016/j.rse.2019.111482
	eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects
	Koltunov A.; Ramirez C.M.; Ustin S.L.; Slaton M.; Haunreiter E.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	The worldwide demand for timely and accurate information about ecosystem dynamics at Landsat spatial scale is growing and as of today still exceeds the availability of information. The diversity of required disturbance metrics and trade-offs between sensitivity, reliability, timelines of information generation, and flexibility toward potential customizations suggests that a single system is not likely to fill such demand in the near future. To address this challenge, the scientific community has been developing and improving various Landsat-based algorithms for land change monitoring. We describe the Ecosystem Disturbance and Recovery Tracker (eDaRT) version 2.9 — a highly automated prototype system in continuous development, which has been operated since 2012 by the USDA Forest Service Pacific Southwest Region to generate most current disturbance maps at Landsat scale and provide customized information services and inputs to science and land management applications in the Region. The eDaRT processing system utilizes all three dimensions of dense Landsat image time series: spectral, temporal, and spatial. Two anomaly detection algorithms are sequentially applied, one estimating pixels’ disturbance status metrics in every processed image and the other detecting disturbance events, the primary output of eDaRT. The first algorithm initially estimates change relative to a user-defined fixed baseline time period, using a stratified version of the Dynamic Detection Model (DDM; Koltunov et al., 2009) applied to Landsat bands and vegetation indexes that reflect canopy greenness, abundance, and moisture content. Using the model residuals and a probabilistic context analysis, the detected anomalies are further classified as disturbed, cloud/snow, or recovered. The resulting residuals, classification maps, and the associated disturbance confidence values provide the most rapid preliminary snapshot of the current cumulative effect of disturbance and regeneration. The second algorithm detects discrete disturbance events as regime changes in the dense time series of the residuals for each pixel. First, the residuals are compared against a recent baseline window and classified to find candidates for disturbance events. Next, candidate-events are accepted based on temporal consistency of their detection. The standard outputs from this algorithm include disturbance event timing (down to 8–16 day precision) and a detection confidence as a proxy for event magnitude. We initially evaluated eDaRT performance with high-resolution imagery and airborne LiDAR data in a test area in California for several types of annual disturbance events at 30-m scale. These tests modeled detection probabilities as functions of canopy cover loss, which estimated detection rates of 96%, 87% and 92% respectively for fire, harvest, and tree mortality events, when canopy loss values follow a uniform prior distribution. The error of commission varied between 10- 20% for most forest types (12% on average). Following ongoing optimizations and extended validations, eDaRT will expand beyond Landsat instruments to improve ecosystem monitoring, as an independent system and potentially as a part of an ensemble method. © 2019 Elsevier Inc.
英文关键词	Change detection; Disturbance detection; Dynamic detection model; Ecosystem disturbance and recovery tracker; eDaRT; Forest disturbance; Landsat; Landscape monitoring; Remote sensing; Time series
语种	英语
scopus关键词	Economic and social effects; Ecosystems; Information management; Information services; Pixels; Probability distributions; Recovery; Reforestation; Remote sensing; Software prototyping; Time series; Change detection; Disturbance detection; Dynamic detection; Ecosystem disturbance; eDaRT; Forest disturbances; LANDSAT; Anomaly detection; algorithm; disturbance; ecosystem dynamics; forest ecosystem; land management; landscape ecology; remote sensing; satellite imagery; time series; California; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179475
作者单位	Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis, One Shields Avenue, Davis, CA 95616, United States; USDA Forest Service, Remote Sensing Laboratory, Pacific Southwest Region, 3237 Peacekeeper Way, Suite 201, McClellan, CA 95652, United States
推荐引用方式 GB/T 7714	Koltunov A.,Ramirez C.M.,Ustin S.L.,et al. eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects[J],2020,238.
APA	Koltunov A.,Ramirez C.M.,Ustin S.L.,Slaton M.,&Haunreiter E..(2020).eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects.Remote Sensing of Environment,238.
MLA	Koltunov A.,et al."eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects".Remote Sensing of Environment 238(2020).