Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

By Robert E. Kennedy, Zhiqiang Yang, Warren B. Cohen. 2010

1,747 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/152da628bb52b50c7c028a0154c21f0041500f4e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Global and Planetary Change

Citations in the corpus

How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms (2017, background): “These areas were defined as the non-overlapping portions of six individual, Landsat frames, known as TSAs (Thiessen Scene Areas; [15]), from the second World Reference System (WRS-2) grid.”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “…change agents (Kennedy et al., 2015), insect infestation detection (Liang et al., 2014; Meigs et al., 2015, 2011; Senf et al., 2015), forest change detection (Grifﬁths et al., 2012; Grogan et al., 2015; Kennedy et al., 2012, 2010), and land cover change detection (Franklin et al., 2015).”
A review of large area monitoring of land cover change using Landsat data (2012, methodology): “single-date time 1and time 2 analyses, as per the PRODES, NCAS, NLCS, LEDAPS and CI methods, will be replaced by more exhaustive and data intensive methods that employ time-series data sets (Huang et al., 2009; Kennedy et al., 2009).”
Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation (2010, methodology): “In this paper, we describe how to use TimeSync to collect data over forested plots in Oregon and Washington, USA, examine the data collected with it, and then compare those data with the output from a new LTS algorithm, LandTrendr, described in a companion paper (Kennedy et al., 2010).”
Current status of Landsat program, science, and applications (2019, background): “Algorithms that focus directly on disturbance have also benefited from the temporally-dense Landsat data (Brooks et al., 2012; Huang et al., 2010; Hughes et al., 2017; Kennedy et al., 2010; Zhu et al., 2012), allowing investigation of drivers of change (Alonzo et al.”
Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “Meigs et al. (2011) used the LandTrendr algorithm (Kennedy et al. 2010) over the Cascade Range in Oregon, United States of America to characterise mortality induced by bark beetle, finding a statistically significant relationship between field-based measures of severity (total basal area killed)…”
A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping (2013, neutral): “in forest ecosystems [22], [29].”
Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive (2011, background): “Dense time-series ‘stacks’ of Landsat images have recently been used in temperate environments for detecting both long-term vegetation trends (Röder et al 2008) and land cover transitions (Huang et al 2010, Kennedy et al 2010).”
Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review (2020, methodology): “average change detection (EWMACD) [31], and Landsat-based detection of Trends (LandTrendr) [32] are available.”
Implementation of the LandTrendr Algorithm on Google Earth Engine (2018, methodology): “The LandTrendr (Landsat-based detection of trends in disturbance and recovery) algorithm was among the ﬁrst wave of such approaches [3], using temporal segmentation of spectral trajectories as a means of distilling and extracting meaningful change information from the rich Landsat archive [4].”
Opening the archive: How free data has enabled the science and monitoring promise of Landsat (2012, positive): “…efforts to develop automated algorithms that leverage the high temporal dimensionality of Landsat data are preparing the user community for applications demanding high temporal resolution over large areas (Hilker et al., 2009; Huang et al., 2010; Kennedy et al., 2010; Zhu et al., this issue).”