Development of time series stacks of Landsat images for reconstructing forest disturbance history

Huang, Chengquan; Goward, Samuel N.; Masek, Jeffrey G.; Gao, Feng; Vermote, Éric; Thomas, Nancy E.; Schleeweis, Karen; Kennedy, Robert E.; Zhu, Zhiliang; Eidenshink, Jeffery C.; Townshend, J. R. G.

doi:10.1080/17538940902801614

Cited by 112 publications

(63 citation statements)

References 52 publications

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“…It was adopted for use with Landsat data and was implemented as part of the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) developed at NASA Goddard Space Flight Center (Masek et al 2006, Huang et al 2009a. As an adaptation of the MODIS Adaptive Processing System for processing Landsat data, the LEDAPS allows rapid processing of large quantities of Landsat images to produce surface reflectance products from the raw radiometry.…”

Section: Production Of Surface Reflectance Imagesmentioning

confidence: 99%

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

Townshend

Masek

Huang

et al. 2012

International Journal of Digital Earth

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274

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The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Nevertheless, there are many challenges in ensuring the creation of high-quality products. And we propose various ways in which the challenges can be overcome. Among the challenges are the need for atmospheric correction, incorrect calibration coefficients in some of the data-sets, the different phenologies between compilations, the need for terrain correction, the lack of consistent reference data for training and accuracy assessment, and the need for highly automated characterization and change detection. We propose and evaluate the creation and use of surface reflectance products, improved selection of scenes to reduce phenological differences, terrain illumination correction, automated training selection, and the use of information extraction procedures robust to errors in training data along with several other issues. At several stages we use Moderate Resolution Spectroradiometer data and products to assist our analysis. A global working prototype product of forest cover and forest cover change is included.

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Section: Production Of Surface Reflectance Imagesmentioning

confidence: 99%

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

Townshend

Masek

Huang

et al. 2012

International Journal of Digital Earth

Self Cite

274

149

View full text Add to dashboard Cite

show abstract

“…The original images were first converted to surface reflectance using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm [61]. Geometrically, no additional correction was performed on these images because they had already been ortho-rectified by the USGS to achieve subpixel geolocation accuracy [44,61]. A detailed description of the procedures involved in assembling LTSSs has been provided in a previous study [44].…”

Section: Ltss Assemblingmentioning

confidence: 99%

“…The compositing algorithm identifies and replaces the cloud and shadow contaminated pixels and adjusts the phenological differences among the affected images [44]. The cloud and cloud shadow were identified using an automated masking algorithm [43].…”

Section: Ltss Assemblingmentioning

confidence: 99%

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

et al. 2016

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Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. With the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S.

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“…We collected annual growing-season Landsat images of Path 47 and Row 27 from 1984 to 2011. These Landsat images were first converted to surface reflectance through the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) [66] to construct a dense time series image stack [25]. This image stack was then analyzed using the vegetation change tracker (VCT) algorithm to produce an annual disturbance product [24].…”

Section: Deriving Reference Datasetsmentioning

confidence: 99%

“…In particular, a number of novel techniques have recently emerged for reconstructing forest change history using dense time series of Landsat images [24][25][26][27]. Consisting of "clear-view" Landsat observations every year or every two years [19], such image stacks allow forest change mapping at annual or biennial time steps.…”

Section: Introductionmentioning

confidence: 99%

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

et al. 2014

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Abstract:We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r 2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data.

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Development of time series stacks of Landsat images for reconstructing forest disturbance history

Cited by 112 publications

References 52 publications

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

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