Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

Schmidt, Gail L.; Jenkerson, Calli B.; Masek, Jeffrey G.; Vermote, E.; Gao, Feng

doi:10.3133/ofr20131057

Cited by 154 publications

(99 citation statements)

References 5 publications

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“…LEDAPS software was originally developed by the National Aeronautics and Space Administration-Goddard Space Flight Center and the University of Maryland [48]. LEDAPS produces top-of-atmosphere (TOA) reflectance from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) Level 1 digital numbers (DN) and applies atmospheric compensation to generate a surface-reflectance product [49]. The atmospheric compensations are based on the second simulation of a satellite signal in the solar spectrum (6S) radiative transfer code [50] used by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team.…”

Section: Landsat Reflectance Datamentioning

confidence: 99%

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Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001-2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region OPEN ACCESSRemote Sens. 2015, 7 6684 interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

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Section: Landsat Reflectance Datamentioning

confidence: 99%

“…At the time of this study, the LEDAPS processing of Landsat imagery does not include terrain correction [49]. Thus a topographic correction was needed to compensate for the very significant terrain illumination effects for the study region.…”

Section: Digital Elevation Modelmentioning

confidence: 99%

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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“…The second step concerns the conversion to SR by applying and correcting atmospheric effect (Song, Woodcock, Seto, Lenney, & Macomber, 2001). After testing different algorithms such as Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Schmidt, Jenkerson, Masek, Vermote, & Gao, 2013), R packages (RStoolbox and Landsat), Grass GIS algorithms (i.landsat.atcorr and i.atcorr), and the Simplifié Modèle d'Atmosphérique Correction (SMAC) (http://www.cesbio.ups-tlse.fr/multitemp/?page_id=2975), the final choice (based on efficiency, reliability, and easiness of integration in the workflow) was the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) algorithm available in the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI 9 ) software (Vermote, Tanre, Deuze, Herman, & Morcette, 1997). Before ingesting any data in the Data Cube, data should be preprocessed.…”

Section: Landsat Scenes Pre-processingmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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“…Landsat time series processing was performed as described in Reiche et al [29]: Landsat images were processed individually using a standard processing chain consisting of Fmask (function of mask) [61] and LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) [62][63][64]. Based on the uncorrected Landsat data in digital numbers, Fmask was used for masking clouds, cloud shadows and Landsat-7 SCL-off gaps as missing data.…”

Section: Landsat Ndvi Datamentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

Cited by 154 publications

References 5 publications

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

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