Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos

Valencia, Germán M.; Anaya, Jesús A.; Caro-Lopera, Francisco J.

doi:10.4995/raet.2016.3582

Cited by 5 publications

(12 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Schmidt et al, 2013;Masek et al, 2006Masek et al, , 2008Masek et al, , 2013 surface reflectance (SR) from the Landsat 5 Thematic Mapper (TM) was used in this study to characterize the type of disturbance and the subsequent pathways of forest regrowth over our study areas. LEDAPS was developed to ensure that spectral changes in Landsat are associated with regrowth dynamics (Masek et al, 2012;Schmidt et al, 2013) and to facilitate robust studies of land surface changes at different temporal and spatial scales in tropical forests (Kim et al, 2014;Valencia et al, 2016;Alonzo et al, 2016). LEDAPS SR Landsat 5 TM (L5 hereinafter) is generated by the United States Geological Survey (USGS) using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) that corrects for the influences of, among others, water vapor, ozone, aerosol optical thickness, and digital elevation on spectral bands (USGS, 2017;Vermote et al, 1997).…”

Section: Landsat Satellite Data and Proceduresmentioning

confidence: 99%

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

et al. 2020

View full text Add to dashboard Cite

Abstract. Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

show abstract

Section: Landsat Satellite Data and Proceduresmentioning

confidence: 99%

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

et al. 2020

View full text Add to dashboard Cite

show abstract

“…También se deben incluir aquí los suelos subyacentes que soportan determinado tipo de vegetación, pues tienden a quedar más expuestos después del fuego y afectar la reflectividad postfuego. Por otro lado, están los cambios que no están asociados al fuego: unos están asociados a las imágenes prefuego y postfuego, como diferencias en iluminación (Roy, et al, 2005), diferencias entre sensores (TM5, ETM+, OLI) (Valencia et al, 2016) y diferencias en la atmósfera en el momento de adquisición de la imagen (Vermote et al, 2002); otros cambios se deben a los procesos fenológicos de la vegetación (Potter et al, 2003). Además, la diferencia temporal dNBR cambia en función del número de días transcurridos entre la ocurrencia del fuego y la toma de la imagen postfuego debido a la resiliencia de la vegetación.…”

Section: Introductionunclassified

Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

2018

View full text Add to dashboard Cite

There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.

show abstract

“…The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Schmidt et al, 2013;Masek et al, 2006;Masek et al, 2013;Masek et al, 2008) surface reflectance (SR) from Landsat 5 Thematic Mapper (TM) was used in this study to characterize the type of disturbance and their subsequent pathways of forest regrowth over our study areas. LEDAPS was developed to ensure that spectral changes in Landsat are associated with regrowth dynamics (Masek et al, 2012;Schmidt et al, 2013) and to facilitate robust studies of land surface changes at different temporal and spatial scales in tropical forests (Kim 150 et al, 2014;Valencia et al, 2016;Alonzo et al, 2016). LEDAPS SR Landsat 5 TM (L5 hereinafter) is generated by the United States Geological Survey (USGS) using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) that corrects for the influences of, among others, water vapor, ozone, aerosol optical thickness, and digital elevation on spectral bands (USGS, 2017;Vermote et al, 1997).…”

mentioning

confidence: 99%

“…Landsat 8 was not used since comparison between Landsat 8 and both L5 and L7 is not straightforward due to differences in the spectral bandwidth of these sensors. We used LEDAPS since has a long time series of data, is promptly available, have high spectral performance comparable to Moderate Resolution Imaging Spectroradiometer (MODIS) (Claverie 160 et al, 2015) and several datasets (Vuolo et al, 2015;Nazeer et al, 2014) and it is suitable for ecological studies in the Amazon (van Doninck and Tuomisto, 2018;Valencia et al, 2016). L5 and L7 are available in Google Earth Engine (Gorelick et al, 2017), which we used to retrieve and analyze these data.…”

mentioning

confidence: 99%

“…For this procedure, visual inspection of visible bands and quality information from L5 and L7 were used. No further corrections were applied due to the robustness of L5 imagery over the Amazon (Valencia et al, 2016). The dates of L5 images used were (Landsat 5 operational imaging ended in 2011) 6/1/19846/1/ , 7/6/19856/1/ , 7/12/19876/1/ , 8/2/19896/1/ , 7/20/19906/1/ , 8/8/19916/1/ , 7/31/19946/1/ , 6/21/19976/1/ , 7/26/19986/1/ , 7/13/19996/1/ , 7/24/20036/1/ , 8/4/20076/1/ , 8/6/20086/1/ , 7/27/20106/1/ , and 8/31/20116/1/ .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Landsat NIR band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Negrón‐Juárez

Holm

Faybishenko

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Forest disturbance and regrowth are key processes in forest dynamics but detailed information of these processes is difficult to obtain in remote forests as the Amazon. We used chronosequences of Landsat satellite imagery to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clearcutting, and burning and their successional pathways of forest regrowth in the Central Amazon. We also assess whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clearcut. The results show that all spectral bands from Landsat satellite were sensitive to the disturbances but after 3 to 6 years only the Near Infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early-successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR will return to pre-disturbance values in about 39 years (consistent with observational data of biomass regrowth following windthrows), and 36 and 56 years for clearcut and burning. The NIR captured the observed successional pathways of forest regrowth after clearcut and burning that diverge through time. ELM-FATES predicted higher peaks of initial forest responses (e.g., biomass, stem density) after clearcuts than after windthrows, similar to the changes in NIR. However, ELM-FATES predicted a faster recovery of forest structure and canopy-coverage back to pre-disturbance conditions for windthrows compared to clearcuts. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clearcut to those of the NIR results suggest that the dynamics of forest regrowth for these disturbances are represented with appropriate fidelity within ELM-FATES and useful as a benchmarking tool.

show abstract

Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos

Cited by 5 publications

References 52 publications

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

Landsat NIR band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Contact Info

Product

Resources

About