Improving near real time tropical forest change monitoring with multiple data sources

Research output: Chapter in Book/Report/Conference proceedingAbstractAcademic

Abstract

Forest cover loss in the tropics is mainly driven by agriculture and other activities such as mining and timber logging. Tropical countries need reliable and timely measurements of the extent of forest disturbances to prevent and reduce unsustainable and illegal activities. Time series-based forest monitoring at near real time (NRT) has the capacity of detect forest changes once a new satellite image is available. NRT forest multi-sensor monitoring approaches have proven to increase accuracy in tropical forest change detection; although, current methods are still not capable of detecting changes with high spatial accuracy after a few observations. The inclusion of ancillary datasets, e.g. road networks, in combination with satellite time series via machine learning approaches has the potential to provide information about the drivers of forest change and therefore to increase the change detection accuracy. The main objective of this study is to develop such a multi-source approach. A NRT scenario will be simulated in the province of Madre de Dios, Peru. Sentinel 1, Sentinel 2 and Peru’s road network datasets will be combined through the approach developed by Reiche et al. (2018) to detect forest changes. This approach calculates the conditional probability of forest cover change once a new image of the input time series is available. The conditional probability of forest change is computed using Bayesian updating, and forest change events are detected. New satellite observations are used to update the conditional probability of forest change along the time axis, and to confirm or reject forest change events detected previously. Very high resolution images, available through Planet Archive, will be used as guided reference data to collect training and validation data. The TimeSync tool will be used to estimate the temporal accuracy of the proposed method. Both spatial and temporal accuracy will be evaluated for the forest cover loss maps; therefore enabling us to discuss the utility of the data combination pipeline, as well as the importance of the single sources.This project was funded by the National Council for Science and Technology of Mexico (CONACYT).
Original languageEnglish
Title of host publicationOnline program of ForestSAT 2018
Subtitle of host publicationEntering a new era in forest observation and analysis
Pages171-171
Publication statusPublished - 2018
EventForestSAT - College Park, United States
Duration: 1 Oct 20185 Oct 2018
https://forestsat2018.forestsat.com/

Conference

ConferenceForestSAT
CountryUnited States
CityCollege Park
Period1/10/185/10/18
Internet address

Fingerprint

tropical forest
monitoring
forest cover
time series
logging (timber)
image resolution
science and technology
planet
sensor
agriculture
disturbance

Cite this

Martin del Campo Munoz, S., Reiche, J., Tuia, D., Verbesselt, J., & Herold, M. (2018). Improving near real time tropical forest change monitoring with multiple data sources. In Online program of ForestSAT 2018: Entering a new era in forest observation and analysis (pp. 171-171)
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title = "Improving near real time tropical forest change monitoring with multiple data sources",
abstract = "Forest cover loss in the tropics is mainly driven by agriculture and other activities such as mining and timber logging. Tropical countries need reliable and timely measurements of the extent of forest disturbances to prevent and reduce unsustainable and illegal activities. Time series-based forest monitoring at near real time (NRT) has the capacity of detect forest changes once a new satellite image is available. NRT forest multi-sensor monitoring approaches have proven to increase accuracy in tropical forest change detection; although, current methods are still not capable of detecting changes with high spatial accuracy after a few observations. The inclusion of ancillary datasets, e.g. road networks, in combination with satellite time series via machine learning approaches has the potential to provide information about the drivers of forest change and therefore to increase the change detection accuracy. The main objective of this study is to develop such a multi-source approach. A NRT scenario will be simulated in the province of Madre de Dios, Peru. Sentinel 1, Sentinel 2 and Peru’s road network datasets will be combined through the approach developed by Reiche et al. (2018) to detect forest changes. This approach calculates the conditional probability of forest cover change once a new image of the input time series is available. The conditional probability of forest change is computed using Bayesian updating, and forest change events are detected. New satellite observations are used to update the conditional probability of forest change along the time axis, and to confirm or reject forest change events detected previously. Very high resolution images, available through Planet Archive, will be used as guided reference data to collect training and validation data. The TimeSync tool will be used to estimate the temporal accuracy of the proposed method. Both spatial and temporal accuracy will be evaluated for the forest cover loss maps; therefore enabling us to discuss the utility of the data combination pipeline, as well as the importance of the single sources.This project was funded by the National Council for Science and Technology of Mexico (CONACYT).",
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booktitle = "Online program of ForestSAT 2018",

}

Martin del Campo Munoz, S, Reiche, J, Tuia, D, Verbesselt, J & Herold, M 2018, Improving near real time tropical forest change monitoring with multiple data sources. in Online program of ForestSAT 2018: Entering a new era in forest observation and analysis. pp. 171-171, College Park, United States, 1/10/18.

Improving near real time tropical forest change monitoring with multiple data sources. / Martin del Campo Munoz, Samantha; Reiche, J.; Tuia, D.; Verbesselt, J.; Herold, M.

Online program of ForestSAT 2018: Entering a new era in forest observation and analysis. 2018. p. 171-171.

Research output: Chapter in Book/Report/Conference proceedingAbstractAcademic

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T1 - Improving near real time tropical forest change monitoring with multiple data sources

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AU - Reiche, J.

AU - Tuia, D.

AU - Verbesselt, J.

AU - Herold, M.

PY - 2018

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N2 - Forest cover loss in the tropics is mainly driven by agriculture and other activities such as mining and timber logging. Tropical countries need reliable and timely measurements of the extent of forest disturbances to prevent and reduce unsustainable and illegal activities. Time series-based forest monitoring at near real time (NRT) has the capacity of detect forest changes once a new satellite image is available. NRT forest multi-sensor monitoring approaches have proven to increase accuracy in tropical forest change detection; although, current methods are still not capable of detecting changes with high spatial accuracy after a few observations. The inclusion of ancillary datasets, e.g. road networks, in combination with satellite time series via machine learning approaches has the potential to provide information about the drivers of forest change and therefore to increase the change detection accuracy. The main objective of this study is to develop such a multi-source approach. A NRT scenario will be simulated in the province of Madre de Dios, Peru. Sentinel 1, Sentinel 2 and Peru’s road network datasets will be combined through the approach developed by Reiche et al. (2018) to detect forest changes. This approach calculates the conditional probability of forest cover change once a new image of the input time series is available. The conditional probability of forest change is computed using Bayesian updating, and forest change events are detected. New satellite observations are used to update the conditional probability of forest change along the time axis, and to confirm or reject forest change events detected previously. Very high resolution images, available through Planet Archive, will be used as guided reference data to collect training and validation data. The TimeSync tool will be used to estimate the temporal accuracy of the proposed method. Both spatial and temporal accuracy will be evaluated for the forest cover loss maps; therefore enabling us to discuss the utility of the data combination pipeline, as well as the importance of the single sources.This project was funded by the National Council for Science and Technology of Mexico (CONACYT).

AB - Forest cover loss in the tropics is mainly driven by agriculture and other activities such as mining and timber logging. Tropical countries need reliable and timely measurements of the extent of forest disturbances to prevent and reduce unsustainable and illegal activities. Time series-based forest monitoring at near real time (NRT) has the capacity of detect forest changes once a new satellite image is available. NRT forest multi-sensor monitoring approaches have proven to increase accuracy in tropical forest change detection; although, current methods are still not capable of detecting changes with high spatial accuracy after a few observations. The inclusion of ancillary datasets, e.g. road networks, in combination with satellite time series via machine learning approaches has the potential to provide information about the drivers of forest change and therefore to increase the change detection accuracy. The main objective of this study is to develop such a multi-source approach. A NRT scenario will be simulated in the province of Madre de Dios, Peru. Sentinel 1, Sentinel 2 and Peru’s road network datasets will be combined through the approach developed by Reiche et al. (2018) to detect forest changes. This approach calculates the conditional probability of forest cover change once a new image of the input time series is available. The conditional probability of forest change is computed using Bayesian updating, and forest change events are detected. New satellite observations are used to update the conditional probability of forest change along the time axis, and to confirm or reject forest change events detected previously. Very high resolution images, available through Planet Archive, will be used as guided reference data to collect training and validation data. The TimeSync tool will be used to estimate the temporal accuracy of the proposed method. Both spatial and temporal accuracy will be evaluated for the forest cover loss maps; therefore enabling us to discuss the utility of the data combination pipeline, as well as the importance of the single sources.This project was funded by the National Council for Science and Technology of Mexico (CONACYT).

M3 - Abstract

SP - 171

EP - 171

BT - Online program of ForestSAT 2018

ER -

Martin del Campo Munoz S, Reiche J, Tuia D, Verbesselt J, Herold M. Improving near real time tropical forest change monitoring with multiple data sources. In Online program of ForestSAT 2018: Entering a new era in forest observation and analysis. 2018. p. 171-171