Wageningen University

Thesis Student / Project Assistant

E worked at Wageningen University as a Thesis Student / Project Assistant

Wageningen University

PhD Candidate

E worked at Wageningen University as a PhD Candidate

CRWRC

Regional Disaster Relief and Preparedness Coordinator

E worked at CRWRC as a Regional Disaster Relief and Preparedness Coordinator

CIFOR - Center for International Forestry Research

Research Consultant

Developed Landsat-based deforestation and forest degradation map products for several sites in Indonesia

University of Maryland

Assistant Research Professor

E worked at University of Maryland as a Assistant Research Professor

University of Maryland

Post-Doctoral Research Associate

E worked at University of Maryland as a Post-Doctoral Research Associate

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Fuelwood Savings and Carbon Emission Reductions by the Use of Improved Cooking Stoves in an Afromontane Forest, Ethiopia

Land

In many Sub-Saharan African countries, fuelwood collection is among the most important drivers of deforestation and particularly forest degradation. In a detailed field study in the Kafa region of southern Ethiopia, we assessed the potential of efficient cooking stoves to mitigate the negative impacts of fuelwood harvesting on forests. Eleven thousand improved cooking stoves (ICS), specifically designed for baking Ethiopia’s staple food injera, referred to locally as “Mirt” stoves, have been distributed here. We found a high acceptance rate of the stove. One hundred forty interviews, including users and non-users of the ICS, revealed fuelwood savings of nearly 40% in injera preparation compared to the traditional three-stone fire, leading to a total annual savings of 1.28 tons of fuelwood per household. Considering the approximated share of fuelwood from unsustainable sources, these savings translate to 11,800 tons of CO2 saved for 11,156 disseminated ICS, corresponding to the amount of carbon stored in over 30 ha of local forest. We further found that stove efficiency increased with longer injera baking sessions, which shows a way of optimizing fuelwood savings by adapted usage of ICS. Our study confirms that efficient cooking stoves, if well adapted to the local cooking habits, can make a significant contribution to the conservation of forests and the avoidance of carbon emission from forest clearing and degradation.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Fuelwood Savings and Carbon Emission Reductions by the Use of Improved Cooking Stoves in an Afromontane Forest, Ethiopia

Land

In many Sub-Saharan African countries, fuelwood collection is among the most important drivers of deforestation and particularly forest degradation. In a detailed field study in the Kafa region of southern Ethiopia, we assessed the potential of efficient cooking stoves to mitigate the negative impacts of fuelwood harvesting on forests. Eleven thousand improved cooking stoves (ICS), specifically designed for baking Ethiopia’s staple food injera, referred to locally as “Mirt” stoves, have been distributed here. We found a high acceptance rate of the stove. One hundred forty interviews, including users and non-users of the ICS, revealed fuelwood savings of nearly 40% in injera preparation compared to the traditional three-stone fire, leading to a total annual savings of 1.28 tons of fuelwood per household. Considering the approximated share of fuelwood from unsustainable sources, these savings translate to 11,800 tons of CO2 saved for 11,156 disseminated ICS, corresponding to the amount of carbon stored in over 30 ha of local forest. We further found that stove efficiency increased with longer injera baking sessions, which shows a way of optimizing fuelwood savings by adapted usage of ICS. Our study confirms that efficient cooking stoves, if well adapted to the local cooking habits, can make a significant contribution to the conservation of forests and the avoidance of carbon emission from forest clearing and degradation.

Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series

Remote Sensing of Environment

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt et al., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of -0.065, overall accuracy was estimated to be 78%, and both producer’s and user’s accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD+ for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Fuelwood Savings and Carbon Emission Reductions by the Use of Improved Cooking Stoves in an Afromontane Forest, Ethiopia

Land

In many Sub-Saharan African countries, fuelwood collection is among the most important drivers of deforestation and particularly forest degradation. In a detailed field study in the Kafa region of southern Ethiopia, we assessed the potential of efficient cooking stoves to mitigate the negative impacts of fuelwood harvesting on forests. Eleven thousand improved cooking stoves (ICS), specifically designed for baking Ethiopia’s staple food injera, referred to locally as “Mirt” stoves, have been distributed here. We found a high acceptance rate of the stove. One hundred forty interviews, including users and non-users of the ICS, revealed fuelwood savings of nearly 40% in injera preparation compared to the traditional three-stone fire, leading to a total annual savings of 1.28 tons of fuelwood per household. Considering the approximated share of fuelwood from unsustainable sources, these savings translate to 11,800 tons of CO2 saved for 11,156 disseminated ICS, corresponding to the amount of carbon stored in over 30 ha of local forest. We further found that stove efficiency increased with longer injera baking sessions, which shows a way of optimizing fuelwood savings by adapted usage of ICS. Our study confirms that efficient cooking stoves, if well adapted to the local cooking habits, can make a significant contribution to the conservation of forests and the avoidance of carbon emission from forest clearing and degradation.

Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series

Remote Sensing of Environment

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt et al., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of -0.065, overall accuracy was estimated to be 78%, and both producer’s and user’s accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD+ for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

Localization of Rheb to the endomembrane is critical for its signaling function.

Biochemical and Biophysical Research Communications

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Fuelwood Savings and Carbon Emission Reductions by the Use of Improved Cooking Stoves in an Afromontane Forest, Ethiopia

Land

In many Sub-Saharan African countries, fuelwood collection is among the most important drivers of deforestation and particularly forest degradation. In a detailed field study in the Kafa region of southern Ethiopia, we assessed the potential of efficient cooking stoves to mitigate the negative impacts of fuelwood harvesting on forests. Eleven thousand improved cooking stoves (ICS), specifically designed for baking Ethiopia’s staple food injera, referred to locally as “Mirt” stoves, have been distributed here. We found a high acceptance rate of the stove. One hundred forty interviews, including users and non-users of the ICS, revealed fuelwood savings of nearly 40% in injera preparation compared to the traditional three-stone fire, leading to a total annual savings of 1.28 tons of fuelwood per household. Considering the approximated share of fuelwood from unsustainable sources, these savings translate to 11,800 tons of CO2 saved for 11,156 disseminated ICS, corresponding to the amount of carbon stored in over 30 ha of local forest. We further found that stove efficiency increased with longer injera baking sessions, which shows a way of optimizing fuelwood savings by adapted usage of ICS. Our study confirms that efficient cooking stoves, if well adapted to the local cooking habits, can make a significant contribution to the conservation of forests and the avoidance of carbon emission from forest clearing and degradation.

Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series

Remote Sensing of Environment

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt et al., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of -0.065, overall accuracy was estimated to be 78%, and both producer’s and user’s accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD+ for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

Localization of Rheb to the endomembrane is critical for its signaling function.

Biochemical and Biophysical Research Communications

The Science of Measuring, Reporting and Verification (MRV)

Cambridge University Press

Chapter 8 in "Law Tropical Forests and Carbon: The Case of REDD+", edited by Rosemary Lyster, Catherine MacKenzie and Constance McDermott. Cambridge University Press. 2013.

Near Real-Time Tropical Forest Disturbance Monitoring Using Landsat Time Series and Local Expert Monitoring Data

Conference: MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Breaks For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Forests

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Characterizing forest change using community-based monitoring data and Landsat time series.

PLoS ONE

In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Remote Sensing of Environment

With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91+/-2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61+/-3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84+/-8.1%), the producer's accuracy was low (56+/-9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data.

Fuelwood Savings and Carbon Emission Reductions by the Use of Improved Cooking Stoves in an Afromontane Forest, Ethiopia

Land

In many Sub-Saharan African countries, fuelwood collection is among the most important drivers of deforestation and particularly forest degradation. In a detailed field study in the Kafa region of southern Ethiopia, we assessed the potential of efficient cooking stoves to mitigate the negative impacts of fuelwood harvesting on forests. Eleven thousand improved cooking stoves (ICS), specifically designed for baking Ethiopia’s staple food injera, referred to locally as “Mirt” stoves, have been distributed here. We found a high acceptance rate of the stove. One hundred forty interviews, including users and non-users of the ICS, revealed fuelwood savings of nearly 40% in injera preparation compared to the traditional three-stone fire, leading to a total annual savings of 1.28 tons of fuelwood per household. Considering the approximated share of fuelwood from unsustainable sources, these savings translate to 11,800 tons of CO2 saved for 11,156 disseminated ICS, corresponding to the amount of carbon stored in over 30 ha of local forest. We further found that stove efficiency increased with longer injera baking sessions, which shows a way of optimizing fuelwood savings by adapted usage of ICS. Our study confirms that efficient cooking stoves, if well adapted to the local cooking habits, can make a significant contribution to the conservation of forests and the avoidance of carbon emission from forest clearing and degradation.

Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series

Remote Sensing of Environment

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt et al., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of -0.065, overall accuracy was estimated to be 78%, and both producer’s and user’s accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD+ for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

Localization of Rheb to the endomembrane is critical for its signaling function.

Biochemical and Biophysical Research Communications

The Science of Measuring, Reporting and Verification (MRV)

Cambridge University Press

Chapter 8 in "Law Tropical Forests and Carbon: The Case of REDD+", edited by Rosemary Lyster, Catherine MacKenzie and Constance McDermott. Cambridge University Press. 2013.

Automated quantification of surface water inundation in wetlands using optical satellite imagery.

Remote Sensing

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.