Combining Satellite Data and Community-Based Observations for Forest Monitoring

Pratihast, Arun Kumar; DeVries, Ben; Avitabile, Valerio; Bruin, S. de; Kooistra, Lammert; Tekle, Mesfin; Herold, Martin

doi:10.3390/f5102464

Cited by 45 publications

(83 citation statements)

References 54 publications

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“…The Geo-Wiki platform, by Fritz et al [15], is successfully providing a large quantity of crowd-sourced samples for global land cover mapping [16] or accuracy assessment of a global forest mask [17]. In other studies, VGI was combined with satellite images for mapping [18,19], to augment image time series [20] or to assist forest monitoring in the context of REDD+ [18,[21][22][23] (Reducing Emissions from Deforestation and forest Degradation in developing countries and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks). Forest mapping [24] and monitoring [25] are natural applications of participatory sensing, especially in communities where forest plays an important socio-economic role.…”

Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

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Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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“…These results suggest that the criterion of two consecutive anomalies is not robust enough to reject false detection as previously thought [1,6,7]. Two consecutive anomalies might be sufficient in areas where forest disturbances occur at large scale and in an abrupt manner (e.g., [6,7]), but not sufficient when dealing with complex small-scale forest disturbances [13,20]. Our results also show that using the magnitude of change, in addition to two consecutive negative anomalies, to confirm forest disturbances does not reduce false detections sufficiently.…”

Section: Discussionmentioning

confidence: 80%

“…The forest in this area is subject to small-scale and gradual disturbance processes, caused mainly by small-holder agriculture, human settlements expansion, industrial coffee plantations, and domestic firewood and charcoal extractions [20,21]. Previous studies in the UNESCO Kafa Biosphere Reserve show that separating forest disturbances from false detections is difficult, especially those that involve partial removal of the forest cover [13,16,22].…”

Section: Study Areamentioning

confidence: 99%

“…The spatial window that defines the neighbourhood should be larger than the size of forest disturbance events one wants to detect, to ensure that there are forest pixels within the window to avoid normalising against a value from the disturbed area [6]. The spatial window of 15 by 15 pixels (≈20 ha) we used in this study was deemed sufficient because forest disturbance patches in the UNESCO Kafa Biosphere Reserve tend to be small [13,20].…”

Section: Spatial Normalisation To Reduce Seasonality In Ndvi Time Seriesmentioning

confidence: 99%

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Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series

et al. 2017

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Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely detection of forest disturbance is often accompanied by many false detections, and existing approaches for reducing false detections either compromise the temporal accuracy or amplify the omission error for forest disturbances. Here, we propose to use a set of space-time features to reduce false detections. We first detect potential forest disturbances in the Landsat time series based on two consecutive negative anomalies, and subsequently use space-time features to confirm forest disturbances. A probability threshold is used to discriminate false detections from forest disturbances. We demonstrated this approach in the UNESCO Kafa Biosphere Reserve located in the southwest of Ethiopia by detecting forest disturbances between 2014 and 2016. Our results show that false detections are reduced significantly without compromising temporal accuracy. The user's accuracy was at least 26% higher than the user's accuracies obtained when using only temporal information (e.g., two consecutive negative anomalies) to confirm forest disturbances. We found the space-time features related to change in spatio-temporal variability, and spatio-temporal association with non-forest areas, to be the main predictors for forest disturbance. The magnitude of change and two consecutive negative anomalies, which are widely used to distinguish real changes from false detections, were not the main predictors for forest disturbance. Overall, our findings indicate that using a set of space-time features to confirm forest disturbances increases the capacity to reject many false detections, without compromising the temporal accuracy.

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“…However there are fewer references exploring the potential of communities to produce activity data or information on forest area; this is one of the contributions of the work by Pratihast et al [3]. In their article, Pratihast et al [3] combine the use of high-resolution satellite imagery and professional measurements to assess the consistency of community monitoring of forest area and forest area changes in Ethiopia in terms of spatial, temporal and thematic accuracy. Community monitoring was used to describe changes associated with deforestation, forest degradation and also reforestation, in terms of their location, size, timing and causes within 10 local administrative units.…”

Section: The Special Issuementioning

confidence: 99%

Special Issue: The Potential Role for Community Monitoring in MRV and in Benefit Sharing in REDD+

Torres

Skutsch

2015

Forests

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Abstract:Since the early design of activities to reduce emissions from deforestation and forest degradation in developing countries (REDD+) under the United Nations Framework Convention on Climate Change (UNFCCC), the need to engage local communities and indigenous groups in monitoring and reporting has been recognized. REDD+ has advanced under the UNFCCC negotiations, but most countries still need to define formally what the role of communities in their national monitoring systems will be. Previous research and experiences have shown that local communities can effectively contribute in the monitoring of natural resources. This editorial introduces a Special Issue of Forests which discusses the implications of and potential for including community based monitoring (CBM) in monitoring and benefit-sharing systems in REDD+. It outlines the main points of the nine contributions to the Special Issue which cover a wide geographical area and report on projects and research which engages more than 150 communities from eight different countries from Africa, Asia and Latin America. The editorial summarizes how the articles and reports build further understanding of the potential of CBM to contribute to the implementation, monitoring and distribution of benefits in REDD+. It also discusses the results of an on-going opinion survey on issues related to CBM and its relation to benefit sharing, which indicates that there is still disagreement on a number of key elements. OPEN ACCESSForests 2015, 6 245

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Combining Satellite Data and Community-Based Observations for Forest Monitoring

Cited by 45 publications

References 54 publications

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series

Special Issue: The Potential Role for Community Monitoring in MRV and in Benefit Sharing in REDD+

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